{ "schemaVersion": "deck-intelligence.v1", "id": "019dd923-5ca1-7489-b633-7a814d47b8dd", "slug": "4d3687fa691fcd8b", "title": "The art of AI maturity Advancing from practice to performance", "subtitle": "Accenture", "author": "Accenture", "pageCount": 40, "kind": "consulting-deck", "orientation": "landscape", "aspectRatio": 1.778, "document": { "id": "019dd923-5ca1-7489-b633-7a814d47b8dd", "slug": "4d3687fa691fcd8b", "title": "The art of AI maturity Advancing from practice to performance", "rawTitle": "The art of AI maturity Advancing from practice to performance", "authorId": "Accenture", "authorName": "Accenture", "authorDisplay": "Accenture", "kind": { "slug": "consulting-deck", "label": "Consulting deck" }, "sourceType": { "slug": "strategy_consulting", "label": "Strategy consulting" }, "fileName": "The-art-of-AI-maturity-Advancing-from-practice-to-performance.pdf", "sourceUrl": "https://www.accenture.com/content/dam/system-files/acom/custom-code/ai-maturity/Accenture-Art-of-AI-Maturity-Report-Global-Revised.pdf", "sourceDate": null, "presentationDate": null, "ingestedAt": "2026-04-23 20:51:26+00", "status": "ready", "pageCount": 40, "orientation": "landscape", "aspectRatio": 1.778, "targetCompany": null, "targetTicker": null, "metadata": { "local_root": "/tmp/legacy-images/4d3687fa691fcd8b", "origin_corpus": "consulting" } }, "coverage": { "pageCount": 40, "pagesIndexed": 40, "pagesWithImages": 40, "pagesWithMetadata": 40, "imageCoveragePct": 100, "metadataCoveragePct": 100, "componentCount": 287, "chartCount": 8, "metricSlides": 24, "toolMatches": 93, "documentToolMatches": 0, "frameworkMatches": 10, "arcMatches": 1, "beatMatches": 5, "loopMatches": 6, "patternMatches": 0, "storymakersMatch": true, "scoreAvailable": true, "reviewAvailable": true, "activistThesisAvailable": false, "pitchdeckMetadataAvailable": false, "pitchdeckProfileAvailable": false }, "blocks": [], "matches": { "arcs": [ { "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null, "categoryName": "Strategy & Business", "categorySlug": "strategy-business" }, "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "evidence": "Classic SCQA: situation (pp 3-7 AI essential), problem (pp 8-12 only 12% Achievers), evidence (pp 13-16 outperformance), solution (pp 17-27 five ways), action (pp 28-31 vision + assessment).", "isPrimary": true, "confidence": 90, "extraction": { "at": "2026-04-29 13:07:28.977035+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "beats": [ { "to": 7, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null, "categoryName": "Strategy & Business", "categorySlug": "strategy-business" }, "from": 3, "name": "Situation & Context", "beatId": "019dd95a-0682-776c-8e32-77ef30635bbc", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Situation & Context", "slug": "consultants-gambit-situation-context", "status": "active", "canonId": "019dd9b8-05ae-741b-992d-65ee83c639b3", "version": 1, "description": null }, "evidence": "AI essential for competitive advantage; transformation accelerating 16 months faster", "position": 1, "isPrimaryArc": true, "parentBeatType": { "name": "Setup", "slug": "setup", "status": "active", "canonId": "019dd9b8-020e-7505-af9b-d9b9a4679bcc", "version": 1, "description": "Where the deck grounds the reader before doing anything else. Almost every arc opens here." }, "alignedBlockIds": null, "matchConfidence": 90 }, { "to": 12, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null, "categoryName": "Strategy & Business", "categorySlug": "strategy-business" }, "from": 8, "name": "Problem & Complication", "beatId": "019dd95a-0682-776c-8e32-79a5f653b5e2", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Problem & Complication", "slug": "consultants-gambit-problem-complication", "status": "active", "canonId": "019dd9b8-064a-777f-a374-e9a8c43dedd4", "version": 1, "description": null }, "evidence": "Segmentation reveals only 12% are AI Achievers; 63% Experimenters", "position": 2, "isPrimaryArc": true, "parentBeatType": { "name": "Complication", "slug": "complication", "status": "active", "canonId": "019dd9b8-0275-7709-b2d6-4f77c3fc7579", "version": 1, "description": "The point in the narrative where the audience should feel discomfort. Required for argumentative arcs." }, "alignedBlockIds": null, "matchConfidence": 90 }, { "to": 27, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null, "categoryName": "Strategy & Business", "categorySlug": "strategy-business" }, "from": 17, "name": "Solution & Approach", "beatId": "019dd95a-0682-776c-8e32-7e8c761f9fc5", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Solution & Approach", "slug": "consultants-gambit-solution-approach", "status": "active", "canonId": "019dd9b8-071c-7161-9184-580fdac7a4f9", "version": 1, "description": null }, "evidence": "Five recommendations to master AI maturity, each with case study", "position": 3, "isPrimaryArc": true, "parentBeatType": { "name": "Turn", "slug": "turn", "status": "active", "canonId": "019dd9b8-0396-702b-ac2f-9901f8dd8b54", "version": 1, "description": "The pivot — solution proposed, catalyst introduced, decision made. Common (15/20 arcs) but not universal: Triple-take, AIDA, PAS, Voyage-return and Pixar substitute it with a strong reflection or development beat." }, "alignedBlockIds": null, "matchConfidence": 90 }, { "to": 16, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null, "categoryName": "Strategy & Business", "categorySlug": "strategy-business" }, "from": 13, "name": "Evidence & Proof", "beatId": "019dd95a-0682-776c-8e32-83fe9528254e", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Evidence & Proof", "slug": "consultants-gambit-evidence-proof", "status": "active", "canonId": "019dd9b8-07b1-7688-8e1e-7637fec3802a", "version": 1, "description": null }, "evidence": "Achievers outperform peers on revenue (50%), scaling (25%), CX (8%)", "position": 4, "isPrimaryArc": true, "parentBeatType": { "name": "Evidence", "slug": "evidence", "status": "active", "canonId": "019dd9b8-03f9-772a-b84a-4f07b415b77a", "version": 1, "description": "A dedicated proof beat exists in only ~4 arcs (Gambit, Sequoia, AIDA, Monroes). Other arcs embed evidence inline within development." }, "alignedBlockIds": null, "matchConfidence": 90 }, { "to": 31, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null, "categoryName": "Strategy & Business", "categorySlug": "strategy-business" }, "from": 28, "name": "Impact & Next Steps", "beatId": "019dd95a-0682-776c-8e32-8716c65168b1", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Impact & Next Steps", "slug": "consultants-gambit-impact-next-steps", "status": "active", "canonId": "019dd9b8-0849-7728-9ee9-fd1609c4c655", "version": 1, "description": null }, "evidence": "Vision (12% to 27% by 2024) plus C-suite assessment questions", "position": 5, "isPrimaryArc": true, "parentBeatType": { "name": "Resolution", "slug": "resolution", "status": "active", "canonId": "019dd9b8-045d-703b-8dd7-e92aba3ba91b", "version": 1, "description": "How the deck ends. Required in 19/20 arcs. The exception (Sparkline) ends with new bliss which is functionally a resolution." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 4, "from": 3, "loop": { "name": "03_aha_moment", "slug": "03-aha-moment", "status": "active", "bestFor": "Data-heavy sections, research findings, analytical arguments", "canonId": "019dd956-66f4-7668-ab25-cec9c996c0bc", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Problem/Question -> What the Data Shows -> The Insight", "description": "Establish a tension, present data, then deliver the insight that changes everything", "familyLabel": null, "categoryName": "Narrative", "categorySlug": "narrative" }, "matchId": "019dd95a-07fe-70ce-8d3a-3bb17d512555", "evidence": "Exec summary states the 12% finding; p4 lists the five ways", "position": 1, "objective": "Reveal the central finding and preview the five differentiators", "confidence": 75, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } }, { "to": 7, "from": 5, "loop": { "name": "15_why_now", "slug": "15-why-now", "status": "active", "bestFor": "Sales pitches, fundraising, requesting immediate budget approval", "canonId": "019dd956-6c00-70cd-bd42-6bacc4af726f", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Context (Trends) -> The Trigger Event -> The Window of Opportunity", "description": "Create temporal urgency by proving that the window of opportunity is opening or closing", "familyLabel": null, "categoryName": "Urgency", "categorySlug": "urgency" }, "matchId": "019dd95a-07fe-70ce-8d3a-3e8a2c756978", "evidence": "Pp 6-7 frame AI as essential and accelerating: 16 months faster, AI-influenced revenue tripling", "position": 2, "objective": "Establish urgency for AI investment", "confidence": 80, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } }, { "to": 12, "from": 8, "loop": { "name": "30_2x2_matrix", "slug": "30-2x2-matrix", "status": "active", "bestFor": "Portfolio analysis, prioritization, strategic positioning", "canonId": "019dd956-7225-756a-9cc7-1e6d3f039818", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "Dimension 1 (X-axis) -> Dimension 2 (Y-axis) -> The Four Quadrants -> The Sweet Spot", "description": "Plot options on two critical dimensions to reveal the optimal quadrant", "familyLabel": null, "categoryName": "Consulting Framework", "categorySlug": "consulting-framework" }, "matchId": "019dd95a-07fe-70ce-8d3a-4369b4be6b2e", "evidence": "p10 explicit 2x2 (AI Foundation x Differentiation) with four named segments", "position": 3, "objective": "Define AI maturity and segment firms via 2x2", "confidence": 90, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } }, { "to": 16, "from": 13, "loop": { "name": "39_benchmark_gap", "slug": "39-benchmark-gap", "status": "active", "bestFor": "Performance improvement, competitive analysis, target setting", "canonId": "019dd956-75a4-7522-8d1a-af94da4703e9", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "Our Performance -> Industry Average -> Best-in-Class -> The Gap = The Opportunity", "description": "Compare performance against best-in-class to quantify the opportunity", "familyLabel": null, "categoryName": "Comparison", "categorySlug": "comparison" }, "matchId": "019dd95a-07fe-70ce-8d3a-4639593ed737", "evidence": "Pp 14-16 quantify gap: 50% revenue growth, 25% scaling, 8% CX, 17 capability heatmap", "position": 4, "objective": "Show Achievers outperform peers on key metrics", "confidence": 85, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } }, { "to": 27, "from": 17, "loop": { "name": "40_mece_breakdown", "slug": "40-mece-breakdown", "status": "active", "bestFor": "Problem structuring, ensuring completeness, strategic analysis", "canonId": "019dd956-763c-765e-8b68-6ee3ae1ef184", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Whole -> Category A (distinct) -> Category B (distinct) -> Category C (distinct) -> Complete Coverage", "description": "Divide a complex topic into mutually exclusive, collectively exhaustive categories", "familyLabel": null, "categoryName": "Consulting Framework", "categorySlug": "consulting-framework" }, "matchId": "019dd95a-07fe-70ce-8d3a-483bc3c6386e", "evidence": "Five distinct recommendation categories (sponsor / talent / AI core / responsible / invest) each with case study", "position": 5, "objective": "Five MECE recommendations to become an Achiever", "confidence": 85, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } }, { "to": 31, "from": 28, "loop": { "name": "41_so_what_cascade", "slug": "41-so-what-cascade", "status": "active", "bestFor": "Data presentations, executive summaries, driving to recommendations", "canonId": "019dd956-769f-7208-afbc-f28985c6f4fb", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Data -> So What? (Insight 1) -> So What? (Insight 2) -> So What? (The Action)", "description": "Chain insights together, each answering 'so what?' until you reach the actionable conclusion", "familyLabel": null, "categoryName": "Consulting Framework", "categorySlug": "consulting-framework" }, "matchId": "019dd95a-07fe-70ce-8d3a-4ee1c0546ad8", "evidence": "p28 vision stat, p30 mantra quote, p31 sample C-suite assessment questions", "position": 6, "objective": "Drive from finding to assessment action", "confidence": 70, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "tools": [ { "tool": { "name": "SCQA Framework", "slug": "scqa-framework", "status": "active", "bestFor": null, "canonId": "019dd956-7bcb-763f-bdf6-1dfedf3bd36c", "version": 1, "bodyDocId": "019df22a-a0b0-719e-937e-2ac31e53d32c", "description": "Situation, Complication, Question, Answer - structured problem framing", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Architect", "layer": "block", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-2893bf72c88f", "evidence": "Whole deck opens with situation (AI evolved), complication (12% lead), question (how) - block anchors here", "pageRefs": null, "priority": "Core", "whenToUse": null, "confidence": 85, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 3, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Curiosity Gap", "slug": "curiosity-gap", "status": "active", "bestFor": null, "canonId": "019dd956-9703-74ef-8b85-96dfd8525025", "version": 1, "bodyDocId": null, "description": "People seek missing information - create open questions that demand resolution", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Storyteller", "layer": "block", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-43d33f34ca50", "evidence": "Question title creates open loop the deck will close", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 4, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "MECE Principle", "slug": "mece-principle", "status": "active", "bestFor": null, "canonId": "019dd956-7c60-71a8-8758-c79d914489c9", "version": 1, "bodyDocId": null, "description": "Mutually Exclusive, Collectively Exhaustive - ensuring complete, non-overlapping structure", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Architect", "layer": "block", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-4ad0dcc84a91", "evidence": "Five distinct, non-overlapping capability categories preview the deck", "pageRefs": null, "priority": "Core", "whenToUse": null, "confidence": 75, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 4, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "MECE Principle", "slug": "mece-principle", "status": "active", "bestFor": null, "canonId": "019dd956-7c60-71a8-8758-c79d914489c9", "version": 1, "bodyDocId": null, "description": "Mutually Exclusive, Collectively Exhaustive - ensuring complete, non-overlapping structure", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Architect", "layer": "block", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-cdb39bdebf42", "evidence": "Five distinct recommendation categories begin here", "pageRefs": null, "priority": "Core", "whenToUse": null, "confidence": 75, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 19, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "MECE Principle", "slug": "mece-principle", "status": "active", "bestFor": null, "canonId": "019dd956-7c60-71a8-8758-c79d914489c9", "version": 1, "bodyDocId": null, "description": "Mutually Exclusive, Collectively Exhaustive - ensuring complete, non-overlapping structure", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Architect", "layer": "block", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987c-3a61f60f79d3", "evidence": "Four MECE pillars (Strategy/Sponsorship, Data/AI Core, Talent/Culture, Responsible AI)", "pageRefs": null, "priority": "Core", "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 31, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Aha! Moment", "slug": "aha-moment", "status": "active", "bestFor": null, "canonId": "019dd956-a5b5-7763-820e-b299c5f27a32", "version": 1, "bodyDocId": null, "description": "The moment when the audience realizes the value - design for insight revelation", "familyLabel": null, "categoryName": "Loop", "categorySlug": "loop" }, "agent": "Storyteller", "layer": "loop", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-38c6052c802e", "evidence": "Loop opens with the surprising 12%-Achiever insight", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 75, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 3, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Chunking", "slug": "chunking", "status": "active", "bestFor": null, "canonId": "019dd956-abab-72ce-b64c-2924cc83cf39", "version": 1, "bodyDocId": null, "description": "Information grouped into chunks is remembered better - structure content in digestible units", "familyLabel": null, "categoryName": "Loop", "categorySlug": "loop" }, "agent": "Architect", "layer": "loop", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-44e1e02996d4", "evidence": "Five labelled chunks listed: champion / talent / AI core / responsible / invest", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 4, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Contrast Pairs", "slug": "contrast-pairs", "status": "active", "bestFor": null, "canonId": "019dd956-9b77-70d3-88b1-f34e26f60860", "version": 1, "bodyDocId": null, "description": "Creating tension through opposites: Before↔After, Problem↔Solution, Pain↔Relief", "familyLabel": null, "categoryName": "Loop", "categorySlug": "loop" }, "agent": "Storyteller", "layer": "loop", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-ae2279deb778", "evidence": "Achievers vs Experimenters comparison anchors the loop", "pageRefs": null, "priority": "Core", "whenToUse": null, "confidence": 75, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 14, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Chunking", "slug": "chunking", "status": "active", "bestFor": null, "canonId": "019dd956-abab-72ce-b64c-2924cc83cf39", "version": 1, "bodyDocId": null, "description": "Information grouped into chunks is remembered better - structure content in digestible units", "familyLabel": null, "categoryName": "Loop", "categorySlug": "loop" }, "agent": "Architect", "layer": "loop", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-cabaf29cb94a", "evidence": "First of five recommendation chunks begins", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 75, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 19, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Slide recipe: Cover", "slug": "cover-slide", "status": "active", "bestFor": null, "canonId": "019df269-2a08-767d-9128-e7431e6fe8a3", "version": 1, "bodyDocId": "019df269-29e5-7738-b5fa-6892e5dc8e65", "description": "The first ten seconds. Build it deliberately.", "familyLabel": "slide-recipe", "categoryName": "Slide", "categorySlug": "slide" }, "agent": "designer", "layer": "slide", "agents": [ "designer" ], "matchId": "b9bb85a3-65d0-4a01-adb8-7db57df6951b", "evidence": "The art of AI maturity", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.9, "extraction": { "at": "2026-07-16 21:02:18.91764+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 1, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-2c21fbe8cd95", "evidence": "Title states insight not topic: 'AI evolved from concept to societal constant'", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 3, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-3558d2012141", "evidence": "Specific 70-year history and 12% figure", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 3, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Executive summary", "slug": "executive-summary", "status": "active", "bestFor": null, "canonId": "019dd9e1-4b63-72d5-9734-da757d91ca61", "version": 1, "bodyDocId": null, "description": "A condensed view of the entire argument on a single slide / block.", "familyLabel": null, "categoryName": null, "categorySlug": null }, "agent": null, "layer": "slide", "agents": null, "matchId": "d3b3eb4d-8a8c-4b6b-9eba-0b3a0b0d5a04", "evidence": "In fewer than 70 years, artificial intelligence (AI) has evolved from a scientific concept to a societal constant.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.9, "extraction": { "at": "2026-07-16 21:02:19.025248+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 3, "whyItWorks": null, "antipattern": null, "cardinality": "common", "narrativePurpose": null }, { "tool": { "name": "Opening Hooks", "slug": "opening-hooks", "status": "active", "bestFor": null, "canonId": "019dd956-aeb0-77d5-bfbd-9cd9a6be8186", "version": 1, "bodyDocId": null, "description": "Techniques to capture attention: Startling statistic, provocative question, short story, analogy, contrast", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-3091a16e04be", "evidence": "Startling-statistic hook: '12% have advanced AI maturity to superior performance'", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 85, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 3, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-3c4ed9b16629", "evidence": "Title 'What do AI Achievers do differently?' frames the answer set", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 4, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Audience Definition", "slug": "audience-definition", "status": "active", "bestFor": null, "canonId": "019dd956-7f2a-775d-aeee-ff099bcb7756", "version": 1, "bodyDocId": null, "description": "Six key questions: Who are they? What do they know? What do they believe? What do they care about? What do they fear? What decisions can they make?", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "25d5bdcc-f9d0-4ec8-b72e-bcf0ef37ce54", "evidence": "The slide is addressed to a specific group, 'AI Achievers'.", "pageRefs": null, "priority": "Core", "whenToUse": null, "confidence": 0.7, "extraction": { "at": "2026-07-16 21:02:36.208078+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 4, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Picture Superiority Effect", "slug": "picture-superiority-effect", "status": "active", "bestFor": null, "canonId": "019dd956-c201-7014-bb58-3e85e3e11dfd", "version": 1, "bodyDocId": null, "description": "Pictures remembered 6x better than words - prioritize visuals over text", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "019dd95a-0f08-73d3-987b-4e6773923526", "evidence": "Half-slide portrait paired with text", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 70, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 4, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Storytelling Effect", "slug": "storytelling-effect", "status": "active", "bestFor": null, "canonId": "019dd956-981b-74f9-9096-5c8df03dab5a", "version": 1, "bodyDocId": null, "description": "Stories are remembered up to 22x better than facts alone", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "9b705281-579e-4f32-8816-16cf5c8865c3", "evidence": "The use of a narrative to describe 'AI Achievers'.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.7, "extraction": { "at": "2026-07-16 21:02:36.243683+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 4, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Section divider", "slug": "section-divider", "status": "active", "bestFor": null, "canonId": "019dd9e1-4c66-707d-8d1f-6e14547134c7", "version": 1, "bodyDocId": null, "description": "A transitional slide marking the start of a new section.", "familyLabel": null, "categoryName": null, "categorySlug": null }, "agent": null, "layer": "slide", "agents": null, "matchId": "9a77e928-26df-4585-a4df-9d0117ec27ad", "evidence": "The slide is titled 'AI maturity: Why it matters' and has a subtitle.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.9, "extraction": { "at": "2026-07-16 21:02:36.316901+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 5, "whyItWorks": null, "antipattern": null, "cardinality": "common", "narrativePurpose": null }, { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-528972455469", "evidence": "Subhead states the insight: 'AI is absolutely essential to competitive advantage'", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 85, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 6, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Annotation", "slug": "annotation", "status": "active", "bestFor": null, "canonId": "019dd956-b5b9-7668-bf0e-3af99823ec5c", "version": 1, "bodyDocId": null, "description": "Adding explanatory labels to highlight key data points", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "019dd95a-0f08-73d3-987b-552d36966511", "evidence": "Chart annotates '16 months faster' delta with callout", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 85, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 6, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Color Strategy", "slug": "color-strategy", "status": "active", "bestFor": null, "canonId": "019dd956-b202-77cf-8a6e-f445b08c0fd3", "version": 1, "bodyDocId": null, "description": "Using color for emphasis, categories, sentiment, and hierarchy", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "019dd95a-0f08-73d3-987b-5bc43a82aa1d", "evidence": "Purple curves distinguish AI vs Digital transformation", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 75, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 6, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-5de8df3c3dc4", "evidence": "Specific stats: 46% CEOs, 75% integrated AI, 30% scaled pilots", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 6, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Data Story Arc", "slug": "data-story-arc", "status": "active", "bestFor": null, "canonId": "019dd956-9c30-728e-b974-d3fb123dcf5a", "version": 1, "bodyDocId": null, "description": "Context → Conflict → Insight → Implication structure for data narratives", "familyLabel": null, "categoryName": "Loop", "categorySlug": "loop" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "d014749d-e228-421f-b6a3-955f65d691bd", "evidence": "The use of data to support the narrative about AI adoption.", "pageRefs": null, "priority": "Core", "whenToUse": null, "confidence": 0.7, "extraction": { "at": "2026-07-16 21:02:36.388623+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 6, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-6005692193a6", "evidence": "Figure 2 title states the trend in revenue evolution", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 75, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 7, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Annotation", "slug": "annotation", "status": "active", "bestFor": null, "canonId": "019dd956-b5b9-7668-bf0e-3af99823ec5c", "version": 1, "bodyDocId": null, "description": "Adding explanatory labels to highlight key data points", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "019dd95a-0f08-73d3-987b-6810b057b8cf", "evidence": "10% / 22% / 31% callouts on the bands", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 7, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Chart Selection Guide", "slug": "chart-selection-guide", "status": "active", "bestFor": null, "canonId": "019dd956-b1a0-700b-ba17-0e16c4d5bcae", "version": 1, "bodyDocId": null, "description": "Choosing the right chart type for your data and message", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "019dd95a-0f08-73d3-987b-673a456d2945", "evidence": "Sankey-style flow chart suits revenue-share migration over time", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 7, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Color Strategy", "slug": "color-strategy", "status": "active", "bestFor": null, "canonId": "019dd956-b202-77cf-8a6e-f445b08c0fd3", "version": 1, "bodyDocId": null, "description": "Using color for emphasis, categories, sentiment, and hierarchy", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "019dd95a-0f08-73d3-987b-6f97b03f1a71", "evidence": "Color encodes High / Medium / Low revenue thresholds", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 75, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 7, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Section divider", "slug": "section-divider", "status": "active", "bestFor": null, "canonId": "019dd9e1-4c66-707d-8d1f-6e14547134c7", "version": 1, "bodyDocId": null, "description": "A transitional slide marking the start of a new section.", "familyLabel": null, "categoryName": null, "categorySlug": null }, "agent": null, "layer": "slide", "agents": null, "matchId": "36cc49e9-11e3-46cf-b723-f6a154ef3210", "evidence": "The slide is titled 'AI maturity: What it is' and has a subtitle.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.9, "extraction": { "at": "2026-07-16 21:02:36.495226+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 8, "whyItWorks": null, "antipattern": null, "cardinality": "common", "narrativePurpose": null }, { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-7106a27a9b04", "evidence": "Defines AI maturity as mastery of capabilities for performance", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 9, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Core Message Extraction", "slug": "core-message-extraction", "status": "active", "bestFor": "Any slide that carries an argument or finding. The diagnostic: if the slide has a chart on it, it needs a core message. Run extraction whenever the current title is a topic (Q3 performance, Customer satisfaction) rather than a claim, or when a reader could reasonably draw more than one conclusion from the visible evidence.", "canonId": "019dd956-bc71-707d-8867-1b2e810175cf", "version": 1, "bodyDocId": "f85613cb-4b77-462f-bfe6-3b862516e1a6", "description": "The discipline of distilling raw analysis, data, or content into a single declarative sentence — the slide's core message — that the audience would walk away with if they remembered nothing else. Process, not artefact: extraction questions run on the raw content until one sentence falls out.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-7798f7fcd36c", "evidence": "Single-paragraph definition crystallizes the construct", "pageRefs": null, "priority": null, "whenToUse": "Any slide that carries an argument or finding. The diagnostic: if the slide has a chart on it, it needs a core message. Run extraction whenever the current title is a topic (Q3 performance, Customer satisfaction) rather than a claim, or when a reader could reasonably draw more than one conclusion from the visible evidence.", "confidence": 75, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 9, "whyItWorks": "Solves the two failure modes of slide writing simultaneously: the topic title (which outsources interpretation to the audience and produces three different conclusions in three different heads) and the multi-thesis stapler (which exceeds working memory and gets remembered as fragments). Aligns with how readers actually consume decks — Heath & Heath's Find the Core, the journalist's nut graf, and Minto's governing thought all converge on the same cognitive economics.", "antipattern": "Topic titles disguised as core messages (Customer satisfaction, no verb, no stake); multi-thesis sentences stapled with and; hedged sentences (may show signs of possible softening); chart-caption titles (Revenue fell 4%) that restate what the chart already shows instead of naming the so-what; meta-commentary (this slide explores...) that describes the slide rather than concluding it.", "cardinality": null, "narrativePurpose": "Forces the slide author to do the interpretive work before the slide ships, so the audience does not have to do it during the meeting. Wins the WYSIATI battle on purpose — the loudest fragment on the page becomes the author's chosen sentence rather than whichever chart label happens to be largest." }, { "tool": { "name": "2x2 matrix", "slug": "matrix-2x2", "status": "active", "bestFor": null, "canonId": "019dd9e1-50ab-72d1-8afb-3ab0a38e32a8", "version": 1, "bodyDocId": null, "description": "BCG matrix, importance/urgency, any axes-and-quadrants frame.", "familyLabel": null, "categoryName": null, "categorySlug": null }, "agent": null, "layer": "slide", "agents": null, "matchId": "fd7baedd-85fa-4169-913e-9438ae2361a8", "evidence": "The slide features a 2x2 matrix showing the distribution of companies across four quadrants.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.9, "extraction": { "at": "2026-07-16 21:02:36.605976+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 10, "whyItWorks": null, "antipattern": null, "cardinality": "common", "narrativePurpose": null }, { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-795353f951c2", "evidence": "Figure 3 title is the insight: 'Only 12% are AI Achievers'", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 90, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 10, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Annotation", "slug": "annotation", "status": "active", "bestFor": null, "canonId": "019dd956-b5b9-7668-bf0e-3af99823ec5c", "version": 1, "bodyDocId": null, "description": "Adding explanatory labels to highlight key data points", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "019dd95a-0f08-73d3-987b-8594f6516ada", "evidence": "Each quadrant annotated with % share and definition", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 10, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Color Strategy", "slug": "color-strategy", "status": "active", "bestFor": null, "canonId": "019dd956-b202-77cf-8a6e-f445b08c0fd3", "version": 1, "bodyDocId": null, "description": "Using color for emphasis, categories, sentiment, and hierarchy", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "019dd95a-0f08-73d3-987b-8361ceb2a62f", "evidence": "Distinct color per quadrant (blue Achievers, magenta Innovators, etc.)", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 85, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 10, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Visual Hierarchy", "slug": "visual-hierarchy", "status": "active", "bestFor": null, "canonId": "019dd956-b2ca-706b-937f-e52441086fd7", "version": 1, "bodyDocId": null, "description": "Order of perception through Size > Color > Position > Weight > Space", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "019dd95a-0f08-73d3-987b-7d57c981c696", "evidence": "Achievers placed top-right; large % numbers in each quadrant", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 10, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-8b604002b260", "evidence": "'AI, applied' frames the world's-largest-firms cut", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 70, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 11, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Authority Bias", "slug": "authority-bias", "status": "active", "bestFor": null, "canonId": "019dd956-c38d-70b9-a284-b2b479763356", "version": 1, "bodyDocId": null, "description": "Expert opinions are weighted more heavily - cite credible sources", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Architect", "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-92675d86845d", "evidence": "Citing top-2,000-by-market-cap sample for credibility", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 70, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 11, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-8e3f821ad536", "evidence": "Specific: 2,000 largest firms by market cap, 10% Achievers", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 11, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-993421352dde", "evidence": "Specific: air temperature data, 25% energy reduction", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 12, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Sinatra Test", "slug": "sinatra-test", "status": "active", "bestFor": null, "canonId": "019dd956-be4a-775f-8038-d4e7e58dfc5a", "version": 1, "bodyDocId": null, "description": "If you can make it there, you can make it anywhere - using flagship example", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-972e282391fb", "evidence": "Metro de Madrid as flagship public-sector AI example", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 12, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Singularity Effect", "slug": "singularity-effect", "status": "active", "bestFor": null, "canonId": "019dd956-dcd3-7287-94d8-5a49496c2ddb", "version": 1, "bodyDocId": null, "description": "People care more about individuals than statistics - use specific examples", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-9c73b2a15d56", "evidence": "Single named organization stands in for sector benefit", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 70, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 12, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-a335f3bc977b", "evidence": "Subhead: 'Achievers thrive on traditional performance metrics'", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 85, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 14, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-ab21e93148a8", "evidence": "50% revenue growth, 3.5x more likely, 30% AI-influenced revenue", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 14, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Small Multiples", "slug": "small-multiples", "status": "active", "bestFor": null, "canonId": "019dd956-b32d-7586-b368-baa1dd388ed4", "version": 1, "bodyDocId": null, "description": "Repeating similar charts for comparison across categories or time", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "019dd95a-0f08-73d3-987b-a7a5e2b8492a", "evidence": "Capability matrix repeats squares per profile across 17 capabilities", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 85, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 14, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-b1be7c29c5ea", "evidence": "'Turning pilots into production' states the insight", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 15, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-b4f8ec86d2f9", "evidence": "25% more likely to scale AI pilots; specific delta", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 15, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-bad6a8d24205", "evidence": "'Focusing beyond financial metrics' frames CX gap", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 75, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 16, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Audience Definition", "slug": "audience-definition", "status": "active", "bestFor": null, "canonId": "019dd956-7f2a-775d-aeee-ff099bcb7756", "version": 1, "bodyDocId": null, "description": "Six key questions: Who are they? What do they know? What do they believe? What do they care about? What do they fear? What decisions can they make?", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "d899529d-6124-4a6b-94f5-7adbbbe93d3c", "evidence": "Accenture’s Sustainable Technology survey of more than 500 multinational companies", "pageRefs": null, "priority": "Core", "whenToUse": null, "confidence": 0.7, "extraction": { "at": "2026-07-16 21:02:35.412514+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 16, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-bf9930bdc5bd", "evidence": "8% higher CX score for Achievers", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 75, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 16, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Storytelling Effect", "slug": "storytelling-effect", "status": "active", "bestFor": null, "canonId": "019dd956-981b-74f9-9096-5c8df03dab5a", "version": 1, "bodyDocId": null, "description": "Stories are remembered up to 22x better than facts alone", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "9f1da67e-de21-4423-85ae-9528ff619ae5", "evidence": "The stakeholder performance model revealed the value-creation gap between Achievers and other companies is significant when it comes to sustainability.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.6, "extraction": { "at": "2026-07-16 21:02:35.452544+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 16, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Aha! Moment", "slug": "aha-moment", "status": "active", "bestFor": null, "canonId": "019dd956-a5b5-7763-820e-b299c5f27a32", "version": 1, "bodyDocId": null, "description": "The moment when the audience realizes the value - design for insight revelation", "familyLabel": null, "categoryName": "Loop", "categorySlug": "loop" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "7e93f200-3442-41dd-b6fb-ab4540da5fe9", "evidence": "High performance today will ultimately become business-as-usual tomorrow.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.8, "extraction": { "at": "2026-07-16 21:02:35.567404+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 18, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-c3b0a0be42da", "evidence": "Title is the directive: 'Champion AI as a strategic priority'", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 90, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 19, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-c6816ee6d35e", "evidence": "83% / 67% / 56% sponsorship comparison across segments", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 19, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Credibility Transfer", "slug": "credibility-transfer", "status": "active", "bestFor": null, "canonId": "019dd956-bd34-759a-8e3b-2f14b571a2a1", "version": 1, "bodyDocId": null, "description": "Borrowing authority from trusted sources or vivid details", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "b01ca547-5760-42f4-8fae-44c5d795fb3c", "evidence": "83% of Achievers have such sponsorship, while only 67% of Builders and just 56% of Experimenters have it.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.6, "extraction": { "at": "2026-07-16 21:02:35.684413+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 19, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Executive summary", "slug": "executive-summary", "status": "active", "bestFor": null, "canonId": "019dd9e1-4b63-72d5-9734-da757d91ca61", "version": 1, "bodyDocId": null, "description": "A condensed view of the entire argument on a single slide / block.", "familyLabel": null, "categoryName": null, "categorySlug": null }, "agent": null, "layer": "slide", "agents": null, "matchId": "8846bb20-ed18-4427-bcbc-02fb67f75077", "evidence": "Champion AI as a strategic priority for the entire organization, with full sponsorship from leadership", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.7, "extraction": { "at": "2026-07-16 21:02:35.644108+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 19, "whyItWorks": null, "antipattern": null, "cardinality": "common", "narrativePurpose": null }, { "tool": { "name": "Bandwagon Effect", "slug": "bandwagon-effect", "status": "active", "bestFor": null, "canonId": "019dd956-c742-724e-9623-54fd8878b87b", "version": 1, "bodyDocId": null, "description": "People adopt beliefs proportionally to how many others hold them - show momentum", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-d54db6f98fc1", "evidence": "Shows what successful peers do (CEO sponsorship 83%)", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 70, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 20, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-d287f6159caf", "evidence": "16% vs 4% on collaboration platforms", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 20, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Evidence Matrix", "slug": "evidence-matrix", "status": "active", "bestFor": null, "canonId": "019dd956-7ecc-74c2-b46c-0b9c1f4c73d7", "version": 1, "bodyDocId": null, "description": "Planning visual assets: Claim → Evidence Needed → Data Source → Visual Type", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "6b3e2464-5dfe-4d26-8a43-621084803f45", "evidence": "We found that 16% of Achievers are already using platforms that allow workers to easily pose questions and share ideas with colleagues across the company—compared to 4% of Experimenters.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.6, "extraction": { "at": "2026-07-16 21:02:35.760556+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 20, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-d8e61606d2c5", "evidence": "Directive title: 'Invest heavily in talent to get more from AI'", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 90, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 21, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-dcd1c9ae87d0", "evidence": "44% / 33% / 30% skills competency stats by segment", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 21, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Pyramid Principle", "slug": "pyramid-principle", "status": "active", "bestFor": "When the document has a single governing decision or recommendation the reader is asked to accept, and the case can be decomposed into a small number of clean groups. Strategy decks, board memos, investment committee papers.", "canonId": "019dd956-7cbd-7219-a477-c871e2bc1aff", "version": 1, "bodyDocId": "88b6ac56-b880-46f9-8621-ef3491ab6235", "description": "Lead with the answer; group three to five MECE supports beneath it; recurse one level down. Top-down hierarchical structure for any document with a single recommendation.", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "c76d5470-0345-41f9-80c6-ebd4b2f03f40", "evidence": "Invest heavily in talent to get more from AI investments", "pageRefs": null, "priority": "Core", "whenToUse": "When the document has a single governing decision or recommendation the reader is asked to accept, and the case can be decomposed into a small number of clean groups. Strategy decks, board memos, investment committee papers.", "confidence": 0.7, "extraction": { "at": "2026-07-16 21:02:35.837334+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 21, "whyItWorks": "Solves the two failure modes of expert communication at once: (1) the long climb where readers wait pages for the answer, and (2) logical mush from non-MECE groupings. Progressive disclosure lets a reader stop at any depth and get the right information.", "antipattern": "Chronological structure (\"what we did, what we found, what we conclude\") instead of logical hierarchy. Top-of-pyramid that is a topic (\"strategic options\") instead of an answer (\"pursue Option B because...\"). Non-MECE supports that overlap or fail to cover the universe.", "cardinality": null, "narrativePurpose": "Transfer conviction with minimum reader effort by mirroring how decision-makers consume information: answer first, scaffolding behind." }, { "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null, "categoryName": null, "categorySlug": null }, "agent": null, "layer": "slide", "agents": null, "matchId": "7f55fdf6-1339-4039-95b2-ff27d997d8ee", "evidence": "78% of Achievers—compared with just 56% of Builders and 51% of Experimenters—have mandatory AI trainings for most employees", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.6, "extraction": { "at": "2026-07-16 21:02:35.878522+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 21, "whyItWorks": null, "antipattern": null, "cardinality": "common", "narrativePurpose": null }, { "tool": { "name": "Bandwagon Effect", "slug": "bandwagon-effect", "status": "active", "bestFor": null, "canonId": "019dd956-c742-724e-9623-54fd8878b87b", "version": 1, "bodyDocId": null, "description": "People adopt beliefs proportionally to how many others hold them - show momentum", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-e6b24338f89c", "evidence": "What Achievers already do as social proof for action", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 70, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 22, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-e21fc2db8c8d", "evidence": "78% mandatory training stat", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 22, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-e82258f4cc15", "evidence": "Directive: 'Industrialize AI tools and teams to create an AI core'", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 90, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 23, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-ec93e02c70c3", "evidence": "32% more likely to build custom ML applications", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 23, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Logical chain", "slug": "logical-chain", "status": "active", "bestFor": null, "canonId": "019dd9e1-5104-702f-8953-85757af6553d", "version": 1, "bodyDocId": null, "description": "If-then, premise-conclusion, deductive chain.", "familyLabel": null, "categoryName": null, "categorySlug": null }, "agent": null, "layer": "slide", "agents": null, "matchId": "cc6b62bb-040a-4cb6-873b-560641c7acb5", "evidence": "To extract value from their data quickly and effectively, Achievers are also 32% more likely, on average, than Experimenters to either develop custom-built machine learning applications or work with a partner", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.7, "extraction": { "at": "2026-07-16 21:02:36.002457+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 23, "whyItWorks": null, "antipattern": null, "cardinality": "common", "narrativePurpose": null }, { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-f4c54ba672a5", "evidence": "$1.5B annual claims, 5% efficiency gains, $100M savings", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 85, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 24, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Sinatra Test", "slug": "sinatra-test", "status": "active", "bestFor": null, "canonId": "019dd956-be4a-775f-8038-d4e7e58dfc5a", "version": 1, "bodyDocId": null, "description": "If you can make it there, you can make it anywhere - using flagship example", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-f02412cd5ea8", "evidence": "Major insurer flagship transformation case", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 24, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Singularity Effect", "slug": "singularity-effect", "status": "active", "bestFor": null, "canonId": "019dd956-dcd3-7287-94d8-5a49496c2ddb", "version": 1, "bodyDocId": null, "description": "People care more about individuals than statistics - use specific examples", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-f89c63c59a0b", "evidence": "Single insurer case stands in for the recommendation", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 70, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 24, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-fe900ba89c82", "evidence": "Directive: 'Design AI responsibly, from the start'", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 90, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 25, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987c-01511abb42ac", "evidence": "53% more likely to be responsible by design", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 25, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Monroe's Motivated Sequence", "slug": "monroes-motivated-sequence", "status": "active", "bestFor": null, "canonId": "019dd956-8758-700f-83cf-d260a54ad24f", "version": 1, "bodyDocId": null, "description": "Five-step persuasion: Attention, Need, Satisfaction, Visualization, Action", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "3f9492ba-373f-47c1-87a2-5fbffb1d8c65", "evidence": "Design AI responsibly, from the start", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.6, "extraction": { "at": "2026-07-16 21:02:36.13041+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 25, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987c-07103bb1d7cf", "evidence": "6% implemented vs 42% aspire by 2024", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 26, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Sinatra Test", "slug": "sinatra-test", "status": "active", "bestFor": null, "canonId": "019dd956-be4a-775f-8038-d4e7e58dfc5a", "version": 1, "bodyDocId": null, "description": "If you can make it there, you can make it anywhere - using flagship example", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987c-0b3725dce97e", "evidence": "Monetary Authority of Singapore Veritas + Novartis flagship cases", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 26, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Von Restorff Effect", "slug": "von-restorff-effect", "status": "active", "bestFor": null, "canonId": "019dd956-beab-73d8-9f62-30d0332e926e", "version": 1, "bodyDocId": null, "description": "Items that stand out are noticed and remembered - make key points visually distinct", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "019dd95a-0f08-73d3-987c-0ea5dae86980", "evidence": "Large purple callout box isolates the 6% / 42% stat", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 26, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987c-1374f0ff8ae3", "evidence": "Directive: 'Prioritize long- and short-term AI investments'", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 90, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 27, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "67a3cf86-5bac-4582-9e03-d9facd8aa706", "evidence": "Prioritize long- and short-term AI investments", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 0.9, "extraction": { "at": "2026-07-16 21:02:36.246447+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 27, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987c-165d09e5a644", "evidence": "14% to 28% to 34% AI budget trajectory", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 27, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987c-1b570017c2e8", "evidence": "Headline is the projection: 12% to 27% by 2024", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 85, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 28, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Emotional Appeal", "slug": "emotional-appeal", "status": "active", "bestFor": null, "canonId": "019dd956-bde9-7668-8be7-0097678fec59", "version": 1, "bodyDocId": null, "description": "Connecting to audience feelings rather than just logic", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987c-23d8ee628917", "evidence": "Triumphant silhouette with raised arms in purple light", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 75, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 28, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Picture Superiority Effect", "slug": "picture-superiority-effect", "status": "active", "bestFor": null, "canonId": "019dd956-c201-7014-bb58-3e85e3e11dfd", "version": 1, "bodyDocId": null, "description": "Pictures remembered 6x better than words - prioritize visuals over text", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "019dd95a-0f08-73d3-987c-27aae6b354e3", "evidence": "Full-bleed photo dominates the slide", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 70, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 28, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Von Restorff Effect", "slug": "von-restorff-effect", "status": "active", "bestFor": null, "canonId": "019dd956-beab-73d8-9f62-30d0332e926e", "version": 1, "bodyDocId": null, "description": "Items that stand out are noticed and remembered - make key points visually distinct", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "019dd95a-0f08-73d3-987c-1f9935c5b7cd", "evidence": "Single oversized stat against silhouette image", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 28, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Closing Techniques", "slug": "closing-techniques", "status": "active", "bestFor": null, "canonId": "019dd956-af13-774b-a4fd-7db0a3a88d51", "version": 1, "bodyDocId": null, "description": "Strong endings: Callback, challenge, vision, clear CTA, memorable line", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987c-2b990d81666a", "evidence": "Memorable line: 'cloud as enabler, data as driver, AI as differentiator'", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 30, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Core Message Extraction", "slug": "core-message-extraction", "status": "active", "bestFor": "Any slide that carries an argument or finding. The diagnostic: if the slide has a chart on it, it needs a core message. Run extraction whenever the current title is a topic (Q3 performance, Customer satisfaction) rather than a claim, or when a reader could reasonably draw more than one conclusion from the visible evidence.", "canonId": "019dd956-bc71-707d-8867-1b2e810175cf", "version": 1, "bodyDocId": "f85613cb-4b77-462f-bfe6-3b862516e1a6", "description": "The discipline of distilling raw analysis, data, or content into a single declarative sentence — the slide's core message — that the audience would walk away with if they remembered nothing else. Process, not artefact: extraction questions run on the raw content until one sentence falls out.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987c-2c29555591ec", "evidence": "Quote distills the strategic thesis", "pageRefs": null, "priority": null, "whenToUse": "Any slide that carries an argument or finding. The diagnostic: if the slide has a chart on it, it needs a core message. Run extraction whenever the current title is a topic (Q3 performance, Customer satisfaction) rather than a claim, or when a reader could reasonably draw more than one conclusion from the visible evidence.", "confidence": 75, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 30, "whyItWorks": "Solves the two failure modes of slide writing simultaneously: the topic title (which outsources interpretation to the audience and produces three different conclusions in three different heads) and the multi-thesis stapler (which exceeds working memory and gets remembered as fragments). Aligns with how readers actually consume decks — Heath & Heath's Find the Core, the journalist's nut graf, and Minto's governing thought all converge on the same cognitive economics.", "antipattern": "Topic titles disguised as core messages (Customer satisfaction, no verb, no stake); multi-thesis sentences stapled with and; hedged sentences (may show signs of possible softening); chart-caption titles (Revenue fell 4%) that restate what the chart already shows instead of naming the so-what; meta-commentary (this slide explores...) that describes the slide rather than concluding it.", "cardinality": null, "narrativePurpose": "Forces the slide author to do the interpretive work before the slide ships, so the audience does not have to do it during the meeting. Wins the WYSIATI battle on purpose — the loudest fragment on the page becomes the author's chosen sentence rather than whichever chart label happens to be largest." }, { "tool": { "name": "Storytelling: audience calibration", "slug": "audience", "status": "active", "bestFor": null, "canonId": "019df269-23ea-723a-87c0-af1d78d702d2", "version": 1, "bodyDocId": "019df269-23c5-72ec-8b94-78fc1da7b656", "description": "The same thesis, written for four different audiences, produces four", "familyLabel": "narrative-block", "categoryName": "Narrative & Transformation", "categorySlug": "narrative-transformation" }, "agent": "storyteller", "layer": "slide", "agents": [ "storyteller" ], "matchId": "c1dc35bc-ff19-465b-bf29-b38f1089ba71", "evidence": "Other organizations should be asking questions to assess their own AI maturity.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.7, "extraction": { "at": "2026-07-16 21:02:26.221061+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 30, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987c-32dece881988", "evidence": "Figure 7 frames assessment for C-suite leaders", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 75, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 31, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Grid System", "slug": "grid-system", "status": "active", "bestFor": null, "canonId": "019dd956-b745-710f-8b70-825c603b1163", "version": 1, "bodyDocId": null, "description": "Aligning elements to invisible grid for visual harmony", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "019dd95a-0f08-73d3-987c-34bfee3863d1", "evidence": "Four-row table aligned by category and key questions", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 31, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Audience Definition", "slug": "audience-definition", "status": "active", "bestFor": null, "canonId": "019dd956-7f2a-775d-aeee-ff099bcb7756", "version": 1, "bodyDocId": null, "description": "Six key questions: Who are they? What do they know? What do they believe? What do they care about? What do they fear? What decisions can they make?", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "027546b1-5f5a-44be-bc40-fe5a703b8736", "evidence": "Research Manager", "pageRefs": null, "priority": "Core", "whenToUse": null, "confidence": 0.7, "extraction": { "at": "2026-07-17 03:14:34.314336+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 35, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Authority citation", "slug": "authority-citation", "status": "active", "bestFor": null, "canonId": "019dd9e1-49ae-7557-89ad-3fca70469b45", "version": 1, "bodyDocId": null, "description": "Quoting an authority figure, study, or precedent to lend weight.", "familyLabel": null, "categoryName": null, "categorySlug": null }, "agent": null, "layer": "slide", "agents": null, "matchId": "8900dd19-a183-43a4-8730-84addc56958a", "evidence": "We interviewed 25 CEOs, Chief Data Officers and Chief Analytics Officers.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.7, "extraction": { "at": "2026-07-17 03:14:34.356392+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 35, "whyItWorks": null, "antipattern": null, "cardinality": "common", "narrativePurpose": null }, { "tool": { "name": "List presentation", "slug": "list-presentation", "status": "active", "bestFor": null, "canonId": "019dd9e1-4ef7-777d-ba68-9074c2b3bcf1", "version": 1, "bodyDocId": null, "description": "Bulleted, numbered, or checklist enumerations.", "familyLabel": null, "categoryName": null, "categorySlug": null }, "agent": null, "layer": "slide", "agents": null, "matchId": "1871c8cd-f1f0-424c-8dc3-a79c8b50a30b", "evidence": "1. Senior Sponsorship: Organizations have an AI strategy that is developed by the Chief Analytics Officer, Chief Data Officer, Chief Digital Officer or an equivalent.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.9, "extraction": { "at": "2026-07-17 03:14:34.436819+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 37, "whyItWorks": null, "antipattern": null, "cardinality": "common", "narrativePurpose": null }, { "tool": { "name": "List presentation", "slug": "list-presentation", "status": "active", "bestFor": null, "canonId": "019dd9e1-4ef7-777d-ba68-9074c2b3bcf1", "version": 1, "bodyDocId": null, "description": "Bulleted, numbered, or checklist enumerations.", "familyLabel": null, "categoryName": null, "categorySlug": null }, "agent": null, "layer": "slide", "agents": null, "matchId": "2bf981c4-fca3-4320-b49d-06f4bbdb0fc7", "evidence": "13. Innovation Culture Embedded: Organizations ensure innovation is part of the day-to-day work environment.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.9, "extraction": { "at": "2026-07-17 03:14:34.515898+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 38, "whyItWorks": null, "antipattern": null, "cardinality": "common", "narrativePurpose": null } ], "frameworks": [ { "matchId": "e6258561-1235-4dcf-99db-d29c52e98265", "evidence": "The slide explicitly defines a 'holistic AI-maturity framework' and describes measuring the degree of mastery of capabilities.", "framework": null, "confidence": 1, "extraction": { "at": "2026-05-02 19:49:13.345+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 9, "frameworkName": "maturity-model" }, { "matchId": "019dd95a-1ca5-70bb-bb9f-3d2900ad658f", "evidence": "Axes labelled 'AI Foundation' (X) x 'AI Differentiation' (Y) with four named quadrants", "framework": null, "confidence": 95, "extraction": { "at": "2026-04-29 13:07:34.826634+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 10, "frameworkName": "2x2 Matrix" }, { "matchId": "20937c33-924f-4ea8-995f-df4d69f7526c", "evidence": "The slide uses a 2x2 grid to segment companies based on two axes: AI Foundation and AI Differentiation.", "framework": null, "confidence": 1, "extraction": { "at": "2026-05-02 19:49:13.378+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 10, "frameworkName": "matrix-2x2" }, { "matchId": "ba834e4a-196d-439f-84ca-730ee9a7a0ff", "evidence": "Levels of AI maturity by industry", "framework": null, "confidence": 0.9, "extraction": { "at": "2026-05-02 19:49:13.612+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 11, "frameworkName": "maturity-model" }, { "matchId": "019dd95a-1ca5-70bb-bb9f-40e303f7e6a0", "evidence": "Four pillars (Strategy & Sponsorship, Data & AI Core, Talent & Culture, Responsible AI) x 17 capabilities heatmap", "framework": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:34.826634+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 14, "frameworkName": "Accenture AI Maturity Capability Framework" }, { "matchId": "1791ae98-3b4c-424b-8356-b507e6aeb903", "evidence": "Categorization of AI profiles into Achievers, Builders, Innovators, and Experimenters across capabilities.", "framework": null, "confidence": 0.9, "extraction": { "at": "2026-05-02 19:49:13.949+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 14, "frameworkName": "maturity-model" }, { "matchId": "019dd95a-1ca5-70bb-bb9f-46577eebc96d", "evidence": "Callout names FEAT principles (Fair, Ethical, Accountable, Transparent)", "framework": null, "confidence": 85, "extraction": { "at": "2026-04-29 13:07:34.826634+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 26, "frameworkName": "FEAT Principles" }, { "matchId": "019dd95a-1ca5-70bb-bb9f-48c7d03ff99f", "evidence": "Four-category assessment (Strategy/Sponsorship, Data/AI Core, Talent/Culture, Responsible AI)", "framework": null, "confidence": 85, "extraction": { "at": "2026-04-29 13:07:34.826634+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 31, "frameworkName": "Accenture AI Maturity Assessment" }, { "matchId": "b40c1ee5-872e-4f0d-9abc-e9801bd15fc1", "evidence": "The slide presents a structured assessment of AI maturity across four key organizational dimensions.", "framework": null, "confidence": 0.9, "extraction": { "at": "2026-05-02 19:49:15.162+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 31, "frameworkName": "maturity-model" }, { "matchId": "51611ba6-98ba-46ee-81a4-268e05c41198", "evidence": "The slide details the specific analytical steps and regression models used to derive study conclusions.", "framework": null, "confidence": 0.9, "extraction": { "at": "2026-05-02 21:24:34.862+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 36, "frameworkName": "analytical-method" } ], "patterns": [] }, "storymakers": { "model": "claude-cli-sonnet-4.6", "backend": null, "rawJson": { "arc": { "id": 1, "slug": "consultants-gambit", "beats": [ { "name": "Situation & Context", "end_page": 7, "evidence": "AI essential for competitive advantage; transformation accelerating 16 months faster", "position": 1, "start_page": 3 }, { "name": "Problem & Complication", "end_page": 12, "evidence": "Segmentation reveals only 12% are AI Achievers; 63% Experimenters", "position": 2, "start_page": 8 }, { "name": "Solution & Approach", "end_page": 27, "evidence": "Five recommendations to master AI maturity, each with case study", "position": 3, "start_page": 17 }, { "name": "Evidence & Proof", "end_page": 16, "evidence": "Achievers outperform peers on revenue (50%), scaling (25%), CX (8%)", "position": 4, "start_page": 13 }, { "name": "Impact & Next Steps", "end_page": 31, "evidence": "Vision (12% to 27% by 2024) plus C-suite assessment questions", "position": 5, "start_page": 28 } ], "evidence": "Classic SCQA: situation (pp 3-7 AI essential), problem (pp 8-12 only 12% Achievers), evidence (pp 13-16 outperformance), solution (pp 17-27 five ways), action (pp 28-31 vision + assessment).", "confidence": 90 }, "loops": [ { "id": 3, "slug": "03-aha-moment", "end_page": 4, "evidence": "Exec summary states the 12% finding; p4 lists the five ways", "position": 1, "objective": "Reveal the central finding and preview the five differentiators", "confidence": 75, "start_page": 3 }, { "id": 15, "slug": "15-why-now", "end_page": 7, "evidence": "Pp 6-7 frame AI as essential and accelerating: 16 months faster, AI-influenced revenue tripling", "position": 2, "objective": "Establish urgency for AI investment", "confidence": 80, "start_page": 5 }, { "id": 30, "slug": "30-2x2-matrix", "end_page": 12, "evidence": "p10 explicit 2x2 (AI Foundation x Differentiation) with four named segments", "position": 3, "objective": "Define AI maturity and segment firms via 2x2", "confidence": 90, "start_page": 8 }, { "id": 39, "slug": "39-benchmark-gap", "end_page": 16, "evidence": "Pp 14-16 quantify gap: 50% revenue growth, 25% scaling, 8% CX, 17 capability heatmap", "position": 4, "objective": "Show Achievers outperform peers on key metrics", "confidence": 85, "start_page": 13 }, { "id": 40, "slug": "40-mece-breakdown", "end_page": 27, "evidence": "Five distinct recommendation categories (sponsor / talent / AI core / responsible / invest) each with case study", "position": 5, "objective": "Five MECE recommendations to become an Achiever", "confidence": 85, "start_page": 17 }, { "id": 41, "slug": "41-so-what-cascade", "end_page": 31, "evidence": "p28 vision stat, p30 mantra quote, p31 sample C-suite assessment questions", "position": 6, "objective": "Drive from finding to assessment action", "confidence": 70, "start_page": 28 } ], "slide_tools": [ { "tools": [ { "id": 1, "slug": "scqa-framework", "layer": "Block", "evidence": "Whole deck opens with situation (AI evolved), complication (12% lead), question (how) - block anchors here", "confidence": 85 }, { "id": 118, "slug": "action-titles", "layer": "Slide", "evidence": "Title states insight not topic: 'AI evolved from concept to societal constant'", "confidence": 80 }, { "id": 123, "slug": "opening-hooks", "layer": "Slide", "evidence": "Startling-statistic hook: '12% have advanced AI maturity to superior performance'", "confidence": 85 }, { "id": 154, "slug": "concrete-language", "layer": "Slide", "evidence": "Specific 70-year history and 12% figure", "confidence": 80 }, { "id": 103, "slug": "aha-moment", "layer": "Loop", "evidence": "Loop opens with the surprising 12%-Achiever insight", "confidence": 75 } ], "page_number": 3 }, { "tools": [ { "id": 118, "slug": "action-titles", "layer": "Slide", "evidence": "Title 'What do AI Achievers do differently?' frames the answer set", "confidence": 80 }, { "id": 67, "slug": "curiosity-gap", "layer": "Block", "evidence": "Question title creates open loop the deck will close", "confidence": 80 }, { "id": 116, "slug": "chunking", "layer": "Loop", "evidence": "Five labelled chunks listed: champion / talent / AI core / responsible / invest", "confidence": 80 }, { "id": 2, "slug": "mece-principle", "layer": "Block", "evidence": "Five distinct, non-overlapping capability categories preview the deck", "confidence": 75 }, { "id": 165, "slug": "picture-superiority-effect", "layer": "Slide", "evidence": "Half-slide portrait paired with text", "confidence": 70 } ], "page_number": 4 }, { "tools": [ { "id": 118, "slug": "action-titles", "layer": "Slide", "evidence": "Subhead states the insight: 'AI is absolutely essential to competitive advantage'", "confidence": 85 }, { "id": 138, "slug": "annotation", "layer": "Slide", "evidence": "Chart annotates '16 months faster' delta with callout", "confidence": 85 }, { "id": 130, "slug": "color-strategy", "layer": "Slide", "evidence": "Purple curves distinguish AI vs Digital transformation", "confidence": 75 }, { "id": 154, "slug": "concrete-language", "layer": "Slide", "evidence": "Specific stats: 46% CEOs, 75% integrated AI, 30% scaled pilots", "confidence": 80 } ], "page_number": 6 }, { "tools": [ { "id": 118, "slug": "action-titles", "layer": "Slide", "evidence": "Figure 2 title states the trend in revenue evolution", "confidence": 75 }, { "id": 129, "slug": "chart-selection-guide", "layer": "Slide", "evidence": "Sankey-style flow chart suits revenue-share migration over time", "confidence": 80 }, { "id": 138, "slug": "annotation", "layer": "Slide", "evidence": "10% / 22% / 31% callouts on the bands", "confidence": 80 }, { "id": 130, "slug": "color-strategy", "layer": "Slide", "evidence": "Color encodes High / Medium / Low revenue thresholds", "confidence": 75 } ], "page_number": 7 }, { "tools": [ { "id": 118, "slug": "action-titles", "layer": "Slide", "evidence": "Defines AI maturity as mastery of capabilities for performance", "confidence": 80 }, { "id": 153, "slug": "core-message-extraction", "layer": "Slide", "evidence": "Single-paragraph definition crystallizes the construct", "confidence": 75 } ], "page_number": 9 }, { "tools": [ { "id": 118, "slug": "action-titles", "layer": "Slide", "evidence": "Figure 3 title is the insight: 'Only 12% are AI Achievers'", "confidence": 90 }, { "id": 132, "slug": "visual-hierarchy", "layer": "Slide", "evidence": "Achievers placed top-right; large % numbers in each quadrant", "confidence": 80 }, { "id": 130, "slug": "color-strategy", "layer": "Slide", "evidence": "Distinct color per quadrant (blue Achievers, magenta Innovators, etc.)", "confidence": 85 }, { "id": 138, "slug": "annotation", "layer": "Slide", "evidence": "Each quadrant annotated with % share and definition", "confidence": 80 } ], "page_number": 10 }, { "tools": [ { "id": 118, "slug": "action-titles", "layer": "Slide", "evidence": "'AI, applied' frames the world's-largest-firms cut", "confidence": 70 }, { "id": 154, "slug": "concrete-language", "layer": "Slide", "evidence": "Specific: 2,000 largest firms by market cap, 10% Achievers", "confidence": 80 }, { "id": 169, "slug": "authority-bias", "layer": "Slide", "evidence": "Citing top-2,000-by-market-cap sample for credibility", "confidence": 70 } ], "page_number": 11 }, { "tools": [ { "id": 157, "slug": "sinatra-test", "layer": "Slide", "evidence": "Metro de Madrid as flagship public-sector AI example", "confidence": 80 }, { "id": 154, "slug": "concrete-language", "layer": "Slide", "evidence": "Specific: air temperature data, 25% energy reduction", "confidence": 80 }, { "id": 224, "slug": "singularity-effect", "layer": "Slide", "evidence": "Single named organization stands in for sector benefit", "confidence": 70 } ], "page_number": 12 }, { "tools": [ { "id": 118, "slug": "action-titles", "layer": "Slide", "evidence": "Subhead: 'Achievers thrive on traditional performance metrics'", "confidence": 85 }, { "id": 133, "slug": "small-multiples", "layer": "Slide", "evidence": "Capability matrix repeats squares per profile across 17 capabilities", "confidence": 85 }, { "id": 154, "slug": "concrete-language", "layer": "Slide", "evidence": "50% revenue growth, 3.5x more likely, 30% AI-influenced revenue", "confidence": 80 }, { "id": 78, "slug": "contrast-pairs", "layer": "Loop", "evidence": "Achievers vs Experimenters comparison anchors the loop", "confidence": 75 } ], "page_number": 14 }, { "tools": [ { "id": 118, "slug": "action-titles", "layer": "Slide", "evidence": "'Turning pilots into production' states the insight", "confidence": 80 }, { "id": 154, "slug": "concrete-language", "layer": "Slide", "evidence": "25% more likely to scale AI pilots; specific delta", "confidence": 80 } ], "page_number": 15 }, { "tools": [ { "id": 118, "slug": "action-titles", "layer": "Slide", "evidence": "'Focusing beyond financial metrics' frames CX gap", "confidence": 75 }, { "id": 154, "slug": "concrete-language", "layer": "Slide", "evidence": "8% higher CX score for Achievers", "confidence": 75 } ], "page_number": 16 }, { "tools": [ { "id": 118, "slug": "action-titles", "layer": "Slide", "evidence": "Title is the directive: 'Champion AI as a strategic priority'", "confidence": 90 }, { "id": 154, "slug": "concrete-language", "layer": "Slide", "evidence": "83% / 67% / 56% sponsorship comparison across segments", "confidence": 80 }, { "id": 116, "slug": "chunking", "layer": "Loop", "evidence": "First of five recommendation chunks begins", "confidence": 75 }, { "id": 2, "slug": "mece-principle", "layer": "Block", "evidence": "Five distinct recommendation categories begin here", "confidence": 75 } ], "page_number": 19 }, { "tools": [ { "id": 154, "slug": "concrete-language", "layer": "Slide", "evidence": "16% vs 4% on collaboration platforms", "confidence": 80 }, { "id": 177, "slug": "bandwagon-effect", "layer": "Slide", "evidence": "Shows what successful peers do (CEO sponsorship 83%)", "confidence": 70 } ], "page_number": 20 }, { "tools": [ { "id": 118, "slug": "action-titles", "layer": "Slide", "evidence": "Directive title: 'Invest heavily in talent to get more from AI'", "confidence": 90 }, { "id": 154, "slug": "concrete-language", "layer": "Slide", "evidence": "44% / 33% / 30% skills competency stats by segment", "confidence": 80 } ], "page_number": 21 }, { "tools": [ { "id": 154, "slug": "concrete-language", "layer": "Slide", "evidence": "78% mandatory training stat", "confidence": 80 }, { "id": 177, "slug": "bandwagon-effect", "layer": "Slide", "evidence": "What Achievers already do as social proof for action", "confidence": 70 } ], "page_number": 22 }, { "tools": [ { "id": 118, "slug": "action-titles", "layer": "Slide", "evidence": "Directive: 'Industrialize AI tools and teams to create an AI core'", "confidence": 90 }, { "id": 154, "slug": "concrete-language", "layer": "Slide", "evidence": "32% more likely to build custom ML applications", "confidence": 80 } ], "page_number": 23 }, { "tools": [ { "id": 157, "slug": "sinatra-test", "layer": "Slide", "evidence": "Major insurer flagship transformation case", "confidence": 80 }, { "id": 154, "slug": "concrete-language", "layer": "Slide", "evidence": "$1.5B annual claims, 5% efficiency gains, $100M savings", "confidence": 85 }, { "id": 224, "slug": "singularity-effect", "layer": "Slide", "evidence": "Single insurer case stands in for the recommendation", "confidence": 70 } ], "page_number": 24 }, { "tools": [ { "id": 118, "slug": "action-titles", "layer": "Slide", "evidence": "Directive: 'Design AI responsibly, from the start'", "confidence": 90 }, { "id": 154, "slug": "concrete-language", "layer": "Slide", "evidence": "53% more likely to be responsible by design", "confidence": 80 } ], "page_number": 25 }, { "tools": [ { "id": 154, "slug": "concrete-language", "layer": "Slide", "evidence": "6% implemented vs 42% aspire by 2024", "confidence": 80 }, { "id": 157, "slug": "sinatra-test", "layer": "Slide", "evidence": "Monetary Authority of Singapore Veritas + Novartis flagship cases", "confidence": 80 }, { "id": 158, "slug": "von-restorff-effect", "layer": "Slide", "evidence": "Large purple callout box isolates the 6% / 42% stat", "confidence": 80 } ], "page_number": 26 }, { "tools": [ { "id": 118, "slug": "action-titles", "layer": "Slide", "evidence": "Directive: 'Prioritize long- and short-term AI investments'", "confidence": 90 }, { "id": 154, "slug": "concrete-language", "layer": "Slide", "evidence": "14% to 28% to 34% AI budget trajectory", "confidence": 80 } ], "page_number": 27 }, { "tools": [ { "id": 118, "slug": "action-titles", "layer": "Slide", "evidence": "Headline is the projection: 12% to 27% by 2024", "confidence": 85 }, { "id": 158, "slug": "von-restorff-effect", "layer": "Slide", "evidence": "Single oversized stat against silhouette image", "confidence": 80 }, { "id": 156, "slug": "emotional-appeal", "layer": "Slide", "evidence": "Triumphant silhouette with raised arms in purple light", "confidence": 75 }, { "id": 165, "slug": "picture-superiority-effect", "layer": "Slide", "evidence": "Full-bleed photo dominates the slide", "confidence": 70 } ], "page_number": 28 }, { "tools": [ { "id": 124, "slug": "closing-techniques", "layer": "Slide", "evidence": "Memorable line: 'cloud as enabler, data as driver, AI as differentiator'", "confidence": 80 }, { "id": 153, "slug": "core-message-extraction", "layer": "Slide", "evidence": "Quote distills the strategic thesis", "confidence": 75 } ], "page_number": 30 }, { "tools": [ { "id": 118, "slug": "action-titles", "layer": "Slide", "evidence": "Figure 7 frames assessment for C-suite leaders", "confidence": 75 }, { "id": 142, "slug": "grid-system", "layer": "Slide", "evidence": "Four-row table aligned by category and key questions", "confidence": 80 }, { "id": 2, "slug": "mece-principle", "layer": "Block", "evidence": "Four MECE pillars (Strategy/Sponsorship, Data/AI Core, Talent/Culture, Responsible AI)", "confidence": 80 } ], "page_number": 31 } ], "slides_seen": 40, "deck_summary": "Textbook Consultant's Gambit (SCQA): Accenture sets up AI urgency, reveals the 12%-Achiever segmentation via a 2x2, benchmarks Achievers' outperformance, prescribes five MECE recommendations with case-study evidence, and closes with a maturity self-assessment.", "secondary_arcs": [], "images_inspected": 9, "slide_frameworks": [ { "frameworks": [ { "name": "2x2 Matrix", "slug": "2x2-matrix", "evidence": "Axes labelled 'AI Foundation' (X) x 'AI Differentiation' (Y) with four named quadrants", "confidence": 95 } ], "page_number": 10 }, { "frameworks": [ { "name": "Accenture AI Maturity Capability Framework", "slug": "accenture-ai-maturity-capability-framework", "evidence": "Four pillars (Strategy & Sponsorship, Data & AI Core, Talent & Culture, Responsible AI) x 17 capabilities heatmap", "confidence": 80 } ], "page_number": 14 }, { "frameworks": [ { "name": "FEAT Principles", "slug": "feat-principles", "evidence": "Callout names FEAT principles (Fair, Ethical, Accountable, Transparent)", "confidence": 85 } ], "page_number": 26 }, { "frameworks": [ { "name": "Accenture AI Maturity Assessment", "slug": "accenture-ai-maturity-assessment", "evidence": "Four-category assessment (Strategy/Sponsorship, Data/AI Core, Talent/Culture, Responsible AI)", "confidence": 85 } ], "page_number": 31 } ] }, "matchedAt": "2026-04-26 01:12:16+00", "slidesSeen": 40, "deckSummary": "Textbook Consultant's Gambit (SCQA): Accenture sets up AI urgency, reveals the 12%-Achiever segmentation via a 2x2, benchmarks Achievers' outperformance, prescribes five MECE recommendations with case-study evidence, and closes with a maturity self-assessment.", "imagesInspected": 9, "extractionSeconds": 399.55115 }, "score": { "backend": null, "scoredAt": "2026-04-24 09:23:24+00", "subScores": { "mece_pillars": 61.6, "scqa_opening": 90, "slide_type_mix": 64, "lead_with_answer": 100, "narrative_pacing": 77.5, "action_title_density": 60.483870967741936 }, "totalScore": 75.2, "coveragePct": 100, "explanations": { "mece_pillars": "6 pillars, balance=0.34", "scqa_opening": "first-third narrative: SCS", "slide_type_mix": "N10% · S52% · W22% · W15%", "lead_with_answer": "executive_summary on page 3", "narrative_pacing": "15% filler/appendix, middle density 85%", "action_title_density": "15/31 substantive slides have action titles (48%)" }, "slidesAnalyzed": 40 }, "review": { "backend": null, "verdict": "A disciplined Accenture thought-leadership deck with a genuine SCQA spine and a clean five-pillar recommend+case-study build — use the divider ladder and pillar pairing as a teaching example, but not the soft landing or the label-style analytical titles.", "reviewedAt": "2026-04-24 11:00:56+00", "slidesSeen": 40, "suggestions": [ "Rewrite topic-label titles as insight titles: p.11 → 'Only 10% of the world's 2,000 largest firms are AI Achievers'; p.7 → 'AI-influenced revenue will roughly triple by 2024'; p.16 → 'Achievers beat peers on CX and sustainability, not just revenue'", "Add an explicit call-to-action slide after p.30 ('Three moves for the next 90 days') and promote the C-suite questions (p.31) to the closer rather than burying them before the appendix", "De-duplicate the p.17/p.18 divider pair and repurpose p.18 as a preview map of the five recommendations so the reader can track position within the pillar build" ], "closingScore": 62, "openingScore": 82, "topStrengths": [ "Five-part recommendation spine (p.19, 21, 23, 25, 27) where each recommend slide is paired with a case-study slide — clean MECE pillars with proof points", "Thesis is quantified and time-bound early: 12%→27% by 2024 surfaces on p.4 and is book-ended on p.28", "Section dividers (p.5, 8, 13, 17, 29) form a coherent narrative ladder rather than topic dumps" ], "topWeaknesses": [ "No explicit call-to-action slide; the deck trails off into author bios (p.32–33) and a six-page appendix (p.34–39), with the C-suite self-assessment (p.31) buried before them", "Several analytical titles are labels not insights — p.11 'AI, applied', p.12 'AI, applied across industries', p.16 'Focusing beyond financial metrics', p.7 'Figure 2: Evolution of…'", "Duplicate section divider at p.17 and p.18 (both 'How AI Achievers master their craft') — a structural seam that wasn't cleaned up", "p.20 and p.22 case-study titles simply restate the stat from the preceding recommendation ('83%…', '78%…') instead of advancing the argument" ], "narrativeScore": 78, "pillarCritique": "Dividers are genuinely MECE and narrative: 'Why it matters' (p.5) → 'What it is' (p.8) → 'Advance from practice to performance' (p.13) → 'How Achievers master their craft' (p.17/18) → 'Practice makes progress' (p.29). Each divider sets up the next act rather than dumping a topic, though p.17 and p.18 are a duplicated divider pair.", "closingCritique": "p.28 restates the thesis and p.30 delivers a quotable close ('cloud as the enabler, data as the driver, AI as the differentiator'), but there is no explicit call-to-action slide — p.31's C-suite self-assessment is the closest thing and is buried just before author bios (p.32–33) and a heavy six-page appendix.", "openingCritique": "The opening leads with the answer: p.3 establishes stakes and p.4 explicitly poses the central question ('What do AI Achievers do differently?') while previewing the 12%→27% thesis. Strong hook with a quantified, time-bound claim inside the first four pages.", "extractionSeconds": 58.04842, "narrativeCritique": "Clean SCQA arc: 'Why AI maturity matters' (situation) → 'only 12% are Achievers' (complication) → 'how Achievers differ' (analysis via five recommendations) → 'Practice makes progress' close (resolution). The tension is slightly soft — the complication is framed as opportunity rather than consequence of inaction — but the act structure is intact.", "titleQualityScore": 72, "titleQualityCritique": "The five recommendation titles are genuinely action-bearing ('Champion AI as a strategic priority…', 'Invest heavily in talent…', 'Industrialize AI tools and teams…'), but several analytical slides revert to topic labels — e.g. p.11 'AI, applied', p.12 'AI, applied across industries', p.16 'Focusing beyond financial metrics', p.7 'Figure 2: Evolution of companies' AI-influenced revenue share' — instead of stating the insight in the title." }, "activistThesis": null, "pitchdeck": { "metadata": null, "profile": null }, "slides": [ { "page": 1, "type": "setup", "function": "front_matter", "imagePath": "https://imgproxy.kitesheet.com/HGjIctp6W3yYr261H85WDeOdF307NdWmTTMCH3NlC_M/rs:fit:1200:1200:0/q:82/f:webp/czM6Ly9raXRlc2hlZXQvY29ycHVzL3NsaWRlcy80ZDM2ODdmYTY5MWZjZDhiL3AwMDEuanBn", "rawType": "cover", "block": null, "metadata": { "slideType": "cover", "slideTypeCanon": { "name": "Cover", "slug": "cover", "status": "active", "canonId": "019de52d-05f2-729d-9f93-e0504f0a2410", "version": 1, "description": null }, "function": "front_matter", "functionCanon": { "name": "Front matter", "slug": "front_matter", "status": "active", "canonId": "019de52d-133d-73f0-9e26-201eda96b098", "version": 1, "description": null }, "density": "balanced", "densityScore": 32, "componentCount": 6, "textChars": 223, "nDataPoints": 0, "notes": null, "elementsJson": [ "headline_text", "subtitle_text", "photo" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:11.073+00", "seconds": 2.499, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/1", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-1", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "From insights to action, the path to extraordinary value starts here.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:44:27+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b401-2b7739b67931", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.166, "x": 0.029, "y": 0.922 }, "kind": "image", "text": "Voices of Change", "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Logo", "slug": "logo", "status": "active", "canonId": "019de52c-fdaf-743d-94c4-d7c6a0e55302", "version": 1, "description": null }, "framework": null }, "subkind": "logo", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:11.073+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "8cf50422-fe85-491b-b6f1-8d49fb3cb81b", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.076, "x": 0.895, "y": 0.922 }, "kind": "image", "text": "accenture", "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Logo", "slug": "logo", "status": "active", "canonId": "019de52c-fdaf-743d-94c4-d7c6a0e55302", "version": 1, "description": null }, "framework": null }, "subkind": "logo", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:11.073+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "d3d33622-8090-482b-b71d-918389533659", "parentComponentId": null }, { "bbox": { "h": 0.025, "w": 0.398, "x": 0.301, "y": 0.93 }, "kind": "paragraph", "text": "From insights to action, the path to extraordinary value starts here.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:11.073+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "5729b78c-f240-419a-a2e7-f857ec0038f9", "parentComponentId": null }, { "bbox": { "h": 0.115, "w": 0.606, "x": 0.197, "y": 0.398 }, "kind": "title", "text": "The art of AI maturity", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:11.073+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "260e1241-b4ad-4f9c-9ef0-6e7105468e6a", "parentComponentId": null }, { "bbox": { "h": 0.045, "w": 0.45, "x": 0.275, "y": 0.513 }, "kind": "title", "text": "Advancing from practice to performance", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Subtitle", "slug": "subtitle", "status": "active", "canonId": "019de52c-fb09-739d-900c-815b0d9ac526", "version": 1, "description": null }, "framework": null }, "subkind": "subtitle", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:11.073+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "f6c77e4e-34b5-4136-bb81-066a14669396", "parentComponentId": null } ], "metrics": [], "tools": [ { "tool": { "name": "Slide recipe: Cover", "slug": "cover-slide", "status": "active", "bestFor": null, "canonId": "019df269-2a08-767d-9128-e7431e6fe8a3", "version": 1, "bodyDocId": "019df269-29e5-7738-b5fa-6892e5dc8e65", "description": "The first ten seconds. Build it deliberately.", "familyLabel": "slide-recipe", "categoryName": "Slide", "categorySlug": "slide" }, "agent": "designer", "layer": "slide", "agents": [ "designer" ], "matchId": "b9bb85a3-65d0-4a01-adb8-7db57df6951b", "evidence": "The art of AI maturity", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.9, "extraction": { "at": "2026-07-16 21:02:18.91764+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 1, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null } ], "frameworks": [], "arcBeats": [], "loops": [], "thumbSrc": "https://imgproxy.kitesheet.com/mzfEOzTNQVyScktrDUGpDkUaeAXWz9yS1MlloELrrj0/rs:fit:480:480:0/q:78/f:webp/czM6Ly9raXRlc2hlZXQvY29ycHVzL3NsaWRlcy80ZDM2ODdmYTY5MWZjZDhiL3AwMDEuanBn", "imagePathAlt": "https://imgproxy.kitesheet.com/srWPjYBS4lBXBIyqRvnKfKyOT9ijjaskjpW4RIw4SH0/rs:fit:1200:1200:0/q:82/f:webp/czM6Ly9raXRlc2hlZXQvY29ycHVzL3NsaWRlcy80ZDM2ODdmYTY5MWZjZDhiL3AwMDEucG5n", "thumbSrcAlt": "https://imgproxy.kitesheet.com/DpHiSj6kzoGDyfnqbinN6ftkIR9BbY6kXzdCkg_t-ZI/rs:fit:480:480:0/q:78/f:webp/czM6Ly9raXRlc2hlZXQvY29ycHVzL3NsaWRlcy80ZDM2ODdmYTY5MWZjZDhiL3AwMDEucG5n" }, { "page": 2, "type": "setup", "function": "front_matter", "rawType": "table_of_contents", "block": null, "metadata": { "slideType": "table_of_contents", "slideTypeCanon": { "name": "Table of contents", "slug": "table_of_contents", "status": "active", "canonId": "019de52d-0643-721b-9b85-69676bb85cf8", "version": 1, "description": null }, "function": "front_matter", "functionCanon": { "name": "Front matter", "slug": "front_matter", "status": "active", "canonId": "019de52d-133d-73f0-9e26-201eda96b098", "version": 1, "description": null }, "density": "sparse", "densityScore": 14, "componentCount": 2, "textChars": 196, "nDataPoints": 6, "notes": null, "elementsJson": [ "headline_text", "bullet_list", "big_number" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:12.892+00", "seconds": 4.033, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/2", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-2", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": { "h": 0.688, "w": 0.884, "x": 0.058, "y": 0.188 }, "kind": "list", "text": "AI maturity: Why it matters 05\nAI maturity: What it is 08\nAI Achievers advance from practice to performance 13\nHow AI Achievers master their craft 17\nPractice makes progress 29\nAppendix 35", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Numbered list", "slug": "numbered", "status": "active", "canonId": "019de52c-fcc0-7718-a284-a4250c805df2", "version": 1, "description": null }, "framework": null }, "subkind": "numbered", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:12.892+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "622ba96b-1a85-4806-b8d1-031ac5551195", "parentComponentId": null }, { "bbox": { "h": 0.055, "w": 0.135, "x": 0.063, "y": 0.106 }, "kind": "title", "text": "Contents", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:12.892+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "523dfdde-0daf-420b-9c1b-f4a60e923151", "parentComponentId": null } ], "metrics": [], "tools": [], "frameworks": [], "arcBeats": [], "loops": [], "locked": true }, { "page": 3, "type": "setup", "title": "Includes a footnote reference (1) and cites Accenture research.", "function": "summarize", "rawType": "executive_summary", "block": null, "metadata": { "slideType": "executive_summary", "slideTypeCanon": { "name": "Executive summary", "slug": "executive_summary", "status": "active", "canonId": "019de52d-06be-73ff-81b3-f99fe7f0025e", "version": 1, "description": null }, "function": "summarize", "functionCanon": { "name": "Summarize", "slug": "summarize", "status": "active", "canonId": "019de52d-1541-704f-b86f-bf7b75056373", "version": 1, "description": null }, "density": "overcrowded", "densityScore": 94, "componentCount": 13, "textChars": 2245, "nDataPoints": 4, "notes": "Includes a footnote reference (1) and cites Accenture research.", "elementsJson": [ "headline_text", "action_title", "paragraph", "big_number", "callout_box" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:12.912+00", "seconds": 4.015, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/3", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-3", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "12% of firms have advanced their AI maturity enough to achieve superior performance and growth.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:44:02+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b400-7955d2ef7f86", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.1, "x": 0.561, "y": 0.66 }, "kind": "metric", "text": "63%", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": null, "subkind": { "name": "Big number", "slug": "big-number", "status": "active", "canonId": "019de52c-fc70-70c8-b965-0584548a9204", "version": 1, "description": null }, "framework": null }, "subkind": "big-number", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:12.912+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "240ab805-6e57-411a-8c27-e872613d5fc8", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.1, "x": 0.742, "y": 0.318 }, "kind": "metric", "text": "12%", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": null, "subkind": { "name": "Big number", "slug": "big-number", "status": "active", "canonId": "019de52c-fc70-70c8-b965-0584548a9204", "version": 1, "description": null }, "framework": null }, "subkind": "big-number", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:12.912+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "b0454b99-cdb1-4577-b91e-d87b48b06dc3", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "Share of firms with AI maturity sufficient for superior performance and growth: 12%", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:44:02+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b400-823ea4885254", "parentComponentId": null }, { "bbox": { "h": 0.25, "w": 0.25, "x": 0.058, "y": 0.475 }, "kind": "paragraph", "text": "Today, so much of what we take for granted in our daily lives stems from machine learning. Every time you use a wayfinding app to get from point A to point B, use dictation to convert speech to text, or unlock your phone using face ID ... you’re relying on AI. And companies across industries are also relying on—and investing in—AI to drive logistics, improve customer service, increase efficiency, empower employees and so much more.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:12.912+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "5b6e12e2-6a1e-4559-8b86-020c24784f63", "parentComponentId": null }, { "bbox": { "h": 0.04, "w": 0.15, "x": 0.561, "y": 0.77 }, "kind": "paragraph", "text": "of firms are still testing the AI waters.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:12.912+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "5db278d2-bbaa-4e56-a26a-34e9207df5a4", "parentComponentId": null }, { "bbox": { "h": 0.06, "w": 0.15, "x": 0.742, "y": 0.445 }, "kind": "paragraph", "text": "of firms have advanced their AI maturity enough to achieve superior performance and growth.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:12.912+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "87ede6ae-4085-4892-97cb-f0f11a302d04", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.25, "x": 0.058, "y": 0.296 }, "kind": "paragraph", "text": "Computer scientist John McCarthy coined the term “artificial intelligence” in 1955, proposing that, “every aspect of learning ... can in principle be so precisely described that a machine can be made to simulate it.”¹", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:12.912+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "9df7a5f2-5b54-4a58-b85a-d6c79218cf7f", "parentComponentId": null }, { "bbox": { "h": 0.25, "w": 0.25, "x": 0.612, "y": 0.296 }, "kind": "paragraph", "text": "This journey to AI maturity has been in high gear for years. Pre-pandemic (2019), AI Achievers already enjoyed 50% greater revenue growth on average, compared with their peers. And in 2021, among executives of the world’s 2,000 largest companies (by market capitalization), those who discussed AI on their earnings calls were 40% more likely to see their firms’ share prices increase—up from 23% in 2018, according to analysis by Accenture.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:12.912+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "caeb36e1-545f-4504-8922-18b399a5beec", "parentComponentId": null }, { "bbox": { "h": 0.35, "w": 0.25, "x": 0.335, "y": 0.296 }, "kind": "paragraph", "text": "Despite these ever-expanding use cases, when it comes to making the most of AI’s full potential and their own investments, most organizations are barely scratching the surface. In fact, only 12% of firms have advanced their AI maturity enough to achieve superior growth and business transformation, according to Accenture’s extensive analysis of approximately 1,200 companies globally. We call them the “AI Achievers.” Another 25% of firms are somewhat advanced in their level of AI maturity, while the remaining 63% (the majority) are still mostly testing the waters.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:12.912+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "ee1147cf-4e8e-4d8c-a558-0958678ab4d5", "parentComponentId": null }, { "bbox": null, "kind": "quote", "text": "\"every aspect of learning ... can in principle be so precisely described that a machine can be made to simulate it.\" — John McCarthy, 1955", "attrs": null, "canon": { "kind": { "name": "Quote", "slug": "quote", "status": "active", "canonId": "019de52c-f8fb-751e-8bc6-d58e2d59562a", "version": 1, "description": "Pull quote or testimonial." }, "tool": { "name": "Authority citation", "slug": "authority-citation", "status": "active", "bestFor": null, "canonId": "019dd9e1-49ae-7557-89ad-3fca70469b45", "version": 1, "bodyDocId": null, "description": "Quoting an authority figure, study, or precedent to lend weight.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:44:02+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b400-7f2d548e20da", "parentComponentId": null }, { "bbox": { "h": 0.075, "w": 0.55, "x": 0.058, "y": 0.208 }, "kind": "title", "text": "In fewer than 70 years, artificial intelligence (AI) has evolved from a scientific concept to a societal constant.", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Action title", "slug": "action-title", "status": "active", "canonId": "019de52c-fb31-734c-a0f5-a3935b73135d", "version": 1, "description": null }, "framework": null }, "subkind": "action-title", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:12.912+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "f47a6f8a-6fc5-48c7-9992-816e314f34da", "parentComponentId": null }, { "bbox": { "h": 0.045, "w": 0.298, "x": 0.058, "y": 0.143 }, "kind": "title", "text": "Executive summary", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:12.912+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "3a947ab1-8c33-4d1d-b0e5-bf6843d702ed", "parentComponentId": null } ], "metrics": [ { "metricRaw": "Share of firms with AI maturity sufficient for superior performance and growth", "numberRaw": "12%", "numberKind": "percent", "actionTitle": "In fewer than 70 years, artificial intelligence (AI) has evolved from a scientific concept to a societal constant.", "calloutText": "12% of firms have advanced their AI maturity enough to achieve superior performance and growth.", "numberScale": null, "numberValue": 12, "metricFamily": "growth_rate", "numberCurrency": null } ], "tools": [ { "tool": { "name": "SCQA Framework", "slug": "scqa-framework", "status": "active", "bestFor": null, "canonId": "019dd956-7bcb-763f-bdf6-1dfedf3bd36c", "version": 1, "bodyDocId": "019df22a-a0b0-719e-937e-2ac31e53d32c", "description": "Situation, Complication, Question, Answer - structured problem framing", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Architect", "layer": "block", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-2893bf72c88f", "evidence": "Whole deck opens with situation (AI evolved), complication (12% lead), question (how) - block anchors here", "pageRefs": null, "priority": "Core", "whenToUse": null, "confidence": 85, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 3, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Aha! Moment", "slug": "aha-moment", "status": "active", "bestFor": null, "canonId": "019dd956-a5b5-7763-820e-b299c5f27a32", "version": 1, "bodyDocId": null, "description": "The moment when the audience realizes the value - design for insight revelation", "familyLabel": null, "categoryName": "Loop", "categorySlug": "loop" }, "agent": "Storyteller", "layer": "loop", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-38c6052c802e", "evidence": "Loop opens with the surprising 12%-Achiever insight", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 75, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 3, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-2c21fbe8cd95", "evidence": "Title states insight not topic: 'AI evolved from concept to societal constant'", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 3, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-3558d2012141", "evidence": "Specific 70-year history and 12% figure", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 3, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Executive summary", "slug": "executive-summary", "status": "active", "bestFor": null, "canonId": "019dd9e1-4b63-72d5-9734-da757d91ca61", "version": 1, "bodyDocId": null, "description": "A condensed view of the entire argument on a single slide / block.", "familyLabel": null, "categoryName": null, "categorySlug": null }, "agent": null, "layer": "slide", "agents": null, "matchId": "d3b3eb4d-8a8c-4b6b-9eba-0b3a0b0d5a04", "evidence": "In fewer than 70 years, artificial intelligence (AI) has evolved from a scientific concept to a societal constant.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.9, "extraction": { "at": "2026-07-16 21:02:19.025248+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 3, "whyItWorks": null, "antipattern": null, "cardinality": "common", "narrativePurpose": null }, { "tool": { "name": "Opening Hooks", "slug": "opening-hooks", "status": "active", "bestFor": null, "canonId": "019dd956-aeb0-77d5-bfbd-9cd9a6be8186", "version": 1, "bodyDocId": null, "description": "Techniques to capture attention: Startling statistic, provocative question, short story, analogy, contrast", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-3091a16e04be", "evidence": "Startling-statistic hook: '12% have advanced AI maturity to superior performance'", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 85, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 3, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null } ], "frameworks": [], "arcBeats": [ { "to": 7, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null }, "from": 3, "name": "Situation & Context", "beatId": "019dd95a-0682-776c-8e32-77ef30635bbc", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Situation & Context", "slug": "consultants-gambit-situation-context", "status": "active", "canonId": "019dd9b8-05ae-741b-992d-65ee83c639b3", "version": 1, "description": null }, "evidence": "AI essential for competitive advantage; transformation accelerating 16 months faster", "position": 1, "isPrimaryArc": true, "parentBeatType": { "name": "Setup", "slug": "setup", "status": "active", "canonId": "019dd9b8-020e-7505-af9b-d9b9a4679bcc", "version": 1, "description": "Where the deck grounds the reader before doing anything else. Almost every arc opens here." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 4, "from": 3, "loop": { "name": "03_aha_moment", "slug": "03-aha-moment", "status": "active", "bestFor": "Data-heavy sections, research findings, analytical arguments", "canonId": "019dd956-66f4-7668-ab25-cec9c996c0bc", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Problem/Question -> What the Data Shows -> The Insight", "description": "Establish a tension, present data, then deliver the insight that changes everything", "familyLabel": null }, "matchId": "019dd95a-07fe-70ce-8d3a-3bb17d512555", "evidence": "Exec summary states the 12% finding; p4 lists the five ways", "position": 1, "objective": "Reveal the central finding and preview the five differentiators", "confidence": 75, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "locked": true }, { "page": 4, "type": "resolution", "function": "summarize", "rawType": "key_takeaways", "block": null, "metadata": { "slideType": "key_takeaways", "slideTypeCanon": { "name": "Key takeaways", "slug": "key_takeaways", "status": "active", "canonId": "019de52d-06e7-7674-a891-9f7de3b11b6d", "version": 1, "description": null }, "function": "summarize", "functionCanon": { "name": "Summarize", "slug": "summarize", "status": "active", "canonId": "019de52d-1541-704f-b86f-bf7b75056373", "version": 1, "description": null }, "density": "overcrowded", "densityScore": 77, "componentCount": 8, "textChars": 1111, "nDataPoints": 2, "notes": null, "elementsJson": [ "headline_text", "paragraph", "numbered_list", "photo" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:15.474+00", "seconds": 6.435, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/4", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-4", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "Our machine learning models suggest that the share of AI Achievers will increase rapidly and significantly, more than doubling from the current 12% to 27% by 2024.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:44:03+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b400-927fec0a795a", "parentComponentId": null }, { "bbox": { "h": 0.867, "w": 0.457, "x": 0.029, "y": 0.065 }, "kind": "image", "text": null, "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Photo", "slug": "photo", "status": "active", "canonId": "019de52c-fd37-757f-b4a8-ca26d4888875", "version": 1, "description": null }, "framework": null }, "subkind": "photo", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.474+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "ba247b95-bf12-485d-8335-386ceb9eeaf7", "parentComponentId": null }, { "bbox": { "h": 0.2, "w": 0.435, "x": 0.515, "y": 0.25 }, "kind": "list", "text": "1. Their top leaders champion AI as a strategic priority for the entire organization.\n2. They invest heavily in talent to get more from their AI investments.\n3. They industrialize AI tools and teams to create a strong AI core.\n4. They design AI responsibly, from the start.\n5. They prioritize long- and short-term AI investments.", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Numbered list", "slug": "numbered", "status": "active", "canonId": "019de52c-fcc0-7718-a284-a4250c805df2", "version": 1, "description": null }, "framework": null }, "subkind": "numbered", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.474+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "9b9365d9-1375-4cd8-892d-6819e193013e", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "share of AI Achievers by 2024: 27%", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:44:03+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b400-944b799e563d", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.435, "x": 0.515, "y": 0.175 }, "kind": "paragraph", "text": "While there’s clearly a science to AI, our findings demonstrate there is also an art to AI maturity. Achievers are not defined by the sophistication of any one capability, but by their ability to combine strengths across strategy, processes and people.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.474+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "34c770d7-3f3b-4744-b1f2-92312b57fe8b", "parentComponentId": null }, { "bbox": { "h": 0.04, "w": 0.435, "x": 0.515, "y": 0.66 }, "kind": "paragraph", "text": "Our machine learning models suggest that the share of AI Achievers will increase rapidly and significantly, more than doubling from the current 12% to 27% by 2024.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.474+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "7846c032-0638-410f-8a95-8642851b569d", "parentComponentId": null }, { "bbox": { "h": 0.04, "w": 0.435, "x": 0.515, "y": 0.715 }, "kind": "paragraph", "text": "In short, advancing AI maturity is no longer a choice. It’s an opportunity facing every industry, every organization and every leader.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.474+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "ebd7ff99-c9c9-41ef-b52d-ce557ddadde1", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.435, "x": 0.515, "y": 0.125 }, "kind": "title", "text": "What do AI Achievers do differently?", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.474+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "a21bb1b8-83ae-47ba-a5f7-2dd092b59643", "parentComponentId": null } ], "metrics": [ { "metricRaw": "share of AI Achievers by 2024", "numberRaw": "27%", "numberKind": "percent", "actionTitle": null, "calloutText": "Our machine learning models suggest that the share of AI Achievers will increase rapidly and significantly, more than doubling from the current 12% to 27% by 2024.", "numberScale": null, "numberValue": 27, "metricFamily": "share_penetration", "numberCurrency": null } ], "tools": [ { "tool": { "name": "Curiosity Gap", "slug": "curiosity-gap", "status": "active", "bestFor": null, "canonId": "019dd956-9703-74ef-8b85-96dfd8525025", "version": 1, "bodyDocId": null, "description": "People seek missing information - create open questions that demand resolution", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Storyteller", "layer": "block", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-43d33f34ca50", "evidence": "Question title creates open loop the deck will close", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 4, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "MECE Principle", "slug": "mece-principle", "status": "active", "bestFor": null, "canonId": "019dd956-7c60-71a8-8758-c79d914489c9", "version": 1, "bodyDocId": null, "description": "Mutually Exclusive, Collectively Exhaustive - ensuring complete, non-overlapping structure", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Architect", "layer": "block", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-4ad0dcc84a91", "evidence": "Five distinct, non-overlapping capability categories preview the deck", "pageRefs": null, "priority": "Core", "whenToUse": null, "confidence": 75, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 4, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Chunking", "slug": "chunking", "status": "active", "bestFor": null, "canonId": "019dd956-abab-72ce-b64c-2924cc83cf39", "version": 1, "bodyDocId": null, "description": "Information grouped into chunks is remembered better - structure content in digestible units", "familyLabel": null, "categoryName": "Loop", "categorySlug": "loop" }, "agent": "Architect", "layer": "loop", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-44e1e02996d4", "evidence": "Five labelled chunks listed: champion / talent / AI core / responsible / invest", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 4, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-3c4ed9b16629", "evidence": "Title 'What do AI Achievers do differently?' frames the answer set", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 4, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Audience Definition", "slug": "audience-definition", "status": "active", "bestFor": null, "canonId": "019dd956-7f2a-775d-aeee-ff099bcb7756", "version": 1, "bodyDocId": null, "description": "Six key questions: Who are they? What do they know? What do they believe? What do they care about? What do they fear? What decisions can they make?", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "25d5bdcc-f9d0-4ec8-b72e-bcf0ef37ce54", "evidence": "The slide is addressed to a specific group, 'AI Achievers'.", "pageRefs": null, "priority": "Core", "whenToUse": null, "confidence": 0.7, "extraction": { "at": "2026-07-16 21:02:36.208078+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 4, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Picture Superiority Effect", "slug": "picture-superiority-effect", "status": "active", "bestFor": null, "canonId": "019dd956-c201-7014-bb58-3e85e3e11dfd", "version": 1, "bodyDocId": null, "description": "Pictures remembered 6x better than words - prioritize visuals over text", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "019dd95a-0f08-73d3-987b-4e6773923526", "evidence": "Half-slide portrait paired with text", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 70, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 4, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Storytelling Effect", "slug": "storytelling-effect", "status": "active", "bestFor": null, "canonId": "019dd956-981b-74f9-9096-5c8df03dab5a", "version": 1, "bodyDocId": null, "description": "Stories are remembered up to 22x better than facts alone", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "9b705281-579e-4f32-8816-16cf5c8865c3", "evidence": "The use of a narrative to describe 'AI Achievers'.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.7, "extraction": { "at": "2026-07-16 21:02:36.243683+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 4, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null } ], "frameworks": [], "arcBeats": [ { "to": 7, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null }, "from": 3, "name": "Situation & Context", "beatId": "019dd95a-0682-776c-8e32-77ef30635bbc", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Situation & Context", "slug": "consultants-gambit-situation-context", "status": "active", "canonId": "019dd9b8-05ae-741b-992d-65ee83c639b3", "version": 1, "description": null }, "evidence": "AI essential for competitive advantage; transformation accelerating 16 months faster", "position": 1, "isPrimaryArc": true, "parentBeatType": { "name": "Setup", "slug": "setup", "status": "active", "canonId": "019dd9b8-020e-7505-af9b-d9b9a4679bcc", "version": 1, "description": "Where the deck grounds the reader before doing anything else. Almost every arc opens here." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 4, "from": 3, "loop": { "name": "03_aha_moment", "slug": "03-aha-moment", "status": "active", "bestFor": "Data-heavy sections, research findings, analytical arguments", "canonId": "019dd956-66f4-7668-ab25-cec9c996c0bc", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Problem/Question -> What the Data Shows -> The Insight", "description": "Establish a tension, present data, then deliver the insight that changes everything", "familyLabel": null }, "matchId": "019dd95a-07fe-70ce-8d3a-3bb17d512555", "evidence": "Exec summary states the 12% finding; p4 lists the five ways", "position": 1, "objective": "Reveal the central finding and preview the five differentiators", "confidence": 75, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "locked": true }, { "page": 5, "type": "setup", "function": "transition", "rawType": "section_divider", "block": null, "metadata": { "slideType": "section_divider", "slideTypeCanon": { "name": "Section divider", "slug": "section_divider", "status": "active", "canonId": "019de52d-066a-7402-9554-6ae504999041", "version": 1, "description": null }, "function": "transition", "functionCanon": { "name": "Transition", "slug": "transition", "status": "active", "canonId": "019de52d-1365-7149-92aa-66df859e75fd", "version": 1, "description": null }, "density": "sparse", "densityScore": 6, "componentCount": 3, "textChars": 49, "nDataPoints": 0, "notes": null, "elementsJson": [ "headline_text", "photo" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:11.133+00", "seconds": 2.027, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/5", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-5", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": { "h": 1, "w": 1, "x": 0, "y": 0 }, "kind": "image", "text": null, "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Illustration", "slug": "illustration", "status": "active", "canonId": "019de52c-fe76-7772-a5ac-c0b7294d7615", "version": 1, "description": null }, "framework": null }, "subkind": "illustration", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:11.133+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "3ae23b6d-f963-4a8a-b05e-d35143c9c47a", "parentComponentId": null }, { "bbox": { "h": 0.075, "w": 0.614, "x": 0.193, "y": 0.488 }, "kind": "title", "text": "AI maturity: Why it matters", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:11.133+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "3d2ab260-d9af-4235-94d0-fd1077bf687f", "parentComponentId": null }, { "bbox": { "h": 0.025, "w": 0.178, "x": 0.058, "y": 0.102 }, "kind": "title", "text": "The art of AI maturity", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Subtitle", "slug": "subtitle", "status": "active", "canonId": "019de52c-fb09-739d-900c-815b0d9ac526", "version": 1, "description": null }, "framework": null }, "subkind": "subtitle", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:11.133+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "9e0c265e-79ea-47bf-adc8-1925180c865b", "parentComponentId": null } ], "metrics": [], "tools": [ { "tool": { "name": "Section divider", "slug": "section-divider", "status": "active", "bestFor": null, "canonId": "019dd9e1-4c66-707d-8d1f-6e14547134c7", "version": 1, "bodyDocId": null, "description": "A transitional slide marking the start of a new section.", "familyLabel": null, "categoryName": null, "categorySlug": null }, "agent": null, "layer": "slide", "agents": null, "matchId": "9a77e928-26df-4585-a4df-9d0117ec27ad", "evidence": "The slide is titled 'AI maturity: Why it matters' and has a subtitle.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.9, "extraction": { "at": "2026-07-16 21:02:36.316901+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 5, "whyItWorks": null, "antipattern": null, "cardinality": "common", "narrativePurpose": null } ], "frameworks": [], "arcBeats": [ { "to": 7, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null }, "from": 3, "name": "Situation & Context", "beatId": "019dd95a-0682-776c-8e32-77ef30635bbc", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Situation & Context", "slug": "consultants-gambit-situation-context", "status": "active", "canonId": "019dd9b8-05ae-741b-992d-65ee83c639b3", "version": 1, "description": null }, "evidence": "AI essential for competitive advantage; transformation accelerating 16 months faster", "position": 1, "isPrimaryArc": true, "parentBeatType": { "name": "Setup", "slug": "setup", "status": "active", "canonId": "019dd9b8-020e-7505-af9b-d9b9a4679bcc", "version": 1, "description": "Where the deck grounds the reader before doing anything else. Almost every arc opens here." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 7, "from": 5, "loop": { "name": "15_why_now", "slug": "15-why-now", "status": "active", "bestFor": "Sales pitches, fundraising, requesting immediate budget approval", "canonId": "019dd956-6c00-70cd-bd42-6bacc4af726f", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Context (Trends) -> The Trigger Event -> The Window of Opportunity", "description": "Create temporal urgency by proving that the window of opportunity is opening or closing", "familyLabel": null }, "matchId": "019dd95a-07fe-70ce-8d3a-3e8a2c756978", "evidence": "Pp 6-7 frame AI as essential and accelerating: 16 months faster, AI-influenced revenue tripling", "position": 2, "objective": "Establish urgency for AI investment", "confidence": 80, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "locked": true }, { "page": 6, "type": "analysis", "title": "The chart illustrates the acceleration of AI adoption compared to digital transformation using a growth curve.", "function": "establish_context", "rawType": "industry_trends", "block": null, "metadata": { "slideType": "industry_trends", "slideTypeCanon": { "name": "Industry trends", "slug": "industry_trends", "status": "active", "canonId": "019de52d-0b1d-7705-a0e0-c663199cd1fa", "version": 1, "description": null }, "function": "establish_context", "functionCanon": { "name": "Establish context", "slug": "establish_context", "status": "active", "canonId": "019de52d-1314-74e6-9074-d213dc0bdcdf", "version": 1, "description": null }, "density": "overcrowded", "densityScore": 88, "componentCount": 9, "textChars": 1785, "nDataPoints": 8, "notes": "The chart illustrates the acceleration of AI adoption compared to digital transformation using a growth curve.", "elementsJson": [ "headline_text", "action_title", "paragraph", "line_chart", "area_chart", "callout_box", "footnote" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 21:24:40.291+00", "seconds": 7.644, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/6", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-6", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "With early successes building confidence in AI as a value-driver, we estimate that AI transformation will happen much faster than digital transformation—on average, 16 months faster (Figure 1).", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:44:11+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b400-d8882178f431", "parentComponentId": null }, { "bbox": { "h": 0.55, "w": 0.43, "x": 0.52, "y": 0.14 }, "kind": "chart", "text": "Figure 1: We project that AI transformation will take less time than digital transformation", "attrs": null, "canon": { "kind": { "name": "Chart", "slug": "chart", "status": "active", "canonId": "019de52c-f9a0-76ef-adc6-0b859109d1f8", "version": 1, "description": "Quantitative chart." }, "tool": null, "subkind": { "name": "Area chart", "slug": "area", "status": "active", "canonId": "019de52c-ff89-71e0-911e-5cab78b90f53", "version": 1, "description": null }, "framework": null }, "subkind": "area", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 21:24:40.291+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "82f6fdf3-a00e-4fc3-9285-dc2aa87dbf98", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "AI vs digital transformation timing gap: 16 months", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:44:11+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b400-df3caca9645d", "parentComponentId": null }, { "bbox": { "h": 0.45, "w": 0.43, "x": 0.06, "y": 0.31 }, "kind": "paragraph", "text": "So, it's no surprise that in 2021, 46% of CEOs (of the world's 2,000 largest companies by market capitalization) mentioned AI on their earnings calls. The companies leading the way are already seeing the results—42% said that the return on their AI initiatives exceeded their expectations, while only 1% said the return didn't meet expectations. Our survey of over 1,600 C-suite executives and data-science leaders found that nearly 75% of companies have integrated AI into their business strategies and reworked their cloud plans to achieve AI success. And companies are putting those plans into practice: Nearly a third (30%) of all AI pilot initiatives are subsequently scaled to deliver wide-ranging outcomes, from accelerating R&D timelines for new products to enhancing customer experiences.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 21:24:40.291+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "1431aa02-1e65-44cf-8ada-fc239afe2c47", "parentComponentId": null }, { "bbox": { "h": 0.08, "w": 0.21, "x": 0.28, "y": 0.55 }, "kind": "paragraph", "text": "With early successes building confidence in AI as a value-driver, we estimate that AI transformation will happen much faster than digital transformation—on average, 16 months faster (Figure 1).", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 21:24:40.291+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "e0f2ba97-2039-4691-b639-e59e59668aaa", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.43, "x": 0.52, "y": 0.66 }, "kind": "source-note", "text": "Source: Accenture Research. Note: Our estimate is derived from a natural language processing analysis of investor calls of the world's 2,000 largest companies (by market cap), from 2010 to 2021, that referenced “AI” and “Digital” in tandem with “business transformation,” respectively. Data was sourced from S&P earnings transcripts.", "attrs": null, "canon": { "kind": { "name": "Source note", "slug": "source-note", "status": "active", "canonId": "23484952-494b-4a5e-b1ad-550d0d70e948", "version": 1, "description": "Attribution / source / caveat placed at the slide footer. Numbered footnotes use attrs.numbered." }, "tool": null, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 21:24:40.291+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "1f325965-1f41-41b8-8936-f61833e8e51c", "parentComponentId": null }, { "bbox": { "h": 0.07, "w": 0.43, "x": 0.06, "y": 0.21 }, "kind": "title", "text": "There is a growing consensus that AI is absolutely essential to competitive advantage.", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Action title", "slug": "action-title", "status": "active", "canonId": "019de52c-fb31-734c-a0f5-a3935b73135d", "version": 1, "description": null }, "framework": null }, "subkind": "action-title", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 21:24:40.291+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "dd950a00-925f-4bdf-8f3f-4751b61b5302", "parentComponentId": null }, { "bbox": { "h": 0.04, "w": 0.39, "x": 0.06, "y": 0.13 }, "kind": "title", "text": "AI maturity: Why it matters", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 21:24:40.291+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "20306b99-a2a6-45b5-be8e-529564e5b640", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.21, "x": 0.28, "y": 0.49 }, "kind": "title", "text": "AI, accelerated", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Subtitle", "slug": "subtitle", "status": "active", "canonId": "019de52c-fb09-739d-900c-815b0d9ac526", "version": 1, "description": null }, "framework": null }, "subkind": "subtitle", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 21:24:40.291+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "c634452c-cd2d-49f7-9669-2d9871bfc529", "parentComponentId": null } ], "metrics": [ { "metricRaw": "AI vs digital transformation timing gap", "numberRaw": "16 months", "numberKind": "plain", "actionTitle": "There is a growing consensus that AI is absolutely essential to competitive advantage.", "calloutText": "With early successes building confidence in AI as a value-driver, we estimate that AI transformation will happen much faster than digital transformation—on average, 16 months faster (Figure 1).", "numberScale": null, "numberValue": 16, "metricFamily": "other", "numberCurrency": null } ], "tools": [ { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-528972455469", "evidence": "Subhead states the insight: 'AI is absolutely essential to competitive advantage'", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 85, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 6, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Annotation", "slug": "annotation", "status": "active", "bestFor": null, "canonId": "019dd956-b5b9-7668-bf0e-3af99823ec5c", "version": 1, "bodyDocId": null, "description": "Adding explanatory labels to highlight key data points", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "019dd95a-0f08-73d3-987b-552d36966511", "evidence": "Chart annotates '16 months faster' delta with callout", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 85, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 6, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Color Strategy", "slug": "color-strategy", "status": "active", "bestFor": null, "canonId": "019dd956-b202-77cf-8a6e-f445b08c0fd3", "version": 1, "bodyDocId": null, "description": "Using color for emphasis, categories, sentiment, and hierarchy", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "019dd95a-0f08-73d3-987b-5bc43a82aa1d", "evidence": "Purple curves distinguish AI vs Digital transformation", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 75, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 6, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-5de8df3c3dc4", "evidence": "Specific stats: 46% CEOs, 75% integrated AI, 30% scaled pilots", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 6, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Data Story Arc", "slug": "data-story-arc", "status": "active", "bestFor": null, "canonId": "019dd956-9c30-728e-b974-d3fb123dcf5a", "version": 1, "bodyDocId": null, "description": "Context → Conflict → Insight → Implication structure for data narratives", "familyLabel": null, "categoryName": "Loop", "categorySlug": "loop" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "d014749d-e228-421f-b6a3-955f65d691bd", "evidence": "The use of data to support the narrative about AI adoption.", "pageRefs": null, "priority": "Core", "whenToUse": null, "confidence": 0.7, "extraction": { "at": "2026-07-16 21:02:36.388623+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 6, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null } ], "frameworks": [], "arcBeats": [ { "to": 7, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null }, "from": 3, "name": "Situation & Context", "beatId": "019dd95a-0682-776c-8e32-77ef30635bbc", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Situation & Context", "slug": "consultants-gambit-situation-context", "status": "active", "canonId": "019dd9b8-05ae-741b-992d-65ee83c639b3", "version": 1, "description": null }, "evidence": "AI essential for competitive advantage; transformation accelerating 16 months faster", "position": 1, "isPrimaryArc": true, "parentBeatType": { "name": "Setup", "slug": "setup", "status": "active", "canonId": "019dd9b8-020e-7505-af9b-d9b9a4679bcc", "version": 1, "description": "Where the deck grounds the reader before doing anything else. Almost every arc opens here." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 7, "from": 5, "loop": { "name": "15_why_now", "slug": "15-why-now", "status": "active", "bestFor": "Sales pitches, fundraising, requesting immediate budget approval", "canonId": "019dd956-6c00-70cd-bd42-6bacc4af726f", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Context (Trends) -> The Trigger Event -> The Window of Opportunity", "description": "Create temporal urgency by proving that the window of opportunity is opening or closing", "familyLabel": null }, "matchId": "019dd95a-07fe-70ce-8d3a-3e8a2c756978", "evidence": "Pp 6-7 frame AI as essential and accelerating: 16 months faster, AI-influenced revenue tripling", "position": 2, "objective": "Establish urgency for AI investment", "confidence": 80, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "locked": true }, { "page": 7, "type": "analysis", "title": "The chart shows the migration of companies between 'High', 'Medium', and 'Low' AI-influenced revenue share categories.", "function": "analyze_data", "rawType": "industry_trends", "block": null, "metadata": { "slideType": "industry_trends", "slideTypeCanon": { "name": "Industry trends", "slug": "industry_trends", "status": "active", "canonId": "019de52d-0b1d-7705-a0e0-c663199cd1fa", "version": 1, "description": null }, "function": "analyze_data", "functionCanon": { "name": "Analyze data", "slug": "analyze_data", "status": "active", "canonId": "019de52d-1590-7413-8c82-cc218cff40b4", "version": 1, "description": null }, "density": "dense", "densityScore": 71, "componentCount": 7, "textChars": 1077, "nDataPoints": 6, "notes": "The chart shows the migration of companies between 'High', 'Medium', and 'Low' AI-influenced revenue share categories.", "elementsJson": [ "paragraph", "sankey", "footnote" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:12.041+00", "seconds": 2.789, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/7", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-7", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "The incentive to move quickly is strong. We found, for example, that the share of companies' revenue that is \"AI-influenced\" more than doubled between 2018 and 2021 and is expected to roughly triple between 2018 and 2024.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:44:09+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b400-ba9c6dd55359", "parentComponentId": null }, { "bbox": { "h": 0.55, "w": 0.55, "x": 0.38, "y": 0.15 }, "kind": "chart", "text": "Sankey diagram showing revenue share migration", "attrs": null, "canon": { "kind": { "name": "Chart", "slug": "chart", "status": "active", "canonId": "019de52c-f9a0-76ef-adc6-0b859109d1f8", "version": 1, "description": "Quantitative chart." }, "tool": null, "subkind": { "name": "Sankey diagram", "slug": "sankey", "status": "active", "canonId": "019de52d-0145-711d-a871-198a7a06733b", "version": 1, "description": null }, "framework": null }, "subkind": "sankey", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:12.041+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "a0d60c2a-6841-4b05-94c5-8768bd65efbe", "parentComponentId": null }, { "bbox": { "h": 0.2, "w": 0.55, "x": 0.38, "y": 0.7 }, "kind": "disclaimer", "text": "Note: Color indicates the achieved AI-influenced revenue threshold within each time period. Source: Accenture Research. Note: *2024 = projected. *Definition of “AI-influenced” revenues: a. Sales of existing products... b. Sales of new products... c. Higher prices...", "attrs": null, "canon": { "kind": { "name": "Disclaimer", "slug": "disclaimer", "status": "active", "canonId": "91173477-d91f-4024-bbaf-d4f93e3f82dc", "version": 1, "description": "Legal/cautionary text that occupies most of the body of its slide." }, "tool": null, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:12.041+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "c320cf24-b903-47f6-bac0-c37a0230c523", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.2, "x": 0.38, "y": 0.62 }, "kind": "legend", "text": "Legend: High (>30%), Medium (10%-30%), Low (<10%)", "attrs": null, "canon": { "kind": { "name": "Legend", "slug": "legend", "status": "active", "canonId": "31361933-3d22-4c70-bbad-0801e9fffc0c", "version": 1, "description": "Color/symbol decoder placed adjacent to a chart or diagram." }, "tool": null, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:12.041+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "d39b45ba-bbd2-4053-8a0d-e591bfb33b2e", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "Average AI-influenced revenue share: 31%", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:44:09+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b400-bf55e75347e3", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.25, "x": 0.05, "y": 0.32 }, "kind": "paragraph", "text": "Given the evidence, it's easy to see why companies plan to increase and accelerate their AI investments. In 2021, 19% of companies dedicated >30% of their tech budgets to AI development. By 2024, 49% of companies intend to.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:12.041+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "cd38ae6f-62a8-474d-b69c-f5be125ddad1", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.25, "x": 0.05, "y": 0.14 }, "kind": "paragraph", "text": "The incentive to move quickly is strong. We found, for example, that the share of companies' revenue that is “AI-influenced” more than doubled between 2018 and 2021 and is expected to roughly triple between 2018 and 2024 (Figure 2).", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:12.041+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "d43593fd-392c-47d3-bd9e-e5279d8a6032", "parentComponentId": null } ], "metrics": [ { "metricRaw": "Average AI-influenced revenue share", "numberRaw": "31%", "numberKind": "percent", "actionTitle": null, "calloutText": "The incentive to move quickly is strong. We found, for example, that the share of companies' revenue that is \"AI-influenced\" more than doubled between 2018 and 2021 and is expected to roughly triple between 2018 and 2024.", "numberScale": null, "numberValue": 31, "metricFamily": "share_penetration", "numberCurrency": null } ], "tools": [ { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-6005692193a6", "evidence": "Figure 2 title states the trend in revenue evolution", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 75, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 7, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Annotation", "slug": "annotation", "status": "active", "bestFor": null, "canonId": "019dd956-b5b9-7668-bf0e-3af99823ec5c", "version": 1, "bodyDocId": null, "description": "Adding explanatory labels to highlight key data points", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "019dd95a-0f08-73d3-987b-6810b057b8cf", "evidence": "10% / 22% / 31% callouts on the bands", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 7, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Chart Selection Guide", "slug": "chart-selection-guide", "status": "active", "bestFor": null, "canonId": "019dd956-b1a0-700b-ba17-0e16c4d5bcae", "version": 1, "bodyDocId": null, "description": "Choosing the right chart type for your data and message", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "019dd95a-0f08-73d3-987b-673a456d2945", "evidence": "Sankey-style flow chart suits revenue-share migration over time", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 7, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Color Strategy", "slug": "color-strategy", "status": "active", "bestFor": null, "canonId": "019dd956-b202-77cf-8a6e-f445b08c0fd3", "version": 1, "bodyDocId": null, "description": "Using color for emphasis, categories, sentiment, and hierarchy", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "019dd95a-0f08-73d3-987b-6f97b03f1a71", "evidence": "Color encodes High / Medium / Low revenue thresholds", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 75, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 7, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null } ], "frameworks": [], "arcBeats": [ { "to": 7, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null }, "from": 3, "name": "Situation & Context", "beatId": "019dd95a-0682-776c-8e32-77ef30635bbc", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Situation & Context", "slug": "consultants-gambit-situation-context", "status": "active", "canonId": "019dd9b8-05ae-741b-992d-65ee83c639b3", "version": 1, "description": null }, "evidence": "AI essential for competitive advantage; transformation accelerating 16 months faster", "position": 1, "isPrimaryArc": true, "parentBeatType": { "name": "Setup", "slug": "setup", "status": "active", "canonId": "019dd9b8-020e-7505-af9b-d9b9a4679bcc", "version": 1, "description": "Where the deck grounds the reader before doing anything else. Almost every arc opens here." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 7, "from": 5, "loop": { "name": "15_why_now", "slug": "15-why-now", "status": "active", "bestFor": "Sales pitches, fundraising, requesting immediate budget approval", "canonId": "019dd956-6c00-70cd-bd42-6bacc4af726f", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Context (Trends) -> The Trigger Event -> The Window of Opportunity", "description": "Create temporal urgency by proving that the window of opportunity is opening or closing", "familyLabel": null }, "matchId": "019dd95a-07fe-70ce-8d3a-3e8a2c756978", "evidence": "Pp 6-7 frame AI as essential and accelerating: 16 months faster, AI-influenced revenue tripling", "position": 2, "objective": "Establish urgency for AI investment", "confidence": 80, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "locked": true }, { "page": 8, "type": "setup", "function": "transition", "rawType": "section_divider", "block": null, "metadata": { "slideType": "section_divider", "slideTypeCanon": { "name": "Section divider", "slug": "section_divider", "status": "active", "canonId": "019de52d-066a-7402-9554-6ae504999041", "version": 1, "description": null }, "function": "transition", "functionCanon": { "name": "Transition", "slug": "transition", "status": "active", "canonId": "019de52d-1365-7149-92aa-66df859e75fd", "version": 1, "description": null }, "density": "sparse", "densityScore": 6, "componentCount": 3, "textChars": 45, "nDataPoints": 0, "notes": null, "elementsJson": [ "headline_text", "photo" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:11.549+00", "seconds": 2.208, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/8", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-8", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": { "h": 1, "w": 1, "x": 0, "y": 0 }, "kind": "image", "text": null, "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Illustration", "slug": "illustration", "status": "active", "canonId": "019de52c-fe76-7772-a5ac-c0b7294d7615", "version": 1, "description": null }, "framework": null }, "subkind": "illustration", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:11.549+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "1a6383b8-3d61-42e5-b007-588a2565f6d6", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.5, "x": 0.25, "y": 0.45 }, "kind": "title", "text": "AI maturity: What it is", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:11.549+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "4169f226-6ae0-46ef-a958-bfa6b3501f3b", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.2, "x": 0.05, "y": 0.1 }, "kind": "title", "text": "The art of AI maturity", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Subtitle", "slug": "subtitle", "status": "active", "canonId": "019de52c-fb09-739d-900c-815b0d9ac526", "version": 1, "description": null }, "framework": null }, "subkind": "subtitle", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:11.549+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "66147d5e-f411-44ce-92ad-aedcf4008b08", "parentComponentId": null } ], "metrics": [], "tools": [ { "tool": { "name": "Section divider", "slug": "section-divider", "status": "active", "bestFor": null, "canonId": "019dd9e1-4c66-707d-8d1f-6e14547134c7", "version": 1, "bodyDocId": null, "description": "A transitional slide marking the start of a new section.", "familyLabel": null, "categoryName": null, "categorySlug": null }, "agent": null, "layer": "slide", "agents": null, "matchId": "36cc49e9-11e3-46cf-b723-f6a154ef3210", "evidence": "The slide is titled 'AI maturity: What it is' and has a subtitle.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.9, "extraction": { "at": "2026-07-16 21:02:36.495226+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 8, "whyItWorks": null, "antipattern": null, "cardinality": "common", "narrativePurpose": null } ], "frameworks": [], "arcBeats": [ { "to": 12, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null }, "from": 8, "name": "Problem & Complication", "beatId": "019dd95a-0682-776c-8e32-79a5f653b5e2", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Problem & Complication", "slug": "consultants-gambit-problem-complication", "status": "active", "canonId": "019dd9b8-064a-777f-a374-e9a8c43dedd4", "version": 1, "description": null }, "evidence": "Segmentation reveals only 12% are AI Achievers; 63% Experimenters", "position": 2, "isPrimaryArc": true, "parentBeatType": { "name": "Complication", "slug": "complication", "status": "active", "canonId": "019dd9b8-0275-7709-b2d6-4f77c3fc7579", "version": 1, "description": "The point in the narrative where the audience should feel discomfort. Required for argumentative arcs." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 12, "from": 8, "loop": { "name": "30_2x2_matrix", "slug": "30-2x2-matrix", "status": "active", "bestFor": "Portfolio analysis, prioritization, strategic positioning", "canonId": "019dd956-7225-756a-9cc7-1e6d3f039818", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "Dimension 1 (X-axis) -> Dimension 2 (Y-axis) -> The Four Quadrants -> The Sweet Spot", "description": "Plot options on two critical dimensions to reveal the optimal quadrant", "familyLabel": null }, "matchId": "019dd95a-07fe-70ce-8d3a-4369b4be6b2e", "evidence": "p10 explicit 2x2 (AI Foundation x Differentiation) with four named segments", "position": 3, "objective": "Define AI maturity and segment firms via 2x2", "confidence": 90, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "locked": true }, { "page": 9, "type": "setup", "title": "The slide introduces a proprietary AI maturity framework developed by Accenture using machine learning to analyze survey data.", "function": "present_framework", "rawType": "diagnosis", "block": null, "metadata": { "slideType": "diagnosis", "slideTypeCanon": { "name": "Diagnosis", "slug": "diagnosis", "status": "active", "canonId": "019de52d-0cd7-7212-898f-0464041ff8d8", "version": 1, "description": null }, "function": "present_framework", "functionCanon": { "name": "Present framework", "slug": "present_framework", "status": "active", "canonId": "019de52d-1403-7667-9100-d2ac6709f750", "version": 1, "description": null }, "density": "dense", "densityScore": 52, "componentCount": 5, "textChars": 1328, "nDataPoints": 0, "notes": "The slide introduces a proprietary AI maturity framework developed by Accenture using machine learning to analyze survey data.", "elementsJson": [ "headline_text", "subtitle_text", "paragraph", "callout_box" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:13.345+00", "seconds": 3.907, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/9", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-9", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": { "h": 0.45, "w": 0.38, "x": 0.55, "y": 0.25 }, "kind": "callout", "text": "AI maturity measures the degree to which organizations have mastered AI-related capabilities in the right combination to achieve high performance for customers, shareholders and employees.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": null, "subkind": { "name": "Primary callout", "slug": "primary", "status": "active", "canonId": "019de52c-fbd1-70eb-a9ce-8997f8674c1d", "version": 1, "description": null }, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.345+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "f7318f31-61e0-44e3-baf2-229d4519dca5", "parentComponentId": null }, { "bbox": null, "kind": "framework", "text": "AI Maturity Framework", "attrs": null, "canon": { "kind": { "name": "Framework", "slug": "framework", "status": "active", "canonId": "019de52c-fa18-77c6-b10a-ccb8277ac0fa", "version": 1, "description": "Named conceptual framework instance." }, "tool": null, "subkind": { "name": "Framework instance", "slug": "instance", "status": "active", "canonId": "019de52d-05a2-72af-be0b-7edc60708a03", "version": 1, "description": null }, "framework": null }, "subkind": "instance", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.345+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "fb269153-0469-425a-844a-e8ceda6db742", "parentComponentId": null }, { "bbox": { "h": 0.5, "w": 0.4, "x": 0.06, "y": 0.32 }, "kind": "paragraph", "text": "To uncover strategies for AI success, Accenture designed a holistic AI-maturity framework. Fittingly, our analysis itself was conducted using AI. We applied machine learning models to unravel massive survey datasets and uncover drivers of AI maturity that would have been impossible to detect using more traditional analytical methods (more on the methodology in the Appendix). Our research found that AI maturity comes down to mastering a set of key capabilities in the right combinations—not only in data and AI, but also in organizational strategy, talent and culture—to give companies a strong competitive advantage. (See pages 36 and 37 for key capability descriptions.) This includes foundational AI capabilities—like cloud platforms and tools, data platforms, architecture and governance—that are required to keep pace with competitors. It also includes “differentiation” AI capabilities, like AI strategy and C-suite sponsorship, combined with a culture of innovation that can set companies apart.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.345+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "c09f2ca4-fe51-4988-a2de-6acc6eb3a6aa", "parentComponentId": null }, { "bbox": { "h": 0.08, "w": 0.4, "x": 0.06, "y": 0.22 }, "kind": "title", "text": "If most organizations are racing to embrace AI, why are some seeing more value than others?", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Action title", "slug": "action-title", "status": "active", "canonId": "019de52c-fb31-734c-a0f5-a3935b73135d", "version": 1, "description": null }, "framework": null }, "subkind": "action-title", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.345+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "4e3d0107-fb2e-4607-ba9a-ebee00c72e94", "parentComponentId": null }, { "bbox": { "h": 0.04, "w": 0.3, "x": 0.06, "y": 0.16 }, "kind": "title", "text": "AI maturity: What it is", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.345+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "d923b872-6ac7-4861-9204-55072bd97f26", "parentComponentId": null } ], "metrics": [], "tools": [ { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-7106a27a9b04", "evidence": "Defines AI maturity as mastery of capabilities for performance", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 9, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Core Message Extraction", "slug": "core-message-extraction", "status": "active", "bestFor": "Any slide that carries an argument or finding. The diagnostic: if the slide has a chart on it, it needs a core message. Run extraction whenever the current title is a topic (Q3 performance, Customer satisfaction) rather than a claim, or when a reader could reasonably draw more than one conclusion from the visible evidence.", "canonId": "019dd956-bc71-707d-8867-1b2e810175cf", "version": 1, "bodyDocId": "f85613cb-4b77-462f-bfe6-3b862516e1a6", "description": "The discipline of distilling raw analysis, data, or content into a single declarative sentence — the slide's core message — that the audience would walk away with if they remembered nothing else. Process, not artefact: extraction questions run on the raw content until one sentence falls out.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-7798f7fcd36c", "evidence": "Single-paragraph definition crystallizes the construct", "pageRefs": null, "priority": null, "whenToUse": "Any slide that carries an argument or finding. The diagnostic: if the slide has a chart on it, it needs a core message. Run extraction whenever the current title is a topic (Q3 performance, Customer satisfaction) rather than a claim, or when a reader could reasonably draw more than one conclusion from the visible evidence.", "confidence": 75, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 9, "whyItWorks": "Solves the two failure modes of slide writing simultaneously: the topic title (which outsources interpretation to the audience and produces three different conclusions in three different heads) and the multi-thesis stapler (which exceeds working memory and gets remembered as fragments). Aligns with how readers actually consume decks — Heath & Heath's Find the Core, the journalist's nut graf, and Minto's governing thought all converge on the same cognitive economics.", "antipattern": "Topic titles disguised as core messages (Customer satisfaction, no verb, no stake); multi-thesis sentences stapled with and; hedged sentences (may show signs of possible softening); chart-caption titles (Revenue fell 4%) that restate what the chart already shows instead of naming the so-what; meta-commentary (this slide explores...) that describes the slide rather than concluding it.", "cardinality": null, "narrativePurpose": "Forces the slide author to do the interpretive work before the slide ships, so the audience does not have to do it during the meeting. Wins the WYSIATI battle on purpose — the loudest fragment on the page becomes the author's chosen sentence rather than whichever chart label happens to be largest." } ], "frameworks": [ { "matchId": "e6258561-1235-4dcf-99db-d29c52e98265", "evidence": "The slide explicitly defines a 'holistic AI-maturity framework' and describes measuring the degree of mastery of capabilities.", "framework": null, "confidence": 1, "extraction": { "at": "2026-05-02 19:49:13.345+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 9, "frameworkName": "maturity-model" } ], "arcBeats": [ { "to": 12, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null }, "from": 8, "name": "Problem & Complication", "beatId": "019dd95a-0682-776c-8e32-79a5f653b5e2", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Problem & Complication", "slug": "consultants-gambit-problem-complication", "status": "active", "canonId": "019dd9b8-064a-777f-a374-e9a8c43dedd4", "version": 1, "description": null }, "evidence": "Segmentation reveals only 12% are AI Achievers; 63% Experimenters", "position": 2, "isPrimaryArc": true, "parentBeatType": { "name": "Complication", "slug": "complication", "status": "active", "canonId": "019dd9b8-0275-7709-b2d6-4f77c3fc7579", "version": 1, "description": "The point in the narrative where the audience should feel discomfort. Required for argumentative arcs." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 12, "from": 8, "loop": { "name": "30_2x2_matrix", "slug": "30-2x2-matrix", "status": "active", "bestFor": "Portfolio analysis, prioritization, strategic positioning", "canonId": "019dd956-7225-756a-9cc7-1e6d3f039818", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "Dimension 1 (X-axis) -> Dimension 2 (Y-axis) -> The Four Quadrants -> The Sweet Spot", "description": "Plot options on two critical dimensions to reveal the optimal quadrant", "familyLabel": null }, "matchId": "019dd95a-07fe-70ce-8d3a-4369b4be6b2e", "evidence": "p10 explicit 2x2 (AI Foundation x Differentiation) with four named segments", "position": 3, "objective": "Define AI maturity and segment firms via 2x2", "confidence": 90, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "locked": true }, { "page": 10, "type": "setup", "title": "The chart is a scatter plot mapped onto a 2x2 matrix framework.", "function": "present_framework", "rawType": "segmentation", "block": null, "metadata": { "slideType": "segmentation", "slideTypeCanon": { "name": "Segmentation", "slug": "segmentation", "status": "active", "canonId": "019de52d-0917-75d5-9172-1b3b3c2f6400", "version": 1, "description": null }, "function": "present_framework", "functionCanon": { "name": "Present framework", "slug": "present_framework", "status": "active", "canonId": "019de52d-1403-7667-9100-d2ac6709f750", "version": 1, "description": null }, "density": "dense", "densityScore": 71, "componentCount": 8, "textChars": 687, "nDataPoints": 4, "notes": "The chart is a scatter plot mapped onto a 2x2 matrix framework.", "elementsJson": [ "headline_text", "paragraph", "quadrant_chart", "scatter_plot", "matrix_2x2", "big_number", "footnote" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:13.378+00", "seconds": 3.858, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/10", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-10", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "Achievers, Builders and Innovators collectively represent just 37% of surveyed organizations—Achievers accounted for 12%, Builders for 12% and Innovators for 13% (Figure 3).", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:44:43+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b401-7040c0143357", "parentComponentId": null }, { "bbox": { "h": 0.55, "w": 0.45, "x": 0.41, "y": 0.21 }, "kind": "chart", "text": "Scatter plot showing distribution of companies across four quadrants", "attrs": null, "canon": { "kind": { "name": "Chart", "slug": "chart", "status": "active", "canonId": "019de52c-f9a0-76ef-adc6-0b859109d1f8", "version": 1, "description": "Quantitative chart." }, "tool": null, "subkind": { "name": "Scatter plot", "slug": "scatter", "status": "active", "canonId": "019de52d-007d-72ec-8689-c79fb78e21a2", "version": 1, "description": null }, "framework": null }, "subkind": "scatter", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.378+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "54f5e020-c4dc-49b7-9a0f-811f1e100147", "parentComponentId": null }, { "bbox": { "h": 0.65, "w": 0.55, "x": 0.357, "y": 0.2 }, "kind": "framework", "text": null, "attrs": null, "canon": { "kind": { "name": "Framework", "slug": "framework", "status": "active", "canonId": "019de52c-fa18-77c6-b10a-ccb8277ac0fa", "version": 1, "description": "Named conceptual framework instance." }, "tool": null, "subkind": { "name": "Framework instance", "slug": "instance", "status": "active", "canonId": "019de52d-05a2-72af-be0b-7edc60708a03", "version": 1, "description": null }, "framework": null }, "subkind": "instance", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.378+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "e7d372d4-8851-44f7-9a45-47c87ffe49ea", "parentComponentId": null }, { "bbox": null, "kind": "framework", "text": "2x2 Matrix", "attrs": null, "canon": { "kind": { "name": "Framework", "slug": "framework", "status": "active", "canonId": "019de52c-fa18-77c6-b10a-ccb8277ac0fa", "version": 1, "description": "Named conceptual framework instance." }, "tool": { "name": "Structuring frame", "slug": "structuring-frame", "status": "active", "bestFor": null, "canonId": "019dd9e1-48ff-708d-8103-a8464743e560", "version": 1, "bodyDocId": null, "description": "A named scaffold for organising content (SCQA, situation-action-result, etc.).", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:44:43+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b401-79d8403ef76a", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "Share of organizations that are AI Achievers: 12%", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:44:43+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b401-7639ca8d70e5", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.25, "x": 0.058, "y": 0.141 }, "kind": "paragraph", "text": "The companies that scored best in both categories are the AI Achievers. AI Builders show strong foundational capabilities and average differentiation capabilities, while AI Innovators show strong differentiation capabilities and average foundational capabilities.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.378+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "a15b6086-8a23-49f0-ad34-7ea10f22e36a", "parentComponentId": null }, { "bbox": { "h": 0.02, "w": 0.4, "x": 0.357, "y": 0.82 }, "kind": "source-note", "text": "Source: Accenture Research analysis based on a sample of 1,200 companies", "attrs": null, "canon": { "kind": { "name": "Source note", "slug": "source-note", "status": "active", "canonId": "23484952-494b-4a5e-b1ad-550d0d70e948", "version": 1, "description": "Attribution / source / caveat placed at the slide footer. Numbered footnotes use attrs.numbered." }, "tool": null, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.378+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "ef8373fe-fcb5-4c0f-8e97-99454429b1e6", "parentComponentId": null }, { "bbox": { "h": 0.02, "w": 0.35, "x": 0.357, "y": 0.141 }, "kind": "title", "text": "Figure 3: Only 12% of organizations are AI Achievers", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.378+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "eec42a26-3a36-4d3f-8d78-75749222f535", "parentComponentId": null } ], "metrics": [ { "metricRaw": "Share of organizations that are AI Achievers", "numberRaw": "12%", "numberKind": "percent", "actionTitle": "Figure 3: Only 12% of organizations are AI Achievers", "calloutText": "Achievers, Builders and Innovators collectively represent just 37% of surveyed organizations—Achievers accounted for 12%, Builders for 12% and Innovators for 13% (Figure 3).", "numberScale": null, "numberValue": 12, "metricFamily": "survey_sentiment", "numberCurrency": null } ], "tools": [ { "tool": { "name": "2x2 matrix", "slug": "matrix-2x2", "status": "active", "bestFor": null, "canonId": "019dd9e1-50ab-72d1-8afb-3ab0a38e32a8", "version": 1, "bodyDocId": null, "description": "BCG matrix, importance/urgency, any axes-and-quadrants frame.", "familyLabel": null, "categoryName": null, "categorySlug": null }, "agent": null, "layer": "slide", "agents": null, "matchId": "fd7baedd-85fa-4169-913e-9438ae2361a8", "evidence": "The slide features a 2x2 matrix showing the distribution of companies across four quadrants.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.9, "extraction": { "at": "2026-07-16 21:02:36.605976+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 10, "whyItWorks": null, "antipattern": null, "cardinality": "common", "narrativePurpose": null }, { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-795353f951c2", "evidence": "Figure 3 title is the insight: 'Only 12% are AI Achievers'", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 90, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 10, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Annotation", "slug": "annotation", "status": "active", "bestFor": null, "canonId": "019dd956-b5b9-7668-bf0e-3af99823ec5c", "version": 1, "bodyDocId": null, "description": "Adding explanatory labels to highlight key data points", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "019dd95a-0f08-73d3-987b-8594f6516ada", "evidence": "Each quadrant annotated with % share and definition", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 10, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Color Strategy", "slug": "color-strategy", "status": "active", "bestFor": null, "canonId": "019dd956-b202-77cf-8a6e-f445b08c0fd3", "version": 1, "bodyDocId": null, "description": "Using color for emphasis, categories, sentiment, and hierarchy", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "019dd95a-0f08-73d3-987b-8361ceb2a62f", "evidence": "Distinct color per quadrant (blue Achievers, magenta Innovators, etc.)", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 85, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 10, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Visual Hierarchy", "slug": "visual-hierarchy", "status": "active", "bestFor": null, "canonId": "019dd956-b2ca-706b-937f-e52441086fd7", "version": 1, "bodyDocId": null, "description": "Order of perception through Size > Color > Position > Weight > Space", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "019dd95a-0f08-73d3-987b-7d57c981c696", "evidence": "Achievers placed top-right; large % numbers in each quadrant", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 10, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null } ], "frameworks": [ { "matchId": "019dd95a-1ca5-70bb-bb9f-3d2900ad658f", "evidence": "Axes labelled 'AI Foundation' (X) x 'AI Differentiation' (Y) with four named quadrants", "framework": null, "confidence": 95, "extraction": { "at": "2026-04-29 13:07:34.826634+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 10, "frameworkName": "2x2 Matrix" }, { "matchId": "20937c33-924f-4ea8-995f-df4d69f7526c", "evidence": "The slide uses a 2x2 grid to segment companies based on two axes: AI Foundation and AI Differentiation.", "framework": null, "confidence": 1, "extraction": { "at": "2026-05-02 19:49:13.378+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 10, "frameworkName": "matrix-2x2" } ], "arcBeats": [ { "to": 12, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null }, "from": 8, "name": "Problem & Complication", "beatId": "019dd95a-0682-776c-8e32-79a5f653b5e2", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Problem & Complication", "slug": "consultants-gambit-problem-complication", "status": "active", "canonId": "019dd9b8-064a-777f-a374-e9a8c43dedd4", "version": 1, "description": null }, "evidence": "Segmentation reveals only 12% are AI Achievers; 63% Experimenters", "position": 2, "isPrimaryArc": true, "parentBeatType": { "name": "Complication", "slug": "complication", "status": "active", "canonId": "019dd9b8-0275-7709-b2d6-4f77c3fc7579", "version": 1, "description": "The point in the narrative where the audience should feel discomfort. Required for argumentative arcs." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 12, "from": 8, "loop": { "name": "30_2x2_matrix", "slug": "30-2x2-matrix", "status": "active", "bestFor": "Portfolio analysis, prioritization, strategic positioning", "canonId": "019dd956-7225-756a-9cc7-1e6d3f039818", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "Dimension 1 (X-axis) -> Dimension 2 (Y-axis) -> The Four Quadrants -> The Sweet Spot", "description": "Plot options on two critical dimensions to reveal the optimal quadrant", "familyLabel": null }, "matchId": "019dd95a-07fe-70ce-8d3a-4369b4be6b2e", "evidence": "p10 explicit 2x2 (AI Foundation x Differentiation) with four named segments", "position": 3, "objective": "Define AI maturity and segment firms via 2x2", "confidence": 90, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "locked": true }, { "page": 11, "type": "analysis", "title": "The chart is a dumbbell plot showing progress in AI maturity index scores.", "function": "analyze_data", "rawType": "industry_trends", "block": null, "metadata": { "slideType": "industry_trends", "slideTypeCanon": { "name": "Industry trends", "slug": "industry_trends", "status": "active", "canonId": "019de52d-0b1d-7705-a0e0-c663199cd1fa", "version": 1, "description": null }, "function": "analyze_data", "functionCanon": { "name": "Analyze data", "slug": "analyze_data", "status": "active", "canonId": "019de52d-1590-7413-8c82-cc218cff40b4", "version": 1, "description": null }, "density": "overcrowded", "densityScore": 92, "componentCount": 11, "textChars": 1269, "nDataPoints": 32, "notes": "The chart is a dumbbell plot showing progress in AI maturity index scores.", "elementsJson": [ "paragraph", "subtitle_text", "bar_chart_horizontal", "footnote" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:13.612+00", "seconds": 3.969, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/11", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-11", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "For the world's 2,000 largest firms by market cap, the percentage of Achievers was even smaller: 10%.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:44:40+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b401-4d0ee5133e39", "parentComponentId": null }, { "bbox": { "h": 0.6, "w": 0.42, "x": 0.528, "y": 0.175 }, "kind": "chart", "text": "Dumbbell plot showing AI maturity index by industry for 2021 and 2024", "attrs": null, "canon": { "kind": { "name": "Chart", "slug": "chart", "status": "active", "canonId": "019de52c-f9a0-76ef-adc6-0b859109d1f8", "version": 1, "description": "Quantitative chart." }, "tool": null, "subkind": { "name": "Horizontal bar chart", "slug": "bar-horizontal", "status": "active", "canonId": "019de52c-fec4-7221-986b-1cb5f5e84b1f", "version": 1, "description": null }, "framework": null }, "subkind": "bar-horizontal", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.612+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "62def3f7-4bbb-4061-a7f7-10ce7cf9bb35", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "Median AI Maturity Index (0-100): 60", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:44:40+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b401-51cc24d7f302", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.21, "x": 0.058, "y": 0.315 }, "kind": "paragraph", "text": "Taken together, Achievers, Builders and Innovators tend to have more resources (such as technology, talent and patents) to deliver on their AI visions and to transform their organizations.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.612+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "7efc9704-59d7-49b5-9124-33d111a79305", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.21, "x": 0.058, "y": 0.14 }, "kind": "paragraph", "text": "For the world's 2,000 largest firms by market cap, the percentage of Achievers was even smaller: 10%. These numbers suggest that large firms may struggle to make the large foundational and cultural shifts needed to become AI Achievers.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.612+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "ae17abf2-b72d-4ab5-b341-4f06b2af09c6", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.21, "x": 0.058, "y": 0.63 }, "kind": "paragraph", "text": "While industries like tech are currently far ahead in their respective AI maturity, the gap will likely narrow considerably by 2024 (Figure 4).", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.612+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "f42eca52-e321-4f93-96d4-2ba7894a45ee", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.21, "x": 0.285, "y": 0.345 }, "kind": "paragraph", "text": "For industry laggards like financial services and healthcare, a range of factors may be contributing to their relatively low AI maturity—including legal and regulatory challenges, inadequate AI infrastructure and a shortage of AI-trained workers.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.612+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "fd46a266-2785-4ede-889e-473316cbe334", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.42, "x": 0.528, "y": 0.845 }, "kind": "source-note", "text": "Note: *2024 = estimated scores. Industries' AI maturity scores represent the arithmetic average of their respective Foundational and Differentiation index.", "attrs": null, "canon": { "kind": { "name": "Source note", "slug": "source-note", "status": "active", "canonId": "23484952-494b-4a5e-b1ad-550d0d70e948", "version": 1, "description": "Attribution / source / caveat placed at the slide footer. Numbered footnotes use attrs.numbered." }, "tool": null, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.612+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "3e8e806e-5d3e-4374-8e6e-9adaa59a0811", "parentComponentId": null }, { "bbox": { "h": 0.02, "w": 0.15, "x": 0.528, "y": 0.815 }, "kind": "source-note", "text": "Source: Accenture Research", "attrs": null, "canon": { "kind": { "name": "Source note", "slug": "source-note", "status": "active", "canonId": "23484952-494b-4a5e-b1ad-550d0d70e948", "version": 1, "description": "Attribution / source / caveat placed at the slide footer. Numbered footnotes use attrs.numbered." }, "tool": null, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.612+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "50594acc-61d9-488d-a437-781676b25bbb", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.1, "x": 0.058, "y": 0.57 }, "kind": "title", "text": "AI, applied", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.612+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "16cc849d-7633-4044-9336-138d9839918a", "parentComponentId": null }, { "bbox": { "h": 0.02, "w": 0.42, "x": 0.528, "y": 0.14 }, "kind": "title", "text": "Figure 4: Levels of AI maturity by industry, 2021 and 2024*", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.612+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "ebc9852e-4c00-4e89-8ffa-2c9b990149d3", "parentComponentId": null } ], "metrics": [ { "metricRaw": "Median AI Maturity Index (0-100)", "numberRaw": "60", "numberKind": "plain", "actionTitle": null, "calloutText": "For the world's 2,000 largest firms by market cap, the percentage of Achievers was even smaller: 10%.", "numberScale": null, "numberValue": 60, "metricFamily": "index_score", "numberCurrency": null } ], "tools": [ { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-8b604002b260", "evidence": "'AI, applied' frames the world's-largest-firms cut", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 70, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 11, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Authority Bias", "slug": "authority-bias", "status": "active", "bestFor": null, "canonId": "019dd956-c38d-70b9-a284-b2b479763356", "version": 1, "bodyDocId": null, "description": "Expert opinions are weighted more heavily - cite credible sources", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Architect", "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-92675d86845d", "evidence": "Citing top-2,000-by-market-cap sample for credibility", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 70, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 11, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-8e3f821ad536", "evidence": "Specific: 2,000 largest firms by market cap, 10% Achievers", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 11, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null } ], "frameworks": [ { "matchId": "ba834e4a-196d-439f-84ca-730ee9a7a0ff", "evidence": "Levels of AI maturity by industry", "framework": null, "confidence": 0.9, "extraction": { "at": "2026-05-02 19:49:13.612+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 11, "frameworkName": "maturity-model" } ], "arcBeats": [ { "to": 12, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null }, "from": 8, "name": "Problem & Complication", "beatId": "019dd95a-0682-776c-8e32-79a5f653b5e2", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Problem & Complication", "slug": "consultants-gambit-problem-complication", "status": "active", "canonId": "019dd9b8-064a-777f-a374-e9a8c43dedd4", "version": 1, "description": null }, "evidence": "Segmentation reveals only 12% are AI Achievers; 63% Experimenters", "position": 2, "isPrimaryArc": true, "parentBeatType": { "name": "Complication", "slug": "complication", "status": "active", "canonId": "019dd9b8-0275-7709-b2d6-4f77c3fc7579", "version": 1, "description": "The point in the narrative where the audience should feel discomfort. Required for argumentative arcs." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 12, "from": 8, "loop": { "name": "30_2x2_matrix", "slug": "30-2x2-matrix", "status": "active", "bestFor": "Portfolio analysis, prioritization, strategic positioning", "canonId": "019dd956-7225-756a-9cc7-1e6d3f039818", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "Dimension 1 (X-axis) -> Dimension 2 (Y-axis) -> The Four Quadrants -> The Sweet Spot", "description": "Plot options on two critical dimensions to reveal the optimal quadrant", "familyLabel": null }, "matchId": "019dd95a-07fe-70ce-8d3a-4369b4be6b2e", "evidence": "p10 explicit 2x2 (AI Foundation x Differentiation) with four named segments", "position": 3, "objective": "Define AI maturity and segment firms via 2x2", "confidence": 90, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "locked": true }, { "page": 12, "type": "evidence", "function": "illustrate_case", "rawType": "client_example", "block": null, "metadata": { "slideType": "client_example", "slideTypeCanon": { "name": "Client example", "slug": "client_example", "status": "active", "canonId": "019de52d-0c87-77cb-9867-613ae22e95ac", "version": 1, "description": null }, "function": "illustrate_case", "functionCanon": { "name": "Illustrate case", "slug": "illustrate_case", "status": "active", "canonId": "019de52d-13b4-772c-bb73-1873612a7463", "version": 1, "description": null }, "density": "overcrowded", "densityScore": 91, "componentCount": 10, "textChars": 2355, "nDataPoints": 5, "notes": null, "elementsJson": [ "headline_text", "bullet_list", "paragraph" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:13.887+00", "seconds": 4.092, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/12", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-12", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "In the public sector, Metro de Madrid, one of the world's oldest urban rail systems, deployed AI algorithms to sift through mountains of data—on everything from air temperature at individual stations, to train frequency and passenger patterns, to electricity prices—to reduce its annual energy intake by 25%.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:44:46+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b401-98d71500b541", "parentComponentId": null }, { "bbox": { "h": 0.12, "w": 0.26, "x": 0.058, "y": 0.678 }, "kind": "list", "text": "A Middle East-based telco uses AI-driven virtual assistants—which can communicate in different Arab dialects as well as in English—to deftly handle some 1.65 million customer calls each month.", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.887+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "283ce4f8-e272-4a4f-8410-d82e6d0e8560", "parentComponentId": null }, { "bbox": { "h": 0.12, "w": 0.26, "x": 0.678, "y": 0.508 }, "kind": "list", "text": "A major US-based beverage bottler used AI to consolidate data sources and measure the effect of promotions on different retailers and markets, boosting the bottler's annual sales by 3%.", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.887+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "4133647d-2190-42ed-8775-55f831ccbdbc", "parentComponentId": null }, { "bbox": { "h": 0.18, "w": 0.26, "x": 0.058, "y": 1 }, "kind": "list", "text": "One food delivery service uses deep learning to guide drivers to the best delivery routes. AI models analyze more than 2,000 variables, from the latest food ordering trends to traffic conditions, to make real-time recommendations.", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.887+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "466a796f-5250-49c9-a194-5a0186c59df8", "parentComponentId": null }, { "bbox": { "h": 0.18, "w": 0.26, "x": 0.058, "y": 0.428 }, "kind": "list", "text": "A large chemicals and energy firm is using drones and AI-powered computer vision to monitor its equipment and remote locations. The upshot: More frequent inspections at lower cost to the company and fewer safety risks for its maintenance workers.", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.887+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "7f7f5588-4b50-4c33-a911-483b6423b001", "parentComponentId": null }, { "bbox": { "h": 0.22, "w": 0.26, "x": 0.678, "y": 0.198 }, "kind": "list", "text": "In the public sector, Metro de Madrid, one of the world's oldest urban rail systems, deployed AI algorithms to sift through mountains of data—on everything from air temperature at individual stations, to train frequency and passenger patterns, to electricity prices—to reduce its annual energy intake by 25%.", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.887+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "c5753690-16ad-455c-bb58-ab6a590a8130", "parentComponentId": null }, { "bbox": { "h": 0.25, "w": 0.26, "x": 0.368, "y": 0.588 }, "kind": "list", "text": "A leading solar-panel installer is using satellite photos and deep-learning algorithms to create fully automated rooftop-installation plans and price estimates. In addition to offering end customers an industry-first ability to self-design their systems, the company expects its AI-led design efforts to ultimately lower the firm's sales costs by 25%.", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.887+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "c871ca53-8536-45df-b399-edb6645cdb61", "parentComponentId": null }, { "bbox": { "h": 0.28, "w": 0.26, "x": 0.368, "y": 0.198 }, "kind": "list", "text": "A large Australian telco deployed AI to quantify the effectiveness of its individual marketing initiatives. The firm was able to measure some 4,000 different marketing metrics and, in the process, has created a world-class marketing performance insights capability, with a range of strategic and tactical applications. The telco is using insights gained from Marketing Mix Modeling (MMM) to optimize the allocation of marketing spend, messaging and media.", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.887+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "e34dde8d-f93f-4aad-bbbb-57d9a3eb125f", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "Metro de Madrid annual energy intake reduction: 25%", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:44:46+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b401-9eb47977dbaf", "parentComponentId": null }, { "bbox": { "h": 0.044, "w": 0.354, "x": 0.058, "y": 0.118 }, "kind": "title", "text": "AI, applied across industries", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.887+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "30a37546-88fe-432b-9083-81b732001f76", "parentComponentId": null } ], "metrics": [ { "metricRaw": "Metro de Madrid annual energy intake reduction", "numberRaw": "25%", "numberKind": "percent", "actionTitle": null, "calloutText": "In the public sector, Metro de Madrid, one of the world's oldest urban rail systems, deployed AI algorithms to sift through mountains of data—on everything from air temperature at individual stations, to train frequency and passenger patterns, to electricity prices—to reduce its annual energy intake by 25%.", "numberScale": null, "numberValue": 25, "metricFamily": "other", "numberCurrency": null } ], "tools": [ { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-993421352dde", "evidence": "Specific: air temperature data, 25% energy reduction", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 12, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Sinatra Test", "slug": "sinatra-test", "status": "active", "bestFor": null, "canonId": "019dd956-be4a-775f-8038-d4e7e58dfc5a", "version": 1, "bodyDocId": null, "description": "If you can make it there, you can make it anywhere - using flagship example", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-972e282391fb", "evidence": "Metro de Madrid as flagship public-sector AI example", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 12, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Singularity Effect", "slug": "singularity-effect", "status": "active", "bestFor": null, "canonId": "019dd956-dcd3-7287-94d8-5a49496c2ddb", "version": 1, "bodyDocId": null, "description": "People care more about individuals than statistics - use specific examples", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-9c73b2a15d56", "evidence": "Single named organization stands in for sector benefit", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 70, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 12, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null } ], "frameworks": [], "arcBeats": [ { "to": 12, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null }, "from": 8, "name": "Problem & Complication", "beatId": "019dd95a-0682-776c-8e32-79a5f653b5e2", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Problem & Complication", "slug": "consultants-gambit-problem-complication", "status": "active", "canonId": "019dd9b8-064a-777f-a374-e9a8c43dedd4", "version": 1, "description": null }, "evidence": "Segmentation reveals only 12% are AI Achievers; 63% Experimenters", "position": 2, "isPrimaryArc": true, "parentBeatType": { "name": "Complication", "slug": "complication", "status": "active", "canonId": "019dd9b8-0275-7709-b2d6-4f77c3fc7579", "version": 1, "description": "The point in the narrative where the audience should feel discomfort. Required for argumentative arcs." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 12, "from": 8, "loop": { "name": "30_2x2_matrix", "slug": "30-2x2-matrix", "status": "active", "bestFor": "Portfolio analysis, prioritization, strategic positioning", "canonId": "019dd956-7225-756a-9cc7-1e6d3f039818", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "Dimension 1 (X-axis) -> Dimension 2 (Y-axis) -> The Four Quadrants -> The Sweet Spot", "description": "Plot options on two critical dimensions to reveal the optimal quadrant", "familyLabel": null }, "matchId": "019dd95a-07fe-70ce-8d3a-4369b4be6b2e", "evidence": "p10 explicit 2x2 (AI Foundation x Differentiation) with four named segments", "position": 3, "objective": "Define AI maturity and segment firms via 2x2", "confidence": 90, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "locked": true }, { "page": 13, "type": "setup", "title": "The slide uses a ballet metaphor to represent the transition from practice to performance.", "function": "transition", "rawType": "transition", "block": null, "metadata": { "slideType": "transition", "slideTypeCanon": { "name": "Transition", "slug": "transition", "status": "active", "canonId": "019de52d-0693-7766-9f05-887f41f8e41e", "version": 1, "description": null }, "function": "transition", "functionCanon": { "name": "Transition", "slug": "transition", "status": "active", "canonId": "019de52d-1365-7149-92aa-66df859e75fd", "version": 1, "description": null }, "density": "sparse", "densityScore": 8, "componentCount": 3, "textChars": 94, "nDataPoints": 0, "notes": "The slide uses a ballet metaphor to represent the transition from practice to performance.", "elementsJson": [ "headline_text", "photo" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:14.188+00", "seconds": 4.301, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/13", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-13", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": { "h": 1, "w": 1, "x": 0, "y": 0 }, "kind": "image", "text": "Ballet dancers on stage", "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Photo", "slug": "photo", "status": "active", "canonId": "019de52c-fd37-757f-b4a8-ca26d4888875", "version": 1, "description": null }, "framework": null }, "subkind": "photo", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:14.188+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "dc1af4ef-78d9-43cd-a6f5-e04a9fa2b32b", "parentComponentId": null }, { "bbox": { "h": 0.166, "w": 0.622, "x": 0.189, "y": 0.418 }, "kind": "title", "text": "AI Achievers advance from practice to performance", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:14.188+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "9165a8ce-eaa3-47e6-95cd-05fb2a33a21d", "parentComponentId": null }, { "bbox": { "h": 0.025, "w": 0.178, "x": 0.058, "y": 0.103 }, "kind": "title", "text": "The art of AI maturity", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Subtitle", "slug": "subtitle", "status": "active", "canonId": "019de52c-fb09-739d-900c-815b0d9ac526", "version": 1, "description": null }, "framework": null }, "subkind": "subtitle", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:14.188+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "f07d2776-80f1-469c-b5fb-a15b700bad32", "parentComponentId": null } ], "metrics": [], "tools": [], "frameworks": [], "arcBeats": [ { "to": 16, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null }, "from": 13, "name": "Evidence & Proof", "beatId": "019dd95a-0682-776c-8e32-83fe9528254e", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Evidence & Proof", "slug": "consultants-gambit-evidence-proof", "status": "active", "canonId": "019dd9b8-07b1-7688-8e1e-7637fec3802a", "version": 1, "description": null }, "evidence": "Achievers outperform peers on revenue (50%), scaling (25%), CX (8%)", "position": 4, "isPrimaryArc": true, "parentBeatType": { "name": "Evidence", "slug": "evidence", "status": "active", "canonId": "019dd9b8-03f9-772a-b84a-4f07b415b77a", "version": 1, "description": "A dedicated proof beat exists in only ~4 arcs (Gambit, Sequoia, AIDA, Monroes). Other arcs embed evidence inline within development." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 16, "from": 13, "loop": { "name": "39_benchmark_gap", "slug": "39-benchmark-gap", "status": "active", "bestFor": "Performance improvement, competitive analysis, target setting", "canonId": "019dd956-75a4-7522-8d1a-af94da4703e9", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "Our Performance -> Industry Average -> Best-in-Class -> The Gap = The Opportunity", "description": "Compare performance against best-in-class to quantify the opportunity", "familyLabel": null }, "matchId": "019dd95a-07fe-70ce-8d3a-4639593ed737", "evidence": "Pp 14-16 quantify gap: 50% revenue growth, 25% scaling, 8% CX, 17 capability heatmap", "position": 4, "objective": "Show Achievers outperform peers on key metrics", "confidence": 85, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "locked": true }, { "page": 14, "type": "setup", "title": "The chart uses a matrix of filled/empty squares to represent performance levels across four segments.", "function": "compare_peers", "rawType": "peer_benchmark", "block": null, "metadata": { "slideType": "peer_benchmark", "slideTypeCanon": { "name": "Peer benchmark", "slug": "peer_benchmark", "status": "active", "canonId": "019de52d-08f0-7158-a78d-d7b5331fefaf", "version": 1, "description": null }, "function": "compare_peers", "functionCanon": { "name": "Compare peers", "slug": "compare_peers", "status": "active", "canonId": "019de52d-1453-72ba-a367-9ba3d34c1630", "version": 1, "description": null }, "density": "overcrowded", "densityScore": 79, "componentCount": 8, "textChars": 845, "nDataPoints": 68, "notes": "The chart uses a matrix of filled/empty squares to represent performance levels across four segments.", "elementsJson": [ "headline_text", "action_title", "subtitle_text", "paragraph", "heatmap", "footnote" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:13.949+00", "seconds": 3.987, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/14", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-14", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "Pre-pandemic (2019), they already enjoyed 50% greater revenue growth on average, versus their peers. And today, they're 3.5 times more likely than Experimenters to see their AI-influenced revenue surpass 30% of their total revenues.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:44:52+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b401-b775b1660234", "parentComponentId": null }, { "bbox": { "h": 0.65, "w": 0.42, "x": 0.52, "y": 0.16 }, "kind": "chart", "text": "Figure 5: AI Achievers outperform in nearly all capabilities", "attrs": null, "canon": { "kind": { "name": "Chart", "slug": "chart", "status": "active", "canonId": "019de52c-f9a0-76ef-adc6-0b859109d1f8", "version": 1, "description": "Quantitative chart." }, "tool": null, "subkind": { "name": "Heatmap", "slug": "heatmap", "status": "active", "canonId": "019de52d-00cd-71ee-bf36-233b7817c304", "version": 1, "description": null }, "framework": null }, "subkind": "heatmap", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.949+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "bf450835-5492-4dc0-bcec-0377358a138f", "parentComponentId": null }, { "bbox": { "h": 0.06, "w": 0.42, "x": 0.52, "y": 0.79 }, "kind": "legend", "text": "Note: Each square represents one of the 17 key capabilities. The square is filled in when the AI profile is outperforming against peers.", "attrs": null, "canon": { "kind": { "name": "Legend", "slug": "legend", "status": "active", "canonId": "31361933-3d22-4c70-bbad-0801e9fffc0c", "version": 1, "description": "Color/symbol decoder placed adjacent to a chart or diagram." }, "tool": null, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.949+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "f5dbadfa-06c1-4e2b-a3c1-af4073417747", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "AI-influenced revenue share of total: 30%", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:44:52+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b401-bae800ce13a0", "parentComponentId": null }, { "bbox": { "h": 0.12, "w": 0.4, "x": 0.06, "y": 0.34 }, "kind": "paragraph", "text": "Pre-pandemic (2019), they already enjoyed 50% greater revenue growth on average, versus their peers. And today, they're 3.5 times more likely than Experimenters to see their AI-influenced revenue surpass 30% of their total revenues.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.949+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "3cffb760-19ee-4e28-8710-32acb37055ba", "parentComponentId": null }, { "bbox": { "h": 0.02, "w": 0.15, "x": 0.52, "y": 0.76 }, "kind": "source-note", "text": "Source: Accenture Research", "attrs": null, "canon": { "kind": { "name": "Source note", "slug": "source-note", "status": "active", "canonId": "23484952-494b-4a5e-b1ad-550d0d70e948", "version": 1, "description": "Attribution / source / caveat placed at the slide footer. Numbered footnotes use attrs.numbered." }, "tool": null, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.949+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "1b22c88a-8267-4ee2-95d4-1e8b671265b2", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.4, "x": 0.06, "y": 0.26 }, "kind": "title", "text": "AI Achievers thrive when it comes to traditional performance metrics.", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Action title", "slug": "action-title", "status": "active", "canonId": "019de52c-fb31-734c-a0f5-a3935b73135d", "version": 1, "description": null }, "framework": null }, "subkind": "action-title", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.949+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "804fe0ab-f03e-4f06-ac6d-1a98342501ad", "parentComponentId": null }, { "bbox": { "h": 0.08, "w": 0.4, "x": 0.06, "y": 0.14 }, "kind": "title", "text": "AI Achievers advance from practice to performance", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.949+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "23150813-8177-40cd-b393-9a6201a92a13", "parentComponentId": null } ], "metrics": [ { "metricRaw": "AI-influenced revenue share of total", "numberRaw": "30%", "numberKind": "percent", "actionTitle": "AI Achievers advance from practice to performance", "calloutText": "Pre-pandemic (2019), they already enjoyed 50% greater revenue growth on average, versus their peers. And today, they're 3.5 times more likely than Experimenters to see their AI-influenced revenue surpass 30% of their total revenues.", "numberScale": null, "numberValue": 30, "metricFamily": "share_penetration", "numberCurrency": null } ], "tools": [ { "tool": { "name": "Contrast Pairs", "slug": "contrast-pairs", "status": "active", "bestFor": null, "canonId": "019dd956-9b77-70d3-88b1-f34e26f60860", "version": 1, "bodyDocId": null, "description": "Creating tension through opposites: Before↔After, Problem↔Solution, Pain↔Relief", "familyLabel": null, "categoryName": "Loop", "categorySlug": "loop" }, "agent": "Storyteller", "layer": "loop", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-ae2279deb778", "evidence": "Achievers vs Experimenters comparison anchors the loop", "pageRefs": null, "priority": "Core", "whenToUse": null, "confidence": 75, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 14, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-a335f3bc977b", "evidence": "Subhead: 'Achievers thrive on traditional performance metrics'", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 85, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 14, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-ab21e93148a8", "evidence": "50% revenue growth, 3.5x more likely, 30% AI-influenced revenue", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 14, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Small Multiples", "slug": "small-multiples", "status": "active", "bestFor": null, "canonId": "019dd956-b32d-7586-b368-baa1dd388ed4", "version": 1, "bodyDocId": null, "description": "Repeating similar charts for comparison across categories or time", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "019dd95a-0f08-73d3-987b-a7a5e2b8492a", "evidence": "Capability matrix repeats squares per profile across 17 capabilities", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 85, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 14, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null } ], "frameworks": [ { "matchId": "019dd95a-1ca5-70bb-bb9f-40e303f7e6a0", "evidence": "Four pillars (Strategy & Sponsorship, Data & AI Core, Talent & Culture, Responsible AI) x 17 capabilities heatmap", "framework": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:34.826634+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 14, "frameworkName": "Accenture AI Maturity Capability Framework" }, { "matchId": "1791ae98-3b4c-424b-8356-b507e6aeb903", "evidence": "Categorization of AI profiles into Achievers, Builders, Innovators, and Experimenters across capabilities.", "framework": null, "confidence": 0.9, "extraction": { "at": "2026-05-02 19:49:13.949+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 14, "frameworkName": "maturity-model" } ], "arcBeats": [ { "to": 16, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null }, "from": 13, "name": "Evidence & Proof", "beatId": "019dd95a-0682-776c-8e32-83fe9528254e", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Evidence & Proof", "slug": "consultants-gambit-evidence-proof", "status": "active", "canonId": "019dd9b8-07b1-7688-8e1e-7637fec3802a", "version": 1, "description": null }, "evidence": "Achievers outperform peers on revenue (50%), scaling (25%), CX (8%)", "position": 4, "isPrimaryArc": true, "parentBeatType": { "name": "Evidence", "slug": "evidence", "status": "active", "canonId": "019dd9b8-03f9-772a-b84a-4f07b415b77a", "version": 1, "description": "A dedicated proof beat exists in only ~4 arcs (Gambit, Sequoia, AIDA, Monroes). Other arcs embed evidence inline within development." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 16, "from": 13, "loop": { "name": "39_benchmark_gap", "slug": "39-benchmark-gap", "status": "active", "bestFor": "Performance improvement, competitive analysis, target setting", "canonId": "019dd956-75a4-7522-8d1a-af94da4703e9", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "Our Performance -> Industry Average -> Best-in-Class -> The Gap = The Opportunity", "description": "Compare performance against best-in-class to quantify the opportunity", "familyLabel": null }, "matchId": "019dd95a-07fe-70ce-8d3a-4639593ed737", "evidence": "Pp 14-16 quantify gap: 50% revenue growth, 25% scaling, 8% CX, 17 capability heatmap", "position": 4, "objective": "Show Achievers outperform peers on key metrics", "confidence": 85, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "locked": true }, { "page": 15, "type": "evidence", "title": "The chart is a dumbbell plot showing the gap between two groups across 8 business functions.", "function": "illustrate_case", "rawType": "case_study", "block": null, "metadata": { "slideType": "case_study", "slideTypeCanon": { "name": "Case study", "slug": "case_study", "status": "active", "canonId": "019de52d-0b95-7344-8090-926f3b8e92c8", "version": 1, "description": null }, "function": "illustrate_case", "functionCanon": { "name": "Illustrate case", "slug": "illustrate_case", "status": "active", "canonId": "019de52d-13b4-772c-bb73-1873612a7463", "version": 1, "description": null }, "density": "overcrowded", "densityScore": 80, "componentCount": 8, "textChars": 1017, "nDataPoints": 24, "notes": "The chart is a dumbbell plot showing the gap between two groups across 8 business functions.", "elementsJson": [ "headline_text", "paragraph", "bar_chart_horizontal", "footnote" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:18.133+00", "seconds": 8.144, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/15", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-15", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "Achievers are 25% more likely to scale AI pilots across the enterprise compared with Experimenters.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:44:53+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b401-d8f37c9fc767", "parentComponentId": null }, { "bbox": { "h": 0.655, "w": 0.538, "x": 0.402, "y": 0.14 }, "kind": "chart", "text": "Figure 6: Achievers excel at turning AI pilots into production", "attrs": null, "canon": { "kind": { "name": "Chart", "slug": "chart", "status": "active", "canonId": "019de52c-f9a0-76ef-adc6-0b859109d1f8", "version": 1, "description": "Quantitative chart." }, "tool": null, "subkind": { "name": "Horizontal bar chart", "slug": "bar-horizontal", "status": "active", "canonId": "019de52c-fec4-7221-986b-1cb5f5e84b1f", "version": 1, "description": null }, "framework": null }, "subkind": "bar-horizontal", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:18.133+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "86061449-4d21-42aa-a5a9-cadb7c9254ab", "parentComponentId": null }, { "bbox": { "h": 0.055, "w": 0.538, "x": 0.402, "y": 0.76 }, "kind": "disclaimer", "text": "Note: Score 0-100, ranging from 0 = AI use case not started, 50 = AI use in early stage, 100 = having AI programs in place for full productization. The chart shows the average scores for AI use cases of different functions, between Achievers and other firms.", "attrs": null, "canon": { "kind": { "name": "Disclaimer", "slug": "disclaimer", "status": "active", "canonId": "91173477-d91f-4024-bbaf-d4f93e3f82dc", "version": 1, "description": "Legal/cautionary text that occupies most of the body of its slide." }, "tool": null, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:18.133+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "37b68142-9bef-4800-a5df-a2ba776affc9", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "Average AI productization score (Achievers vs Experimenters): 55", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:44:53+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b401-dd3ebf976428", "parentComponentId": null }, { "bbox": { "h": 0.095, "w": 0.321, "x": 0.058, "y": 0.191 }, "kind": "paragraph", "text": "Achievers have largely moved beyond the AI investment “tipping point,” going from experimenting with new AI in isolation to applying AI at scale to solve critical business problems (Figure 6). Achievers are 25% more likely to scale AI pilots across the enterprise compared with Experimenters.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:18.133+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "ba2795bf-6b05-46c1-965a-1f95b3834705", "parentComponentId": null }, { "bbox": { "h": 0.155, "w": 0.321, "x": 0.058, "y": 0.374 }, "kind": "paragraph", "text": "Take product development as an example: Procter & Gamble (P&G) uses “explainable AI” algorithms to harness its proprietary data and formulation models and recommend product improvements.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:18.133+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "c36adae7-d9d3-4429-8d4b-be794ccc429c", "parentComponentId": null }, { "bbox": { "h": 0.013, "w": 0.135, "x": 0.402, "y": 0.735 }, "kind": "source-note", "text": "Source: Accenture Research", "attrs": null, "canon": { "kind": { "name": "Source note", "slug": "source-note", "status": "active", "canonId": "23484952-494b-4a5e-b1ad-550d0d70e948", "version": 1, "description": "Attribution / source / caveat placed at the slide footer. Numbered footnotes use attrs.numbered." }, "tool": null, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:18.133+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "4a2dbb5c-c91b-4ded-b617-0fca5545b618", "parentComponentId": null }, { "bbox": { "h": 0.028, "w": 0.295, "x": 0.058, "y": 0.128 }, "kind": "title", "text": "Turning pilots into production", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:18.133+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "9f35c61e-812a-4866-9041-88fb493530a6", "parentComponentId": null } ], "metrics": [ { "metricRaw": "Average AI productization score (Achievers vs Experimenters)", "numberRaw": "55", "numberKind": "plain", "actionTitle": "Turning pilots into production", "calloutText": "Achievers are 25% more likely to scale AI pilots across the enterprise compared with Experimenters.", "numberScale": null, "numberValue": 55, "metricFamily": "index_score", "numberCurrency": null } ], "tools": [ { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-b1be7c29c5ea", "evidence": "'Turning pilots into production' states the insight", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 15, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-b4f8ec86d2f9", "evidence": "25% more likely to scale AI pilots; specific delta", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 15, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null } ], "frameworks": [], "arcBeats": [ { "to": 16, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null }, "from": 13, "name": "Evidence & Proof", "beatId": "019dd95a-0682-776c-8e32-83fe9528254e", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Evidence & Proof", "slug": "consultants-gambit-evidence-proof", "status": "active", "canonId": "019dd9b8-07b1-7688-8e1e-7637fec3802a", "version": 1, "description": null }, "evidence": "Achievers outperform peers on revenue (50%), scaling (25%), CX (8%)", "position": 4, "isPrimaryArc": true, "parentBeatType": { "name": "Evidence", "slug": "evidence", "status": "active", "canonId": "019dd9b8-03f9-772a-b84a-4f07b415b77a", "version": 1, "description": "A dedicated proof beat exists in only ~4 arcs (Gambit, Sequoia, AIDA, Monroes). Other arcs embed evidence inline within development." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 16, "from": 13, "loop": { "name": "39_benchmark_gap", "slug": "39-benchmark-gap", "status": "active", "bestFor": "Performance improvement, competitive analysis, target setting", "canonId": "019dd956-75a4-7522-8d1a-af94da4703e9", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "Our Performance -> Industry Average -> Best-in-Class -> The Gap = The Opportunity", "description": "Compare performance against best-in-class to quantify the opportunity", "familyLabel": null }, "matchId": "019dd95a-07fe-70ce-8d3a-4639593ed737", "evidence": "Pp 14-16 quantify gap: 50% revenue growth, 25% scaling, 8% CX, 17 capability heatmap", "position": 4, "objective": "Show Achievers outperform peers on key metrics", "confidence": 85, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "locked": true }, { "page": 16, "type": "resolution", "title": "The slide uses a narrative approach to link AI maturity with broader stakeholder performance metrics.", "function": "summarize", "rawType": "key_takeaways", "block": null, "metadata": { "slideType": "key_takeaways", "slideTypeCanon": { "name": "Key takeaways", "slug": "key_takeaways", "status": "active", "canonId": "019de52d-06e7-7674-a891-9f7de3b11b6d", "version": 1, "description": null }, "function": "summarize", "functionCanon": { "name": "Summarize", "slug": "summarize", "status": "active", "canonId": "019de52d-1541-704f-b86f-bf7b75056373", "version": 1, "description": null }, "density": "overcrowded", "densityScore": 86, "componentCount": 9, "textChars": 1599, "nDataPoints": 3, "notes": "The slide uses a narrative approach to link AI maturity with broader stakeholder performance metrics.", "elementsJson": [ "headline_text", "paragraph", "photo" ], "confidence": 0.9, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:13.337+00", "seconds": 3.326, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/16", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-16", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "Achievers score 8% higher than Experimenters on customer experience", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:45:04+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b402-0a3d8a63d219", "parentComponentId": null }, { "bbox": { "h": 0.871, "w": 0.456, "x": 0.029, "y": 0.062 }, "kind": "image", "text": null, "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Photo", "slug": "photo", "status": "active", "canonId": "019de52c-fd37-757f-b4a8-ca26d4888875", "version": 1, "description": null }, "framework": null }, "subkind": "photo", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.337+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "460626ab-f67b-431e-9561-6c5bf6b97c89", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "Achiever lead in customer experience: 8%", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:45:04+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b402-0fcaf158cf13", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.235, "x": 0.514, "y": 0.376 }, "kind": "paragraph", "text": "Additionally, they double down on their commitment to sustainability by, for instance, rigorously measuring and reducing their greenhouse gas emissions, consuming water and other natural resources more economically and using AI responsibly.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.337+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "13ecd6c2-092a-4a9e-b9b1-d56bb280843b", "parentComponentId": null }, { "bbox": { "h": 0.06, "w": 0.235, "x": 0.742, "y": 0.508 }, "kind": "paragraph", "text": "The stakeholder performance model revealed the value-creation gap between Achievers and other companies is significant when it comes to sustainability.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.337+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "141f8d1f-a729-41bd-a2a2-d0b478acefdd", "parentComponentId": null }, { "bbox": { "h": 0.12, "w": 0.235, "x": 0.742, "y": 0.376 }, "kind": "paragraph", "text": "One US-based utility company conducts remote monitoring of its extensive grid infrastructure via satellites, drones and other surveillance tools. With the help of advanced analytics, machine learning and computer vision, the company is able to quickly identify and prioritize areas for maintenance, improve public safety and mitigate the effects of climate change.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.337+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "27d501dc-f35b-438e-afd2-e0cf5260164c", "parentComponentId": null }, { "bbox": { "h": 0.12, "w": 0.235, "x": 0.514, "y": 0.244 }, "kind": "paragraph", "text": "Achievers also develop strong relationships with customers—by building trust, reducing churn and boosting the quality and safety of offerings. Our stakeholder performance model showed with high statistical significance that Achievers score 8% higher than Experimenters on customer experience (see Appendix for more on methodology).", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.337+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "8fda741f-454b-4f42-bad6-ab4d7c71c24c", "parentComponentId": null }, { "bbox": { "h": 0.14, "w": 0.235, "x": 0.742, "y": 0.162 }, "kind": "paragraph", "text": "Accenture’s Sustainable Technology survey of more than 500 multinational companies found that of the surveyed firms that managed to reduce emissions from their operations, 70% used AI to achieve those reductions. Likewise, of the surveyed companies that made strides in measuring and disclosing their carbon footprints more transparently, 75% used AI to make such progress.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.337+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "93a1fd39-0062-48a5-92ce-2a2dd739e578", "parentComponentId": null }, { "bbox": { "h": 0.054, "w": 0.235, "x": 0.514, "y": 0.144 }, "kind": "title", "text": "Focusing beyond financial metrics", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.337+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "459e9484-c7c3-4de2-89a2-3051b0534b3b", "parentComponentId": null } ], "metrics": [ { "metricRaw": "Achiever lead in customer experience", "numberRaw": "8%", "numberKind": "percent", "actionTitle": null, "calloutText": "Achievers score 8% higher than Experimenters on customer experience", "numberScale": null, "numberValue": 8, "metricFamily": "other", "numberCurrency": null } ], "tools": [ { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-bad6a8d24205", "evidence": "'Focusing beyond financial metrics' frames CX gap", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 75, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 16, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Audience Definition", "slug": "audience-definition", "status": "active", "bestFor": null, "canonId": "019dd956-7f2a-775d-aeee-ff099bcb7756", "version": 1, "bodyDocId": null, "description": "Six key questions: Who are they? What do they know? What do they believe? What do they care about? What do they fear? What decisions can they make?", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "d899529d-6124-4a6b-94f5-7adbbbe93d3c", "evidence": "Accenture’s Sustainable Technology survey of more than 500 multinational companies", "pageRefs": null, "priority": "Core", "whenToUse": null, "confidence": 0.7, "extraction": { "at": "2026-07-16 21:02:35.412514+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 16, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-bf9930bdc5bd", "evidence": "8% higher CX score for Achievers", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 75, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 16, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Storytelling Effect", "slug": "storytelling-effect", "status": "active", "bestFor": null, "canonId": "019dd956-981b-74f9-9096-5c8df03dab5a", "version": 1, "bodyDocId": null, "description": "Stories are remembered up to 22x better than facts alone", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "9f1da67e-de21-4423-85ae-9528ff619ae5", "evidence": "The stakeholder performance model revealed the value-creation gap between Achievers and other companies is significant when it comes to sustainability.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.6, "extraction": { "at": "2026-07-16 21:02:35.452544+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 16, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null } ], "frameworks": [], "arcBeats": [ { "to": 16, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null }, "from": 13, "name": "Evidence & Proof", "beatId": "019dd95a-0682-776c-8e32-83fe9528254e", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Evidence & Proof", "slug": "consultants-gambit-evidence-proof", "status": "active", "canonId": "019dd9b8-07b1-7688-8e1e-7637fec3802a", "version": 1, "description": null }, "evidence": "Achievers outperform peers on revenue (50%), scaling (25%), CX (8%)", "position": 4, "isPrimaryArc": true, "parentBeatType": { "name": "Evidence", "slug": "evidence", "status": "active", "canonId": "019dd9b8-03f9-772a-b84a-4f07b415b77a", "version": 1, "description": "A dedicated proof beat exists in only ~4 arcs (Gambit, Sequoia, AIDA, Monroes). Other arcs embed evidence inline within development." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 16, "from": 13, "loop": { "name": "39_benchmark_gap", "slug": "39-benchmark-gap", "status": "active", "bestFor": "Performance improvement, competitive analysis, target setting", "canonId": "019dd956-75a4-7522-8d1a-af94da4703e9", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "Our Performance -> Industry Average -> Best-in-Class -> The Gap = The Opportunity", "description": "Compare performance against best-in-class to quantify the opportunity", "familyLabel": null }, "matchId": "019dd95a-07fe-70ce-8d3a-4639593ed737", "evidence": "Pp 14-16 quantify gap: 50% revenue growth, 25% scaling, 8% CX, 17 capability heatmap", "position": 4, "objective": "Show Achievers outperform peers on key metrics", "confidence": 85, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "locked": true }, { "page": 17, "type": "setup", "title": "The slide uses a DJ metaphor to represent 'mastering a craft'.", "function": "transition", "rawType": "section_divider", "block": null, "metadata": { "slideType": "section_divider", "slideTypeCanon": { "name": "Section divider", "slug": "section_divider", "status": "active", "canonId": "019de52d-066a-7402-9554-6ae504999041", "version": 1, "description": null }, "function": "transition", "functionCanon": { "name": "Transition", "slug": "transition", "status": "active", "canonId": "019de52d-1365-7149-92aa-66df859e75fd", "version": 1, "description": null }, "density": "sparse", "densityScore": 13, "componentCount": 4, "textChars": 101, "nDataPoints": 0, "notes": "The slide uses a DJ metaphor to represent 'mastering a craft'.", "elementsJson": [ "headline_text", "subtitle_text", "photo" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:12.998+00", "seconds": 2.892, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/17", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-17", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": { "h": 1, "w": 1, "x": 0, "y": 0 }, "kind": "image", "text": "DJ mixing on a turntable", "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Photo", "slug": "photo", "status": "active", "canonId": "019de52c-fd37-757f-b4a8-ca26d4888875", "version": 1, "description": null }, "framework": null }, "subkind": "photo", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:12.998+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "8ec608e4-56cd-41f8-bc16-a8e74bc64de8", "parentComponentId": null }, { "bbox": { "h": 0.2, "w": 0.5, "x": 0.25, "y": 0.4 }, "kind": "title", "text": "How AI Achievers master their craft", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:12.998+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "1e392576-5088-4880-a58d-5cab86f3eda8", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.2, "x": 0.05, "y": 0.1 }, "kind": "title", "text": "The art of AI maturity", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Subtitle", "slug": "subtitle", "status": "active", "canonId": "019de52c-fb09-739d-900c-815b0d9ac526", "version": 1, "description": null }, "framework": null }, "subkind": "subtitle", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:12.998+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "1aa2358b-1da4-4656-a164-4ed8a275bf75", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.2, "x": 0.4, "y": 0.6 }, "kind": "title", "text": "Five success factors", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Subtitle", "slug": "subtitle", "status": "active", "canonId": "019de52c-fb09-739d-900c-815b0d9ac526", "version": 1, "description": null }, "framework": null }, "subkind": "subtitle", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:12.998+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "4f147307-6173-4294-acaf-ef8c15e3116e", "parentComponentId": null } ], "metrics": [], "tools": [], "frameworks": [], "arcBeats": [ { "to": 27, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null }, "from": 17, "name": "Solution & Approach", "beatId": "019dd95a-0682-776c-8e32-7e8c761f9fc5", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Solution & Approach", "slug": "consultants-gambit-solution-approach", "status": "active", "canonId": "019dd9b8-071c-7161-9184-580fdac7a4f9", "version": 1, "description": null }, "evidence": "Five recommendations to master AI maturity, each with case study", "position": 3, "isPrimaryArc": true, "parentBeatType": { "name": "Turn", "slug": "turn", "status": "active", "canonId": "019dd9b8-0396-702b-ac2f-9901f8dd8b54", "version": 1, "description": "The pivot — solution proposed, catalyst introduced, decision made. Common (15/20 arcs) but not universal: Triple-take, AIDA, PAS, Voyage-return and Pixar substitute it with a strong reflection or development beat." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 27, "from": 17, "loop": { "name": "40_mece_breakdown", "slug": "40-mece-breakdown", "status": "active", "bestFor": "Problem structuring, ensuring completeness, strategic analysis", "canonId": "019dd956-763c-765e-8b68-6ee3ae1ef184", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Whole -> Category A (distinct) -> Category B (distinct) -> Category C (distinct) -> Complete Coverage", "description": "Divide a complex topic into mutually exclusive, collectively exhaustive categories", "familyLabel": null }, "matchId": "019dd95a-07fe-70ce-8d3a-483bc3c6386e", "evidence": "Five distinct recommendation categories (sponsor / talent / AI core / responsible / invest) each with case study", "position": 5, "objective": "Five MECE recommendations to become an Achiever", "confidence": 85, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "locked": true }, { "page": 18, "type": "setup", "function": "transition", "rawType": "transition", "block": null, "metadata": { "slideType": "transition", "slideTypeCanon": { "name": "Transition", "slug": "transition", "status": "active", "canonId": "019de52d-0693-7766-9f05-887f41f8e41e", "version": 1, "description": null }, "function": "transition", "functionCanon": { "name": "Transition", "slug": "transition", "status": "active", "canonId": "019de52d-1365-7149-92aa-66df859e75fd", "version": 1, "description": null }, "density": "balanced", "densityScore": 35, "componentCount": 4, "textChars": 648, "nDataPoints": 0, "notes": null, "elementsJson": [ "headline_text", "paragraph", "photo" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:13.136+00", "seconds": 2.399, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/18", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-18", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "High performance today will ultimately become business-as-usual tomorrow.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:45:07+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b402-222187c89158", "parentComponentId": null }, { "bbox": { "h": 0.87, "w": 0.645, "x": 0.355, "y": 0.036 }, "kind": "image", "text": null, "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Photo", "slug": "photo", "status": "active", "canonId": "019de52c-fd37-757f-b4a8-ca26d4888875", "version": 1, "description": null }, "framework": null }, "subkind": "photo", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.136+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "cca739ec-5d81-4e92-8c0e-0a1f79a7c104", "parentComponentId": null }, { "bbox": { "h": 0.45, "w": 0.25, "x": 0.058, "y": 0.268 }, "kind": "paragraph", "text": "It's worth noting that the potential for AI-mature organizations will evolve along with the technology itself. High performance today will ultimately become business-as-usual tomorrow. Today's AI Achievers have set the standard and are poised to remain leaders. While there is clearly a science to AI, they've shown us there is also an art to AI maturity. They have demonstrated that excellence in areas like vision and culture are just as critical as algorithmic integrity. Our research uncovered five key success factors for AI Achievers.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.136+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "94259e1c-cafa-407d-99e8-2406c7aa7dd0", "parentComponentId": null }, { "bbox": { "h": 0.08, "w": 0.24, "x": 0.058, "y": 0.148 }, "kind": "title", "text": "How AI Achievers master their craft", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.136+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "f60b1886-7bf1-499d-9178-b0aa050fab79", "parentComponentId": null } ], "metrics": [], "tools": [ { "tool": { "name": "Aha! Moment", "slug": "aha-moment", "status": "active", "bestFor": null, "canonId": "019dd956-a5b5-7763-820e-b299c5f27a32", "version": 1, "bodyDocId": null, "description": "The moment when the audience realizes the value - design for insight revelation", "familyLabel": null, "categoryName": "Loop", "categorySlug": "loop" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "7e93f200-3442-41dd-b6fb-ab4540da5fe9", "evidence": "High performance today will ultimately become business-as-usual tomorrow.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.8, "extraction": { "at": "2026-07-16 21:02:35.567404+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 18, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null } ], "frameworks": [], "arcBeats": [ { "to": 27, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null }, "from": 17, "name": "Solution & Approach", "beatId": "019dd95a-0682-776c-8e32-7e8c761f9fc5", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Solution & Approach", "slug": "consultants-gambit-solution-approach", "status": "active", "canonId": "019dd9b8-071c-7161-9184-580fdac7a4f9", "version": 1, "description": null }, "evidence": "Five recommendations to master AI maturity, each with case study", "position": 3, "isPrimaryArc": true, "parentBeatType": { "name": "Turn", "slug": "turn", "status": "active", "canonId": "019dd9b8-0396-702b-ac2f-9901f8dd8b54", "version": 1, "description": "The pivot — solution proposed, catalyst introduced, decision made. Common (15/20 arcs) but not universal: Triple-take, AIDA, PAS, Voyage-return and Pixar substitute it with a strong reflection or development beat." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 27, "from": 17, "loop": { "name": "40_mece_breakdown", "slug": "40-mece-breakdown", "status": "active", "bestFor": "Problem structuring, ensuring completeness, strategic analysis", "canonId": "019dd956-763c-765e-8b68-6ee3ae1ef184", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Whole -> Category A (distinct) -> Category B (distinct) -> Category C (distinct) -> Complete Coverage", "description": "Divide a complex topic into mutually exclusive, collectively exhaustive categories", "familyLabel": null }, "matchId": "019dd95a-07fe-70ce-8d3a-483bc3c6386e", "evidence": "Five distinct recommendation categories (sponsor / talent / AI core / responsible / invest) each with case study", "position": 5, "objective": "Five MECE recommendations to become an Achiever", "confidence": 85, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "locked": true }, { "page": 19, "type": "setup", "title": "The slide uses a 'Success Factor' label, suggesting it is part of a series of recommendations.", "function": "summarize", "rawType": "key_messages", "block": null, "metadata": { "slideType": "key_messages", "slideTypeCanon": { "name": "Key messages", "slug": "key_messages", "status": "active", "canonId": "019de52d-0712-703f-89f5-bda6688faf3f", "version": 1, "description": null }, "function": "summarize", "functionCanon": { "name": "Summarize", "slug": "summarize", "status": "active", "canonId": "019de52d-1541-704f-b86f-bf7b75056373", "version": 1, "description": null }, "density": "overcrowded", "densityScore": 91, "componentCount": 11, "textChars": 1789, "nDataPoints": 4, "notes": "The slide uses a 'Success Factor' label, suggesting it is part of a series of recommendations.", "elementsJson": [ "headline_text", "action_title", "subtitle_text", "paragraph", "quote_block" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:14.482+00", "seconds": 3.643, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/19", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-19", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "83% of Achievers have such sponsorship, while only 67% of Builders and just 56% of Experimenters have it.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:45:23+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b402-74b1843203c2", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "Achievers with formal senior sponsorship for AI strategies: 83%", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:45:23+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b402-7deb40f94e5e", "parentComponentId": null }, { "bbox": { "h": 0.2, "w": 0.2, "x": 0.28, "y": 0.35 }, "kind": "paragraph", "text": "And for the CEOs of Achievers, creating a culture of innovation is itself a deliberate, strategic move—one that is used as a vehicle for experimentation and learning across the organization. In fact, 48% of Achievers embed innovation in their organizational strategies, while just 33% of Experimenters do.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:14.482+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "0afd3949-10e2-48ab-a760-888a1e011f2b", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.2, "x": 0.05, "y": 0.75 }, "kind": "paragraph", "text": "Our research also suggests that the best AI strategies tend to be bold, even when they have modest beginnings. Bold AI strategies, in turn, help spur innovation.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:14.482+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "53323178-8e07-402d-8e89-a518b4db01aa", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.2, "x": 0.05, "y": 0.35 }, "kind": "paragraph", "text": "Companies can create strong AI strategies, but unless those strategies receive enthusiastic support from the CEO and the rest of the C-suite, they're likely to flounder, competing with other initiatives for attention and resources.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:14.482+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "ef5a72d8-8691-4c08-ae39-b11eb204381c", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.2, "x": 0.05, "y": 0.55 }, "kind": "paragraph", "text": "Achievers are more likely to have formal senior sponsorship for their AI strategies: we found that 83% of Achievers have such sponsorship, while only 67% of Builders and just 56% of Experimenters have it.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:14.482+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "fab81dfd-408c-42e9-85bf-a9302014250f", "parentComponentId": null }, { "bbox": { "h": 0.5, "w": 0.35, "x": 0.55, "y": 0.2 }, "kind": "quote", "text": "In the last five years, we started to use AI as one of our main drivers in the business. Five years ago, AI was not critical at all ... [but today] it's becoming critical. I would say, between 0 and 10, [AI] has become something like an 8.", "attrs": null, "canon": { "kind": { "name": "Quote", "slug": "quote", "status": "active", "canonId": "019de52c-f8fb-751e-8bc6-d58e2d59562a", "version": 1, "description": "Pull quote or testimonial." }, "tool": null, "subkind": { "name": "Testimonial", "slug": "testimonial", "status": "active", "canonId": "019de52c-fc21-71b6-9ac3-8266a64945e8", "version": 1, "description": null }, "framework": null }, "subkind": "testimonial", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:14.482+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "02554ce8-dfcf-4e5b-9e4c-372c541025b6", "parentComponentId": null }, { "bbox": null, "kind": "quote", "text": "\"In the last five years, we started to use AI as one of our main drivers in the business. Five years ago, AI was not critical at all ... [but today] it's becoming critical. I would say, between 0 and 10, [AI] has become something like an 8.\" — CEO of a German automotive parts and equipment manufacturer", "attrs": null, "canon": { "kind": { "name": "Quote", "slug": "quote", "status": "active", "canonId": "019de52c-f8fb-751e-8bc6-d58e2d59562a", "version": 1, "description": "Pull quote or testimonial." }, "tool": { "name": "Authority citation", "slug": "authority-citation", "status": "active", "bestFor": null, "canonId": "019dd9e1-49ae-7557-89ad-3fca70469b45", "version": 1, "bodyDocId": null, "description": "Quoting an authority figure, study, or precedent to lend weight.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:45:23+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b402-7ad15aeaf174", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.3, "x": 0.55, "y": 0.65 }, "kind": "source-note", "text": "CEO of a German automotive parts and equipment manufacturer", "attrs": null, "canon": { "kind": { "name": "Source note", "slug": "source-note", "status": "active", "canonId": "23484952-494b-4a5e-b1ad-550d0d70e948", "version": 1, "description": "Attribution / source / caveat placed at the slide footer. Numbered footnotes use attrs.numbered." }, "tool": null, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:14.482+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "3b8992b0-daa0-441e-8d9b-8eee1f7563d8", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.4, "x": 0.05, "y": 0.15 }, "kind": "title", "text": "Champion AI as a strategic priority for the entire organization, with full sponsorship from leadership", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:14.482+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "ffcab912-91f4-49f8-9fde-e7319f7b0423", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.2, "x": 0.05, "y": 0.1 }, "kind": "title", "text": "Success Factor 01", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Subtitle", "slug": "subtitle", "status": "active", "canonId": "019de52c-fb09-739d-900c-815b0d9ac526", "version": 1, "description": null }, "framework": null }, "subkind": "subtitle", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:14.482+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "8c987ccb-2ca7-4904-ad52-8a02e42f877e", "parentComponentId": null } ], "metrics": [ { "metricRaw": "Achievers with formal senior sponsorship for AI strategies", "numberRaw": "83%", "numberKind": "percent", "actionTitle": "Champion AI as a strategic priority for the entire organization, with full sponsorship from leadership", "calloutText": "83% of Achievers have such sponsorship, while only 67% of Builders and just 56% of Experimenters have it.", "numberScale": null, "numberValue": 83, "metricFamily": "other", "numberCurrency": null } ], "tools": [ { "tool": { "name": "MECE Principle", "slug": "mece-principle", "status": "active", "bestFor": null, "canonId": "019dd956-7c60-71a8-8758-c79d914489c9", "version": 1, "bodyDocId": null, "description": "Mutually Exclusive, Collectively Exhaustive - ensuring complete, non-overlapping structure", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Architect", "layer": "block", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-cdb39bdebf42", "evidence": "Five distinct recommendation categories begin here", "pageRefs": null, "priority": "Core", "whenToUse": null, "confidence": 75, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 19, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Chunking", "slug": "chunking", "status": "active", "bestFor": null, "canonId": "019dd956-abab-72ce-b64c-2924cc83cf39", "version": 1, "bodyDocId": null, "description": "Information grouped into chunks is remembered better - structure content in digestible units", "familyLabel": null, "categoryName": "Loop", "categorySlug": "loop" }, "agent": "Architect", "layer": "loop", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-cabaf29cb94a", "evidence": "First of five recommendation chunks begins", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 75, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 19, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-c3b0a0be42da", "evidence": "Title is the directive: 'Champion AI as a strategic priority'", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 90, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 19, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-c6816ee6d35e", "evidence": "83% / 67% / 56% sponsorship comparison across segments", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 19, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Credibility Transfer", "slug": "credibility-transfer", "status": "active", "bestFor": null, "canonId": "019dd956-bd34-759a-8e3b-2f14b571a2a1", "version": 1, "bodyDocId": null, "description": "Borrowing authority from trusted sources or vivid details", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "b01ca547-5760-42f4-8fae-44c5d795fb3c", "evidence": "83% of Achievers have such sponsorship, while only 67% of Builders and just 56% of Experimenters have it.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.6, "extraction": { "at": "2026-07-16 21:02:35.684413+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 19, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Executive summary", "slug": "executive-summary", "status": "active", "bestFor": null, "canonId": "019dd9e1-4b63-72d5-9734-da757d91ca61", "version": 1, "bodyDocId": null, "description": "A condensed view of the entire argument on a single slide / block.", "familyLabel": null, "categoryName": null, "categorySlug": null }, "agent": null, "layer": "slide", "agents": null, "matchId": "8846bb20-ed18-4427-bcbc-02fb67f75077", "evidence": "Champion AI as a strategic priority for the entire organization, with full sponsorship from leadership", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.7, "extraction": { "at": "2026-07-16 21:02:35.644108+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 19, "whyItWorks": null, "antipattern": null, "cardinality": "common", "narrativePurpose": null } ], "frameworks": [], "arcBeats": [ { "to": 27, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null }, "from": 17, "name": "Solution & Approach", "beatId": "019dd95a-0682-776c-8e32-7e8c761f9fc5", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Solution & Approach", "slug": "consultants-gambit-solution-approach", "status": "active", "canonId": "019dd9b8-071c-7161-9184-580fdac7a4f9", "version": 1, "description": null }, "evidence": "Five recommendations to master AI maturity, each with case study", "position": 3, "isPrimaryArc": true, "parentBeatType": { "name": "Turn", "slug": "turn", "status": "active", "canonId": "019dd9b8-0396-702b-ac2f-9901f8dd8b54", "version": 1, "description": "The pivot — solution proposed, catalyst introduced, decision made. Common (15/20 arcs) but not universal: Triple-take, AIDA, PAS, Voyage-return and Pixar substitute it with a strong reflection or development beat." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 27, "from": 17, "loop": { "name": "40_mece_breakdown", "slug": "40-mece-breakdown", "status": "active", "bestFor": "Problem structuring, ensuring completeness, strategic analysis", "canonId": "019dd956-763c-765e-8b68-6ee3ae1ef184", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Whole -> Category A (distinct) -> Category B (distinct) -> Category C (distinct) -> Complete Coverage", "description": "Divide a complex topic into mutually exclusive, collectively exhaustive categories", "familyLabel": null }, "matchId": "019dd95a-07fe-70ce-8d3a-483bc3c6386e", "evidence": "Five distinct recommendation categories (sponsor / talent / AI core / responsible / invest) each with case study", "position": 5, "objective": "Five MECE recommendations to become an Achiever", "confidence": 85, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "locked": true }, { "page": 20, "type": "evidence", "function": "illustrate_case", "rawType": "client_example", "block": null, "metadata": { "slideType": "client_example", "slideTypeCanon": { "name": "Client example", "slug": "client_example", "status": "active", "canonId": "019de52d-0c87-77cb-9867-613ae22e95ac", "version": 1, "description": null }, "function": "illustrate_case", "functionCanon": { "name": "Illustrate case", "slug": "illustrate_case", "status": "active", "canonId": "019de52d-13b4-772c-bb73-1873612a7463", "version": 1, "description": null }, "density": "dense", "densityScore": 72, "componentCount": 7, "textChars": 1286, "nDataPoints": 3, "notes": null, "elementsJson": [ "paragraph", "big_number", "photo" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:14.238+00", "seconds": 3.38, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/20", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-20", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "We found that 16% of Achievers are already using platforms that allow workers to easily pose questions and share ideas with colleagues across the company—compared to 4% of Experimenters.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:45:11+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b402-38121a4c1e75", "parentComponentId": null }, { "bbox": { "h": 0.85, "w": 0.45, "x": 0.03, "y": 0.05 }, "kind": "image", "text": "Two professionals working in an office setting", "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Photo", "slug": "photo", "status": "active", "canonId": "019de52c-fd37-757f-b4a8-ca26d4888875", "version": 1, "description": null }, "framework": null }, "subkind": "photo", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:14.238+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "a38d6d19-be15-4f73-bcee-4750c099cc98", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.15, "x": 0.75, "y": 0.5 }, "kind": "metric", "text": "83%", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": null, "subkind": { "name": "Big number", "slug": "big-number", "status": "active", "canonId": "019de52c-fc70-70c8-b965-0584548a9204", "version": 1, "description": null }, "framework": null }, "subkind": "big-number", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:14.238+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "8db32a23-3032-45c9-a8cd-ab90611dcf5e", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "Achievers with CEO and senior sponsorship: 83%", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:45:11+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b402-3ef6b2929b47", "parentComponentId": null }, { "bbox": { "h": 0.25, "w": 0.22, "x": 0.75, "y": 0.22 }, "kind": "paragraph", "text": "We found that 16% of Achievers are already using platforms that allow workers to easily pose questions and share ideas with colleagues across the company—compared to 4% of Experimenters. That number will only grow as these companies expand their pools of AI talent.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:14.238+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "9b8fa9cf-b831-4de6-98e7-458c47b54e1f", "parentComponentId": null }, { "bbox": { "h": 0.5, "w": 0.22, "x": 0.51, "y": 0.22 }, "kind": "paragraph", "text": "For instance, Lendlease Digital (part of multinational Lendlease Group) hopes to produce architectural blueprints for buildings using generative design and AI, then use those blueprints to manufacture actual buildings in factories—fitting together all the pieces like LEGO sets. The company’s bold vision starts at the top, led by the CEO of Lendlease Digital, William (Bill) Ruh. To encourage such end-to-end innovation, Achievers implement systems and structures that help employees showcase their innovation experiments and seek constructive feedback from leadership. For instance, Achievers tend to be the first to embrace new tools that encourage their employees to experiment and innovate.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:14.238+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "b07acb0a-8dcc-4b5e-924d-22cf3042cf5f", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.15, "x": 0.75, "y": 0.65 }, "kind": "paragraph", "text": "of Achievers have CEO and senior sponsorship.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:14.238+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "f1b4f286-03cf-4d53-8c4c-005ce5808f7d", "parentComponentId": null } ], "metrics": [ { "metricRaw": "Achievers with CEO and senior sponsorship", "numberRaw": "83%", "numberKind": "percent", "actionTitle": "83% of Achievers have CEO and senior sponsorship.", "calloutText": "We found that 16% of Achievers are already using platforms that allow workers to easily pose questions and share ideas with colleagues across the company—compared to 4% of Experimenters.", "numberScale": null, "numberValue": 83, "metricFamily": "other", "numberCurrency": null } ], "tools": [ { "tool": { "name": "Bandwagon Effect", "slug": "bandwagon-effect", "status": "active", "bestFor": null, "canonId": "019dd956-c742-724e-9623-54fd8878b87b", "version": 1, "bodyDocId": null, "description": "People adopt beliefs proportionally to how many others hold them - show momentum", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-d54db6f98fc1", "evidence": "Shows what successful peers do (CEO sponsorship 83%)", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 70, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 20, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-d287f6159caf", "evidence": "16% vs 4% on collaboration platforms", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 20, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Evidence Matrix", "slug": "evidence-matrix", "status": "active", "bestFor": null, "canonId": "019dd956-7ecc-74c2-b46c-0b9c1f4c73d7", "version": 1, "bodyDocId": null, "description": "Planning visual assets: Claim → Evidence Needed → Data Source → Visual Type", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "6b3e2464-5dfe-4d26-8a43-621084803f45", "evidence": "We found that 16% of Achievers are already using platforms that allow workers to easily pose questions and share ideas with colleagues across the company—compared to 4% of Experimenters.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.6, "extraction": { "at": "2026-07-16 21:02:35.760556+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 20, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null } ], "frameworks": [], "arcBeats": [ { "to": 27, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null }, "from": 17, "name": "Solution & Approach", "beatId": "019dd95a-0682-776c-8e32-7e8c761f9fc5", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Solution & Approach", "slug": "consultants-gambit-solution-approach", "status": "active", "canonId": "019dd9b8-071c-7161-9184-580fdac7a4f9", "version": 1, "description": null }, "evidence": "Five recommendations to master AI maturity, each with case study", "position": 3, "isPrimaryArc": true, "parentBeatType": { "name": "Turn", "slug": "turn", "status": "active", "canonId": "019dd9b8-0396-702b-ac2f-9901f8dd8b54", "version": 1, "description": "The pivot — solution proposed, catalyst introduced, decision made. Common (15/20 arcs) but not universal: Triple-take, AIDA, PAS, Voyage-return and Pixar substitute it with a strong reflection or development beat." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 27, "from": 17, "loop": { "name": "40_mece_breakdown", "slug": "40-mece-breakdown", "status": "active", "bestFor": "Problem structuring, ensuring completeness, strategic analysis", "canonId": "019dd956-763c-765e-8b68-6ee3ae1ef184", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Whole -> Category A (distinct) -> Category B (distinct) -> Category C (distinct) -> Complete Coverage", "description": "Divide a complex topic into mutually exclusive, collectively exhaustive categories", "familyLabel": null }, "matchId": "019dd95a-07fe-70ce-8d3a-483bc3c6386e", "evidence": "Five distinct recommendation categories (sponsor / talent / AI core / responsible / invest) each with case study", "position": 5, "objective": "Five MECE recommendations to become an Achiever", "confidence": 85, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "locked": true }, { "page": 21, "type": "resolution", "title": "Success Factor 02. Mentions 'Achievers', 'Builders', 'Innovators', and 'Experimenters' as segments.", "function": "summarize", "rawType": "key_takeaways", "block": null, "metadata": { "slideType": "key_takeaways", "slideTypeCanon": { "name": "Key takeaways", "slug": "key_takeaways", "status": "active", "canonId": "019de52d-06e7-7674-a891-9f7de3b11b6d", "version": 1, "description": null }, "function": "summarize", "functionCanon": { "name": "Summarize", "slug": "summarize", "status": "active", "canonId": "019de52d-1541-704f-b86f-bf7b75056373", "version": 1, "description": null }, "density": "overcrowded", "densityScore": 90, "componentCount": 11, "textChars": 1415, "nDataPoints": 4, "notes": "Success Factor 02. Mentions 'Achievers', 'Builders', 'Innovators', and 'Experimenters' as segments.", "elementsJson": [ "headline_text", "action_title", "subtitle_text", "paragraph", "photo" ], "confidence": 0.95, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:18.711+00", "seconds": 7.774, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/21", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-21", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "Nearly half (44%) of Achievers have employees with consistently high AI skills competencies, while Innovators (33%) and Experimenters (30%) have significantly fewer such employees, on average.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:45:20+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b402-55bbcc96528e", "parentComponentId": null }, { "bbox": { "h": 0.02, "w": 0.27, "x": 0.029, "y": 0.94 }, "kind": "disclaimer", "text": "The art of AI maturity—Advancing from practice to performance", "attrs": null, "canon": { "kind": { "name": "Disclaimer", "slug": "disclaimer", "status": "active", "canonId": "91173477-d91f-4024-bbaf-d4f93e3f82dc", "version": 1, "description": "Legal/cautionary text that occupies most of the body of its slide." }, "tool": null, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:18.711+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "7e469cb1-b2f2-4f33-9506-915cce0bdde6", "parentComponentId": null }, { "bbox": { "h": 0.871, "w": 0.428, "x": 0.514, "y": 0.058 }, "kind": "image", "text": "Two people working on laptops", "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Photo", "slug": "photo", "status": "active", "canonId": "019de52c-fd37-757f-b4a8-ca26d4888875", "version": 1, "description": null }, "framework": null }, "subkind": "photo", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:18.711+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "d76fc951-aef0-4672-ae8c-658193d95da2", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "Achievers with mandatory AI training for most employees: 78%", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:45:20+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b402-58f9246fcf08", "parentComponentId": null }, { "bbox": { "h": 0.16, "w": 0.19, "x": 0.285, "y": 0.445 }, "kind": "paragraph", "text": "Nearly half (44%) of Achievers have employees with consistently high AI skills competencies, while Innovators (33%) and Experimenters (30%) have significantly fewer such employees, on average. Furthermore, Achievers have employees with higher competencies in almost all data- and AI-related skills.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:18.711+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "1f43184c-5dd5-4852-9ab6-165e27490fb5", "parentComponentId": null }, { "bbox": { "h": 0.12, "w": 0.22, "x": 0.058, "y": 0.495 }, "kind": "paragraph", "text": "We found, for example, that 78% of Achievers—compared with just 56% of Builders and 51% of Experimenters—have mandatory AI trainings for most employees, from product development engineers to C-suite executives.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:18.711+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "6c628937-9854-4823-ac79-becfd4626f7c", "parentComponentId": null }, { "bbox": { "h": 0.08, "w": 0.19, "x": 0.285, "y": 0.315 }, "kind": "paragraph", "text": "This makes it much easier to scale human and AI collaboration and ensure that AI permeates the organization.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:18.711+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "7156f199-ed67-4877-a17a-8b428c4a25f2", "parentComponentId": null }, { "bbox": { "h": 0.12, "w": 0.22, "x": 0.058, "y": 0.315 }, "kind": "paragraph", "text": "With a clear AI strategy and strong CEO sponsorship, organizations are more likely to invest heavily in creating data and AI fluency across their workforces. While AI proficiency must start at the top, it can't end there.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:18.711+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "e5d3f6cf-3aaa-4d5a-8675-f3d222defbc1", "parentComponentId": null }, { "bbox": { "h": 0.08, "w": 0.22, "x": 0.058, "y": 0.675 }, "kind": "paragraph", "text": "Because Achievers prioritize efforts to build AI literacy in their workforces, it's no surprise that their employees are also more proficient in AI-related skills.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:18.711+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "f43f436e-0572-4bfb-8fda-eaadf81202f0", "parentComponentId": null }, { "bbox": { "h": 0.08, "w": 0.42, "x": 0.058, "y": 0.175 }, "kind": "title", "text": "Invest heavily in talent to get more from AI investments", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:18.711+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "3f391afd-1363-4505-817d-cb4c836a31ef", "parentComponentId": null }, { "bbox": { "h": 0.025, "w": 0.15, "x": 0.058, "y": 0.103 }, "kind": "title", "text": "Success Factor 02", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Subtitle", "slug": "subtitle", "status": "active", "canonId": "019de52c-fb09-739d-900c-815b0d9ac526", "version": 1, "description": null }, "framework": null }, "subkind": "subtitle", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:18.711+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "266ad51c-994b-4e7f-945d-922839ad1d82", "parentComponentId": null } ], "metrics": [ { "metricRaw": "Achievers with mandatory AI training for most employees", "numberRaw": "78%", "numberKind": "percent", "actionTitle": "Invest heavily in talent to get more from AI investments", "calloutText": "Nearly half (44%) of Achievers have employees with consistently high AI skills competencies, while Innovators (33%) and Experimenters (30%) have significantly fewer such employees, on average.", "numberScale": null, "numberValue": 78, "metricFamily": "employment", "numberCurrency": null } ], "tools": [ { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-d8e61606d2c5", "evidence": "Directive title: 'Invest heavily in talent to get more from AI'", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 90, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 21, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-dcd1c9ae87d0", "evidence": "44% / 33% / 30% skills competency stats by segment", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 21, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Pyramid Principle", "slug": "pyramid-principle", "status": "active", "bestFor": "When the document has a single governing decision or recommendation the reader is asked to accept, and the case can be decomposed into a small number of clean groups. Strategy decks, board memos, investment committee papers.", "canonId": "019dd956-7cbd-7219-a477-c871e2bc1aff", "version": 1, "bodyDocId": "88b6ac56-b880-46f9-8621-ef3491ab6235", "description": "Lead with the answer; group three to five MECE supports beneath it; recurse one level down. Top-down hierarchical structure for any document with a single recommendation.", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "c76d5470-0345-41f9-80c6-ebd4b2f03f40", "evidence": "Invest heavily in talent to get more from AI investments", "pageRefs": null, "priority": "Core", "whenToUse": "When the document has a single governing decision or recommendation the reader is asked to accept, and the case can be decomposed into a small number of clean groups. Strategy decks, board memos, investment committee papers.", "confidence": 0.7, "extraction": { "at": "2026-07-16 21:02:35.837334+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 21, "whyItWorks": "Solves the two failure modes of expert communication at once: (1) the long climb where readers wait pages for the answer, and (2) logical mush from non-MECE groupings. Progressive disclosure lets a reader stop at any depth and get the right information.", "antipattern": "Chronological structure (\"what we did, what we found, what we conclude\") instead of logical hierarchy. Top-of-pyramid that is a topic (\"strategic options\") instead of an answer (\"pursue Option B because...\"). Non-MECE supports that overlap or fail to cover the universe.", "cardinality": null, "narrativePurpose": "Transfer conviction with minimum reader effort by mirroring how decision-makers consume information: answer first, scaffolding behind." }, { "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null, "categoryName": null, "categorySlug": null }, "agent": null, "layer": "slide", "agents": null, "matchId": "7f55fdf6-1339-4039-95b2-ff27d997d8ee", "evidence": "78% of Achievers—compared with just 56% of Builders and 51% of Experimenters—have mandatory AI trainings for most employees", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.6, "extraction": { "at": "2026-07-16 21:02:35.878522+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 21, "whyItWorks": null, "antipattern": null, "cardinality": "common", "narrativePurpose": null } ], "frameworks": [], "arcBeats": [ { "to": 27, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null }, "from": 17, "name": "Solution & Approach", "beatId": "019dd95a-0682-776c-8e32-7e8c761f9fc5", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Solution & Approach", "slug": "consultants-gambit-solution-approach", "status": "active", "canonId": "019dd9b8-071c-7161-9184-580fdac7a4f9", "version": 1, "description": null }, "evidence": "Five recommendations to master AI maturity, each with case study", "position": 3, "isPrimaryArc": true, "parentBeatType": { "name": "Turn", "slug": "turn", "status": "active", "canonId": "019dd9b8-0396-702b-ac2f-9901f8dd8b54", "version": 1, "description": "The pivot — solution proposed, catalyst introduced, decision made. Common (15/20 arcs) but not universal: Triple-take, AIDA, PAS, Voyage-return and Pixar substitute it with a strong reflection or development beat." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 27, "from": 17, "loop": { "name": "40_mece_breakdown", "slug": "40-mece-breakdown", "status": "active", "bestFor": "Problem structuring, ensuring completeness, strategic analysis", "canonId": "019dd956-763c-765e-8b68-6ee3ae1ef184", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Whole -> Category A (distinct) -> Category B (distinct) -> Category C (distinct) -> Complete Coverage", "description": "Divide a complex topic into mutually exclusive, collectively exhaustive categories", "familyLabel": null }, "matchId": "019dd95a-07fe-70ce-8d3a-483bc3c6386e", "evidence": "Five distinct recommendation categories (sponsor / talent / AI core / responsible / invest) each with case study", "position": 5, "objective": "Five MECE recommendations to become an Achiever", "confidence": 85, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "locked": true }, { "page": 22, "type": "evidence", "title": "The slide uses a mix of narrative case studies and a prominent callout metric to support the thesis of proactive talent development.", "function": "illustrate_case", "rawType": "client_example", "block": null, "metadata": { "slideType": "client_example", "slideTypeCanon": { "name": "Client example", "slug": "client_example", "status": "active", "canonId": "019de52d-0c87-77cb-9867-613ae22e95ac", "version": 1, "description": null }, "function": "illustrate_case", "functionCanon": { "name": "Illustrate case", "slug": "illustrate_case", "status": "active", "canonId": "019de52d-13b4-772c-bb73-1873612a7463", "version": 1, "description": null }, "density": "overcrowded", "densityScore": 86, "componentCount": 9, "textChars": 1963, "nDataPoints": 2, "notes": "The slide uses a mix of narrative case studies and a prominent callout metric to support the thesis of proactive talent development.", "elementsJson": [ "paragraph", "big_number", "callout_box" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:18.51+00", "seconds": 7.522, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/22", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-22", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": { "h": 0.15, "w": 0.22, "x": 0.64, "y": 0.435 }, "kind": "callout", "text": "of Achievers have mandatory AI trainings for most employees, from product development engineers to C-suite executives.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": null, "subkind": { "name": "Primary callout", "slug": "primary", "status": "active", "canonId": "019de52c-fbd1-70eb-a9ce-8997f8674c1d", "version": 1, "description": null }, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:18.51+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "339f8178-334c-4e9e-aa73-ac06fafb55da", "parentComponentId": null }, { "bbox": null, "kind": "callout", "text": "78% of Achievers have mandatory AI trainings for most employees, from product development engineers to C-suite executives.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:45:26+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b402-b42d9af6567d", "parentComponentId": null }, { "bbox": { "h": 0.02, "w": 0.26, "x": 0.028, "y": 0.94 }, "kind": "disclaimer", "text": "The art of AI maturity—Advancing from practice to performance", "attrs": null, "canon": { "kind": { "name": "Disclaimer", "slug": "disclaimer", "status": "active", "canonId": "91173477-d91f-4024-bbaf-d4f93e3f82dc", "version": 1, "description": "Legal/cautionary text that occupies most of the body of its slide." }, "tool": null, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:18.51+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "2baa039d-6e09-41a9-bb35-448facd512da", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.15, "x": 0.64, "y": 0.3 }, "kind": "metric", "text": "78%", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": null, "subkind": { "name": "Big number", "slug": "big-number", "status": "active", "canonId": "019de52c-fc70-70c8-b965-0584548a9204", "version": 1, "description": null }, "framework": null }, "subkind": "big-number", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:18.51+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "10f2bee1-7417-46fd-ac5e-06f72e24e5d2", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "Achievers with mandatory AI training: 78%", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:45:26+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b402-bbf453317298", "parentComponentId": null }, { "bbox": { "h": 0.33, "w": 0.18, "x": 0.286, "y": 0.4 }, "kind": "paragraph", "text": "And a leading Southeast Asian oil and gas firm built an AI-powered, “gamified” learning platform to expand employees’ digital fluency. It also created a cloud-based performance reviewer that scrutinized a decade’s worth of employee data to recommend workers best suited for various digital roles. The innovation saved the firm’s HR department significant time filling positions. It also reduced scope for managerial bias in promotional decisions and helped workers assess and close digital-skills gaps.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:18.51+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "1f12b261-bceb-4888-873a-27c657c6f783", "parentComponentId": null }, { "bbox": { "h": 0.25, "w": 0.25, "x": 0.058, "y": 0.505 }, "kind": "paragraph", "text": "In 2018, US utility Exelon established an Analytics Academy. This training upskilled employees like Jeffrey Swiatek, who, at the age of 41, transitioned from his longtime role as a maintenance worker into a higher-paying position as a quantitative engineer. Swiatek has since used his training to write predictive software that saved Exelon an estimated $1 million over eight years on equipment maintenance.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:18.51+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "328c07a3-32f7-49b3-92c7-28b6c7cfc2e7", "parentComponentId": null }, { "bbox": { "h": 0.17, "w": 0.18, "x": 0.286, "y": 0.192 }, "kind": "paragraph", "text": "Japanese e-commerce giant Rakuten established an “AI Promotion Department” in 2016 to accelerate efforts to infuse AI into the company’s 70+ diverse businesses. By 2018, the department helped turn more than 30 AI pilot projects into successful offerings.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:18.51+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "55ac7993-cbd3-41ae-86c1-f9565ec02825", "parentComponentId": null }, { "bbox": { "h": 0.47, "w": 0.25, "x": 0.058, "y": 0.192 }, "kind": "paragraph", "text": "Achievers also develop proactive AI talent strategies to stay at the forefront of industry trends. In addition to hiring, this could mean partnering with or acquiring specialist companies to fill critical roles (such as data or behavioral scientists, social scientists and ethicists). It also means having a plan to get these diverse, multidisciplinary workers to collaborate, create and sustain maximum value from the company's data-science capabilities.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:18.51+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "a8e1d7f8-0c8f-4afd-80a5-e810b1d9879b", "parentComponentId": null } ], "metrics": [ { "metricRaw": "Achievers with mandatory AI training", "numberRaw": "78%", "numberKind": "percent", "actionTitle": "78% of Achievers have mandatory AI trainings for most employees, from product development engineers to C-suite executives.", "calloutText": "78% of Achievers have mandatory AI trainings for most employees, from product development engineers to C-suite executives.", "numberScale": null, "numberValue": 78, "metricFamily": "other", "numberCurrency": null } ], "tools": [ { "tool": { "name": "Bandwagon Effect", "slug": "bandwagon-effect", "status": "active", "bestFor": null, "canonId": "019dd956-c742-724e-9623-54fd8878b87b", "version": 1, "bodyDocId": null, "description": "People adopt beliefs proportionally to how many others hold them - show momentum", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-e6b24338f89c", "evidence": "What Achievers already do as social proof for action", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 70, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 22, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-e21fc2db8c8d", "evidence": "78% mandatory training stat", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 22, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null } ], "frameworks": [], "arcBeats": [ { "to": 27, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null }, "from": 17, "name": "Solution & Approach", "beatId": "019dd95a-0682-776c-8e32-7e8c761f9fc5", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Solution & Approach", "slug": "consultants-gambit-solution-approach", "status": "active", "canonId": "019dd9b8-071c-7161-9184-580fdac7a4f9", "version": 1, "description": null }, "evidence": "Five recommendations to master AI maturity, each with case study", "position": 3, "isPrimaryArc": true, "parentBeatType": { "name": "Turn", "slug": "turn", "status": "active", "canonId": "019dd9b8-0396-702b-ac2f-9901f8dd8b54", "version": 1, "description": "The pivot — solution proposed, catalyst introduced, decision made. Common (15/20 arcs) but not universal: Triple-take, AIDA, PAS, Voyage-return and Pixar substitute it with a strong reflection or development beat." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 27, "from": 17, "loop": { "name": "40_mece_breakdown", "slug": "40-mece-breakdown", "status": "active", "bestFor": "Problem structuring, ensuring completeness, strategic analysis", "canonId": "019dd956-763c-765e-8b68-6ee3ae1ef184", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Whole -> Category A (distinct) -> Category B (distinct) -> Category C (distinct) -> Complete Coverage", "description": "Divide a complex topic into mutually exclusive, collectively exhaustive categories", "familyLabel": null }, "matchId": "019dd95a-07fe-70ce-8d3a-483bc3c6386e", "evidence": "Five distinct recommendation categories (sponsor / talent / AI core / responsible / invest) each with case study", "position": 5, "objective": "Five MECE recommendations to become an Achiever", "confidence": 85, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "locked": true }, { "page": 23, "type": "setup", "title": "Part of a series on AI maturity; mentions 'Achievers' as a specific segment.", "function": "summarize", "rawType": "key_messages", "block": null, "metadata": { "slideType": "key_messages", "slideTypeCanon": { "name": "Key messages", "slug": "key_messages", "status": "active", "canonId": "019de52d-0712-703f-89f5-bda6688faf3f", "version": 1, "description": null }, "function": "summarize", "functionCanon": { "name": "Summarize", "slug": "summarize", "status": "active", "canonId": "019de52d-1541-704f-b86f-bf7b75056373", "version": 1, "description": null }, "density": "overcrowded", "densityScore": 89, "componentCount": 10, "textChars": 1951, "nDataPoints": 1, "notes": "Part of a series on AI maturity; mentions 'Achievers' as a specific segment.", "elementsJson": [ "headline_text", "action_title", "subtitle_text", "paragraph", "photo" ], "confidence": 0.95, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:15.31+00", "seconds": 4.315, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/23", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-23", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "To extract value from their data quickly and effectively, Achievers are also 32% more likely, on average, than Experimenters to either develop custom-built machine learning applications or work with a partner that offers solutions-as-a-service.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:45:26+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b402-97105ee49377", "parentComponentId": null }, { "bbox": { "h": 0.868, "w": 0.431, "x": 0.512, "y": 0.057 }, "kind": "image", "text": "Two people working at a computer", "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Photo", "slug": "photo", "status": "active", "canonId": "019de52c-fd37-757f-b4a8-ca26d4888875", "version": 1, "description": null }, "framework": null }, "subkind": "photo", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.31+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "3d347fff-bd00-4c83-959e-323f53a9c8a0", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "Achievers more likely than Experimenters to productize AI: 32%", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:45:26+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b402-9811a752c8d9", "parentComponentId": null }, { "bbox": { "h": 0.25, "w": 0.205, "x": 0.285, "y": 0.45 }, "kind": "paragraph", "text": "To extract value from their data quickly and effectively, Achievers are also 32% more likely, on average, than Experimenters to either develop custom-built machine learning applications or work with a partner that offers solutions-as-a-service. Achievers are also more likely than Innovators to use AI for innovation, tapping into readily available developer networks that can swiftly productionize and scale successful pilots.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.31+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "1145479d-c0b5-4f59-914b-2fc835b49b51", "parentComponentId": null }, { "bbox": { "h": 0.18, "w": 0.25, "x": 0.057, "y": 0.321 }, "kind": "paragraph", "text": "Another priority for Achievers involves building an AI core: an operational data and AI platform that taps into companies’ talent, technology and data ecosystems, allowing firms to balance experimentation and execution. An AI core helps organizations productize their AI applications and integrate AI into other applications, which makes differentiation with AI more seamless.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.31+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "13f06001-d607-4154-a5a8-76285761f86c", "parentComponentId": null }, { "bbox": { "h": 0.02, "w": 0.267, "x": 0.029, "y": 0.94 }, "kind": "paragraph", "text": "The art of AI maturity—Advancing from practice to performance", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.31+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "45172b44-fbfe-4c1c-bc62-74e7b918a8af", "parentComponentId": null }, { "bbox": { "h": 0.12, "w": 0.205, "x": 0.285, "y": 0.321 }, "kind": "paragraph", "text": "To build AI cores, Achievers harness the power of internal and external data, making that data trustworthy and storing it in a single enterprise-grade cloud platform—complete with appropriate usage, monitoring and security policies.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.31+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "5a78da8c-81db-432f-8686-71a1faa3c38b", "parentComponentId": null }, { "bbox": { "h": 0.25, "w": 0.25, "x": 0.057, "y": 0.515 }, "kind": "paragraph", "text": "An AI core also works across the cloud continuum (e.g. from migration to innovation), provides end-to-end data capabilities (foundation, management and governance), manages the machine learning lifecycle (workflow, model training, model deployment) and provides self-service capabilities. AI cores are, in turn, managed by dedicated interdisciplinary teams of machine learning engineers, data scientists, data-domain experts and systems engineers.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.31+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "6fb8d635-9202-4f6d-8a1e-a12ba7e7c6cc", "parentComponentId": null }, { "bbox": { "h": 0.075, "w": 0.405, "x": 0.057, "y": 0.175 }, "kind": "title", "text": "Industrialize AI tools and teams to create an AI core", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.31+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "a0d62642-bdf7-42fd-9594-5854c802d915", "parentComponentId": null }, { "bbox": { "h": 0.032, "w": 0.205, "x": 0.057, "y": 0.103 }, "kind": "title", "text": "Success Factor 03", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Subtitle", "slug": "subtitle", "status": "active", "canonId": "019de52c-fb09-739d-900c-815b0d9ac526", "version": 1, "description": null }, "framework": null }, "subkind": "subtitle", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.31+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "fdc19a21-b1d4-4ba7-8762-0a0edb842474", "parentComponentId": null } ], "metrics": [ { "metricRaw": "Achievers more likely than Experimenters to productize AI", "numberRaw": "32%", "numberKind": "percent", "actionTitle": "Industrialize AI tools and teams to create an AI core", "calloutText": "To extract value from their data quickly and effectively, Achievers are also 32% more likely, on average, than Experimenters to either develop custom-built machine learning applications or work with a partner that offers solutions-as-a-service.", "numberScale": null, "numberValue": 32, "metricFamily": "other", "numberCurrency": null } ], "tools": [ { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-e82258f4cc15", "evidence": "Directive: 'Industrialize AI tools and teams to create an AI core'", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 90, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 23, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-ec93e02c70c3", "evidence": "32% more likely to build custom ML applications", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 23, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Logical chain", "slug": "logical-chain", "status": "active", "bestFor": null, "canonId": "019dd9e1-5104-702f-8953-85757af6553d", "version": 1, "bodyDocId": null, "description": "If-then, premise-conclusion, deductive chain.", "familyLabel": null, "categoryName": null, "categorySlug": null }, "agent": null, "layer": "slide", "agents": null, "matchId": "cc6b62bb-040a-4cb6-873b-560641c7acb5", "evidence": "To extract value from their data quickly and effectively, Achievers are also 32% more likely, on average, than Experimenters to either develop custom-built machine learning applications or work with a partner", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.7, "extraction": { "at": "2026-07-16 21:02:36.002457+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 23, "whyItWorks": null, "antipattern": null, "cardinality": "common", "narrativePurpose": null } ], "frameworks": [], "arcBeats": [ { "to": 27, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null }, "from": 17, "name": "Solution & Approach", "beatId": "019dd95a-0682-776c-8e32-7e8c761f9fc5", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Solution & Approach", "slug": "consultants-gambit-solution-approach", "status": "active", "canonId": "019dd9b8-071c-7161-9184-580fdac7a4f9", "version": 1, "description": null }, "evidence": "Five recommendations to master AI maturity, each with case study", "position": 3, "isPrimaryArc": true, "parentBeatType": { "name": "Turn", "slug": "turn", "status": "active", "canonId": "019dd9b8-0396-702b-ac2f-9901f8dd8b54", "version": 1, "description": "The pivot — solution proposed, catalyst introduced, decision made. Common (15/20 arcs) but not universal: Triple-take, AIDA, PAS, Voyage-return and Pixar substitute it with a strong reflection or development beat." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 27, "from": 17, "loop": { "name": "40_mece_breakdown", "slug": "40-mece-breakdown", "status": "active", "bestFor": "Problem structuring, ensuring completeness, strategic analysis", "canonId": "019dd956-763c-765e-8b68-6ee3ae1ef184", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Whole -> Category A (distinct) -> Category B (distinct) -> Category C (distinct) -> Complete Coverage", "description": "Divide a complex topic into mutually exclusive, collectively exhaustive categories", "familyLabel": null }, "matchId": "019dd95a-07fe-70ce-8d3a-483bc3c6386e", "evidence": "Five distinct recommendation categories (sponsor / talent / AI core / responsible / invest) each with case study", "position": 5, "objective": "Five MECE recommendations to become an Achiever", "confidence": 85, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "locked": true }, { "page": 24, "type": "evidence", "title": "The slide uses narrative case studies to illustrate the benefits of AI adoption.", "function": "illustrate_case", "rawType": "case_study", "block": null, "metadata": { "slideType": "case_study", "slideTypeCanon": { "name": "Case study", "slug": "case_study", "status": "active", "canonId": "019de52d-0b95-7344-8090-926f3b8e92c8", "version": 1, "description": null }, "function": "illustrate_case", "functionCanon": { "name": "Illustrate case", "slug": "illustrate_case", "status": "active", "canonId": "019de52d-13b4-772c-bb73-1873612a7463", "version": 1, "description": null }, "density": "overcrowded", "densityScore": 85, "componentCount": 9, "textChars": 1217, "nDataPoints": 6, "notes": "The slide uses narrative case studies to illustrate the benefits of AI adoption.", "elementsJson": [ "paragraph", "photo" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:19.06+00", "seconds": 7.734, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/24", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-24", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "By industrializing its AI tools and teams, the company targeted efficiency gains of 5% in its first year alone, with longer-term cost savings of more than $100 million annually.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:45:39+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b402-e82fc48f9d69", "parentComponentId": null }, { "bbox": { "h": 0.808, "w": 0.486, "x": 0, "y": 0.102 }, "kind": "image", "text": null, "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Photo", "slug": "photo", "status": "active", "canonId": "019de52c-fd37-757f-b4a8-ca26d4888875", "version": 1, "description": null }, "framework": null }, "subkind": "photo", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:19.06+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "b127e724-4ec3-4847-b4cc-093556258a95", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "expected bottom-line boost by 2025: $1.5 billion annually", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:45:39+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b402-ef79cc546162", "parentComponentId": null }, { "bbox": { "h": 0.25, "w": 0.215, "x": 0.515, "y": 0.53 }, "kind": "paragraph", "text": "A European energy company created a digital factory to help empower employees to use analytics and AI-driven insights in their daily jobs.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:19.06+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "000e3f29-e414-4741-841c-ca3410d921b9", "parentComponentId": null }, { "bbox": { "h": 0.02, "w": 0.25, "x": 0.029, "y": 0.94 }, "kind": "paragraph", "text": "The art of AI maturity—Advancing from practice to performance", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:19.06+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "10fea553-a862-4080-979b-a3ea01ba1384", "parentComponentId": null }, { "bbox": { "h": 0.25, "w": 0.215, "x": 0.743, "y": 0.4 }, "kind": "paragraph", "text": "To strengthen their AI cores, Achievers often collaborate with external experts to stay on top of of breakthroughs in science and engineering. In 2020, for example, American Express partnered with the Indian Institute of Technology Madras to create a Data Analytics, Risk and Technology laboratory at the prestigious university.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:19.06+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "19a49b38-b74c-4224-a877-a5de677b1557", "parentComponentId": null }, { "bbox": { "h": 0.25, "w": 0.215, "x": 0.743, "y": 0.114 }, "kind": "paragraph", "text": "Thanks to the organization’s increased investment in AI-savvy talent, its business department now receives new, AI-powered apps within five months of initiating their development—compared with an 18-month wait, on average, before the digital factory was built.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:19.06+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "65f8f0a7-5e18-4cfe-abf2-4ba93d6c323e", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.215, "x": 0.515, "y": 0.114 }, "kind": "paragraph", "text": "A major insurer used to rely on its employees to manually administer claims—a tedious process that cost over $500 million annually—even before the firm’s billion-dollar payouts to policyholders.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:19.06+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "77048e7c-c4ae-4884-8cf2-8b2871eed34a", "parentComponentId": null }, { "bbox": { "h": 0.02, "w": 0.01, "x": 0.96, "y": 0.94 }, "kind": "paragraph", "text": "24", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:19.06+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "d8076ec3-cdc7-41a7-82be-104c76948dc5", "parentComponentId": null } ], "metrics": [ { "metricRaw": "expected bottom-line boost by 2025", "numberRaw": "$1.5 billion annually", "numberKind": "money", "actionTitle": null, "calloutText": "By industrializing its AI tools and teams, the company targeted efficiency gains of 5% in its first year alone, with longer-term cost savings of more than $100 million annually.", "numberScale": "b", "numberValue": 1.5, "metricFamily": "other", "numberCurrency": "$" } ], "tools": [ { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-f4c54ba672a5", "evidence": "$1.5B annual claims, 5% efficiency gains, $100M savings", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 85, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 24, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Sinatra Test", "slug": "sinatra-test", "status": "active", "bestFor": null, "canonId": "019dd956-be4a-775f-8038-d4e7e58dfc5a", "version": 1, "bodyDocId": null, "description": "If you can make it there, you can make it anywhere - using flagship example", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-f02412cd5ea8", "evidence": "Major insurer flagship transformation case", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 24, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Singularity Effect", "slug": "singularity-effect", "status": "active", "bestFor": null, "canonId": "019dd956-dcd3-7287-94d8-5a49496c2ddb", "version": 1, "bodyDocId": null, "description": "People care more about individuals than statistics - use specific examples", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987b-f89c63c59a0b", "evidence": "Single insurer case stands in for the recommendation", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 70, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 24, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null } ], "frameworks": [], "arcBeats": [ { "to": 27, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null }, "from": 17, "name": "Solution & Approach", "beatId": "019dd95a-0682-776c-8e32-7e8c761f9fc5", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Solution & Approach", "slug": "consultants-gambit-solution-approach", "status": "active", "canonId": "019dd9b8-071c-7161-9184-580fdac7a4f9", "version": 1, "description": null }, "evidence": "Five recommendations to master AI maturity, each with case study", "position": 3, "isPrimaryArc": true, "parentBeatType": { "name": "Turn", "slug": "turn", "status": "active", "canonId": "019dd9b8-0396-702b-ac2f-9901f8dd8b54", "version": 1, "description": "The pivot — solution proposed, catalyst introduced, decision made. Common (15/20 arcs) but not universal: Triple-take, AIDA, PAS, Voyage-return and Pixar substitute it with a strong reflection or development beat." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 27, "from": 17, "loop": { "name": "40_mece_breakdown", "slug": "40-mece-breakdown", "status": "active", "bestFor": "Problem structuring, ensuring completeness, strategic analysis", "canonId": "019dd956-763c-765e-8b68-6ee3ae1ef184", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Whole -> Category A (distinct) -> Category B (distinct) -> Category C (distinct) -> Complete Coverage", "description": "Divide a complex topic into mutually exclusive, collectively exhaustive categories", "familyLabel": null }, "matchId": "019dd95a-07fe-70ce-8d3a-483bc3c6386e", "evidence": "Five distinct recommendation categories (sponsor / talent / AI core / responsible / invest) each with case study", "position": 5, "objective": "Five MECE recommendations to become an Achiever", "confidence": 85, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "locked": true }, { "page": 25, "type": "setup", "title": "Success Factor 04 in a series.", "function": "summarize", "rawType": "key_messages", "block": null, "metadata": { "slideType": "key_messages", "slideTypeCanon": { "name": "Key messages", "slug": "key_messages", "status": "active", "canonId": "019de52d-0712-703f-89f5-bda6688faf3f", "version": 1, "description": null }, "function": "summarize", "functionCanon": { "name": "Summarize", "slug": "summarize", "status": "active", "canonId": "019de52d-1541-704f-b86f-bf7b75056373", "version": 1, "description": null }, "density": "overcrowded", "densityScore": 89, "componentCount": 10, "textChars": 1743, "nDataPoints": 3, "notes": "Success Factor 04 in a series.", "elementsJson": [ "headline_text", "action_title", "subtitle_text", "paragraph", "photo" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:15.186+00", "seconds": 3.795, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/25", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-25", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "Achievers are 53% more likely, on average, than Builders and Innovators to be responsible by design: designing, developing and deploying AI with good intention to empower employees and businesses, and to fairly impact customers and society—allowing companies to engender trust and scale AI with confidence.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:45:36+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b402-cf3684157edd", "parentComponentId": null }, { "bbox": { "h": 1, "w": 0.486, "x": 0.514, "y": 0 }, "kind": "image", "text": null, "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Photo", "slug": "photo", "status": "active", "canonId": "019de52c-fd37-757f-b4a8-ca26d4888875", "version": 1, "description": null }, "framework": null }, "subkind": "photo", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.186+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "b4166146-9dc4-491c-b3ed-ce252fde8eec", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "Achievers more likely than Builders/Innovators to be responsible by design: 53%", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:45:36+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b402-d02e4cf043f9", "parentComponentId": null }, { "bbox": { "h": 0.135, "w": 0.215, "x": 0.058, "y": 0.271 }, "kind": "paragraph", "text": "As companies deploy AI for a growing range of tasks, adhering to laws, regulations and ethical norms is critical to building a sound data and AI foundation. The potential for regulatory changes in many countries makes the challenge even more daunting.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.186+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "4ba601bd-dc28-4b6d-aacc-a0138189679d", "parentComponentId": null }, { "bbox": { "h": 0.135, "w": 0.215, "x": 0.058, "y": 0.608 }, "kind": "paragraph", "text": "Nearly all (97%) respondents believed that regulation will impact them to some extent, and 77% indicated that compliance is a company-wide priority. Interestingly, many organizations view AI regulation as a boon rather than a barrier to success.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.186+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "4e9f1a05-f7b7-4ed0-9960-231742b53052", "parentComponentId": null }, { "bbox": { "h": 0.071, "w": 0.215, "x": 0.058, "y": 0.475 }, "kind": "paragraph", "text": "In a separate Accenture study of 850 C-suite executives, we sought to gauge attitudes towards AI regulation and assess organizations’ readiness to comply.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.186+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "59ec40ec-2ef4-413d-90c5-3c03533658b2", "parentComponentId": null }, { "bbox": { "h": 0.235, "w": 0.215, "x": 0.286, "y": 0.475 }, "kind": "paragraph", "text": "Achievers are consciously applying responsible AI with greater urgency than their peers. Achievers are 53% more likely, on average, than Builders and Innovators to be responsible by design: designing, developing and deploying AI with good intention to empower employees and businesses, and to fairly impact customers and society—allowing companies to engender trust and scale AI with confidence.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.186+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "62ac2c0c-ba22-46aa-81d6-fbdd47977f0c", "parentComponentId": null }, { "bbox": { "h": 0.135, "w": 0.215, "x": 0.286, "y": 0.271 }, "kind": "paragraph", "text": "The ability to demonstrate high-quality, trustworthy AI systems that are “regulation ready” will give first movers a significant advantage in the short- and long-term, enabling them to attract new customers, retain existing ones and build investor confidence.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.186+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "9f637de6-6304-460e-b5cf-20286bfa8667", "parentComponentId": null }, { "bbox": { "h": 0.038, "w": 0.405, "x": 0.058, "y": 0.173 }, "kind": "title", "text": "Design AI responsibly, from the start", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.186+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "012c2e30-8b2d-42a8-94c9-7b0d84042e4f", "parentComponentId": null }, { "bbox": { "h": 0.028, "w": 0.145, "x": 0.058, "y": 0.103 }, "kind": "title", "text": "Success Factor 04", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Subtitle", "slug": "subtitle", "status": "active", "canonId": "019de52c-fb09-739d-900c-815b0d9ac526", "version": 1, "description": null }, "framework": null }, "subkind": "subtitle", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.186+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "f16c2fb7-e910-4b6f-9b91-2bf8912ef714", "parentComponentId": null } ], "metrics": [ { "metricRaw": "Achievers more likely than Builders/Innovators to be responsible by design", "numberRaw": "53%", "numberKind": "percent", "actionTitle": "Design AI responsibly, from the start", "calloutText": "Achievers are 53% more likely, on average, than Builders and Innovators to be responsible by design: designing, developing and deploying AI with good intention to empower employees and businesses, and to fairly impact customers and society—allowing companies to engender trust and scale AI with confidence.", "numberScale": null, "numberValue": 53, "metricFamily": "other", "numberCurrency": null } ], "tools": [ { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987b-fe900ba89c82", "evidence": "Directive: 'Design AI responsibly, from the start'", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 90, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 25, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987c-01511abb42ac", "evidence": "53% more likely to be responsible by design", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 25, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Monroe's Motivated Sequence", "slug": "monroes-motivated-sequence", "status": "active", "bestFor": null, "canonId": "019dd956-8758-700f-83cf-d260a54ad24f", "version": 1, "bodyDocId": null, "description": "Five-step persuasion: Attention, Need, Satisfaction, Visualization, Action", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "3f9492ba-373f-47c1-87a2-5fbffb1d8c65", "evidence": "Design AI responsibly, from the start", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.6, "extraction": { "at": "2026-07-16 21:02:36.13041+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 25, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null } ], "frameworks": [], "arcBeats": [ { "to": 27, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null }, "from": 17, "name": "Solution & Approach", "beatId": "019dd95a-0682-776c-8e32-7e8c761f9fc5", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Solution & Approach", "slug": "consultants-gambit-solution-approach", "status": "active", "canonId": "019dd9b8-071c-7161-9184-580fdac7a4f9", "version": 1, "description": null }, "evidence": "Five recommendations to master AI maturity, each with case study", "position": 3, "isPrimaryArc": true, "parentBeatType": { "name": "Turn", "slug": "turn", "status": "active", "canonId": "019dd9b8-0396-702b-ac2f-9901f8dd8b54", "version": 1, "description": "The pivot — solution proposed, catalyst introduced, decision made. Common (15/20 arcs) but not universal: Triple-take, AIDA, PAS, Voyage-return and Pixar substitute it with a strong reflection or development beat." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 27, "from": 17, "loop": { "name": "40_mece_breakdown", "slug": "40-mece-breakdown", "status": "active", "bestFor": "Problem structuring, ensuring completeness, strategic analysis", "canonId": "019dd956-763c-765e-8b68-6ee3ae1ef184", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Whole -> Category A (distinct) -> Category B (distinct) -> Category C (distinct) -> Complete Coverage", "description": "Divide a complex topic into mutually exclusive, collectively exhaustive categories", "familyLabel": null }, "matchId": "019dd95a-07fe-70ce-8d3a-483bc3c6386e", "evidence": "Five distinct recommendation categories (sponsor / talent / AI core / responsible / invest) each with case study", "position": 5, "objective": "Five MECE recommendations to become an Achiever", "confidence": 85, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "locked": true }, { "page": 26, "type": "evidence", "title": "The slide uses a purple gradient background with a callout box containing the key statistic.", "function": "illustrate_case", "rawType": "client_example", "block": null, "metadata": { "slideType": "client_example", "slideTypeCanon": { "name": "Client example", "slug": "client_example", "status": "active", "canonId": "019de52d-0c87-77cb-9867-613ae22e95ac", "version": 1, "description": null }, "function": "illustrate_case", "functionCanon": { "name": "Illustrate case", "slug": "illustrate_case", "status": "active", "canonId": "019de52d-13b4-772c-bb73-1873612a7463", "version": 1, "description": null }, "density": "overcrowded", "densityScore": 81, "componentCount": 8, "textChars": 1712, "nDataPoints": 2, "notes": "The slide uses a purple gradient background with a callout box containing the key statistic.", "elementsJson": [ "paragraph", "callout_box", "big_number", "quote_block" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:15.275+00", "seconds": 3.834, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/26", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-26", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": { "h": 0.65, "w": 0.4, "x": 0.54, "y": 0.13 }, "kind": "callout", "text": "Even though only 6% of the companies surveyed had already implemented responsible AI practices, 42% of surveyed companies aspire to do so by the end of 2024.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": null, "subkind": { "name": "Primary callout", "slug": "primary", "status": "active", "canonId": "019de52c-fbd1-70eb-a9ce-8997f8674c1d", "version": 1, "description": null }, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.275+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "5beb06e6-86cd-4985-a706-93fbd2e6f7c8", "parentComponentId": null }, { "bbox": null, "kind": "framework", "text": "FEAT", "attrs": null, "canon": { "kind": { "name": "Framework", "slug": "framework", "status": "active", "canonId": "019de52c-fa18-77c6-b10a-ccb8277ac0fa", "version": 1, "description": "Named conceptual framework instance." }, "tool": { "name": "Structuring frame", "slug": "structuring-frame", "status": "active", "bestFor": null, "canonId": "019dd9e1-48ff-708d-8103-a8464743e560", "version": 1, "bodyDocId": null, "description": "A named scaffold for organising content (SCQA, situation-action-result, etc.).", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:45:45+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b403-1096ab4c8cd7", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "companies aspiring to implement responsible AI practices by end of 2024: 42%", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:45:45+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b403-0d08e57a81ed", "parentComponentId": null }, { "bbox": { "h": 0.25, "w": 0.25, "x": 0.058, "y": 0.375 }, "kind": "paragraph", "text": "For instance, the Monetary Authority of Singapore (MAS), the country's central bank and financial regulator, recognized the benefits that AI can provide to financial firms. Yet MAS was also wary of the threat posed to firms and markets by the illegal and/or unethical use of AI. To guard against this, MAS helped launch the Veritas initiative together with the financial industry, which aims to support the responsible use of AI in the finance industry.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.275+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "22b7999a-5d69-4920-95f2-e46ee0a6d557", "parentComponentId": null }, { "bbox": { "h": 0.25, "w": 0.25, "x": 0.286, "y": 0.4 }, "kind": "paragraph", "text": "Novartis is creating effective accountability mechanisms and risk-management controls to keep AI-powered operations and services aligned with the company's core values. In addition, notes Klaus Moosmayer, Novartis' Chief Ethics, Risk and Compliance Officer, it's designed to, \"provide ... a clear framework for ethical use of AI—enabling [employees] to challenge their own decisions and biases.\"7", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.275+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "c12db218-e141-46cd-922b-a2caf09acc89", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.25, "x": 0.058, "y": 0.188 }, "kind": "paragraph", "text": "For companies, the upshot of being responsible by design is an improved ability to meet future requirements, better mitigate risks and create sustainable value for themselves and their stakeholders.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.275+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "eaf79767-316b-40d6-b16b-c0293f7f9bc2", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.25, "x": 0.286, "y": 0.188 }, "kind": "paragraph", "text": "The initiative has, among other achievements, produced a practical methodology and toolkit—the first of its kind—that offers detailed guidance on how to use AI leveraging the FEAT principles, i.e. fair, ethical, accountable and transparent.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.275+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "fb6c2ec1-0eac-481b-b48c-286a48162c4c", "parentComponentId": null }, { "bbox": null, "kind": "quote", "text": "provide ... a clear framework for ethical use of AI—enabling [employees] to challenge their own decisions and biases. — Klaus Moosmayer, Novartis' Chief Ethics, Risk and Compliance Officer", "attrs": null, "canon": { "kind": { "name": "Quote", "slug": "quote", "status": "active", "canonId": "019de52c-f8fb-751e-8bc6-d58e2d59562a", "version": 1, "description": "Pull quote or testimonial." }, "tool": { "name": "Authority citation", "slug": "authority-citation", "status": "active", "bestFor": null, "canonId": "019dd9e1-49ae-7557-89ad-3fca70469b45", "version": 1, "bodyDocId": null, "description": "Quoting an authority figure, study, or precedent to lend weight.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:45:45+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b403-0ab3e65788b1", "parentComponentId": null } ], "metrics": [ { "metricRaw": "companies aspiring to implement responsible AI practices by end of 2024", "numberRaw": "42%", "numberKind": "percent", "actionTitle": "Even though only 6% of the companies surveyed had already implemented responsible AI practices, 42% of surveyed companies aspire to do so by the end of 2024.", "calloutText": "Even though only 6% of the companies surveyed had already implemented responsible AI practices, 42% of surveyed companies aspire to do so by the end of 2024.", "numberScale": null, "numberValue": 42, "metricFamily": "other", "numberCurrency": null } ], "tools": [ { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987c-07103bb1d7cf", "evidence": "6% implemented vs 42% aspire by 2024", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 26, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Sinatra Test", "slug": "sinatra-test", "status": "active", "bestFor": null, "canonId": "019dd956-be4a-775f-8038-d4e7e58dfc5a", "version": 1, "bodyDocId": null, "description": "If you can make it there, you can make it anywhere - using flagship example", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987c-0b3725dce97e", "evidence": "Monetary Authority of Singapore Veritas + Novartis flagship cases", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 26, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Von Restorff Effect", "slug": "von-restorff-effect", "status": "active", "bestFor": null, "canonId": "019dd956-beab-73d8-9f62-30d0332e926e", "version": 1, "bodyDocId": null, "description": "Items that stand out are noticed and remembered - make key points visually distinct", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "019dd95a-0f08-73d3-987c-0ea5dae86980", "evidence": "Large purple callout box isolates the 6% / 42% stat", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 26, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null } ], "frameworks": [ { "matchId": "019dd95a-1ca5-70bb-bb9f-46577eebc96d", "evidence": "Callout names FEAT principles (Fair, Ethical, Accountable, Transparent)", "framework": null, "confidence": 85, "extraction": { "at": "2026-04-29 13:07:34.826634+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 26, "frameworkName": "FEAT Principles" } ], "arcBeats": [ { "to": 27, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null }, "from": 17, "name": "Solution & Approach", "beatId": "019dd95a-0682-776c-8e32-7e8c761f9fc5", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Solution & Approach", "slug": "consultants-gambit-solution-approach", "status": "active", "canonId": "019dd9b8-071c-7161-9184-580fdac7a4f9", "version": 1, "description": null }, "evidence": "Five recommendations to master AI maturity, each with case study", "position": 3, "isPrimaryArc": true, "parentBeatType": { "name": "Turn", "slug": "turn", "status": "active", "canonId": "019dd9b8-0396-702b-ac2f-9901f8dd8b54", "version": 1, "description": "The pivot — solution proposed, catalyst introduced, decision made. Common (15/20 arcs) but not universal: Triple-take, AIDA, PAS, Voyage-return and Pixar substitute it with a strong reflection or development beat." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 27, "from": 17, "loop": { "name": "40_mece_breakdown", "slug": "40-mece-breakdown", "status": "active", "bestFor": "Problem structuring, ensuring completeness, strategic analysis", "canonId": "019dd956-763c-765e-8b68-6ee3ae1ef184", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Whole -> Category A (distinct) -> Category B (distinct) -> Category C (distinct) -> Complete Coverage", "description": "Divide a complex topic into mutually exclusive, collectively exhaustive categories", "familyLabel": null }, "matchId": "019dd95a-07fe-70ce-8d3a-483bc3c6386e", "evidence": "Five distinct recommendation categories (sponsor / talent / AI core / responsible / invest) each with case study", "position": 5, "objective": "Five MECE recommendations to become an Achiever", "confidence": 85, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "locked": true }, { "page": 27, "type": "evidence", "function": "illustrate_case", "rawType": "client_example", "block": null, "metadata": { "slideType": "client_example", "slideTypeCanon": { "name": "Client example", "slug": "client_example", "status": "active", "canonId": "019de52d-0c87-77cb-9867-613ae22e95ac", "version": 1, "description": null }, "function": "illustrate_case", "functionCanon": { "name": "Illustrate case", "slug": "illustrate_case", "status": "active", "canonId": "019de52d-13b4-772c-bb73-1873612a7463", "version": 1, "description": null }, "density": "overcrowded", "densityScore": 82, "componentCount": 8, "textChars": 2097, "nDataPoints": 3, "notes": null, "elementsJson": [ "headline_text", "subtitle_text", "quote_block", "paragraph" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:14.553+00", "seconds": 3.064, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/27", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-27", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "We found that in 2018, Achievers devoted 14% of their total technology budgets to AI, while in 2021 they devoted 28%. In 2024, they plan to devote 34%.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:45:46+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b403-279e85124134", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "Achievers' share of tech budget devoted to AI: 34%", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:45:46+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b403-2d5145532be9", "parentComponentId": null }, { "bbox": { "h": 0.55, "w": 0.21, "x": 0.515, "y": 0.31 }, "kind": "paragraph", "text": "To avoid being left behind, most companies need to aggressively increase their spending on data and AI. One reason Achievers get more out of AI is simply because they invest more in it. We found that in 2018, Achievers devoted 14% of their total technology budgets to AI, while in 2021 they devoted 28%. In 2024, they plan to devote 34%. Achievers also understand that their AI investment journey doesn't have a finish line. There is, they frequently note, no \"peak AI.\" These companies know they have only scratched the surface of their AI transformations and that the quality of their investments matters just as much as the quantity.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:14.553+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "44faf64a-eb5d-4311-b7d9-392d6fe6397a", "parentComponentId": null }, { "bbox": { "h": 0.55, "w": 0.21, "x": 0.745, "y": 0.31 }, "kind": "paragraph", "text": "For Achievers, continued investment largely involves expanding the scope of AI to deliver maximum impact, while \"cross-pollinating\" AI solutions and redeploying resources in the process. As part of its efforts to create a more data-driven organization that can offer customers highly personalized digital service, Walgreens Boots Alliance migrated from legacy databases to advanced cloud databases and analytics, and built some 100 high-value AI products that create detailed customer profiles and help the company better optimize inventory and prices. The company has been committed to its data- and AI-led transformation since 2020 and doesn't intend to slow down anytime soon.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:14.553+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "e39f610b-88c2-4cef-9dbf-4caf64a96dfc", "parentComponentId": null }, { "bbox": { "h": 0.65, "w": 0.38, "x": 0.08, "y": 0.00125 }, "kind": "quote", "text": "\"I'm on a journey that doesn't stop with a move to the cloud. I'm in the middle of rebuilding an organization shifting from being extremely operational into an organization that is transformational.\" Francesco Tinto, global CIO, SVP, Walgreens Boots Alliance", "attrs": null, "canon": { "kind": { "name": "Quote", "slug": "quote", "status": "active", "canonId": "019de52c-f8fb-751e-8bc6-d58e2d59562a", "version": 1, "description": "Pull quote or testimonial." }, "tool": null, "subkind": { "name": "Testimonial", "slug": "testimonial", "status": "active", "canonId": "019de52c-fc21-71b6-9ac3-8266a64945e8", "version": 1, "description": null }, "framework": null }, "subkind": "testimonial", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:14.553+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "d298e4e3-2a7d-4779-96d5-9134e2e15071", "parentComponentId": null }, { "bbox": null, "kind": "quote", "text": "\"I'm on a journey that doesn't stop with a move to the cloud. I'm in the middle of rebuilding an organization shifting from being extremely operational into an organization that is transformational.\" — Francesco Tinto, global CIO, SVP, Walgreens Boots Alliance", "attrs": null, "canon": { "kind": { "name": "Quote", "slug": "quote", "status": "active", "canonId": "019de52c-f8fb-751e-8bc6-d58e2d59562a", "version": 1, "description": "Pull quote or testimonial." }, "tool": { "name": "Authority citation", "slug": "authority-citation", "status": "active", "bestFor": null, "canonId": "019dd9e1-49ae-7557-89ad-3fca70469b45", "version": 1, "bodyDocId": null, "description": "Quoting an authority figure, study, or precedent to lend weight.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:45:46+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b403-28550d361f77", "parentComponentId": null }, { "bbox": { "h": 0.08, "w": 0.4, "x": 0.515, "y": 0.195 }, "kind": "title", "text": "Prioritize long- and short-term AI investments", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Action title", "slug": "action-title", "status": "active", "canonId": "019de52c-fb31-734c-a0f5-a3935b73135d", "version": 1, "description": null }, "framework": null }, "subkind": "action-title", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:14.553+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "cba8a3ca-76b7-47c5-82d2-dbde1a88ad7d", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.16, "x": 0.515, "y": 0.125 }, "kind": "title", "text": "Success Factor 05", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Subtitle", "slug": "subtitle", "status": "active", "canonId": "019de52c-fb09-739d-900c-815b0d9ac526", "version": 1, "description": null }, "framework": null }, "subkind": "subtitle", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:14.553+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "210e8d96-dba3-47d5-b83e-4b1eb4b64e86", "parentComponentId": null } ], "metrics": [ { "metricRaw": "Achievers' share of tech budget devoted to AI", "numberRaw": "34%", "numberKind": "percent", "actionTitle": "Prioritize long- and short-term AI investments", "calloutText": "We found that in 2018, Achievers devoted 14% of their total technology budgets to AI, while in 2021 they devoted 28%. In 2024, they plan to devote 34%.", "numberScale": null, "numberValue": 34, "metricFamily": "market_size", "numberCurrency": null } ], "tools": [ { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987c-1374f0ff8ae3", "evidence": "Directive: 'Prioritize long- and short-term AI investments'", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 90, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 27, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "67a3cf86-5bac-4582-9e03-d9facd8aa706", "evidence": "Prioritize long- and short-term AI investments", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 0.9, "extraction": { "at": "2026-07-16 21:02:36.246447+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 27, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Concrete Language", "slug": "concrete-language", "status": "active", "bestFor": null, "canonId": "019dd956-bcd4-7398-9723-6aa72346170b", "version": 1, "bodyDocId": null, "description": "Using specific sensory details instead of abstract terms", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987c-165d09e5a644", "evidence": "14% to 28% to 34% AI budget trajectory", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 27, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null } ], "frameworks": [], "arcBeats": [ { "to": 27, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null }, "from": 17, "name": "Solution & Approach", "beatId": "019dd95a-0682-776c-8e32-7e8c761f9fc5", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Solution & Approach", "slug": "consultants-gambit-solution-approach", "status": "active", "canonId": "019dd9b8-071c-7161-9184-580fdac7a4f9", "version": 1, "description": null }, "evidence": "Five recommendations to master AI maturity, each with case study", "position": 3, "isPrimaryArc": true, "parentBeatType": { "name": "Turn", "slug": "turn", "status": "active", "canonId": "019dd9b8-0396-702b-ac2f-9901f8dd8b54", "version": 1, "description": "The pivot — solution proposed, catalyst introduced, decision made. Common (15/20 arcs) but not universal: Triple-take, AIDA, PAS, Voyage-return and Pixar substitute it with a strong reflection or development beat." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 27, "from": 17, "loop": { "name": "40_mece_breakdown", "slug": "40-mece-breakdown", "status": "active", "bestFor": "Problem structuring, ensuring completeness, strategic analysis", "canonId": "019dd956-763c-765e-8b68-6ee3ae1ef184", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Whole -> Category A (distinct) -> Category B (distinct) -> Category C (distinct) -> Complete Coverage", "description": "Divide a complex topic into mutually exclusive, collectively exhaustive categories", "familyLabel": null }, "matchId": "019dd95a-07fe-70ce-8d3a-483bc3c6386e", "evidence": "Five distinct recommendation categories (sponsor / talent / AI core / responsible / invest) each with case study", "position": 5, "objective": "Five MECE recommendations to become an Achiever", "confidence": 85, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "locked": true }, { "page": 28, "type": "setup", "title": "This is a 'hero' or 'transition' slide using a dramatic image to emphasize a key finding.", "function": "summarize", "rawType": "thesis_headline", "block": null, "metadata": { "slideType": "thesis_headline", "slideTypeCanon": { "name": "Thesis headline", "slug": "thesis_headline", "status": "active", "canonId": "019de52d-073a-70cb-b9f6-772a597bb5af", "version": 1, "description": null }, "function": "summarize", "functionCanon": { "name": "Summarize", "slug": "summarize", "status": "active", "canonId": "019de52d-1541-704f-b86f-bf7b75056373", "version": 1, "description": null }, "density": "balanced", "densityScore": 26, "componentCount": 4, "textChars": 315, "nDataPoints": 2, "notes": "This is a 'hero' or 'transition' slide using a dramatic image to emphasize a key finding.", "elementsJson": [ "headline_text", "action_title", "photo" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:13.721+00", "seconds": 2.216, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/28", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-28", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "The share of AI Achievers will increase rapidly and significantly, more than doubling from the current 12% to 27% by 2024.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:46:22+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b403-b92d169a3f68", "parentComponentId": null }, { "bbox": { "h": 1, "w": 1, "x": 0, "y": 0 }, "kind": "image", "text": "Person reaching up in purple lighting", "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Photo", "slug": "photo", "status": "active", "canonId": "019de52c-fd37-757f-b4a8-ca26d4888875", "version": 1, "description": null }, "framework": null }, "subkind": "photo", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.721+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "b1a4ed78-d794-416e-a57b-bdbf90232309", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "Share of AI Achievers by 2024: 27%", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:46:22+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b403-bd6038d9614b", "parentComponentId": null }, { "bbox": { "h": 0.3, "w": 0.42, "x": 0.07, "y": 0.35 }, "kind": "title", "text": "The share of AI Achievers will increase rapidly and significantly, more than doubling from the current 12% to 27% by 2024.", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Action title", "slug": "action-title", "status": "active", "canonId": "019de52c-fb31-734c-a0f5-a3935b73135d", "version": 1, "description": null }, "framework": null }, "subkind": "action-title", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:13.721+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "acec79b6-6de3-4d7a-ab6a-9f3064e3e4d0", "parentComponentId": null } ], "metrics": [ { "metricRaw": "Share of AI Achievers by 2024", "numberRaw": "27%", "numberKind": "percent", "actionTitle": "The share of AI Achievers will increase rapidly and significantly, more than doubling from the current 12% to 27% by 2024.", "calloutText": "The share of AI Achievers will increase rapidly and significantly, more than doubling from the current 12% to 27% by 2024.", "numberScale": null, "numberValue": 27, "metricFamily": "share_penetration", "numberCurrency": null } ], "tools": [ { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987c-1b570017c2e8", "evidence": "Headline is the projection: 12% to 27% by 2024", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 85, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 28, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Emotional Appeal", "slug": "emotional-appeal", "status": "active", "bestFor": null, "canonId": "019dd956-bde9-7668-8be7-0097678fec59", "version": 1, "bodyDocId": null, "description": "Connecting to audience feelings rather than just logic", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987c-23d8ee628917", "evidence": "Triumphant silhouette with raised arms in purple light", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 75, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 28, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Picture Superiority Effect", "slug": "picture-superiority-effect", "status": "active", "bestFor": null, "canonId": "019dd956-c201-7014-bb58-3e85e3e11dfd", "version": 1, "bodyDocId": null, "description": "Pictures remembered 6x better than words - prioritize visuals over text", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "019dd95a-0f08-73d3-987c-27aae6b354e3", "evidence": "Full-bleed photo dominates the slide", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 70, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 28, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Von Restorff Effect", "slug": "von-restorff-effect", "status": "active", "bestFor": null, "canonId": "019dd956-beab-73d8-9f62-30d0332e926e", "version": 1, "bodyDocId": null, "description": "Items that stand out are noticed and remembered - make key points visually distinct", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "019dd95a-0f08-73d3-987c-1f9935c5b7cd", "evidence": "Single oversized stat against silhouette image", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 28, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null } ], "frameworks": [], "arcBeats": [ { "to": 31, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null }, "from": 28, "name": "Impact & Next Steps", "beatId": "019dd95a-0682-776c-8e32-8716c65168b1", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Impact & Next Steps", "slug": "consultants-gambit-impact-next-steps", "status": "active", "canonId": "019dd9b8-0849-7728-9ee9-fd1609c4c655", "version": 1, "description": null }, "evidence": "Vision (12% to 27% by 2024) plus C-suite assessment questions", "position": 5, "isPrimaryArc": true, "parentBeatType": { "name": "Resolution", "slug": "resolution", "status": "active", "canonId": "019dd9b8-045d-703b-8dd7-e92aba3ba91b", "version": 1, "description": "How the deck ends. Required in 19/20 arcs. The exception (Sparkline) ends with new bliss which is functionally a resolution." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 31, "from": 28, "loop": { "name": "41_so_what_cascade", "slug": "41-so-what-cascade", "status": "active", "bestFor": "Data presentations, executive summaries, driving to recommendations", "canonId": "019dd956-769f-7208-afbc-f28985c6f4fb", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Data -> So What? (Insight 1) -> So What? (Insight 2) -> So What? (The Action)", "description": "Chain insights together, each answering 'so what?' until you reach the actionable conclusion", "familyLabel": null }, "matchId": "019dd95a-07fe-70ce-8d3a-4ee1c0546ad8", "evidence": "p28 vision stat, p30 mantra quote, p31 sample C-suite assessment questions", "position": 6, "objective": "Drive from finding to assessment action", "confidence": 70, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "locked": true }, { "page": 29, "type": "setup", "title": "The slide uses a metaphor of pottery to represent the development process.", "function": "transition", "rawType": "section_divider", "block": null, "metadata": { "slideType": "section_divider", "slideTypeCanon": { "name": "Section divider", "slug": "section_divider", "status": "active", "canonId": "019de52d-066a-7402-9554-6ae504999041", "version": 1, "description": null }, "function": "transition", "functionCanon": { "name": "Transition", "slug": "transition", "status": "active", "canonId": "019de52d-1365-7149-92aa-66df859e75fd", "version": 1, "description": null }, "density": "sparse", "densityScore": 6, "componentCount": 3, "textChars": 45, "nDataPoints": 0, "notes": "The slide uses a metaphor of pottery to represent the development process.", "elementsJson": [ "headline_text", "photo" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:14.239+00", "seconds": 2.462, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/29", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-29", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": { "h": 1, "w": 1, "x": 0, "y": 0 }, "kind": "image", "text": null, "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Illustration", "slug": "illustration", "status": "active", "canonId": "019de52c-fe76-7772-a5ac-c0b7294d7615", "version": 1, "description": null }, "framework": null }, "subkind": "illustration", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:14.239+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "b3aceaec-ddfe-4dcf-b2ea-12c0376111ab", "parentComponentId": null }, { "bbox": { "h": 0.08, "w": 0.58, "x": 0.21, "y": 0.48 }, "kind": "title", "text": "Practice makes progress", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:14.239+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "331eb55f-cc75-43ac-a7dc-c3d8c47bbe9f", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.18, "x": 0.06, "y": 0.1 }, "kind": "title", "text": "The art of AI maturity", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Subtitle", "slug": "subtitle", "status": "active", "canonId": "019de52c-fb09-739d-900c-815b0d9ac526", "version": 1, "description": null }, "framework": null }, "subkind": "subtitle", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:14.239+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "1b39a10b-d5cc-43a3-9b47-9262d89c2dfb", "parentComponentId": null } ], "metrics": [], "tools": [], "frameworks": [], "arcBeats": [ { "to": 31, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null }, "from": 28, "name": "Impact & Next Steps", "beatId": "019dd95a-0682-776c-8e32-8716c65168b1", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Impact & Next Steps", "slug": "consultants-gambit-impact-next-steps", "status": "active", "canonId": "019dd9b8-0849-7728-9ee9-fd1609c4c655", "version": 1, "description": null }, "evidence": "Vision (12% to 27% by 2024) plus C-suite assessment questions", "position": 5, "isPrimaryArc": true, "parentBeatType": { "name": "Resolution", "slug": "resolution", "status": "active", "canonId": "019dd9b8-045d-703b-8dd7-e92aba3ba91b", "version": 1, "description": "How the deck ends. Required in 19/20 arcs. The exception (Sparkline) ends with new bliss which is functionally a resolution." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 31, "from": 28, "loop": { "name": "41_so_what_cascade", "slug": "41-so-what-cascade", "status": "active", "bestFor": "Data presentations, executive summaries, driving to recommendations", "canonId": "019dd956-769f-7208-afbc-f28985c6f4fb", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Data -> So What? (Insight 1) -> So What? (Insight 2) -> So What? (The Action)", "description": "Chain insights together, each answering 'so what?' until you reach the actionable conclusion", "familyLabel": null }, "matchId": "019dd95a-07fe-70ce-8d3a-4ee1c0546ad8", "evidence": "p28 vision stat, p30 mantra quote, p31 sample C-suite assessment questions", "position": 6, "objective": "Drive from finding to assessment action", "confidence": 70, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "locked": true }, { "page": 30, "type": "setup", "title": "Includes a testimonial from a Walgreens Boots Alliance executive.", "function": "summarize", "rawType": "key_messages", "block": null, "metadata": { "slideType": "key_messages", "slideTypeCanon": { "name": "Key messages", "slug": "key_messages", "status": "active", "canonId": "019de52d-0712-703f-89f5-bda6688faf3f", "version": 1, "description": null }, "function": "summarize", "functionCanon": { "name": "Summarize", "slug": "summarize", "status": "active", "canonId": "019de52d-1541-704f-b86f-bf7b75056373", "version": 1, "description": null }, "density": "overcrowded", "densityScore": 88, "componentCount": 12, "textChars": 2203, "nDataPoints": 0, "notes": "Includes a testimonial from a Walgreens Boots Alliance executive.", "elementsJson": [ "headline_text", "subtitle_text", "paragraph", "quote_block" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:16.874+00", "seconds": 4.941, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/30", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-30", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": { "h": 0.022, "w": 0.26, "x": 0.638, "y": 0.426 }, "kind": "callout", "text": "How can AI help you differentiate?", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": null, "subkind": { "name": "Primary callout", "slug": "primary", "status": "active", "canonId": "019de52c-fbd1-70eb-a9ce-8997f8674c1d", "version": 1, "description": null }, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.874+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "8d15e9a7-627f-45ac-8b31-93bc657509d9", "parentComponentId": null }, { "bbox": null, "kind": "callout", "text": "Those who transform will be the ones whose teams master the art of AI maturity, using cloud as the enabler, data as the driver and AI as the differentiator.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:45:54+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b403-57ae48d26809", "parentComponentId": null }, { "bbox": { "h": 0.135, "w": 0.26, "x": 0.058, "y": 0.271 }, "kind": "paragraph", "text": "By 2019, there was evidence that scaling AI beyond proofs of concept had a significant impact on ROI. Then the pandemic hit. For many organizations, enterprise-wide transformation was an urgent means of survival. For others, it quickly became a catalyst to thrive.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.874+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "23a32d43-1672-477e-8b23-f5f93f04ade5", "parentComponentId": null }, { "bbox": { "h": 0.135, "w": 0.26, "x": 0.348, "y": 0.398 }, "kind": "paragraph", "text": "Other organizations should be asking questions to assess their own AI maturity. To help get started, Figure 7 has some sample questions for C-suite leaders, according to Accenture's AI maturity assessment. There are also tools available to help benchmark AI maturity and establish clear paths to progress and performance.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.874+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "3d829d5f-c495-4ac5-8df3-27145bdfdf66", "parentComponentId": null }, { "bbox": { "h": 0.068, "w": 0.26, "x": 0.348, "y": 0.271 }, "kind": "paragraph", "text": "While Achievers are advanced relative to their peers, they'll set new standards for high performance as their own maturity evolves.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.874+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "85bd7a27-5a9c-40eb-b09a-9057ae2ce6df", "parentComponentId": null }, { "bbox": { "h": 0.068, "w": 0.26, "x": 0.348, "y": 0.655 }, "kind": "paragraph", "text": "As AI technologies become more prevalent, the future of all businesses is going to look very different—some will lead the change, and some will be subjected to it.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.874+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "c0639fba-2caf-475e-99df-55ba192a0521", "parentComponentId": null }, { "bbox": { "h": 0.235, "w": 0.26, "x": 0.058, "y": 0.478 }, "kind": "paragraph", "text": "Al Achievers are thriving. Across industries, they've moved past cloud migration to scale and computing power to tap into new data sources and AI technologies that are widely available. But AI isn't their secret to superior performance. It's how they're approaching AI that makes them different. They've established that AI maturity is as much about people as it is about technology. As much about strategy as it is about implementation. As much about responsibility as it is about agility.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.874+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "dd095172-c590-4128-be12-7203127b9fe9", "parentComponentId": null }, { "bbox": { "h": 0.068, "w": 0.26, "x": 0.638, "y": 0.271 }, "kind": "paragraph", "text": "Those who transform will be the ones whose teams master the art of AI maturity, using cloud as the enabler, data as the driver and AI as the differentiator.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.874+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "f5622489-7077-4785-b8f4-cb6a911ff805", "parentComponentId": null }, { "bbox": { "h": 0.215, "w": 0.26, "x": 0.638, "y": 0.493 }, "kind": "quote", "text": "\"AI, for us, is really the technology that we're pushing...We want to make sure that AI and machine learning is embedded in everything we do.\"\nFrancesco Tinto, global CIO, SVP, Walgreens Boots Alliance", "attrs": null, "canon": { "kind": { "name": "Quote", "slug": "quote", "status": "active", "canonId": "019de52c-f8fb-751e-8bc6-d58e2d59562a", "version": 1, "description": "Pull quote or testimonial." }, "tool": null, "subkind": { "name": "Testimonial", "slug": "testimonial", "status": "active", "canonId": "019de52c-fc21-71b6-9ac3-8266a64945e8", "version": 1, "description": null }, "framework": null }, "subkind": "testimonial", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.874+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "29a2c4a9-f317-4bee-b537-c5b9f2b42bba", "parentComponentId": null }, { "bbox": null, "kind": "quote", "text": "\"AI, for us, is really the technology that we're pushing...We want to make sure that AI and machine learning is embedded in everything we do.\" — Francesco Tinto, global CIO, SVP, Walgreens Boots Alliance", "attrs": null, "canon": { "kind": { "name": "Quote", "slug": "quote", "status": "active", "canonId": "019de52c-f8fb-751e-8bc6-d58e2d59562a", "version": 1, "description": "Pull quote or testimonial." }, "tool": { "name": "Authority citation", "slug": "authority-citation", "status": "active", "bestFor": null, "canonId": "019dd9e1-49ae-7557-89ad-3fca70469b45", "version": 1, "bodyDocId": null, "description": "Quoting an authority figure, study, or precedent to lend weight.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:45:54+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b403-5a7d3214f5ff", "parentComponentId": null }, { "bbox": { "h": 0.026, "w": 0.508, "x": 0.058, "y": 0.211 }, "kind": "title", "text": "The concept of using AI to solve business problems isn't new.", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Action title", "slug": "action-title", "status": "active", "canonId": "019de52c-fb31-734c-a0f5-a3935b73135d", "version": 1, "description": null }, "framework": null }, "subkind": "action-title", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.874+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "2389fc23-1bb2-47f9-a81c-9c1919c08d89", "parentComponentId": null }, { "bbox": { "h": 0.045, "w": 0.367, "x": 0.058, "y": 0.143 }, "kind": "title", "text": "Practice makes progress", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.874+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "80bc8d92-c522-4baf-b993-074a0a7067c5", "parentComponentId": null } ], "metrics": [], "tools": [ { "tool": { "name": "Closing Techniques", "slug": "closing-techniques", "status": "active", "bestFor": null, "canonId": "019dd956-af13-774b-a4fd-7db0a3a88d51", "version": 1, "bodyDocId": null, "description": "Strong endings: Callback, challenge, vision, clear CTA, memorable line", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "019dd95a-0f08-73d3-987c-2b990d81666a", "evidence": "Memorable line: 'cloud as enabler, data as driver, AI as differentiator'", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 30, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Core Message Extraction", "slug": "core-message-extraction", "status": "active", "bestFor": "Any slide that carries an argument or finding. The diagnostic: if the slide has a chart on it, it needs a core message. Run extraction whenever the current title is a topic (Q3 performance, Customer satisfaction) rather than a claim, or when a reader could reasonably draw more than one conclusion from the visible evidence.", "canonId": "019dd956-bc71-707d-8867-1b2e810175cf", "version": 1, "bodyDocId": "f85613cb-4b77-462f-bfe6-3b862516e1a6", "description": "The discipline of distilling raw analysis, data, or content into a single declarative sentence — the slide's core message — that the audience would walk away with if they remembered nothing else. Process, not artefact: extraction questions run on the raw content until one sentence falls out.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987c-2c29555591ec", "evidence": "Quote distills the strategic thesis", "pageRefs": null, "priority": null, "whenToUse": "Any slide that carries an argument or finding. The diagnostic: if the slide has a chart on it, it needs a core message. Run extraction whenever the current title is a topic (Q3 performance, Customer satisfaction) rather than a claim, or when a reader could reasonably draw more than one conclusion from the visible evidence.", "confidence": 75, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 30, "whyItWorks": "Solves the two failure modes of slide writing simultaneously: the topic title (which outsources interpretation to the audience and produces three different conclusions in three different heads) and the multi-thesis stapler (which exceeds working memory and gets remembered as fragments). Aligns with how readers actually consume decks — Heath & Heath's Find the Core, the journalist's nut graf, and Minto's governing thought all converge on the same cognitive economics.", "antipattern": "Topic titles disguised as core messages (Customer satisfaction, no verb, no stake); multi-thesis sentences stapled with and; hedged sentences (may show signs of possible softening); chart-caption titles (Revenue fell 4%) that restate what the chart already shows instead of naming the so-what; meta-commentary (this slide explores...) that describes the slide rather than concluding it.", "cardinality": null, "narrativePurpose": "Forces the slide author to do the interpretive work before the slide ships, so the audience does not have to do it during the meeting. Wins the WYSIATI battle on purpose — the loudest fragment on the page becomes the author's chosen sentence rather than whichever chart label happens to be largest." }, { "tool": { "name": "Storytelling: audience calibration", "slug": "audience", "status": "active", "bestFor": null, "canonId": "019df269-23ea-723a-87c0-af1d78d702d2", "version": 1, "bodyDocId": "019df269-23c5-72ec-8b94-78fc1da7b656", "description": "The same thesis, written for four different audiences, produces four", "familyLabel": "narrative-block", "categoryName": "Narrative & Transformation", "categorySlug": "narrative-transformation" }, "agent": "storyteller", "layer": "slide", "agents": [ "storyteller" ], "matchId": "c1dc35bc-ff19-465b-bf29-b38f1089ba71", "evidence": "Other organizations should be asking questions to assess their own AI maturity.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.7, "extraction": { "at": "2026-07-16 21:02:26.221061+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 30, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null } ], "frameworks": [], "arcBeats": [ { "to": 31, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null }, "from": 28, "name": "Impact & Next Steps", "beatId": "019dd95a-0682-776c-8e32-8716c65168b1", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Impact & Next Steps", "slug": "consultants-gambit-impact-next-steps", "status": "active", "canonId": "019dd9b8-0849-7728-9ee9-fd1609c4c655", "version": 1, "description": null }, "evidence": "Vision (12% to 27% by 2024) plus C-suite assessment questions", "position": 5, "isPrimaryArc": true, "parentBeatType": { "name": "Resolution", "slug": "resolution", "status": "active", "canonId": "019dd9b8-045d-703b-8dd7-e92aba3ba91b", "version": 1, "description": "How the deck ends. Required in 19/20 arcs. The exception (Sparkline) ends with new bliss which is functionally a resolution." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 31, "from": 28, "loop": { "name": "41_so_what_cascade", "slug": "41-so-what-cascade", "status": "active", "bestFor": "Data presentations, executive summaries, driving to recommendations", "canonId": "019dd956-769f-7208-afbc-f28985c6f4fb", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Data -> So What? (Insight 1) -> So What? (Insight 2) -> So What? (The Action)", "description": "Chain insights together, each answering 'so what?' until you reach the actionable conclusion", "familyLabel": null }, "matchId": "019dd95a-07fe-70ce-8d3a-4ee1c0546ad8", "evidence": "p28 vision stat, p30 mantra quote, p31 sample C-suite assessment questions", "position": 6, "objective": "Drive from finding to assessment action", "confidence": 70, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "locked": true }, { "page": 31, "type": "analysis", "title": "The slide uses a structured table to categorize diagnostic questions, serving as a maturity assessment framework.", "function": "diagnose", "rawType": "diagnosis", "block": null, "metadata": { "slideType": "diagnosis", "slideTypeCanon": { "name": "Diagnosis", "slug": "diagnosis", "status": "active", "canonId": "019de52d-0cd7-7212-898f-0464041ff8d8", "version": 1, "description": null }, "function": "diagnose", "functionCanon": { "name": "Diagnose", "slug": "diagnose", "status": "active", "canonId": "019de52d-1657-7223-acc5-7437d878c850", "version": 1, "description": null }, "density": "sparse", "densityScore": 15, "componentCount": 4, "textChars": 160, "nDataPoints": 0, "notes": "The slide uses a structured table to categorize diagnostic questions, serving as a maturity assessment framework.", "elementsJson": [ "headline_text", "comparison_table", "footnote" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:15.162+00", "seconds": 2.839, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/31", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-31", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": { "h": 0.75, "w": 0.84, "x": 0.08, "y": 0.13 }, "kind": "framework", "text": "AI Maturity Assessment Framework", "attrs": null, "canon": { "kind": { "name": "Framework", "slug": "framework", "status": "active", "canonId": "019de52c-fa18-77c6-b10a-ccb8277ac0fa", "version": 1, "description": "Named conceptual framework instance." }, "tool": null, "subkind": { "name": "Framework instance", "slug": "instance", "status": "active", "canonId": "019de52d-05a2-72af-be0b-7edc60708a03", "version": 1, "description": null }, "framework": null }, "subkind": "instance", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.162+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "27ab6346-b0c8-4e95-8f2d-a9ebe529d983", "parentComponentId": null }, { "bbox": { "h": 0.02, "w": 0.15, "x": 0.08, "y": 0.87 }, "kind": "source-note", "text": "Source: Accenture Research", "attrs": null, "canon": { "kind": { "name": "Source note", "slug": "source-note", "status": "active", "canonId": "23484952-494b-4a5e-b1ad-550d0d70e948", "version": 1, "description": "Attribution / source / caveat placed at the slide footer. Numbered footnotes use attrs.numbered." }, "tool": null, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.162+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "dc4f9ca7-223f-472e-b54f-89660aeac59a", "parentComponentId": null }, { "bbox": { "h": 0.75, "w": 0.84, "x": 0.08, "y": 0.13 }, "kind": "table", "text": "Category and Key questions table", "attrs": null, "canon": { "kind": { "name": "Table", "slug": "table", "status": "active", "canonId": "019de52c-f9c8-7148-8119-eaac9594cd28", "version": 1, "description": "Tabular data layout." }, "tool": null, "subkind": { "name": "Data table", "slug": "data", "status": "active", "canonId": "019de52d-02d5-75b8-8abf-88e92bf63569", "version": 1, "description": null }, "framework": null }, "subkind": "data", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.162+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "402b84e7-a1b6-4d94-9d94-be4e14bb81d0", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.4, "x": 0.08, "y": 0.08 }, "kind": "title", "text": "Figure 7: AI maturity assessment: sample questions for C-suite leaders", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:15.162+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "bdd1d04f-816f-4777-b805-532e23ca71e8", "parentComponentId": null } ], "metrics": [], "tools": [ { "tool": { "name": "MECE Principle", "slug": "mece-principle", "status": "active", "bestFor": null, "canonId": "019dd956-7c60-71a8-8758-c79d914489c9", "version": 1, "bodyDocId": null, "description": "Mutually Exclusive, Collectively Exhaustive - ensuring complete, non-overlapping structure", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Architect", "layer": "block", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987c-3a61f60f79d3", "evidence": "Four MECE pillars (Strategy/Sponsorship, Data/AI Core, Talent/Culture, Responsible AI)", "pageRefs": null, "priority": "Core", "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 31, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Action Titles", "slug": "action-titles", "status": "active", "bestFor": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "canonId": "019dd956-ac70-700b-a608-ba58e78f5dcc", "version": 1, "bodyDocId": "816dad3c-9983-41c1-a86e-978ebd582841", "description": "The McKinsey-bred discipline of writing every slide title as a complete declarative sentence with a verb and an insight, not a topic label. Each title is a sub-claim that ladders up to the deck's governing thought; read in sequence, the titles reconstruct the executive summary.", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Architect", "layer": "slide", "agents": [ "Architect" ], "matchId": "019dd95a-0f08-73d3-987c-32dece881988", "evidence": "Figure 7 frames assessment for C-suite leaders", "pageRefs": null, "priority": "Core", "whenToUse": "Any decision-grade deliverable — strategy recommendation, board update, investment memo, M&A review, post-mortem — where the reader is being asked to agree, decide, or act and will skim the title bar at speed.", "confidence": 75, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 31, "whyItWorks": "Solves two failure modes at once. (1) The buried claim: action titles place the conclusion on the page before any analysis is read, so a busy reader doesn't reverse-engineer it from the chart. (2) The spineless deck: because each title is a claim, slides have to ladder up to the governing thought; logical gaps become visible as topic-shaped titles in a sequence that no longer reads as a story.", "antipattern": "Topic labels disguised as titles — Volume by quarter, Key findings, Pricing strategy, Margins have been impacted. No verb, or a passive verb with no agent, or a fact (Revenue grew 12%) without the so-what. Also: titles that overreach the chart's evidence, multi-claim X-grew-but-Y-fell welded titles, and mechanical repetition of the same X-drove-Y template across the deck.", "cardinality": null, "narrativePurpose": "Forces the deck to carry its argument in the title bar so a senior reader can extract the recommendation without opening a single slide; converts a binder of topics into a navigable pyramid where every slide is a node defending the apex." }, { "tool": { "name": "Grid System", "slug": "grid-system", "status": "active", "bestFor": null, "canonId": "019dd956-b745-710f-8b70-825c603b1163", "version": 1, "bodyDocId": null, "description": "Aligning elements to invisible grid for visual harmony", "familyLabel": null, "categoryName": "Slide", "categorySlug": "slide" }, "agent": "Designer", "layer": "slide", "agents": [ "Designer" ], "matchId": "019dd95a-0f08-73d3-987c-34bfee3863d1", "evidence": "Four-row table aligned by category and key questions", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 80, "extraction": { "at": "2026-04-29 13:07:31.336924+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 31, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null } ], "frameworks": [ { "matchId": "019dd95a-1ca5-70bb-bb9f-48c7d03ff99f", "evidence": "Four-category assessment (Strategy/Sponsorship, Data/AI Core, Talent/Culture, Responsible AI)", "framework": null, "confidence": 85, "extraction": { "at": "2026-04-29 13:07:34.826634+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 31, "frameworkName": "Accenture AI Maturity Assessment" }, { "matchId": "b40c1ee5-872e-4f0d-9abc-e9801bd15fc1", "evidence": "The slide presents a structured assessment of AI maturity across four key organizational dimensions.", "framework": null, "confidence": 0.9, "extraction": { "at": "2026-05-02 19:49:15.162+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 31, "frameworkName": "maturity-model" } ], "arcBeats": [ { "to": 31, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null }, "from": 28, "name": "Impact & Next Steps", "beatId": "019dd95a-0682-776c-8e32-8716c65168b1", "matchId": "019dd95a-0542-7728-a78e-625dbccf944b", "beatType": { "name": "Impact & Next Steps", "slug": "consultants-gambit-impact-next-steps", "status": "active", "canonId": "019dd9b8-0849-7728-9ee9-fd1609c4c655", "version": 1, "description": null }, "evidence": "Vision (12% to 27% by 2024) plus C-suite assessment questions", "position": 5, "isPrimaryArc": true, "parentBeatType": { "name": "Resolution", "slug": "resolution", "status": "active", "canonId": "019dd9b8-045d-703b-8dd7-e92aba3ba91b", "version": 1, "description": "How the deck ends. Required in 19/20 arcs. The exception (Sparkline) ends with new bliss which is functionally a resolution." }, "alignedBlockIds": null, "matchConfidence": 90 } ], "loops": [ { "to": 31, "from": 28, "loop": { "name": "41_so_what_cascade", "slug": "41-so-what-cascade", "status": "active", "bestFor": "Data presentations, executive summaries, driving to recommendations", "canonId": "019dd956-769f-7208-afbc-f28985c6f4fb", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Data -> So What? (Insight 1) -> So What? (Insight 2) -> So What? (The Action)", "description": "Chain insights together, each answering 'so what?' until you reach the actionable conclusion", "familyLabel": null }, "matchId": "019dd95a-07fe-70ce-8d3a-4ee1c0546ad8", "evidence": "p28 vision stat, p30 mantra quote, p31 sample C-suite assessment questions", "position": 6, "objective": "Drive from finding to assessment action", "confidence": 70, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "locked": true }, { "page": 32, "type": "setup", "function": "other", "rawType": "team_bio", "block": null, "metadata": { "slideType": "team_bio", "slideTypeCanon": { "name": "Team bio", "slug": "team_bio", "status": "active", "canonId": "019de52d-0a7d-7394-ae5b-037478e71f1e", "version": 1, "description": null }, "function": "other", "functionCanon": { "name": "Other (fallback)", "slug": "other", "status": "active", "canonId": "019de52d-15e1-75bc-99dc-07732380e2ce", "version": 1, "description": null }, "density": "dense", "densityScore": 66, "componentCount": 12, "textChars": 418, "nDataPoints": 0, "notes": null, "elementsJson": [ "headline_text", "headshot", "subtitle_text" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:16.129+00", "seconds": 3.406, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/32", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-32", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": { "h": 0.139, "w": 0.078, "x": 0.368, "y": 0.343 }, "kind": "image", "text": "Ajay Vasal", "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Headshot", "slug": "headshot", "status": "active", "canonId": "019de52c-fd5f-765c-9282-38b54f61bbbf", "version": 1, "description": null }, "framework": null }, "subkind": "headshot", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.129+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "011ffb02-60ef-4390-b51a-e62cc39f1483", "parentComponentId": null }, { "bbox": { "h": 0.139, "w": 0.078, "x": 0.628, "y": 0.337 }, "kind": "image", "text": "Praveen Tanguturi", "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Headshot", "slug": "headshot", "status": "active", "canonId": "019de52c-fd5f-765c-9282-38b54f61bbbf", "version": 1, "description": null }, "framework": null }, "subkind": "headshot", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.129+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "0b3e5db0-bdd0-4035-9e90-a186b0d85c46", "parentComponentId": null }, { "bbox": { "h": 0.139, "w": 0.078, "x": 0.108, "y": 0.614 }, "kind": "image", "text": "Philippe Roussiere", "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Headshot", "slug": "headshot", "status": "active", "canonId": "019de52c-fd5f-765c-9282-38b54f61bbbf", "version": 1, "description": null }, "framework": null }, "subkind": "headshot", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.129+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "5d278f4b-2341-4423-9937-4d3c3fd40ccf", "parentComponentId": null }, { "bbox": { "h": 0.139, "w": 0.078, "x": 0.108, "y": 0.343 }, "kind": "image", "text": "Sanjeev Vohra", "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Headshot", "slug": "headshot", "status": "active", "canonId": "019de52c-fd5f-765c-9282-38b54f61bbbf", "version": 1, "description": null }, "framework": null }, "subkind": "headshot", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.129+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "8f104493-00a3-441f-adff-668de8a6166f", "parentComponentId": null }, { "bbox": { "h": 0.139, "w": 0.078, "x": 0.368, "y": 0.614 }, "kind": "image", "text": "Lan Guan", "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Headshot", "slug": "headshot", "status": "active", "canonId": "019de52c-fd5f-765c-9282-38b54f61bbbf", "version": 1, "description": null }, "framework": null }, "subkind": "headshot", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.129+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "df531037-6e20-4cc2-ae98-b33a36e2fb7d", "parentComponentId": null }, { "bbox": { "h": 0.06, "w": 0.14, "x": 0.716, "y": 0.348 }, "kind": "paragraph", "text": "Praveen Tanguturi, PhD, Thought Leadership Research Principal Director", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.129+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "5fe0fc96-e1f9-4c3e-aa95-0c750d4dbc97", "parentComponentId": null }, { "bbox": { "h": 0.06, "w": 0.14, "x": 0.196, "y": 0.348 }, "kind": "paragraph", "text": "Sanjeev Vohra, Global Lead - Applied Intelligence", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.129+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "992bef24-32a4-4811-9df1-2408b4332dfc", "parentComponentId": null }, { "bbox": { "h": 0.08, "w": 0.14, "x": 0.456, "y": 0.34 }, "kind": "paragraph", "text": "Ajay Vasal, Growth & Strategy Lead and Centre for Data & Insights Lead - Applied Intelligence", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.129+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "a2a7db69-52e5-4465-8f29-e23985dbcc2d", "parentComponentId": null }, { "bbox": { "h": 0.06, "w": 0.14, "x": 0.196, "y": 0.619 }, "kind": "paragraph", "text": "Philippe Roussiere, Accenture Research Innovation and AI Global Lead", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.129+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "b73cd818-ab0d-4c47-8d90-e03cebbf8b4f", "parentComponentId": null }, { "bbox": { "h": 0.06, "w": 0.14, "x": 0.456, "y": 0.619 }, "kind": "paragraph", "text": "Lan Guan, Lead, Cloud First - Data & AI", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.129+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "e2dc1027-755e-4bcc-a6fa-e0cdb535f55c", "parentComponentId": null }, { "bbox": { "h": 0.042, "w": 0.214, "x": 0.628, "y": 0.2 }, "kind": "title", "text": "Research director", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.129+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "4f553329-a103-41e5-9882-3fbe85bd2302", "parentComponentId": null }, { "bbox": { "h": 0.042, "w": 0.286, "x": 0.108, "y": 0.198 }, "kind": "title", "text": "Meet the authors", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.129+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "d6bedd4e-3467-4e43-b2bb-a3efba00dda9", "parentComponentId": null } ], "metrics": [], "tools": [], "frameworks": [], "arcBeats": [], "loops": [], "locked": true }, { "page": 33, "type": "appendix", "function": "other", "rawType": "appendix", "block": null, "metadata": { "slideType": "appendix", "slideTypeCanon": { "name": "Appendix", "slug": "appendix", "status": "active", "canonId": "019de52d-0762-71f8-b564-89ab863301cc", "version": 1, "description": null }, "function": "other", "functionCanon": { "name": "Other (fallback)", "slug": "other", "status": "active", "canonId": "019de52d-15e1-75bc-99dc-07732380e2ce", "version": 1, "description": null }, "density": "dense", "densityScore": 58, "componentCount": 8, "textChars": 472, "nDataPoints": 0, "notes": null, "elementsJson": [ "headline_text", "headshot", "paragraph" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:16.664+00", "seconds": 3.581, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/33", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-33", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": { "h": 0.12, "w": 0.067, "x": 0.329, "y": 0.414 }, "kind": "image", "text": null, "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Headshot", "slug": "headshot", "status": "active", "canonId": "019de52c-fd5f-765c-9282-38b54f61bbbf", "version": 1, "description": null }, "framework": null }, "subkind": "headshot", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.664+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "a594a037-b6e4-4740-a23c-7600fd7ed648", "parentComponentId": null }, { "bbox": { "h": 0.12, "w": 0.067, "x": 0.108, "y": 0.414 }, "kind": "image", "text": null, "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Headshot", "slug": "headshot", "status": "active", "canonId": "019de52c-fd5f-765c-9282-38b54f61bbbf", "version": 1, "description": null }, "framework": null }, "subkind": "headshot", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.664+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "e88e090a-5faa-43c1-ba38-540ebb4cbd0d", "parentComponentId": null }, { "bbox": { "h": 0.017, "w": 0.31, "x": 0.108, "y": 0.739 }, "kind": "list", "text": "Marketing + Communications: Kathy King, Alisyn Abney, Alexa Mouta", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.664+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "709cbe9a-d4ea-4c59-9dc5-cd3d9e7433da", "parentComponentId": null }, { "bbox": { "h": 0.103, "w": 0.42, "x": 0.108, "y": 0.609 }, "kind": "list", "text": "Contributors: Rahul Basole, Mariusz Bidelski, Marcin Bodziak, Tomas Castagnino, Joe Depa, Ray Eitel-Porter, Michelle Ganchinho, Michal Hadrys, Andy Hickl, David Kimble, Carrie Kleiner, Andra Najem, Linda Ringnalda, Paridhi Sharma, Joanna Syczewska, Ezequiel Tacsir, Jonathan Thomas, Jakub Wiatrak, and Yingchuan Zhu", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.664+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "76ce5f25-8694-4f0d-91e4-efa9ccbea0cf", "parentComponentId": null }, { "bbox": { "h": 0.051, "w": 0.115, "x": 0.41, "y": 0.419 }, "kind": "paragraph", "text": "Deeksha Khare Patnaik\nResearch Manager", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.664+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "05c61150-9281-4e08-ae2d-b2cbffa6af0f", "parentComponentId": null }, { "bbox": { "h": 0.017, "w": 0.085, "x": 0.108, "y": 0.351 }, "kind": "paragraph", "text": "Research Leads", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.664+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "122c9c05-ac3d-4f79-a10f-c80dd2cbddd2", "parentComponentId": null }, { "bbox": { "h": 0.051, "w": 0.085, "x": 0.188, "y": 0.419 }, "kind": "paragraph", "text": "Yuhui Xiong\nResearch Manager", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.664+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "4f443331-94ed-4905-b939-714d2929ae7b", "parentComponentId": null }, { "bbox": { "h": 0.036, "w": 0.155, "x": 0.108, "y": 0.203 }, "kind": "title", "text": "Contributors", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.664+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "b6dde841-169e-43ba-8336-79f2f48b45c6", "parentComponentId": null } ], "metrics": [], "tools": [], "frameworks": [], "arcBeats": [], "loops": [], "locked": true }, { "page": 34, "type": "setup", "title": "Survey of 1,615 executives from large organizations ($1B+ revenue) conducted Aug-Sept 2021.", "function": "summarize", "rawType": "appendix_methodology", "block": null, "metadata": { "slideType": "appendix_methodology", "slideTypeCanon": { "name": "Appendix methodology", "slug": "appendix_methodology", "status": "active", "canonId": "019de52d-07d9-726c-8599-167f2d2ffaa7", "version": 1, "description": null }, "function": "summarize", "functionCanon": { "name": "Summarize", "slug": "summarize", "status": "active", "canonId": "019de52d-1541-704f-b86f-bf7b75056373", "version": 1, "description": null }, "density": "dense", "densityScore": 71, "componentCount": 7, "textChars": 843, "nDataPoints": 31, "notes": "Survey of 1,615 executives from large organizations ($1B+ revenue) conducted Aug-Sept 2021.", "elementsJson": [ "headline_text", "paragraph", "bar_chart_horizontal", "donut_chart", "map", "data_table" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:16.1+00", "seconds": 2.856, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/34", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-34", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "Accenture surveyed 1,615 executives (including CEOs, C-Suite, CAIO's and Data Science Leaders) from the world's largest organizations (with revenues greater than $1 billion). The survey was carried out across 15 countries and 16 industries, and fielded between August and September, 2021.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:46:22+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b403-cec9163eaef2", "parentComponentId": null }, { "bbox": { "h": 0.46, "w": 0.58, "x": 0.358, "y": 0.11 }, "kind": "chart", "text": "Industries", "attrs": null, "canon": { "kind": { "name": "Chart", "slug": "chart", "status": "active", "canonId": "019de52c-f9a0-76ef-adc6-0b859109d1f8", "version": 1, "description": "Quantitative chart." }, "tool": null, "subkind": { "name": "Horizontal bar chart", "slug": "bar-horizontal", "status": "active", "canonId": "019de52c-fec4-7221-986b-1cb5f5e84b1f", "version": 1, "description": null }, "framework": null }, "subkind": "bar-horizontal", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.1+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "a8c9e85a-e0d5-4d27-8ccf-eb573c230cf7", "parentComponentId": null }, { "bbox": { "h": 0.26, "w": 0.25, "x": 0.058, "y": 0.615 }, "kind": "chart", "text": "Revenue (USD)", "attrs": null, "canon": { "kind": { "name": "Chart", "slug": "chart", "status": "active", "canonId": "019de52c-f9a0-76ef-adc6-0b859109d1f8", "version": 1, "description": "Quantitative chart." }, "tool": null, "subkind": { "name": "Donut chart", "slug": "donut", "status": "active", "canonId": "019de52d-0006-752c-9528-0fef476b8004", "version": 1, "description": null }, "framework": null }, "subkind": "donut", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.1+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "57225cc6-79b7-4ae9-87e0-2b910afc3fc2", "parentComponentId": null }, { "bbox": { "h": 0.26, "w": 0.32, "x": 0.62, "y": 0.615 }, "kind": "image", "text": "Countries", "attrs": null, "canon": { "kind": { "name": "Image", "slug": "image", "status": "active", "canonId": "019de52c-f978-701f-a720-51d029d769e7", "version": 1, "description": "Photo, illustration, screenshot or icon." }, "tool": null, "subkind": { "name": "Map", "slug": "map", "status": "active", "canonId": "019de52c-fe4d-77bd-b0a4-d72d58b9e5a4", "version": 1, "description": null }, "framework": null }, "subkind": "map", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.1+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "b497e4ea-eaa8-43b2-aeea-ccfc0a2e9f59", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "Survey respondents by industry: 1,615", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:46:22+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b403-d17d0f1b6de5", "parentComponentId": null }, { "bbox": { "h": 0.22, "w": 0.265, "x": 0.058, "y": 0.185 }, "kind": "paragraph", "text": "Accenture surveyed 1,615 executives (including CEOs, C-Suite, CAIO's and Data Science Leaders) from the world's largest organizations (with revenues greater than $1 billion). The survey was carried out across 15 countries and 16 industries, and fielded between August and September, 2021. Survey includes those organizations who have at least agreed upon some basic AI strategy and have begun implementing relevant tools, to those who have a core AI strategy in place.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.1+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "ed4d4cab-dc86-4fdc-b8d4-70c2ff2011ce", "parentComponentId": null }, { "bbox": { "h": 0.035, "w": 0.26, "x": 0.058, "y": 0.095 }, "kind": "title", "text": "About the research", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.1+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "8a1ac15b-c8eb-465c-b6fa-9c91e57ffe43", "parentComponentId": null } ], "metrics": [ { "metricRaw": "Survey respondents by industry", "numberRaw": "1,615", "numberKind": "plain", "actionTitle": null, "calloutText": "Accenture surveyed 1,615 executives (including CEOs, C-Suite, CAIO's and Data Science Leaders) from the world's largest organizations (with revenues greater than $1 billion). The survey was carried out across 15 countries and 16 industries, and fielded between August and September, 2021.", "numberScale": null, "numberValue": 1.615, "metricFamily": "survey_sentiment", "numberCurrency": null } ], "tools": [], "frameworks": [], "arcBeats": [], "loops": [], "locked": true }, { "page": 35, "type": "appendix", "title": "Describes the statistical approach (Lasso model, K-fold cross-validation) and data sources (survey, interviews, case studies).", "function": "appendix", "rawType": "appendix_methodology", "block": null, "metadata": { "slideType": "appendix_methodology", "slideTypeCanon": { "name": "Appendix methodology", "slug": "appendix_methodology", "status": "active", "canonId": "019de52d-07d9-726c-8599-167f2d2ffaa7", "version": 1, "description": null }, "function": "appendix", "functionCanon": { "name": "Appendix", "slug": "appendix", "status": "active", "canonId": "019de52d-138d-750b-83c3-49b4bc7a7a38", "version": 1, "description": null }, "density": "overcrowded", "densityScore": 92, "componentCount": 11, "textChars": 1767, "nDataPoints": 6, "notes": "Describes the statistical approach (Lasso model, K-fold cross-validation) and data sources (survey, interviews, case studies).", "elementsJson": [ "headline_text", "paragraph", "callout_box", "numbered_list" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:17.296+00", "seconds": 3.959, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/35", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-35", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": { "h": 0.3, "w": 0.22, "x": 0.53, "y": 0.18 }, "kind": "callout", "text": "R_i represents the level and evolution of a company's AI-influenced revenues (sustaining at >10%, reaching >30%) With i = company, t = 2021 and t-1 = 2018, X_it includes controls for industry, firm size and company location (country). The model is a linear probability Lasso model, a K-fold cross-validation with 10 folds performed.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": null, "subkind": { "name": "Primary callout", "slug": "primary", "status": "active", "canonId": "019de52c-fbd1-70eb-a9ce-8997f8674c1d", "version": 1, "description": null }, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:17.296+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "860c7460-1cf9-4051-954e-fbc3d88fdebc", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.22, "x": 0.29, "y": 0.69 }, "kind": "callout", "text": "R_i = β_0+β_1X_it + β_2 Capabilities_it-1 + β_3 ΔCapabilities_it + β_4Capabilities Interactions_it,t-1 + e_it", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": null, "subkind": { "name": "Primary callout", "slug": "primary", "status": "active", "canonId": "019de52c-fbd1-70eb-a9ce-8997f8674c1d", "version": 1, "description": null }, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:17.296+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "b8fc4c91-7eba-4f01-b344-89ea8f23e42a", "parentComponentId": null }, { "bbox": null, "kind": "callout", "text": "The median maturity index of all companies is 36/100.", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:46:16+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b403-9f37f61102a2", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "Median AI maturity index: 36/100", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:46:16+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b403-a01349603a09", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.22, "x": 0.05, "y": 0.75 }, "kind": "paragraph", "text": "Design thinking: We ran a MURAL session with more than 15 senior data scientists to validate our AI maturity model.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:17.296+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "2bddc672-5c79-48a0-86ec-d73e1599e6b9", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.22, "x": 0.05, "y": 0.18 }, "kind": "paragraph", "text": "Survey: From August to September 2021, Accenture surveyed 1,615 C-suite executives at 1,176 of the world's largest companies—present in 16 industries and headquartered in 15 countries.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:17.296+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "45f8e4fd-73e6-406d-b0c6-902c6b21e36b", "parentComponentId": null }, { "bbox": { "h": 0.25, "w": 0.22, "x": 0.05, "y": 0.36 }, "kind": "paragraph", "text": "Interviews and case studies: We interviewed 25 CEOs, Chief Data Officers and Chief Analytics Officers. We also interviewed Renée Richardson Gosline... and Christine Foster... as well as numerous AI experts at Accenture.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:17.296+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "472e467b-2425-408f-af32-0cd0d8461a3c", "parentComponentId": null }, { "bbox": { "h": 0.3, "w": 0.22, "x": 0.53, "y": 0.5 }, "kind": "paragraph", "text": "2. Defined \"foundational\" and \"differentiation\" capabilities: In our models, we classified Capabilities_it-1 and ΔCapabilities_it as AI foundational capabilities; Capabilities Interactions_it,t-1 are—as the name suggests—capabilities with interaction, with strong senior sponsorship and a well-defined AI strategy.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:17.296+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "891ecbae-e313-41b4-a80a-964cf8dc8090", "parentComponentId": null }, { "bbox": { "h": 0.4, "w": 0.22, "x": 0.29, "y": 0.18 }, "kind": "paragraph", "text": "Economic modeling and data science: To assess companies' AI maturity, as well as other measures of performance, we took the following steps: 1. Identified key capabilities of AI maturity...", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:17.296+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "b1e3a1a6-dc9d-4627-ab77-9e9a84bb4f7d", "parentComponentId": null }, { "bbox": { "h": 0.3, "w": 0.22, "x": 0.77, "y": 0.5 }, "kind": "paragraph", "text": "3. Built the AI maturity index: We built two indexes that measure companies' AI foundational capabilities and AI differentiation capabilities, respectively... The median maturity index of all companies is 36/100.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:17.296+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "eacea000-7c9a-40b0-80b0-451cccd06687", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.15, "x": 0.05, "y": 0.08 }, "kind": "title", "text": "Appendix", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:17.296+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "60976894-44d9-4dcb-9eff-962e8674331c", "parentComponentId": null } ], "metrics": [ { "metricRaw": "Median AI maturity index", "numberRaw": "36/100", "numberKind": "plain", "actionTitle": null, "calloutText": "The median maturity index of all companies is 36/100.", "numberScale": null, "numberValue": 36, "metricFamily": "index_score", "numberCurrency": null } ], "tools": [ { "tool": { "name": "Audience Definition", "slug": "audience-definition", "status": "active", "bestFor": null, "canonId": "019dd956-7f2a-775d-aeee-ff099bcb7756", "version": 1, "bodyDocId": null, "description": "Six key questions: Who are they? What do they know? What do they believe? What do they care about? What do they fear? What decisions can they make?", "familyLabel": null, "categoryName": "Block", "categorySlug": "block" }, "agent": "Storyteller", "layer": "slide", "agents": [ "Storyteller" ], "matchId": "027546b1-5f5a-44be-bc40-fe5a703b8736", "evidence": "Research Manager", "pageRefs": null, "priority": "Core", "whenToUse": null, "confidence": 0.7, "extraction": { "at": "2026-07-17 03:14:34.314336+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 35, "whyItWorks": null, "antipattern": null, "cardinality": null, "narrativePurpose": null }, { "tool": { "name": "Authority citation", "slug": "authority-citation", "status": "active", "bestFor": null, "canonId": "019dd9e1-49ae-7557-89ad-3fca70469b45", "version": 1, "bodyDocId": null, "description": "Quoting an authority figure, study, or precedent to lend weight.", "familyLabel": null, "categoryName": null, "categorySlug": null }, "agent": null, "layer": "slide", "agents": null, "matchId": "8900dd19-a183-43a4-8730-84addc56958a", "evidence": "We interviewed 25 CEOs, Chief Data Officers and Chief Analytics Officers.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.7, "extraction": { "at": "2026-07-17 03:14:34.356392+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 35, "whyItWorks": null, "antipattern": null, "cardinality": "common", "narrativePurpose": null } ], "frameworks": [], "arcBeats": [], "loops": [], "locked": true }, { "page": 36, "type": "setup", "title": "This slide outlines the research methodology for a study on AI maturity and performance.", "function": "summarize", "rawType": "appendix_methodology", "block": null, "metadata": { "slideType": "appendix_methodology", "slideTypeCanon": { "name": "Appendix methodology", "slug": "appendix_methodology", "status": "active", "canonId": "019de52d-07d9-726c-8599-167f2d2ffaa7", "version": 1, "description": null }, "function": "summarize", "functionCanon": { "name": "Summarize", "slug": "summarize", "status": "active", "canonId": "019de52d-1541-704f-b86f-bf7b75056373", "version": 1, "description": null }, "density": "sparse", "densityScore": 23, "componentCount": 3, "textChars": 429, "nDataPoints": 0, "notes": "This slide outlines the research methodology for a study on AI maturity and performance.", "elementsJson": [ "headline_text", "paragraph", "bullet_list", "numbered_list" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 21:24:34.862+00", "seconds": 2.045, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/36", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-36", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "AI Achievers—the top quartile on both foundational and differentiation median maturity index: 64/100", "attrs": null, "canon": { "kind": { "name": "Callout", "slug": "callout", "status": "active", "canonId": "019de52c-f8d3-761a-8953-7d37080de5e2", "version": 1, "description": "Highlighted callout box." }, "tool": { "name": "Visual emphasis", "slug": "visual-emphasis", "status": "active", "bestFor": null, "canonId": "019dd9e1-474b-736b-a8eb-a794dd88241c", "version": 1, "bodyDocId": null, "description": "Drawing the eye to one element above the rest of the slide.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:46:22+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b403-f14af29e8932", "parentComponentId": null }, { "bbox": { "h": 0.8, "w": 0.9, "x": 0.05, "y": 0.1 }, "kind": "list", "text": "4. Constructed AI profiles based on foundational and differentiation capabilities. 5. Measured Achievers' financial premium. 6. Measured Achievers' stakeholder performance. 7. Measured the speed of AI transformation vs. the speed of digital transformation.", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 21:24:34.862+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "d4aea5ba-7b63-4e9f-9ac9-2e1f43bb0104", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "AI Achievers foundational + differentiation median maturity index: 64/100", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:46:22+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b403-f5a48c102103", "parentComponentId": null } ], "metrics": [ { "metricRaw": "AI Achievers foundational + differentiation median maturity index", "numberRaw": "64/100", "numberKind": "plain", "actionTitle": null, "calloutText": "AI Achievers—the top quartile on both foundational and differentiation median maturity index: 64/100", "numberScale": null, "numberValue": 64, "metricFamily": "index_score", "numberCurrency": null } ], "tools": [], "frameworks": [ { "matchId": "51611ba6-98ba-46ee-81a4-268e05c41198", "evidence": "The slide details the specific analytical steps and regression models used to derive study conclusions.", "framework": null, "confidence": 0.9, "extraction": { "at": "2026-05-02 21:24:34.862+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 36, "frameworkName": "analytical-method" } ], "arcBeats": [], "loops": [], "locked": true }, { "page": 37, "type": "setup", "title": "The slide lists 10 points, though the numbering is slightly non-sequential in visual layout. It outlines strategic and operational requirements for AI.", "function": "summarize", "rawType": "initiative_list", "block": null, "metadata": { "slideType": "initiative_list", "slideTypeCanon": { "name": "Initiative list", "slug": "initiative_list", "status": "active", "canonId": "019de52d-0caf-770e-9b91-b99239121f50", "version": 1, "description": null }, "function": "summarize", "functionCanon": { "name": "Summarize", "slug": "summarize", "status": "active", "canonId": "019de52d-1541-704f-b86f-bf7b75056373", "version": 1, "description": null }, "density": "balanced", "densityScore": 40, "componentCount": 2, "textChars": 2525, "nDataPoints": 0, "notes": "The slide lists 10 points, though the numbering is slightly non-sequential in visual layout. It outlines strategic and operational requirements for AI.", "elementsJson": [ "headline_text", "numbered_list", "subtitle_text", "paragraph" ], "confidence": 0.9, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:16.908+00", "seconds": 3.387, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/37", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-37", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": { "h": 0.65, "w": 0.88, "x": 0.058, "y": 0.24 }, "kind": "list", "text": "1. Senior Sponsorship: Organizations have an AI strategy that is developed by the Chief Analytics Officer, Chief Data Officer, Chief Digital Officer or an equivalent. The CEO and the Board actively sponsor and share accountability for the strategy and associated AI initiatives.\n2. AI Strategy: Organizations not only have a core AI strategy aligned to the overall business strategy, but they also dedicate tools and tactics to execute it and continuously track their performance against that strategy.\n3. Proactive vs. Reactive: Organizations have the resources (such as technology, talent and patents) to proactively define and demonstrate how AI can create value vs. apply AI as a reaction to a need. They're first-movers instead of fast followers in terms of applying AI for business value.\n4. Readily Available AI and ML tools: Organizations work with an ecosystem of technology partners to access machine learning models and tools to help innovate new products and services.\n5. Readily Available Developer Networks: Organizations tap into an ecosystem of technology partners to access developer networks that support the development of new products and services.\n6. Build vs. Buy: Organizations develop custom-built AI applications or work with a partner who offers solutions-as-a-service, vs. purchase “off-the-shelf” AI solutions with little-to-no customization.\n7. Platform and Technology: Organizations apply the necessary cloud, data and AI infrastructure, software, self-serve capabilities and industry best practices, and they adopt the latest tools available from platform and technology partners.\n8. Experimentation Data—Change: Organizations improved their use of experimentation data between 2018 and 2021, effectively translating into a higher data and AI maturity. Experimentation data is the use of internal and external data to design new models and generate new insights. To do that, organizations use enterprise-grade cloud platforms to keep data clean and trustworthy, and to support decision making at greater speed and scale.\n9. Data Management and Governance: Organizations scale their data management and governance practices to increase data quality, trust and ethics across entities —e.g. by implementing master data management and ensuring security, compliance and interoperability.\n10. Data Management and Governance—Change: Organizations improved their data management and governance practices between 2018 and 2021, effectively translating into a higher data and AI maturity.", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Numbered list", "slug": "numbered", "status": "active", "canonId": "019de52c-fcc0-7718-a284-a4250c805df2", "version": 1, "description": null }, "framework": null }, "subkind": "numbered", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.908+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "c63ecfd9-42f3-4caf-b3ac-abd20d71b1d3", "parentComponentId": null }, { "bbox": { "h": 0.055, "w": 0.226, "x": 0.058, "y": 0.12 }, "kind": "title", "text": "Key Capabilities", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.908+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "157a140c-77bb-47ca-8334-d2c4a6cd1b06", "parentComponentId": null } ], "metrics": [], "tools": [ { "tool": { "name": "List presentation", "slug": "list-presentation", "status": "active", "bestFor": null, "canonId": "019dd9e1-4ef7-777d-ba68-9074c2b3bcf1", "version": 1, "bodyDocId": null, "description": "Bulleted, numbered, or checklist enumerations.", "familyLabel": null, "categoryName": null, "categorySlug": null }, "agent": null, "layer": "slide", "agents": null, "matchId": "1871c8cd-f1f0-424c-8dc3-a79c8b50a30b", "evidence": "1. Senior Sponsorship: Organizations have an AI strategy that is developed by the Chief Analytics Officer, Chief Data Officer, Chief Digital Officer or an equivalent.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.9, "extraction": { "at": "2026-07-17 03:14:34.436819+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 37, "whyItWorks": null, "antipattern": null, "cardinality": "common", "narrativePurpose": null } ], "frameworks": [], "arcBeats": [], "loops": [], "locked": true }, { "page": 38, "type": "setup", "title": "The slide continues a numbered list of initiatives, starting at 11 and ending at 17.", "function": "present_solution", "rawType": "initiative_list", "block": null, "metadata": { "slideType": "initiative_list", "slideTypeCanon": { "name": "Initiative list", "slug": "initiative_list", "status": "active", "canonId": "019de52d-0caf-770e-9b91-b99239121f50", "version": 1, "description": null }, "function": "present_solution", "functionCanon": { "name": "Present solution", "slug": "present_solution", "status": "active", "canonId": "019de52d-15b8-71da-bd23-2b767850cbce", "version": 1, "description": null }, "density": "dense", "densityScore": 54, "componentCount": 5, "textChars": 1919, "nDataPoints": 0, "notes": "The slide continues a numbered list of initiatives, starting at 11 and ending at 17.", "elementsJson": [ "headline_text", "numbered_list", "paragraph" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:16.88+00", "seconds": 3.287, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/38", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-38", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": { "h": 0.68, "w": 0.255, "x": 0.632, "y": 0.32 }, "kind": "list", "text": "16. Responsible AI: Organizations have an industrialized, responsible approach to data and AI across the complete lifecycle of their AI models—an approach that can meet changing regulatory requirements, mitigate risks, and support sustainable, trustworthy AI.\n\n17. Responsible AI—Change: Organizations have improved their responsible data and AI practices between 2018 and 2021, effectively translating into a higher data and AI maturity.", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Numbered list", "slug": "numbered", "status": "active", "canonId": "019de52c-fcc0-7718-a284-a4250c805df2", "version": 1, "description": null }, "framework": null }, "subkind": "numbered", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.88+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "3d7af0c3-025f-438d-949a-63e45717f557", "parentComponentId": null }, { "bbox": { "h": 0.73, "w": 0.255, "x": 0.345, "y": 0.165 }, "kind": "list", "text": "13. Innovation Culture Embedded: Organizations ensure innovation is part of the day-to-day work environment. They encourage mindsets, behaviors and routines that all serve as a vehicle for experimentation, collaboration and learning from ideation to product development to market launch.\n\n14. Innovation Culture Encouraged: Organizations promote and reward innovative mindsets and behaviors including entrepreneurship, collaboration and thoughtful risk-taking.\n\n15. AI Talent Strategy: Organizations have an AI talent strategy—hiring, acquiring, retention—that evolves to keep pace with market or business needs. They also have an AI talent roadmap for hiring diverse AI-related roles, beyond just ML engineers—such as behavioral scientists, social scientists, and ethicists.", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Numbered list", "slug": "numbered", "status": "active", "canonId": "019de52c-fcc0-7718-a284-a4250c805df2", "version": 1, "description": null }, "framework": null }, "subkind": "numbered", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.88+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "a406ff3f-f872-407b-8791-6784a977d906", "parentComponentId": null }, { "bbox": { "h": 0.73, "w": 0.255, "x": 0.058, "y": 0.218 }, "kind": "list", "text": "11. Mandatory AI Training: Organizations enforce AI-specific training programs to improve AI fluency, which are tailored for senior leadership and specific functions, e.g. salesforce, product engineers, etc. They also create deliberate opportunities for employees to learn and apply AI in their roles.\n\n12. Employee Competency in AI-Related Skills: Organizations regularly measure the competency level of employees to determine where further training is needed to improve overall acumen. They measure and build expertise in critical areas like coding, data processing and exploration, business analytics, domain and business acumen, machine learning, visualization and more.", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Numbered list", "slug": "numbered", "status": "active", "canonId": "019de52c-fcc0-7718-a284-a4250c805df2", "version": 1, "description": null }, "framework": null }, "subkind": "numbered", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.88+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "d2b6f07a-8dce-4ece-95b9-8524ce0144df", "parentComponentId": null }, { "bbox": { "h": 0.022, "w": 0.11, "x": 0.632, "y": 0.27 }, "kind": "title", "text": "Responsible AI", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.88+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "5b29fc0f-d639-436e-bf93-f350f3a8dcee", "parentComponentId": null }, { "bbox": { "h": 0.022, "w": 0.148, "x": 0.058, "y": 0.165 }, "kind": "title", "text": "Talent and culture", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.88+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "fd0e136b-40e2-437b-81b6-0af48b42ab2e", "parentComponentId": null } ], "metrics": [], "tools": [ { "tool": { "name": "List presentation", "slug": "list-presentation", "status": "active", "bestFor": null, "canonId": "019dd9e1-4ef7-777d-ba68-9074c2b3bcf1", "version": 1, "bodyDocId": null, "description": "Bulleted, numbered, or checklist enumerations.", "familyLabel": null, "categoryName": null, "categorySlug": null }, "agent": null, "layer": "slide", "agents": null, "matchId": "2bf981c4-fca3-4320-b49d-06f4bbdb0fc7", "evidence": "13. Innovation Culture Embedded: Organizations ensure innovation is part of the day-to-day work environment.", "pageRefs": null, "priority": null, "whenToUse": null, "confidence": 0.9, "extraction": { "at": "2026-07-17 03:14:34.515898+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": null }, "pageNumber": 38, "whyItWorks": null, "antipattern": null, "cardinality": "common", "narrativePurpose": null } ], "frameworks": [], "arcBeats": [], "loops": [], "locked": true }, { "page": 39, "type": "appendix", "function": "appendix", "rawType": "appendix_disclosure", "block": null, "metadata": { "slideType": "appendix_disclosure", "slideTypeCanon": { "name": "Appendix disclosure", "slug": "appendix_disclosure", "status": "active", "canonId": "019de52d-07b2-7549-8e8b-932a19f6e616", "version": 1, "description": null }, "function": "appendix", "functionCanon": { "name": "Appendix", "slug": "appendix", "status": "active", "canonId": "019de52d-138d-750b-83c3-49b4bc7a7a38", "version": 1, "description": null }, "density": "balanced", "densityScore": 38, "componentCount": 3, "textChars": 1026, "nDataPoints": 0, "notes": null, "elementsJson": [ "headline_text", "numbered_list", "footnote" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:16.99+00", "seconds": 3.323, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/39", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-39", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": { "h": 0.45, "w": 0.41, "x": 0.075, "y": 0.322 }, "kind": "list", "text": "1 https://news.stanford.edu/news/2011/october/john-mccarthy-obit-102511.html#:~:text=John%20McCarthy%20a%20professor%20emeritus,He%20was%202084\n2 Accenture Research analysis of the world's 2,000 largest companies by market capitalization mentioning AI in their earnings calls. Formula is based on CEOs of companies that had earnings call in 2020, and CEO was present at the call, and CEO mentioned AI. 46% of these CEOs mentioned AI in their earnings calls, in 2021 up from ~35% in 2017.\n3 Accenture Interview\n4 https://www.nytimes.com/2020/07/13/business/coronavirus-retraining-workers.html", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.99+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "444865ea-32e2-4d40-a154-ff7102e9d6de", "parentComponentId": null }, { "bbox": { "h": 0.45, "w": 0.41, "x": 0.481, "y": 0.322 }, "kind": "list", "text": "5 https://global.rakuten.com/corp/careers/topics/engineering3/\n6 https://economictimes.indiatimes.com/tech/software/american-express-sets-up-data-analytics-risk-technology-lab-in-iit-madras/articleshow/77925793.cms\n7 https://www.novartis.com/about/strategy/data-and-digital/artificial-intelligence/our-commitment-ethical-and-responsible-use-ai\n8 https://www.accenture.com/us-en/insights/artificial-intelligence/ai-investments", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.99+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "dd5be363-48cc-4420-9640-468b716d54d9", "parentComponentId": null }, { "bbox": { "h": 0.035, "w": 0.175, "x": 0.058, "y": 0.096 }, "kind": "title", "text": "References", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:16.99+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "2dc47177-60a5-442b-819d-7d6edfb7f53c", "parentComponentId": null } ], "metrics": [], "tools": [], "frameworks": [], "arcBeats": [], "loops": [], "locked": true }, { "page": 40, "type": "setup", "title": "This is a standard corporate boilerplate slide often found at the end of consulting decks.", "function": "front_matter", "rawType": "appendix_disclosure", "block": null, "metadata": { "slideType": "appendix_disclosure", "slideTypeCanon": { "name": "Appendix disclosure", "slug": "appendix_disclosure", "status": "active", "canonId": "019de52d-07b2-7549-8e8b-932a19f6e616", "version": 1, "description": null }, "function": "front_matter", "functionCanon": { "name": "Front matter", "slug": "front_matter", "status": "active", "canonId": "019de52d-133d-73f0-9e26-201eda96b098", "version": 1, "description": null }, "density": "overcrowded", "densityScore": 87, "componentCount": 9, "textChars": 1879, "nDataPoints": 3, "notes": "This is a standard corporate boilerplate slide often found at the end of consulting decks.", "elementsJson": [ "headline_text", "paragraph", "footnote" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:49:21.698+00", "seconds": 7.923, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/40", "deck": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd", "deckAnchor": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-40", "deckJson": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json" }, "components": [ { "bbox": { "h": 0.1, "w": 0.9, "x": 0.05, "y": 0.8 }, "kind": "disclaimer", "text": "This content is provided for general information purposes and is not intended to be used in place of consultation with our professional advisors. Copyright © 2022 Accenture. All rights reserved. Accenture and its logo are registered trademarks of Accenture.", "attrs": null, "canon": { "kind": { "name": "Disclaimer", "slug": "disclaimer", "status": "active", "canonId": "91173477-d91f-4024-bbaf-d4f93e3f82dc", "version": 1, "description": "Legal/cautionary text that occupies most of the body of its slide." }, "tool": null, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:21.698+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "01776628-2f1f-4e26-8fa8-97e9ee9d2703", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "Accenture employees: 710,000", "attrs": null, "canon": { "kind": { "name": "Metric", "slug": "metric", "status": "active", "canonId": "019de52c-f923-73bf-8081-d0b1a8c5264d", "version": 1, "description": "Big-number or KPI value." }, "tool": { "name": "Quantification", "slug": "quantification", "status": "active", "bestFor": null, "canonId": "019dd9e1-47ab-72ce-a079-aab1f5299e88", "version": 1, "bodyDocId": null, "description": "Translating a claim into numbers, charts, or tables.", "familyLabel": null }, "subkind": null, "framework": null }, "subkind": "primary", "groupRole": null, "confidence": null, "extraction": { "at": "2026-04-23 22:46:40+00", "model": "claude-code-default", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-9f64-72ba-b404-32ab1e9bcaa0", "parentComponentId": null }, { "bbox": { "h": 0.4, "w": 0.28, "x": 0.05, "y": 0.25 }, "kind": "paragraph", "text": "Accenture is a global professional services company with leading capabilities in digital, cloud and security. Combining unmatched experience and specialized skills across more than 40 industries, we offer Strategy and Consulting, Interactive, Technology and Operations services—all powered by the world's largest network of Advanced Technology and Intelligent Operations centers. Our 710,000 people deliver on the promise of technology and human ingenuity every day, serving clients in more than 120 countries. We embrace the power of change to create value and shared success for our clients, people, shareholders, partners and communities. Visit us at www.accenture.com.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:21.698+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "2d154d48-0be2-4e12-953b-f31c90d2aeb2", "parentComponentId": null }, { "bbox": { "h": 0.4, "w": 0.28, "x": 0.36, "y": 0.25 }, "kind": "paragraph", "text": "Accenture Research creates thought leadership about the most pressing business issues organizations face. Combining innovative research techniques, such as data science led analysis, with a deep understanding of industry and technology, our team of 300 researchers in 20 countries publish hundreds of reports, articles and points of view every year. Our thought-provoking research developed with world leading organizations helps our clients embrace change, create value, and deliver on the power of technology and human ingenuity.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:21.698+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "33d339a4-b7d5-461f-b565-d938de709e05", "parentComponentId": null }, { "bbox": { "h": 0.4, "w": 0.28, "x": 0.67, "y": 0.25 }, "kind": "paragraph", "text": "Applied Intelligence is Accenture's approach to scaling AI for clients by embedding AI-powered data, analytics and automation capabilities into business workflows, accelerating time to value with a powerful global alliance, innovation and delivery network that can deploy and scale AI within any market and industry.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:21.698+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "41974a82-fce3-4102-b0e7-3d01e4e84fd0", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.2, "x": 0.67, "y": 0.2 }, "kind": "title", "text": "About Applied Intelligence", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:21.698+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "27fb1ce9-16fa-48d3-b290-7ca3f601bea3", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.2, "x": 0.36, "y": 0.2 }, "kind": "title", "text": "About Accenture Research", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:21.698+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "34249636-0846-4f25-9b62-1a04f758745d", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.2, "x": 0.05, "y": 0.2 }, "kind": "title", "text": "About Accenture", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:21.698+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "486e586b-09b3-4d0c-9c79-7b052a4fb2a7", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.1, "x": 0.05, "y": 0.75 }, "kind": "title", "text": "Disclaimer", "attrs": null, "canon": { "kind": { "name": "Title", "slug": "title", "status": "active", "canonId": "019de52c-f7dd-76d9-b848-b1f12bd85cf4", "version": 1, "description": "Slide title or headline." }, "tool": null, "subkind": { "name": "Headline", "slug": "headline", "status": "active", "canonId": "019de52c-fae1-7463-9a7c-d60668eced0d", "version": 1, "description": null }, "framework": null }, "subkind": "headline", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:49:21.698+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "e792cf05-c808-4011-b624-558f2d54c1ef", "parentComponentId": null } ], "metrics": [ { "metricRaw": "Accenture employees", "numberRaw": "710,000", "numberKind": "plain", "actionTitle": null, "calloutText": null, "numberScale": null, "numberValue": 710, "metricFamily": "employment", "numberCurrency": null } ], "tools": [], "frameworks": [], "arcBeats": [], "loops": [], "locked": true } ], "arcBeats": [ { "from": 3, "to": 7, "label": "Situation & Context", "description": "AI essential for competitive advantage; transformation accelerating 16 months faster" }, { "from": 8, "to": 12, "label": "Problem & Complication", "description": "Segmentation reveals only 12% are AI Achievers; 63% Experimenters" }, { "from": 17, "to": 27, "label": "Solution & Approach", "description": "Five recommendations to master AI maturity, each with case study" }, { "from": 13, "to": 16, "label": "Evidence & Proof", "description": "Achievers outperform peers on revenue (50%), scaling (25%), CX (8%)" }, { "from": 28, "to": 31, "label": "Impact & Next Steps", "description": "Vision (12% to 27% by 2024) plus C-suite assessment questions" } ], "loops": [ { "from": 3, "to": 4, "label": "Aha Moment", "description": "Reveal the central finding and preview the five differentiators" }, { "from": 5, "to": 7, "label": "Why Now", "description": "Establish urgency for AI investment" }, { "from": 8, "to": 12, "label": "2x2 Matrix", "description": "Define AI maturity and segment firms via 2x2" }, { "from": 13, "to": 16, "label": "Benchmark Gap", "description": "Show Achievers outperform peers on key metrics" }, { "from": 17, "to": 27, "label": "Mece Breakdown", "description": "Five MECE recommendations to become an Achiever" }, { "from": 28, "to": 31, "label": "So What Cascade", "description": "Drive from finding to assessment action" } ] }