{ "schemaVersion": "deck-intelligence.v1", "id": "019dd923-5ca1-7489-b639-6b12903e0170", "slug": "32e0484e19423b1f", "title": "Emerging GenAI Use Cases Credit", "subtitle": "McKinsey", "author": "McKinsey", "pageCount": 17, "kind": "consulting-deck", "orientation": "landscape", "aspectRatio": 1.778, "document": { "id": "019dd923-5ca1-7489-b639-6b12903e0170", "slug": "32e0484e19423b1f", "title": "Emerging GenAI Use Cases Credit", "rawTitle": "Emerging GenAI Use Cases Credit", "authorId": "McKinsey", "authorName": "McKinsey", "authorDisplay": "McKinsey", "kind": { "slug": "consulting-deck", "label": "Consulting deck" }, "sourceType": { "slug": "strategy_consulting", "label": "Strategy consulting" }, "fileName": "2025-Emerging-GenAI-Use-Cases-Credit.pdf", "sourceUrl": "https://iacpm.org/wp-content/uploads/2025/03/IACPM-McKinsey-Gen-AI-Webinar-2025.pdf", "sourceDate": "2025-03-15 00:00:00", "presentationDate": null, "ingestedAt": "2026-04-23 20:51:41+00", "status": "ready", "pageCount": 17, "orientation": "landscape", "aspectRatio": 1.778, "targetCompany": null, "targetTicker": null, "metadata": { "local_root": "/tmp/legacy-images/32e0484e19423b1f", "origin_corpus": "consulting" } }, "coverage": { "pageCount": 17, "pagesIndexed": 17, "pagesWithImages": 17, "pagesWithMetadata": 17, "imageCoveragePct": 100, "metadataCoveragePct": 100, "componentCount": 118, "chartCount": 11, "metricSlides": 11, "toolMatches": 0, "documentToolMatches": 0, "frameworkMatches": 0, "arcMatches": 1, "beatMatches": 4, "loopMatches": 2, "patternMatches": 0, "storymakersMatch": false, "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": "a9bd079d-1f1a-4e8f-a06f-59ec3ae8fde0", "evidence": "The deck follows a clear structure, starting with an executive summary and context, followed by data tables and case studies, and concluding with key challenges and insights.", "isPrimary": true, "confidence": 0.8, "extraction": { "at": "2026-07-16 21:07:48.807461+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": "doc-narrative-v1" } } ], "beats": [ { "to": 3, "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": 2, "name": "Situation & Context", "beatId": "81239e13-b9a5-4aed-a774-9f06abd7da31", "matchId": "a9bd079d-1f1a-4e8f-a06f-59ec3ae8fde0", "beatType": { "name": "Situation & Context", "slug": "consultants-gambit-situation-context", "status": "active", "canonId": "019dd9b8-05ae-741b-992d-65ee83c639b3", "version": 1, "description": null }, "evidence": "The deck provides an overview of the research and survey methodology.", "position": 0, "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": 0.8 }, { "to": 6, "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": 5, "name": "Problem & Complication", "beatId": "f5d86c1c-52d0-4f9a-89ca-e99703e2bbfe", "matchId": "a9bd079d-1f1a-4e8f-a06f-59ec3ae8fde0", "beatType": { "name": "Problem & Complication", "slug": "consultants-gambit-problem-complication", "status": "active", "canonId": "019dd9b8-064a-777f-a374-e9a8c43dedd4", "version": 1, "description": null }, "evidence": "The deck presents data tables on use case prioritization and challenges.", "position": 1, "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": 0.8 }, { "to": 9, "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": "Evidence & Proof", "beatId": "b71bc877-1b5d-4e6f-8f51-921e65779214", "matchId": "a9bd079d-1f1a-4e8f-a06f-59ec3ae8fde0", "beatType": { "name": "Evidence & Proof", "slug": "consultants-gambit-evidence-proof", "status": "active", "canonId": "019dd9b8-07b1-7688-8e1e-7637fec3802a", "version": 1, "description": null }, "evidence": "The deck includes case studies and data tables to support the findings.", "position": 2, "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": 0.8 }, { "to": 17, "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": 15, "name": "Impact & Next Steps", "beatId": "82e3868d-3723-4acf-96c8-ab2c6f5e65e6", "matchId": "a9bd079d-1f1a-4e8f-a06f-59ec3ae8fde0", "beatType": { "name": "Impact & Next Steps", "slug": "consultants-gambit-impact-next-steps", "status": "active", "canonId": "019dd9b8-0849-7728-9ee9-fd1609c4c655", "version": 1, "description": null }, "evidence": "The deck concludes with insights on key challenges and next steps.", "position": 3, "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": 0.8 } ], "loops": [ { "to": 9, "from": 8, "loop": { "name": "06_zoom_in", "slug": "06-zoom-in", "status": "active", "bestFor": "Technical deep-dives, case studies, detailed analysis", "canonId": "019dd956-6846-74db-82be-dff5b058b072", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication", "description": "Start broad, then progressively focus on specific details that prove your point", "familyLabel": null, "categoryName": "Analysis", "categorySlug": "analysis" }, "matchId": "0a970929-37e3-4f81-a22a-42b870c6e0b0", "evidence": "The deck provides a case study on a bank automating credit memo drafting using GenAI.", "position": 0, "objective": "What are specific examples of GenAI use cases in credit?", "confidence": 0.7, "extraction": { "at": "2026-07-16 21:07:49.004051+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": "doc-narrative-v1" } }, { "to": 17, "from": 16, "loop": { "name": "27_cost_of_inaction", "slug": "27-cost-of-inaction", "status": "active", "bestFor": "Urgent budget requests, compliance, risk mitigation", "canonId": "019dd956-70d1-7395-a15b-857ba858b394", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Status Quo -> The Hidden Costs Accumulating -> The Future State of Inaction -> The Tipping Point", "description": "Quantify what happens if the audience does nothing", "familyLabel": null, "categoryName": "Urgency", "categorySlug": "urgency" }, "matchId": "df877742-caf0-4a08-89f6-3b5208d86821", "evidence": "The deck highlights the challenges and pain points institutions face in adopting GenAI use cases.", "position": 1, "objective": "What are the consequences of not adopting GenAI use cases in credit?", "confidence": 0.6, "extraction": { "at": "2026-07-16 21:07:49.043188+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": "doc-narrative-v1" } } ], "tools": [], "frameworks": [], "patterns": [] }, "storymakers": null, "score": { "backend": "claude", "scoredAt": "2026-05-02 07:22:20.899+00", "subScores": { "scqa_arc": 68, "action_titles": 86, "mece_structure": 70, "closing_strength": 55, "evidence_quality": 84, "clarity_of_thesis": 68, "production_quality": 74, "visual_storytelling": 70 }, "totalScore": 72, "coveragePct": 100, "explanations": { "scqa_arc": "Situation/Complication land cleanly in slides 2-3 (slower-than-expected adoption), but the Answer is delivered as an analytical data dump (slides 5-14) rather than a building narrative, and ends on diagnosis rather than resolution.", "action_titles": "Slides 4-17 carry insight-bearing action titles (e.g. slide 5: 'Most institutions are testing GenAI use cases, with super regionals leading the deployment'); only slides 1-3 use topic labels.", "mece_structure": "Sections flow methodology (1-4) → adoption landscape (5-9) → enablers (10-13) → risk appetite (14) → recommendations (15) → barriers (16-17), with mild overlap between use-case slides 6, 7, and 9.", "closing_strength": "Slide 15 offers a recommendation grid of leader behaviors, but the deck then closes on barriers and pain points (16-17) with no explicit call to action or next steps for the reader.", "evidence_quality": "Claims are consistently backed by the 33-institution survey with specific figures (47% prioritize productivity, 52% position GenAI as priority, 36% conservative, 20-60% productivity gain on slide 8).", "clarity_of_thesis": "Exec summary on slide 3 frames the gap between 2023 expectations and 2025 reality, but no single declarative thesis statement appears in the first 5 slides — the central argument is distributed.", "production_quality": "Consistent McKinsey framework with action-title discipline on 14 of 17 slides and footnotes present, but density tagged 'overcrowded' on four slides drags polish below McKinsey norm.", "visual_storytelling": "Healthy variety — bubble chart (5), icon grid (7, 15), big numbers (8), stacked bars, tables — but four slides (6, 10, 12, 14) are flagged as overcrowded, blunting message-tool fit." }, "slidesAnalyzed": 17 }, "review": { "backend": null, "verdict": "A competent McKinsey survey readout with strong action titles and a sharp opening tension, but it inverts its own ending and lacks visible MECE structure — use it as an exemplar for action-title writing, not for narrative arc.", "reviewedAt": "2026-04-25 02:56:45+00", "slidesSeen": 17, "suggestions": [ "Re-sequence the close: move pain-points (pp.16-17) before p.15 so the deck ends on 'what leaders do differently' as the resolution, then add an explicit recommendations / next-steps slide.", "Rewrite p.3 as an answer-first executive summary slide ('GenAI adoption in credit is running 50 points behind expectations — three patterns separate leaders') instead of the generic label.", "Insert 2-3 section dividers (e.g. 'State of adoption', 'Where the value is', 'What's holding it back', 'What leaders do differently') to make the MECE structure visible." ], "closingScore": 42, "openingScore": 72, "topStrengths": [ "Strong tension hook in p.2→p.3 (80% expected vs ~30% delivered) sets up a clear SCQA Complication", "Action titles consistently embed the metric or finding (pp.5, 12, 13, 14, 16) rather than naming the topic", "Good use of a concrete case study (p.8) with quantified outcomes ('20-60% CA productivity gain; ~30% faster decisioning')" ], "topWeaknesses": [ "Closes on pain-points (pp.16-17) instead of recommendation — the 'what leaders do' slide (p.15) is mis-sequenced", "No section dividers / MECE pillars — 17 slides flow as a continuous survey readout", "p.3 'Executive summary' is a topic label, not an answer-first thesis statement; the actual headline is hidden in the callout" ], "narrativeScore": 58, "pillarCritique": "No section dividers visible in the inventory; the deck reads as a topic-ordered survey readout (priorities → use cases → enablers → risk appetite → challenges) rather than MECE pillars. A divider structure like 'Where they are / What they're doing / What's blocking them / What leaders do differently' would tighten it.", "closingCritique": "The deck ends on diagnosis (pp.16-17 'top reasons slowing GenAI') rather than a call to action — p.15's 'leading institutions are taking the following actions' should have been the closer. No explicit recommendation, next-steps, or memorable rallying slide.", "openingCritique": "Strong opening: p.2 establishes stakes ('80% of CROs expected GenAI by Q4 2024') and p.3 immediately pivots to the tension ('only ~30% have actually deployed'), which is textbook SCQA setup. Slightly weakened by p.4 reverting to methodology rather than driving the thesis forward.", "extractionSeconds": 54.875313, "narrativeCritique": "The deck has a clear Situation (p.2 context) and Complication (p.3 'progress slower than expected'), but the Resolution is buried — recommendations on p.15 are followed by diagnosis slides on pp.16-17, breaking the S→C→A→R arc. The analytical middle is solid, but ending on pain-points instead of action inverts the natural close.", "titleQualityScore": 74, "titleQualityCritique": "Most titles are genuinely declarative and insight-bearing — e.g. p.5 'Most institutions are testing GenAI use cases, with super regionals leading the deployment' and p.12 'Institutions prioritize productivity improvement as the most important factor.' Weak spots are the topic-label titles on p.2 ('Context and overview'), p.3 ('Executive summary'), and p.9 ('The use cases vary based on institutional priorities' — vague)." }, "activistThesis": null, "pitchdeck": { "metadata": null, "profile": null }, "slides": [ { "page": 1, "type": "setup", "function": "front_matter", "imagePath": "https://imgproxy.kitesheet.com/7n0s05N0Fn5GSr1Bqn0FemviWol3wyVzrgZ3GYB1lt4/rs:fit:1200:1200:0/q:82/f:webp/czM6Ly9raXRlc2hlZXQvY29ycHVzL3NsaWRlcy8zMmUwNDg0ZTE5NDIzYjFmL3AwMDEuanBn", "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": 29, "componentCount": 5, "textChars": 328, "nDataPoints": 0, "notes": null, "elementsJson": [ "logo_grid" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:13:20.005+00", "seconds": 5.7, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b639-6b12903e0170/1", "deck": "/decks/019dd923-5ca1-7489-b639-6b12903e0170", "deckAnchor": "/decks/019dd923-5ca1-7489-b639-6b12903e0170#slide-1", "deckJson": "/decks/019dd923-5ca1-7489-b639-6b12903e0170.json" }, "components": [ { "bbox": { "h": 0.05, "w": 0.4, "x": 0.05, "y": 0.9 }, "kind": "disclaimer", "text": "CONFIDENTIAL AND PROPRIETARY | © 2025 McKinsey & Company. This material is intended solely for your internal use and any use of this material without specific permission of McKinsey & Company is strictly prohibited. All rights reserved.", "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 19:13:20.005+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "713a9ade-a42c-4247-8ee9-18a6e62b7f80", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.15, "x": 0.05, "y": 0.05 }, "kind": "image", "text": "McKinsey & Company", "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 19:13:20.005+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "0724fd4a-ef34-401f-8eb2-55d2f958efcd", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.1, "x": 0.25, "y": 0.05 }, "kind": "image", "text": "IACPM", "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 19:13:20.005+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "be72e1a7-098e-4e97-b60b-4eb3a947282f", "parentComponentId": null }, { "bbox": { "h": 0.2, "w": 0.45, "x": 0.05, "y": 0.3 }, "kind": "title", "text": "Emerging Generative AI Use Cases in Credit", "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 19:13:20.005+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "044baa4e-5f22-4df6-9593-df5dbf55f6fe", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.2, "x": 0.05, "y": 0.55 }, "kind": "title", "text": "Research results\nMarch 2025", "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 19:13:20.005+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "32089955-2efd-4593-9635-0c4ed14d11c2", "parentComponentId": null } ], "metrics": [], "tools": [], "frameworks": [], "arcBeats": [], "loops": [], "thumbSrc": "https://imgproxy.kitesheet.com/qtRbJS9yLfEc1opHj6cRkw54TRINGraxHEuMyMRWuT8/rs:fit:480:480:0/q:78/f:webp/czM6Ly9raXRlc2hlZXQvY29ycHVzL3NsaWRlcy8zMmUwNDg0ZTE5NDIzYjFmL3AwMDEuanBn", "imagePathAlt": "https://imgproxy.kitesheet.com/czl16nEtNZ0L4bgawrxskkDJ75jUF4OqhY2pUYavB3M/rs:fit:1200:1200:0/q:82/f:webp/czM6Ly9raXRlc2hlZXQvY29ycHVzL3NsaWRlcy8zMmUwNDg0ZTE5NDIzYjFmL3AwMDEucG5n", "thumbSrcAlt": "https://imgproxy.kitesheet.com/nXNzCEWxOL7zJUZiUJJphjmIlEoEDfmvK9eVQ-pwt3Q/rs:fit:480:480:0/q:78/f:webp/czM6Ly9raXRlc2hlZXQvY29ycHVzL3NsaWRlcy8zMmUwNDg0ZTE5NDIzYjFmL3AwMDEucG5n" }, { "page": 2, "type": "setup", "title": "Includes a stacked bar chart representing historical survey data from Oct 2023.", "function": "establish_context", "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": "establish_context", "functionCanon": { "name": "Establish context", "slug": "establish_context", "status": "active", "canonId": "019de52d-1314-74e6-9074-d213dc0bdcdf", "version": 1, "description": null }, "density": "overcrowded", "densityScore": 93, "componentCount": 12, "textChars": 1853, "nDataPoints": 5, "notes": "Includes a stacked bar chart representing historical survey data from Oct 2023.", "elementsJson": [ "headline_text", "paragraph", "bar_chart_100pct", "footnote" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:13:18.501+00", "seconds": 3.955, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b639-6b12903e0170/2", "deck": "/decks/019dd923-5ca1-7489-b639-6b12903e0170", "deckAnchor": "/decks/019dd923-5ca1-7489-b639-6b12903e0170#slide-2", "deckJson": "/decks/019dd923-5ca1-7489-b639-6b12903e0170.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "We heard from 80% of the CROs in North America that they were expecting to implement the first GenAI application by Q4 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": "2026-04-24 13:24:10+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-cdee-73ea-b05e-4e8d6caa4a48", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.92, "x": 0.04, "y": 0.68 }, "kind": "chart", "text": "Stacked bar chart showing implementation expectations of CROs in Oct 2023", "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": "Stacked bar chart (100%)", "slug": "bar-stacked-100pct", "status": "active", "canonId": "019de52c-ff14-71b8-8d88-860f8d7da84b", "version": 1, "description": null }, "framework": null }, "subkind": "bar-stacked-100pct", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:13:18.501+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "d5e7c11e-885b-4c86-967b-bc9f0c1bc329", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "CRO adoption expectations: 80%", "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": "2026-04-24 13:24:10+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-cdee-73ea-b05e-530f2bb2db38", "parentComponentId": null }, { "bbox": { "h": 0.07, "w": 0.92, "x": 0.04, "y": 0.23 }, "kind": "paragraph", "text": "This research was conducted using a combination of interviews carried out in Q3 2024 and a flash survey conducted in Dec 2024. 33 institutions participated in the interviews and 33 institutions participated in the online survey, representing a total of 44 unique institutions.", "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 19:13:18.501+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "8bb9fa80-09b0-4217-ba9c-038691aa108d", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.92, "x": 0.04, "y": 0.87 }, "kind": "paragraph", "text": "We will be sharing the results with participating members as well as conduct webinars in March 2025 which are open to all IACPM members", "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 19:13:18.501+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "8f2e509e-ed92-4322-8772-7246bfd62847", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.92, "x": 0.04, "y": 0.39 }, "kind": "paragraph", "text": "In this webinar, we will discuss the findings from our jointly conducted research, including both interview and flash survey results. We will also compare the findings with an article McKinsey published in July 2024 based on insights from roundtable conducted in Oct 2023 with CROs of 24 North American financial institutions to understand the state of adoption for GenAI use cases within a year of the launch of ChatGPT. CROs were enthusiastic about GenAI use cases: We heard from 80% of the CROs in North America that they were expecting to implement the first GenAI application by Q4 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 19:13:18.501+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "96569f0f-6b75-4bc4-ac14-b0d11c4d1efe", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.92, "x": 0.04, "y": 0.32 }, "kind": "paragraph", "text": "These financial institutions encompassed Mega Banks, Super Regionals, Core Regionals, and others across North America, Europe, Asia, and Africa", "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 19:13:18.501+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "acc20fbe-a282-4247-8d2a-2fde99f15e6a", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.92, "x": 0.04, "y": 0.57 }, "kind": "paragraph", "text": "Since then, LLM models have evolved rapidly and so have the use cases and factors defining 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 19:13:18.501+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "b8bebf82-e576-4785-8ab3-9ba3e80695e9", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.92, "x": 0.04, "y": 0.16 }, "kind": "paragraph", "text": "IACPM and McKinsey jointly conducted a research in 2024 to understand key emerging GenAI use cases in Credit and its challenges, risks, investments and returns.", "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 19:13:18.501+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "e7799909-9e7d-41b1-8c6e-e15dfeedb190", "parentComponentId": null }, { "bbox": { "h": 0.02, "w": 0.6, "x": 0.04, "y": 0.95 }, "kind": "source-note", "text": "1. Link to the roundtable publication: https://www.mckinsey.com/capabilities/risk-and-resilience/our-insights/embracing-generative-ai-in-credit-risk", "attrs": { "numbered": true }, "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 19:13:18.501+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "c68c6697-4be3-42fb-9885-bbc65d40513c", "parentComponentId": null }, { "bbox": { "h": 0.04, "w": 0.4, "x": 0.04, "y": 0.08 }, "kind": "title", "text": "Context and overview of 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 19:13:18.501+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "25a47bee-23bc-4bb9-a130-911c7af01014", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.3, "x": 0.04, "y": 0.62 }, "kind": "title", "text": "Expectation of CROs from NA in Oct 2023", "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 19:13:18.501+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "efbce736-62c8-4e8f-a51c-c67b4616d05f", "parentComponentId": null } ], "metrics": [ { "metricRaw": "CRO adoption expectations", "numberRaw": "80%", "numberKind": "percent", "actionTitle": null, "calloutText": "We heard from 80% of the CROs in North America that they were expecting to implement the first GenAI application by Q4 2024.", "numberScale": null, "numberValue": 80, "metricFamily": "share_penetration", "numberCurrency": null } ], "tools": [], "frameworks": [], "arcBeats": [ { "to": 3, "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": 2, "name": "Situation & Context", "beatId": "81239e13-b9a5-4aed-a774-9f06abd7da31", "matchId": "a9bd079d-1f1a-4e8f-a06f-59ec3ae8fde0", "beatType": { "name": "Situation & Context", "slug": "consultants-gambit-situation-context", "status": "active", "canonId": "019dd9b8-05ae-741b-992d-65ee83c639b3", "version": 1, "description": null }, "evidence": "The deck provides an overview of the research and survey methodology.", "position": 0, "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": 0.8 } ], "loops": [], "locked": true }, { "page": 3, "type": "setup", "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": 93, "componentCount": 10, "textChars": 2539, "nDataPoints": 13, "notes": null, "elementsJson": [ "headline_text", "bullet_list" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:13:24.944+00", "seconds": 9.981, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b639-6b12903e0170/3", "deck": "/decks/019dd923-5ca1-7489-b639-6b12903e0170", "deckAnchor": "/decks/019dd923-5ca1-7489-b639-6b12903e0170#slide-3", "deckJson": "/decks/019dd923-5ca1-7489-b639-6b12903e0170.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "The progress, however, is slower than what executives expected in Q4 2023 – where 80% of NA FIs expected to implement GenAI use case within a year, while only ~30% of survey respondents from NA have reached the deployment stage.", "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": "2026-04-24 13:24:10+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-cdee-73ea-b05e-1fc4db772d16", "parentComponentId": null }, { "bbox": { "h": 0.06, "w": 0.91, "x": 0.045, "y": 0.79 }, "kind": "list", "text": "Institutions are continuing to see challenges they observed in Q4 2023 related to defining uses cases and value at stake. In this survey, we heard institutions that top reasons for abandoning use cases are insufficient performance (41%) and articulation of benefit (41%)", "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 19:13:24.944+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "1d051bf3-5d42-4817-acbe-70a30ec4c6ef", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.91, "x": 0.045, "y": 0.14 }, "kind": "list", "text": "All participating institutions are testing one or more GenAI use cases in Credit, with super regionals leading the deployment. While institutions are experimenting with a wide range of use cases, most prevalent uses cases are synthesizing for credit decisioning, drafting memos, data assessment and early warning. The progress, however, is slower than what executives expected in Q4 2023 – where 80% of NA FIs expected to implement GenAI use case within a year, while only ~30% of survey respondents from NA have reached the deployment stage", "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 19:13:24.944+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "331afaea-9261-4da9-a9d7-b1687d3bc21c", "parentComponentId": null }, { "bbox": { "h": 0.08, "w": 0.91, "x": 0.045, "y": 0.61 }, "kind": "list", "text": "With large number of use cases emerging, institutions are considering several factors to determine prioritization with productivity improvement (47% ranked as 2nd top factor) and business need (44% ranked as 2nd top factor) being the top drivers while return on investment being the least important metric", "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 19:13:24.944+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "34547af7-dd37-47a2-88e8-0b4400bd03e9", "parentComponentId": null }, { "bbox": { "h": 0.08, "w": 0.91, "x": 0.045, "y": 0.41 }, "kind": "list", "text": "Institutions are taking fast actions to address the challenges they anticipated in Q4 2023: “Sixty-seven percent of the participants highlighted potential shortages of gen AI capabilities inside the organization. Further challenges, cited by around 50 percent of participants, include difficulties defining uses cases and value at stake.”", "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 19:13:24.944+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "606b90e6-c00a-4bdf-8959-897d753f2e04", "parentComponentId": null }, { "bbox": { "h": 0.08, "w": 0.91, "x": 0.045, "y": 0.31 }, "kind": "list", "text": "80% institutions have access to external GenAI tools, though some institutions take caution in making it available to all employees. To ensure safe use, more than 50% of the institutions require understanding of internal guidelines as a prerequisite to access them", "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 19:13:24.944+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "92073499-2d39-4f2e-9b32-ec7fdd669a48", "parentComponentId": null }, { "bbox": { "h": 0.08, "w": 0.91, "x": 0.045, "y": 0.51 }, "kind": "list", "text": "To address this, institutions have prioritized investment in building talent (87%), Center of Excellence (CoE) (82%), secure environment (94%) and processes (79%). Focus on building CoE is consistent with the research McKinsey conducted earlier in 2024 where participants preferred a centralized GenAI operating model", "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 19:13:24.944+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "9ac2df27-b803-46e4-8b22-98710242ca6c", "parentComponentId": null }, { "bbox": { "h": 0.06, "w": 0.91, "x": 0.045, "y": 0.71 }, "kind": "list", "text": "Other important factors that are playing a role in an institution’s adoption of GenAI are leadership commitment to prioritize GenAI (52%) and risk tolerance where 36% institutions are being conservative and considering incremental adoption", "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 19:13:24.944+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "9e78a081-e679-449c-bd8f-ccba8793dbaf", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "deployment rate: 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": "2026-04-24 13:24:10+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-cdee-73ea-b05e-224292114230", "parentComponentId": null }, { "bbox": { "h": 0.07, "w": 0.31, "x": 0.045, "y": 0.03 }, "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 19:13:24.944+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "77ab3d89-7e47-484c-816c-6ab9703cf70d", "parentComponentId": null } ], "metrics": [ { "metricRaw": "deployment rate", "numberRaw": "30%", "numberKind": "percent", "actionTitle": null, "calloutText": "The progress, however, is slower than what executives expected in Q4 2023 – where 80% of NA FIs expected to implement GenAI use case within a year, while only ~30% of survey respondents from NA have reached the deployment stage.", "numberScale": null, "numberValue": 30, "metricFamily": "rate_catchall", "numberCurrency": null } ], "tools": [], "frameworks": [], "arcBeats": [ { "to": 3, "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": 2, "name": "Situation & Context", "beatId": "81239e13-b9a5-4aed-a774-9f06abd7da31", "matchId": "a9bd079d-1f1a-4e8f-a06f-59ec3ae8fde0", "beatType": { "name": "Situation & Context", "slug": "consultants-gambit-situation-context", "status": "active", "canonId": "019dd9b8-05ae-741b-992d-65ee83c639b3", "version": 1, "description": null }, "evidence": "The deck provides an overview of the research and survey methodology.", "position": 0, "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": 0.8 } ], "loops": [], "locked": true }, { "page": 4, "type": "setup", "title": "The slide provides demographic context for the survey data.", "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": "balanced", "densityScore": 49, "componentCount": 6, "textChars": 450, "nDataPoints": 10, "notes": "The slide provides demographic context for the survey data.", "elementsJson": [ "headline_text", "bar_chart_horizontal", "pie_chart", "footnote" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:13:20.257+00", "seconds": 5.282, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b639-6b12903e0170/4", "deck": "/decks/019dd923-5ca1-7489-b639-6b12903e0170", "deckAnchor": "/decks/019dd923-5ca1-7489-b639-6b12903e0170#slide-4", "deckJson": "/decks/019dd923-5ca1-7489-b639-6b12903e0170.json" }, "components": [ { "bbox": { "h": 0.6, "w": 0.4, "x": 0.05, "y": 0.2 }, "kind": "chart", "text": "Size of institution", "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 19:13:20.257+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "5cc78b6e-6b2e-429d-a811-ba69f4072d7e", "parentComponentId": null }, { "bbox": { "h": 0.6, "w": 0.4, "x": 0.5, "y": 0.2 }, "kind": "chart", "text": "Geography of institution", "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": "Pie chart", "slug": "pie", "status": "active", "canonId": "019de52c-ffdb-71bb-a800-5c5ab9f1c373", "version": 1, "description": null }, "framework": null }, "subkind": "pie", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:13:20.257+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "ba31da65-04f3-46bf-bb77-581072f64339", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "Number of institutions: 33", "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": "2026-04-24 13:24:10+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-cdee-73ea-b05e-357537b0f44a", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.6, "x": 0.05, "y": 0.88 }, "kind": "source-note", "text": "1. Mega Banks include institutions with >$1,000Bn in assets; Super Regionals include institutions with $500-$1,000Bn in assets; Core Regionals include institutions with $100-500Bn in assets; Others include insurance companies/brokers and development banks", "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 19:13:20.257+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "c43ed71b-22a4-4069-a51c-225b2c98b8cc", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.4, "x": 0.05, "y": 0.13 }, "kind": "subtitle", "text": "Number of institutions who responded to the online survey", "attrs": null, "canon": { "kind": { "name": "Subtitle", "slug": "subtitle", "status": "active", "canonId": "82d821ee-2793-42e7-8f16-c6131385a3ce", "version": 1, "description": "Header-zone supporting text near the title (= ex body.description with bbox)." }, "tool": null, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:13:20.257+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "452f452f-2d5d-4553-9c5a-9ef8af9ac08a", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.9, "x": 0.05, "y": 0.08 }, "kind": "title", "text": "33 institutions participated in the survey with global representation", "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 19:13:20.257+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "39b874d6-ae28-4a77-b946-936ebeca8997", "parentComponentId": null } ], "metrics": [ { "metricRaw": "Number of institutions", "numberRaw": "33", "numberKind": "plain", "actionTitle": "33 institutions participated in the survey with global representation", "calloutText": null, "numberScale": null, "numberValue": 33, "metricFamily": "volume_count", "numberCurrency": null } ], "tools": [], "frameworks": [], "arcBeats": [], "loops": [], "locked": true }, { "page": 5, "type": "analysis", "title": "The chart uses bubble size to represent the number of use cases, with specific counts labeled inside.", "function": "analyze_data", "rawType": "data_table", "block": null, "metadata": { "slideType": "data_table", "slideTypeCanon": { "name": "Data table", "slug": "data_table", "status": "active", "canonId": "019de52d-0bbd-71b9-9918-417f904abbf8", "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": 61, "componentCount": 6, "textChars": 817, "nDataPoints": 20, "notes": "The chart uses bubble size to represent the number of use cases, with specific counts labeled inside.", "elementsJson": [ "headline_text", "paragraph", "bubble_chart", "footnote" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:13:18.643+00", "seconds": 3.493, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b639-6b12903e0170/5", "deck": "/decks/019dd923-5ca1-7489-b639-6b12903e0170", "deckAnchor": "/decks/019dd923-5ca1-7489-b639-6b12903e0170#slide-5", "deckJson": "/decks/019dd923-5ca1-7489-b639-6b12903e0170.json" }, "components": [ { "bbox": { "h": 0.65, "w": 0.9, "x": 0.05, "y": 0.2 }, "kind": "chart", "text": "Bubble chart showing counts of use cases by institution type and development stage", "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": "Bubble chart", "slug": "bubble", "status": "active", "canonId": "019de52d-00a4-756f-8978-8d5e48905dbc", "version": 1, "description": null }, "framework": null }, "subkind": "bubble", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:13:18.643+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "1a3909e5-2f09-41ca-90c8-8ec5f6254601", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.15, "x": 0.77, "y": 0.19 }, "kind": "legend", "text": "Number of use cases as indicated by survey participants", "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 19:13:18.643+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "2bfb529a-841b-445b-8737-e900b7306883", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "Number of use cases: 54", "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": "2026-04-24 13:24:10+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-cdee-73ea-b05e-6de48c996e22", "parentComponentId": null }, { "bbox": { "h": 0.08, "w": 0.9, "x": 0.04, "y": 0.9 }, "kind": "source-note", "text": "1. Includes Optimization & Maintenance and Expansion & Scaling\n2. Mega Banks include institutions with >$1,000Bn in assets; Super Regionals include institutions with $500-$1,000Bn in assets; Core Regionals include institutions with $100-500Bn in assets; Others include insurance companies/brokers and development banks\nSource: IACPM/ McKinsey Study on the Use of Generative AI in Credit Portfolio Management (Survey)", "attrs": { "numbered": true }, "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 19:13:18.643+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "0ae6d76a-3c61-411e-b729-3e74de5dc94c", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.8, "x": 0.04, "y": 0.14 }, "kind": "subtitle", "text": "Question: Which GenAI use cases is your institution currently implementing in commercial credit and what are their development stage? [multiple choice]", "attrs": null, "canon": { "kind": { "name": "Subtitle", "slug": "subtitle", "status": "active", "canonId": "82d821ee-2793-42e7-8f16-c6131385a3ce", "version": 1, "description": "Header-zone supporting text near the title (= ex body.description with bbox)." }, "tool": null, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:13:18.643+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "d5dd56ea-cb84-4fc0-93bb-e32bdffd8634", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.85, "x": 0.04, "y": 0.03 }, "kind": "title", "text": "Most institutions are testing GenAI use cases, with super regionals leading the deployment", "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 19:13:18.643+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "050f69bb-6b36-427c-a68a-7a3021e97646", "parentComponentId": null } ], "metrics": [ { "metricRaw": "Number of use cases", "numberRaw": "54", "numberKind": "plain", "actionTitle": "Most institutions are testing GenAI use cases, with super regionals leading the deployment", "calloutText": null, "numberScale": null, "numberValue": 54, "metricFamily": "volume_count", "numberCurrency": null } ], "tools": [], "frameworks": [], "arcBeats": [ { "to": 6, "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": 5, "name": "Problem & Complication", "beatId": "f5d86c1c-52d0-4f9a-89ca-e99703e2bbfe", "matchId": "a9bd079d-1f1a-4e8f-a06f-59ec3ae8fde0", "beatType": { "name": "Problem & Complication", "slug": "consultants-gambit-problem-complication", "status": "active", "canonId": "019dd9b8-064a-777f-a374-e9a8c43dedd4", "version": 1, "description": null }, "evidence": "The deck presents data tables on use case prioritization and challenges.", "position": 1, "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": 0.8 } ], "loops": [], "locked": true }, { "page": 6, "type": "analysis", "title": "The chart displays survey responses across 8 use cases, categorized into Concision, Content generation, and Customer engagement. It shows both the count of resp", "function": "analyze_data", "rawType": "data_table", "block": null, "metadata": { "slideType": "data_table", "slideTypeCanon": { "name": "Data table", "slug": "data_table", "status": "active", "canonId": "019de52d-0bbd-71b9-9918-417f904abbf8", "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": 74, "componentCount": 8, "textChars": 624, "nDataPoints": 128, "notes": "The chart displays survey responses across 8 use cases, categorized into Concision, Content generation, and Customer engagement. It shows both the count of respondents by bank type and the percentage distribution of development stages.", "elementsJson": [ "headline_text", "data_table", "bar_chart_stacked", "bar_chart_horizontal" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:13:18.163+00", "seconds": 2.735, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b639-6b12903e0170/6", "deck": "/decks/019dd923-5ca1-7489-b639-6b12903e0170", "deckAnchor": "/decks/019dd923-5ca1-7489-b639-6b12903e0170#slide-6", "deckJson": "/decks/019dd923-5ca1-7489-b639-6b12903e0170.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "Institutions are prioritizing use cases like supporting underwriting (synthesizing, drafting memo) and portfolio monitoring (early warning).", "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": "2026-04-24 13:24:10+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-cdee-73ea-b05e-8a7422ce177c", "parentComponentId": null }, { "bbox": { "h": 0.7, "w": 0.9, "x": 0.05, "y": 0.2 }, "kind": "chart", "text": "Survey responses by bank type and development stage", "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": "Stacked bar chart", "slug": "bar-stacked", "status": "active", "canonId": "019de52c-feec-74bf-9028-15b74f84f0a7", "version": 1, "description": null }, "framework": null }, "subkind": "bar-stacked", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:13:18.163+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "8f9f0941-18fd-4af9-a8da-e1031197ad0b", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.2, "x": 0.75, "y": 0.1 }, "kind": "legend", "text": "Mega Banks, Core regionals, Super Regionals, Other", "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 19:13:18.163+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "629486a3-3102-47ab-bdd4-55bdb9112095", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "Development stage", "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": "2026-04-24 13:24:10+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-cdee-73ea-b05e-8f7f7368779d", "parentComponentId": null }, { "bbox": { "h": 0.02, "w": 0.1, "x": 0.05, "y": 0.98 }, "kind": "source-note", "text": "Source: Survey", "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 19:13:18.163+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "34b69616-96a2-41f5-b300-90989f6cbb0b", "parentComponentId": null }, { "bbox": { "h": 0.02, "w": 0.3, "x": 0.05, "y": 0.95 }, "kind": "source-note", "text": "1. Includes Optimization & Maintenance and Expansion & Scaling", "attrs": { "numbered": true }, "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 19:13:18.163+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "35952711-b13b-4059-a789-203a4c63fa6c", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.5, "x": 0.05, "y": 0.13 }, "kind": "subtitle", "text": "Question: Which GenAI use cases is your institution currently implementing in commercial credit and what are their development stage? [multiple choice]", "attrs": null, "canon": { "kind": { "name": "Subtitle", "slug": "subtitle", "status": "active", "canonId": "82d821ee-2793-42e7-8f16-c6131385a3ce", "version": 1, "description": "Header-zone supporting text near the title (= ex body.description with bbox)." }, "tool": null, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:13:18.163+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "a4b81ef0-94cb-4dd9-ba8f-5f2a7f8555af", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.9, "x": 0.05, "y": 0.05 }, "kind": "title", "text": "Institutions are prioritizing use cases like supporting underwriting (synthesizing, drafting memo) and portfolio monitoring (early warning)", "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 19:13:18.163+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "f1f7b04e-d294-4bbf-9739-02edf0ebf7ce", "parentComponentId": null } ], "metrics": [], "tools": [], "frameworks": [], "arcBeats": [ { "to": 6, "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": 5, "name": "Problem & Complication", "beatId": "f5d86c1c-52d0-4f9a-89ca-e99703e2bbfe", "matchId": "a9bd079d-1f1a-4e8f-a06f-59ec3ae8fde0", "beatType": { "name": "Problem & Complication", "slug": "consultants-gambit-problem-complication", "status": "active", "canonId": "019dd9b8-064a-777f-a374-e9a8c43dedd4", "version": 1, "description": null }, "evidence": "The deck presents data tables on use case prioritization and challenges.", "position": 1, "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": 0.8 } ], "loops": [], "locked": true }, { "page": 7, "type": "analysis", "title": "The chart uses a dot-matrix representation to visualize percentages.", "function": "analyze_data", "rawType": "data_table", "block": null, "metadata": { "slideType": "data_table", "slideTypeCanon": { "name": "Data table", "slug": "data_table", "status": "active", "canonId": "019de52d-0bbd-71b9-9918-417f904abbf8", "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": "balanced", "densityScore": 41, "componentCount": 5, "textChars": 451, "nDataPoints": 7, "notes": "The chart uses a dot-matrix representation to visualize percentages.", "elementsJson": [ "headline_text", "paragraph", "icon_grid", "footnote" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:13:18.367+00", "seconds": 2.935, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b639-6b12903e0170/7", "deck": "/decks/019dd923-5ca1-7489-b639-6b12903e0170", "deckAnchor": "/decks/019dd923-5ca1-7489-b639-6b12903e0170#slide-7", "deckJson": "/decks/019dd923-5ca1-7489-b639-6b12903e0170.json" }, "components": [ { "bbox": { "h": 0.4, "w": 0.9, "x": 0.04, "y": 0.27 }, "kind": "chart", "text": "Areas of GenAI application in the credit risk life cycle, % of respondents", "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": "Dot plot", "slug": "dot", "status": "active", "canonId": "019de52d-0235-738b-bd2b-78f8e24d7cb0", "version": 1, "description": null }, "framework": null }, "subkind": "dot", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:13:18.367+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "465b827b-dacb-4f9f-8174-3be732c2c515", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "Percentage of respondents: 58%", "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": "2026-04-24 13:24:10+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-cdee-73ea-b05e-ab0b1856b87e", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.6, "x": 0.04, "y": 0.16 }, "kind": "paragraph", "text": "Question (October 2023 CRO roundtable): Which areas are you applying (or planning to apply) GenAI? [multiple choice]", "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 19:13:18.367+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "9d57b128-20a9-4671-88b7-d8d0fede4601", "parentComponentId": null }, { "bbox": { "h": 0.02, "w": 0.6, "x": 0.04, "y": 0.97 }, "kind": "source-note", "text": "Source: Roundtable of CROs from NA (https://www.mckinsey.com/capabilities/risk-and-resilience/our-insights/embracing-generative-ai-in-credit-risk)", "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 19:13:18.367+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "51b64337-359d-484c-8937-3c7f18d918b0", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.9, "x": 0.04, "y": 0.05 }, "kind": "title", "text": "Survey results on use case prioritization aligns with CRO expectation in October 2023", "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 19:13:18.367+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "6ab98b9b-20b8-4687-88d3-1db934f9d9c2", "parentComponentId": null } ], "metrics": [ { "metricRaw": "Percentage of respondents", "numberRaw": "58%", "numberKind": "percent", "actionTitle": "Survey results on use case prioritization aligns with CRO expectation in October 2023", "calloutText": null, "numberScale": null, "numberValue": 58, "metricFamily": "survey_sentiment", "numberCurrency": null } ], "tools": [], "frameworks": [], "arcBeats": [], "loops": [], "locked": true }, { "page": 8, "type": "evidence", "title": "The slide serves as a placeholder for a video presentation.", "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": "dense", "densityScore": 52, "componentCount": 8, "textChars": 245, "nDataPoints": 2, "notes": "The slide serves as a placeholder for a video presentation.", "elementsJson": [ "headline_text", "big_number", "other" ], "confidence": 0.95, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:13:18.366+00", "seconds": 2.542, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b639-6b12903e0170/8", "deck": "/decks/019dd923-5ca1-7489-b639-6b12903e0170", "deckAnchor": "/decks/019dd923-5ca1-7489-b639-6b12903e0170#slide-8", "deckJson": "/decks/019dd923-5ca1-7489-b639-6b12903e0170.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "20-60% CA productivity gain; ~30% Faster decisioning speed", "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": "2026-04-24 13:24:11+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-cdee-73ea-b05e-d902f26427bf", "parentComponentId": null }, { "bbox": { "h": 0.6, "w": 0.6, "x": 0.06, "y": 0.25 }, "kind": "image", "text": "A video will play during the Webinar", "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 19:13:18.366+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "5eae4778-83ab-4fd1-b2e6-545572f88604", "parentComponentId": null }, { "bbox": { "h": 0.08, "w": 0.2, "x": 0.74, "y": 0.38 }, "kind": "metric", "text": "~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": 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 19:13:18.366+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "71b6e5ff-ef26-46d9-9034-40483cb24fee", "parentComponentId": null }, { "bbox": { "h": 0.08, "w": 0.2, "x": 0.74, "y": 0.22 }, "kind": "metric", "text": "20-60%", "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": 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 19:13:18.366+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "83b00944-facc-4bdb-b263-21c16871e21a", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "Productivity gain: 20-60%", "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": "2026-04-24 13:24:11+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-cdee-73ea-b05e-de0c4b0b0d57", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.2, "x": 0.74, "y": 0.47 }, "kind": "paragraph", "text": "Faster decisioning speed", "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 19:13:18.366+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "011b533c-1122-4e5e-95af-02b3f959f9d0", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.2, "x": 0.74, "y": 0.31 }, "kind": "paragraph", "text": "CA productivity gain", "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 19:13:18.366+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "f388902a-563c-42a7-a97a-da4eff693a1d", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.6, "x": 0.04, "y": 0.03 }, "kind": "title", "text": "A: Case example of how a bank automated credit memo drafting using GenAI", "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 19:13:18.366+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "b3d68485-85e7-43a9-b3d5-daf712bfc020", "parentComponentId": null } ], "metrics": [ { "metricRaw": "Productivity gain", "numberRaw": "20-60%", "numberKind": "percent", "actionTitle": "A: Case example of how a bank automated credit memo drafting using GenAI", "calloutText": "20-60% CA productivity gain; ~30% Faster decisioning speed", "numberScale": null, "numberValue": -60, "metricFamily": "employment", "numberCurrency": null } ], "tools": [], "frameworks": [], "arcBeats": [ { "to": 9, "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": "Evidence & Proof", "beatId": "b71bc877-1b5d-4e6f-8f51-921e65779214", "matchId": "a9bd079d-1f1a-4e8f-a06f-59ec3ae8fde0", "beatType": { "name": "Evidence & Proof", "slug": "consultants-gambit-evidence-proof", "status": "active", "canonId": "019dd9b8-07b1-7688-8e1e-7637fec3802a", "version": 1, "description": null }, "evidence": "The deck includes case studies and data tables to support the findings.", "position": 2, "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": 0.8 } ], "loops": [ { "to": 9, "from": 8, "loop": { "name": "06_zoom_in", "slug": "06-zoom-in", "status": "active", "bestFor": "Technical deep-dives, case studies, detailed analysis", "canonId": "019dd956-6846-74db-82be-dff5b058b072", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication", "description": "Start broad, then progressively focus on specific details that prove your point", "familyLabel": null }, "matchId": "0a970929-37e3-4f81-a22a-42b870c6e0b0", "evidence": "The deck provides a case study on a bank automating credit memo drafting using GenAI.", "position": 0, "objective": "What are specific examples of GenAI use cases in credit?", "confidence": 0.7, "extraction": { "at": "2026-07-16 21:07:49.004051+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": "doc-narrative-v1" } } ], "locked": true }, { "page": 9, "type": "evidence", "title": "The slide uses a pie-chart-like icon to represent the maturity stage of each use case (Ideation, PoC, Pilot, Deployment).", "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": "balanced", "densityScore": 30, "componentCount": 5, "textChars": 360, "nDataPoints": 0, "notes": "The slide uses a pie-chart-like icon to represent the maturity stage of each use case (Ideation, PoC, Pilot, Deployment).", "elementsJson": [ "headline_text", "comparison_table", "icon_grid" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:13:22.019+00", "seconds": 5.618, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b639-6b12903e0170/9", "deck": "/decks/019dd923-5ca1-7489-b639-6b12903e0170", "deckAnchor": "/decks/019dd923-5ca1-7489-b639-6b12903e0170#slide-9", "deckJson": "/decks/019dd923-5ca1-7489-b639-6b12903e0170.json" }, "components": [ { "bbox": { "h": 0.02, "w": 0.4, "x": 0.04, "y": 0.97 }, "kind": "disclaimer", "text": "Source: IACPM/ McKinsey Study on the Use of Generative AI in Credit Portfolio Management (Interviews)", "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 19:13:22.019+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "a9c55848-8a2b-4f6b-9b45-99e819cd76ab", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.35, "x": 0.55, "y": 0.18 }, "kind": "legend", "text": "Ideation & Planning, PoC, Pilot, Deployment", "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 19:13:22.019+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "99037f99-517a-4d51-9ab0-38097b0102bd", "parentComponentId": null }, { "bbox": { "h": 0.02, "w": 0.25, "x": 0.04, "y": 0.13 }, "kind": "subtitle", "text": "Examples sourced from the 2024 interviews", "attrs": null, "canon": { "kind": { "name": "Subtitle", "slug": "subtitle", "status": "active", "canonId": "82d821ee-2793-42e7-8f16-c6131385a3ce", "version": 1, "description": "Header-zone supporting text near the title (= ex body.description with bbox)." }, "tool": null, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:13:22.019+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "6cec11e7-4c9a-4d6a-ae3e-72b76faf26f8", "parentComponentId": null }, { "bbox": { "h": 0.65, "w": 0.92, "x": 0.04, "y": 0.25 }, "kind": "table", "text": "Table showing use cases (Concision, Content generation, Customer engagement) and their examples with maturity status icons.", "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 19:13:22.019+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "cb9fbfb3-0b8a-4a15-bfaf-72cbe6dcd4ad", "parentComponentId": null }, { "bbox": { "h": 0.04, "w": 0.5, "x": 0.04, "y": 0.08 }, "kind": "title", "text": "The use cases vary based on institutional priorities", "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 19:13:22.019+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "3f9677d0-2e70-45dd-b5ea-9637e5dd3e7f", "parentComponentId": null } ], "metrics": [], "tools": [], "frameworks": [], "arcBeats": [ { "to": 9, "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": "Evidence & Proof", "beatId": "b71bc877-1b5d-4e6f-8f51-921e65779214", "matchId": "a9bd079d-1f1a-4e8f-a06f-59ec3ae8fde0", "beatType": { "name": "Evidence & Proof", "slug": "consultants-gambit-evidence-proof", "status": "active", "canonId": "019dd9b8-07b1-7688-8e1e-7637fec3802a", "version": 1, "description": null }, "evidence": "The deck includes case studies and data tables to support the findings.", "position": 2, "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": 0.8 } ], "loops": [ { "to": 9, "from": 8, "loop": { "name": "06_zoom_in", "slug": "06-zoom-in", "status": "active", "bestFor": "Technical deep-dives, case studies, detailed analysis", "canonId": "019dd956-6846-74db-82be-dff5b058b072", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication", "description": "Start broad, then progressively focus on specific details that prove your point", "familyLabel": null }, "matchId": "0a970929-37e3-4f81-a22a-42b870c6e0b0", "evidence": "The deck provides a case study on a bank automating credit memo drafting using GenAI.", "position": 0, "objective": "What are specific examples of GenAI use cases in credit?", "confidence": 0.7, "extraction": { "at": "2026-07-16 21:07:49.004051+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": "doc-narrative-v1" } } ], "locked": true }, { "page": 10, "type": "analysis", "title": "The slide uses stacked bar charts to segment responses by institution type (Mega Banks, Super Regionals, Core Regionals, Other).", "function": "analyze_data", "rawType": "data_table", "block": null, "metadata": { "slideType": "data_table", "slideTypeCanon": { "name": "Data table", "slug": "data_table", "status": "active", "canonId": "019de52d-0bbd-71b9-9918-417f904abbf8", "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": 78, "componentCount": 8, "textChars": 858, "nDataPoints": 28, "notes": "The slide uses stacked bar charts to segment responses by institution type (Mega Banks, Super Regionals, Core Regionals, Other).", "elementsJson": [ "headline_text", "bar_chart_stacked", "bar_chart_stacked" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:13:19.431+00", "seconds": 2.939, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b639-6b12903e0170/10", "deck": "/decks/019dd923-5ca1-7489-b639-6b12903e0170", "deckAnchor": "/decks/019dd923-5ca1-7489-b639-6b12903e0170#slide-10", "deckJson": "/decks/019dd923-5ca1-7489-b639-6b12903e0170.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "More than 50% of institutions require understanding of internal guidelines on when and how to use the tool as a prerequisite to gain access to the tool.", "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": "2026-04-24 13:24:11+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-cdee-73ea-b05f-2b96727d6c30", "parentComponentId": null }, { "bbox": { "h": 0.55, "w": 0.45, "x": 0.04, "y": 0.25 }, "kind": "chart", "text": "~80% of the institutions have access to external GenAI solutions (number of institutions)", "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": "Stacked bar chart", "slug": "bar-stacked", "status": "active", "canonId": "019de52c-feec-74bf-9028-15b74f84f0a7", "version": 1, "description": null }, "framework": null }, "subkind": "bar-stacked", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:13:19.431+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "3c3cf910-d4b5-43db-8ee2-4214a773264b", "parentComponentId": null }, { "bbox": { "h": 0.55, "w": 0.45, "x": 0.52, "y": 0.25 }, "kind": "chart", "text": "More than 50% of institutions require understanding of internal guidelines on when and how to use the tool as a prerequisite to gain access to the tool (number of institutions)", "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": "Stacked bar chart", "slug": "bar-stacked", "status": "active", "canonId": "019de52c-feec-74bf-9028-15b74f84f0a7", "version": 1, "description": null }, "framework": null }, "subkind": "bar-stacked", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:13:19.431+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "e9d6d583-6d82-415e-aaf2-b3a4be88fa5c", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.45, "x": 0.04, "y": 0.96 }, "kind": "disclaimer", "text": "Source: IACPM/ McKinsey Study on the Use of Generative AI in Credit Portfolio Management (Survey)", "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 19:13:19.431+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "12be8d23-2ee0-403d-b590-8a00a6a987c5", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.2, "x": 0.75, "y": 0.12 }, "kind": "legend", "text": "Mega Banks, Core Regionals, Super Regionals, Other", "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 19:13:19.431+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "a16c3787-0fe6-4ad8-b7d4-18ca933143f9", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "number of institutions: 80%", "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": "2026-04-24 13:24:11+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-cdee-73ea-b05f-2f9e46d60536", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.45, "x": 0.04, "y": 0.12 }, "kind": "subtitle", "text": "Question: How open is the access to external GenAI solutions [single choice] and what pre-requisites are required? [multiple choice]", "attrs": null, "canon": { "kind": { "name": "Subtitle", "slug": "subtitle", "status": "active", "canonId": "82d821ee-2793-42e7-8f16-c6131385a3ce", "version": 1, "description": "Header-zone supporting text near the title (= ex body.description with bbox)." }, "tool": null, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:13:19.431+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "f74ada97-e7cc-47f9-8dba-27fad945f09e", "parentComponentId": null }, { "bbox": { "h": 0.08, "w": 0.92, "x": 0.04, "y": 0.03 }, "kind": "title", "text": "Most institutions have access to external GenAI tools and require understanding of internal guidelines as a prerequisite to access them", "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 19:13:19.431+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "a83725fd-8964-4c1b-973d-d880da9190af", "parentComponentId": null } ], "metrics": [ { "metricRaw": "number of institutions", "numberRaw": "80%", "numberKind": "percent", "actionTitle": "Most institutions have access to external GenAI tools and require understanding of internal guidelines as a prerequisite to access them", "calloutText": "More than 50% of institutions require understanding of internal guidelines on when and how to use the tool as a prerequisite to gain access to the tool.", "numberScale": null, "numberValue": 80, "metricFamily": "volume_count", "numberCurrency": null } ], "tools": [], "frameworks": [], "arcBeats": [], "loops": [], "locked": true }, { "page": 11, "type": "analysis", "title": "The data is segmented by bank type: Mega Banks, Super Regionals, Core Regionals, and Other.", "function": "analyze_data", "rawType": "data_table", "block": null, "metadata": { "slideType": "data_table", "slideTypeCanon": { "name": "Data table", "slug": "data_table", "status": "active", "canonId": "019de52d-0bbd-71b9-9918-417f904abbf8", "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": 51, "componentCount": 6, "textChars": 435, "nDataPoints": 56, "notes": "The data is segmented by bank type: Mega Banks, Super Regionals, Core Regionals, and Other.", "elementsJson": [ "headline_text", "data_table", "paragraph" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:13:19.135+00", "seconds": 2.64, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b639-6b12903e0170/11", "deck": "/decks/019dd923-5ca1-7489-b639-6b12903e0170", "deckAnchor": "/decks/019dd923-5ca1-7489-b639-6b12903e0170#slide-11", "deckJson": "/decks/019dd923-5ca1-7489-b639-6b12903e0170.json" }, "components": [ { "bbox": { "h": 0.03, "w": 0.35, "x": 0.59, "y": 0.18 }, "kind": "legend", "text": "Mega Banks, Super Regionals, Core Regionals, Other", "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 19:13:19.135+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "ceb79daa-246c-445d-a153-5aab8bc1adda", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "Number of institutions: 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": "2026-04-24 16:08:27+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd952-2e34-7719-b4bb-9a6f0d29749f", "parentComponentId": null }, { "bbox": { "h": 0.02, "w": 0.45, "x": 0.04, "y": 0.96 }, "kind": "source-note", "text": "Source: IACPM/ McKinsey Study on the Use of Generative AI in Credit Portfolio Management (Survey)", "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 19:13:19.135+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "a3c44702-2981-4b6e-b2c6-1f8e85d489f6", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.6, "x": 0.04, "y": 0.12 }, "kind": "subtitle", "text": "Question: Has your institution invested in the following categories to drive adoption of GenAI? [multiple choice]", "attrs": null, "canon": { "kind": { "name": "Subtitle", "slug": "subtitle", "status": "active", "canonId": "82d821ee-2793-42e7-8f16-c6131385a3ce", "version": 1, "description": "Header-zone supporting text near the title (= ex body.description with bbox)." }, "tool": null, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:13:19.135+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "de45f629-8f5c-44a7-82ec-eb46d8b6b68a", "parentComponentId": null }, { "bbox": { "h": 0.7, "w": 0.92, "x": 0.04, "y": 0.2 }, "kind": "table", "text": "Investment activity table by category and bank type", "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 19:13:19.135+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "c00b29dd-ad9c-47f8-bbed-9a8663964460", "parentComponentId": null }, { "bbox": { "h": 0.08, "w": 0.85, "x": 0.04, "y": 0.03 }, "kind": "title", "text": "Institutions have prioritized investment in building talent, CoE, secure environment and processes", "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 19:13:19.135+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "db11381e-a0bb-4ef9-a228-46e1270436f6", "parentComponentId": null } ], "metrics": [], "tools": [], "frameworks": [], "arcBeats": [], "loops": [], "locked": true }, { "page": 12, "type": "analysis", "title": "The chart uses a custom stacked bar representation where each segment represents a count of institutions for a specific rank (1-4).", "function": "analyze_data", "rawType": "data_table", "block": null, "metadata": { "slideType": "data_table", "slideTypeCanon": { "name": "Data table", "slug": "data_table", "status": "active", "canonId": "019de52d-0bbd-71b9-9918-417f904abbf8", "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": 57, "componentCount": 6, "textChars": 593, "nDataPoints": 48, "notes": "The chart uses a custom stacked bar representation where each segment represents a count of institutions for a specific rank (1-4).", "elementsJson": [ "data_table", "bar_chart_stacked" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:13:19.195+00", "seconds": 2.53, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b639-6b12903e0170/12", "deck": "/decks/019dd923-5ca1-7489-b639-6b12903e0170", "deckAnchor": "/decks/019dd923-5ca1-7489-b639-6b12903e0170#slide-12", "deckJson": "/decks/019dd923-5ca1-7489-b639-6b12903e0170.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "47% of the institutions have ranked productivity improvement as their top factor for prioritization.", "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": "2026-04-24 13:24:12+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-cdee-73ea-b05f-605d7fff10c0", "parentComponentId": null }, { "bbox": { "h": 0.65, "w": 0.9, "x": 0.04, "y": 0.25 }, "kind": "chart", "text": "Stacked bar charts showing ranking distribution by institution type", "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": "Stacked bar chart", "slug": "bar-stacked", "status": "active", "canonId": "019de52c-feec-74bf-9028-15b74f84f0a7", "version": 1, "description": null }, "framework": null }, "subkind": "bar-stacked", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:13:19.195+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "d3a50387-dc0e-4a0c-bec1-3b63276af3bf", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "Prioritization ranking: 47%", "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": "2026-04-24 13:24:12+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-cdee-73ea-b05f-6668efbf5857", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.45, "x": 0.04, "y": 0.96 }, "kind": "source-note", "text": "Source: IACPM/ McKinsey Study on the Use of Generative AI in Credit Portfolio Management (Survey)", "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 19:13:19.195+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "1534c047-d011-4b27-a7c2-a9475c293d48", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.6, "x": 0.04, "y": 0.14 }, "kind": "subtitle", "text": "Question: How would you rank the following factors in terms of their prioritization / importance in the initiation/development of GenAI use cases in your institution? [rank order]", "attrs": null, "canon": { "kind": { "name": "Subtitle", "slug": "subtitle", "status": "active", "canonId": "82d821ee-2793-42e7-8f16-c6131385a3ce", "version": 1, "description": "Header-zone supporting text near the title (= ex body.description with bbox)." }, "tool": null, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:13:19.195+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "ba5d177f-1c41-46fa-8643-bd6c7428bddc", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.85, "x": 0.04, "y": 0.03 }, "kind": "title", "text": "Institutions prioritize productivity improvement as the most important factor when initiating or developing GenAI use cases", "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 19:13:19.195+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "376e700f-014c-401e-8051-71b32b48a413", "parentComponentId": null } ], "metrics": [ { "metricRaw": "Prioritization ranking", "numberRaw": "47%", "numberKind": "percent", "actionTitle": "Institutions prioritize productivity improvement as the most important factor when initiating or developing GenAI use cases", "calloutText": "47% of the institutions have ranked productivity improvement as their top factor for prioritization.", "numberScale": null, "numberValue": 47, "metricFamily": "index_score", "numberCurrency": null } ], "tools": [], "frameworks": [], "arcBeats": [], "loops": [], "locked": true }, { "page": 13, "type": "analysis", "title": "The chart displays the number of institutions per category (Mega Banks, Super Regionals, Core Regionals, Other) across three levels of commitment.", "function": "analyze_data", "rawType": "data_table", "block": null, "metadata": { "slideType": "data_table", "slideTypeCanon": { "name": "Data table", "slug": "data_table", "status": "active", "canonId": "019de52d-0bbd-71b9-9918-417f904abbf8", "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": 68, "componentCount": 8, "textChars": 545, "nDataPoints": 10, "notes": "The chart displays the number of institutions per category (Mega Banks, Super Regionals, Core Regionals, Other) across three levels of commitment.", "elementsJson": [ "headline_text", "bar_chart_grouped", "data_table" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:13:20.032+00", "seconds": 3.292, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b639-6b12903e0170/13", "deck": "/decks/019dd923-5ca1-7489-b639-6b12903e0170", "deckAnchor": "/decks/019dd923-5ca1-7489-b639-6b12903e0170#slide-13", "deckJson": "/decks/019dd923-5ca1-7489-b639-6b12903e0170.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "52% of institutions position Gen AI adoption as a priority.", "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": "2026-04-24 13:24:11+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-cdee-73ea-b05f-0f62255018eb", "parentComponentId": null }, { "bbox": { "h": 0.65, "w": 0.35, "x": 0.62, "y": 0.25 }, "kind": "chart", "text": "Grouped bar chart showing institution counts by commitment level and type", "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": "Grouped bar chart", "slug": "bar-grouped", "status": "active", "canonId": "019de52c-ff3b-751e-a8ff-f21ff11097b1", "version": 1, "description": null }, "framework": null }, "subkind": "bar-grouped", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:13:20.032+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "a6900ddb-03cb-4bfb-89cc-2f0763e71970", "parentComponentId": null }, { "bbox": { "h": 0.02, "w": 0.5, "x": 0.04, "y": 0.97 }, "kind": "disclaimer", "text": "Source: IACPM/ McKinsey Study on the Use of Generative AI in Credit Portfolio Management (Survey)", "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 19:13:20.032+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "c838653a-07a6-446c-bb41-ebd740cff7ff", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.35, "x": 0.64, "y": 0.1 }, "kind": "legend", "text": "Mega Banks, Super Regionals, Core Regionals, Other", "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 19:13:20.032+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "463f23ba-e04b-49cb-8f4a-72a285334d4a", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "Percentage of institutions: 52%", "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": "2026-04-24 13:24:11+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-cdee-73ea-b05f-1181569f10b2", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.6, "x": 0.04, "y": 0.14 }, "kind": "subtitle", "text": "Question: How would you describe your institution's leadership commitment to the adoption of GenAI? [single choice]", "attrs": null, "canon": { "kind": { "name": "Subtitle", "slug": "subtitle", "status": "active", "canonId": "82d821ee-2793-42e7-8f16-c6131385a3ce", "version": 1, "description": "Header-zone supporting text near the title (= ex body.description with bbox)." }, "tool": null, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:13:20.032+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "5877774b-1705-4cb1-beb1-6a0a4cc737a8", "parentComponentId": null }, { "bbox": { "h": 0.68, "w": 0.92, "x": 0.04, "y": 0.22 }, "kind": "table", "text": "Level, Description, Number of institutions", "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 19:13:20.032+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "29008712-1e64-4f51-a089-668571ad4745", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.9, "x": 0.04, "y": 0.03 }, "kind": "title", "text": "Leadership at majority of the institutions are positioning GenAI as a priority", "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 19:13:20.032+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "0f1dd798-a8c5-4261-b3e2-9d2a914d1168", "parentComponentId": null } ], "metrics": [ { "metricRaw": "Percentage of institutions", "numberRaw": "52%", "numberKind": "percent", "actionTitle": "Leadership at majority of the institutions are positioning GenAI as a priority", "calloutText": "52% of institutions position Gen AI adoption as a priority.", "numberScale": null, "numberValue": 52, "metricFamily": "survey_sentiment", "numberCurrency": null } ], "tools": [], "frameworks": [], "arcBeats": [], "loops": [], "locked": true }, { "page": 14, "type": "analysis", "title": "The chart displays the number of institutions per risk category, segmented by bank type (Mega, Super Regional, Core Regional, Other).", "function": "analyze_data", "rawType": "data_table", "block": null, "metadata": { "slideType": "data_table", "slideTypeCanon": { "name": "Data table", "slug": "data_table", "status": "active", "canonId": "019de52d-0bbd-71b9-9918-417f904abbf8", "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": 68, "componentCount": 7, "textChars": 741, "nDataPoints": 18, "notes": "The chart displays the number of institutions per risk category, segmented by bank type (Mega, Super Regional, Core Regional, Other).", "elementsJson": [ "headline_text", "bar_chart_grouped", "paragraph" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:13:20.058+00", "seconds": 3.138, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b639-6b12903e0170/14", "deck": "/decks/019dd923-5ca1-7489-b639-6b12903e0170", "deckAnchor": "/decks/019dd923-5ca1-7489-b639-6b12903e0170#slide-14", "deckJson": "/decks/019dd923-5ca1-7489-b639-6b12903e0170.json" }, "components": [ { "bbox": null, "kind": "callout", "text": "36% of Institutions are Conservative, Incremental Adoption", "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": "2026-04-24 13:24:12+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-cdee-73ea-b05f-44c3f2b4b26e", "parentComponentId": null }, { "bbox": { "h": 0.75, "w": 0.35, "x": 0.62, "y": 0.15 }, "kind": "chart", "text": "Number of institutions", "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": "Grouped bar chart", "slug": "bar-grouped", "status": "active", "canonId": "019de52c-ff3b-751e-a8ff-f21ff11097b1", "version": 1, "description": null }, "framework": null }, "subkind": "bar-grouped", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 19:13:20.058+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "2322f39b-a5e8-4d38-a820-22377c9aa557", "parentComponentId": null }, { "bbox": { "h": 0.7, "w": 0.55, "x": 0.04, "y": 0.22 }, "kind": "list", "text": "36% of Institutions are Conservative, Incremental Adoption; 27% of Institutions are balanced but Risk-Aware; 18% of Institutions are Cautious Risk-Averse; 12% of Institutions are Moderate Risk-Seeking; 6% of Institutions are Bold, Risk-Seeking Innovators", "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 19:13:20.058+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "139814e2-477a-4a4c-857d-e81a5a864c4f", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "Number of institutions: 36%", "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": "2026-04-24 13:24:12+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-cdee-73ea-b05f-497cb5df0719", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.45, "x": 0.04, "y": 0.15 }, "kind": "paragraph", "text": "Question: How would you describe your institution's risk profile when it comes to the adoption of GenAI? [single choice]", "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 19:13:20.058+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "9b2e5426-4256-4cc8-8300-7449ca34a29f", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.45, "x": 0.04, "y": 0.96 }, "kind": "source-note", "text": "Source: IACPM/ McKinsey Study on the Use of Generative AI in Credit Portfolio Management (Survey)", "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 19:13:20.058+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "6eccafe8-18af-4be7-a166-67abf9c25740", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.92, "x": 0.04, "y": 0.04 }, "kind": "title", "text": "Majority of the institutions are moving ahead cautiously with GenAI applications, with Super Regionals and Core Regionals showing slightly higher appetite for risk", "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 19:13:20.058+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "3a53da47-1701-4a59-b750-42d56913c1f9", "parentComponentId": null } ], "metrics": [ { "metricRaw": "Number of institutions", "numberRaw": "36%", "numberKind": "percent", "actionTitle": "Majority of the institutions are moving ahead cautiously with GenAI applications, with Super Regionals and Core Regionals showing slightly higher appetite for risk", "calloutText": "36% of Institutions are Conservative, Incremental Adoption", "numberScale": null, "numberValue": 36, "metricFamily": "volume_count", "numberCurrency": null } ], "tools": [], "frameworks": [], "arcBeats": [], "loops": [], "locked": true }, { "page": 15, "type": "setup", "title": "The slide uses a 2-column layout with icons to present a list of strategic initiatives.", "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": "overcrowded", "densityScore": 77, "componentCount": 9, "textChars": 1032, "nDataPoints": 0, "notes": "The slide uses a 2-column layout with icons to present a list of strategic initiatives.", "elementsJson": [ "headline_text", "icon_grid", "bullet_list", "footnote" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:13:20.201+00", "seconds": 3.124, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b639-6b12903e0170/15", "deck": "/decks/019dd923-5ca1-7489-b639-6b12903e0170", "deckAnchor": "/decks/019dd923-5ca1-7489-b639-6b12903e0170#slide-15", "deckJson": "/decks/019dd923-5ca1-7489-b639-6b12903e0170.json" }, "components": [ { "bbox": { "h": 0.03, "w": 0.9, "x": 0.04, "y": 0.96 }, "kind": "disclaimer", "text": "Source: IACPM/ McKinsey Study on the Use of Generative AI in Credit Portfolio Management (Interview) & Roundtable of CROs", "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 19:13:20.201+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "7c6829ea-5faf-42c3-aa96-57d5df60cd73", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.35, "x": 0.58, "y": 0.3 }, "kind": "list", "text": "Investing in infrastructure, such as machine learning operations (MLOps) and data processing pipelines, to ensure sufficient preparation for a new GenAI use case", "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 19:13:20.201+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "1fb334e2-a004-4b11-8b36-5dcb8803a41f", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.35, "x": 0.12, "y": 0.3 }, "kind": "list", "text": "Engaging with regulators proactively to understand regulatory expectations and ensure compliance", "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 19:13:20.201+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "51782b9b-38e5-4cc5-a765-366d39185d19", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.35, "x": 0.58, "y": 0.62 }, "kind": "list", "text": "Piloting on use cases that involve the least level of risk (e.g., bots for ad hoc internal use) to test the feasibility and effectiveness of GenAI before scaling up", "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 19:13:20.201+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "96f7551b-a941-44b3-967e-4843736d422d", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.35, "x": 0.12, "y": 0.46 }, "kind": "list", "text": "Establishing an institution-wide CoE driven by technologically able experts while maintaining close collaboration with IT", "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 19:13:20.201+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "ce06ca59-fc23-4c4a-a3d2-71e00f66f785", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.35, "x": 0.58, "y": 0.46 }, "kind": "list", "text": "Developing a modular solution architecture that allows parallel development and customizable connections across different layers", "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 19:13:20.201+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "da108150-17ea-4a2d-a324-ec4d812fac1b", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.35, "x": 0.12, "y": 0.62 }, "kind": "list", "text": "Aligning processes with the institution stakeholders for building GenAI tools to support End-to-End experimentation and deployment", "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 19:13:20.201+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "f3009259-6b4a-4011-8e56-52a1505c9114", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.8, "x": 0.04, "y": 0.04 }, "kind": "title", "text": "Institutions leading GenAI development are typically taking the following actions", "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 19:13:20.201+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "ff249932-8ebe-4a53-95bf-95f9afbad5a0", "parentComponentId": null }, { "bbox": { "h": 0.03, "w": 0.2, "x": 0.04, "y": 0.14 }, "kind": "title", "text": "Highlights from the interviews", "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 19:13:20.201+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "eb84e3b7-167b-4745-8b4b-e00f8b845bf0", "parentComponentId": null } ], "metrics": [], "tools": [], "frameworks": [], "arcBeats": [ { "to": 17, "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": 15, "name": "Impact & Next Steps", "beatId": "82e3868d-3723-4acf-96c8-ab2c6f5e65e6", "matchId": "a9bd079d-1f1a-4e8f-a06f-59ec3ae8fde0", "beatType": { "name": "Impact & Next Steps", "slug": "consultants-gambit-impact-next-steps", "status": "active", "canonId": "019dd9b8-0849-7728-9ee9-fd1609c4c655", "version": 1, "description": null }, "evidence": "The deck concludes with insights on key challenges and next steps.", "position": 3, "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": 0.8 } ], "loops": [], "locked": true }, { "page": 16, "type": "analysis", "title": "The chart uses a custom dot-plot style visualization to show the number of institutions per category.", "function": "analyze_data", "rawType": "data_table", "block": null, "metadata": { "slideType": "data_table", "slideTypeCanon": { "name": "Data table", "slug": "data_table", "status": "active", "canonId": "019de52d-0bbd-71b9-9918-417f904abbf8", "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": "balanced", "densityScore": 40, "componentCount": 5, "textChars": 380, "nDataPoints": 32, "notes": "The chart uses a custom dot-plot style visualization to show the number of institutions per category.", "elementsJson": [ "headline_text", "bullet_list", "data_table", "other" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:13:19.782+00", "seconds": 2.621, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b639-6b12903e0170/16", "deck": "/decks/019dd923-5ca1-7489-b639-6b12903e0170", "deckAnchor": "/decks/019dd923-5ca1-7489-b639-6b12903e0170#slide-16", "deckJson": "/decks/019dd923-5ca1-7489-b639-6b12903e0170.json" }, "components": [ { "bbox": { "h": 0.03, "w": 0.45, "x": 0.04, "y": 0.96 }, "kind": "disclaimer", "text": "Source: IACPM/ McKinsey Study on the Use of Generative AI in Credit Portfolio Management (Survey)", "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 19:13:19.782+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "7884ec4d-38ce-469e-a372-dc218ad0ad95", "parentComponentId": null }, { "bbox": { "h": 0.05, "w": 0.2, "x": 0.75, "y": 0.07 }, "kind": "legend", "text": "Mega Banks, Super Regionals, Core Regionals, Other", "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 19:13:19.782+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "6474bd72-f073-4cc7-9eaa-6cbd0253d393", "parentComponentId": null }, { "bbox": null, "kind": "metric", "text": "% of institutions: 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": { "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": "2026-04-24 13:24:11+00", "model": "or:google/gemini-3.1-flash-lite-preview", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "019dd951-cdee-73ea-b05e-f27958cf3667", "parentComponentId": null }, { "bbox": { "h": 0.7, "w": 0.92, "x": 0.04, "y": 0.22 }, "kind": "table", "text": "Reasons for abandoning or pausing GenAI use cases categorized by relevance and institution type counts.", "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 19:13:19.782+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "f775723b-b858-44dc-b97a-bf9dcc9c64ee", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.8, "x": 0.04, "y": 0.03 }, "kind": "title", "text": "Insufficient performance and complexity challenge are the top reasons noted for slowing down GenAI use cases", "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 19:13:19.782+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "9c523e57-affa-4c8f-a5f1-79a81fb92852", "parentComponentId": null } ], "metrics": [ { "metricRaw": "% of institutions", "numberRaw": "63%", "numberKind": "percent", "actionTitle": "Insufficient performance and complexity challenge are the top reasons noted for slowing down GenAI use cases", "calloutText": null, "numberScale": null, "numberValue": 63, "metricFamily": "other", "numberCurrency": null } ], "tools": [], "frameworks": [], "arcBeats": [ { "to": 17, "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": 15, "name": "Impact & Next Steps", "beatId": "82e3868d-3723-4acf-96c8-ab2c6f5e65e6", "matchId": "a9bd079d-1f1a-4e8f-a06f-59ec3ae8fde0", "beatType": { "name": "Impact & Next Steps", "slug": "consultants-gambit-impact-next-steps", "status": "active", "canonId": "019dd9b8-0849-7728-9ee9-fd1609c4c655", "version": 1, "description": null }, "evidence": "The deck concludes with insights on key challenges and next steps.", "position": 3, "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": 0.8 } ], "loops": [ { "to": 17, "from": 16, "loop": { "name": "27_cost_of_inaction", "slug": "27-cost-of-inaction", "status": "active", "bestFor": "Urgent budget requests, compliance, risk mitigation", "canonId": "019dd956-70d1-7395-a15b-857ba858b394", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Status Quo -> The Hidden Costs Accumulating -> The Future State of Inaction -> The Tipping Point", "description": "Quantify what happens if the audience does nothing", "familyLabel": null }, "matchId": "df877742-caf0-4a08-89f6-3b5208d86821", "evidence": "The deck highlights the challenges and pain points institutions face in adopting GenAI use cases.", "position": 1, "objective": "What are the consequences of not adopting GenAI use cases in credit?", "confidence": 0.6, "extraction": { "at": "2026-07-16 21:07:49.043188+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": "doc-narrative-v1" } } ], "locked": true }, { "page": 17, "type": "analysis", "title": "The slide uses a table format to map specific reasons for abandoning use cases to detailed qualitative insights.", "function": "diagnose", "rawType": "pain_points", "block": null, "metadata": { "slideType": "pain_points", "slideTypeCanon": { "name": "Pain points", "slug": "pain_points", "status": "active", "canonId": "019de52d-08a0-761d-a14f-1dbc5e447381", "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": 23, "componentCount": 4, "textChars": 336, "nDataPoints": 0, "notes": "The slide uses a table format to map specific reasons for abandoning use cases to detailed qualitative insights.", "elementsJson": [ "headline_text", "comparison_table" ], "confidence": 1, "extraction": { "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "at": "2026-05-02 19:13:19.848+00", "seconds": 2.609, "promptVersion": "v2-canon-conditioned" } }, "links": { "slide": "/slides/019dd923-5ca1-7489-b639-6b12903e0170/17", "deck": "/decks/019dd923-5ca1-7489-b639-6b12903e0170", "deckAnchor": "/decks/019dd923-5ca1-7489-b639-6b12903e0170#slide-17", "deckJson": "/decks/019dd923-5ca1-7489-b639-6b12903e0170.json" }, "components": [ { "bbox": { "h": 0.03, "w": 0.25, "x": 0.04, "y": 0.14 }, "kind": "paragraph", "text": "Highlights from the 2024 interviews", "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 19:13:19.848+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "e5165c24-23ba-4789-8d22-600e5271f16c", "parentComponentId": null }, { "bbox": { "h": 0.02, "w": 0.45, "x": 0.04, "y": 0.97 }, "kind": "source-note", "text": "Source: IACPM/ McKinsey Study on the Use of Generative AI in Credit Portfolio Management (Interview)", "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 19:13:19.848+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "7c51c595-a2b0-479c-87fd-759b955cb379", "parentComponentId": null }, { "bbox": { "h": 0.72, "w": 0.92, "x": 0.04, "y": 0.19 }, "kind": "table", "text": "Table mapping 'Reason for abandoning use cases' to 'Highlights from the interviews'", "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 19:13:19.848+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "22d179db-e946-45d5-8102-3d5782efaac8", "parentComponentId": null }, { "bbox": { "h": 0.11, "w": 0.92, "x": 0.04, "y": 0.03 }, "kind": "title", "text": "Interview insights have revealed similar themes across institutions on the key challenges for adopting GenAI use cases", "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 19:13:19.848+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "0671a797-cdc0-4d41-8743-f694b1028a85", "parentComponentId": null } ], "metrics": [], "tools": [], "frameworks": [], "arcBeats": [ { "to": 17, "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": 15, "name": "Impact & Next Steps", "beatId": "82e3868d-3723-4acf-96c8-ab2c6f5e65e6", "matchId": "a9bd079d-1f1a-4e8f-a06f-59ec3ae8fde0", "beatType": { "name": "Impact & Next Steps", "slug": "consultants-gambit-impact-next-steps", "status": "active", "canonId": "019dd9b8-0849-7728-9ee9-fd1609c4c655", "version": 1, "description": null }, "evidence": "The deck concludes with insights on key challenges and next steps.", "position": 3, "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": 0.8 } ], "loops": [ { "to": 17, "from": 16, "loop": { "name": "27_cost_of_inaction", "slug": "27-cost-of-inaction", "status": "active", "bestFor": "Urgent budget requests, compliance, risk mitigation", "canonId": "019dd956-70d1-7395-a15b-857ba858b394", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Status Quo -> The Hidden Costs Accumulating -> The Future State of Inaction -> The Tipping Point", "description": "Quantify what happens if the audience does nothing", "familyLabel": null }, "matchId": "df877742-caf0-4a08-89f6-3b5208d86821", "evidence": "The deck highlights the challenges and pain points institutions face in adopting GenAI use cases.", "position": 1, "objective": "What are the consequences of not adopting GenAI use cases in credit?", "confidence": 0.6, "extraction": { "at": "2026-07-16 21:07:49.043188+00", "model": "or:meta-llama/llama-4-scout", "runId": null, "seconds": null, "promptVersion": "doc-narrative-v1" } } ], "locked": true } ], "arcBeats": [ { "from": 2, "to": 3, "label": "Situation & Context", "description": "The deck provides an overview of the research and survey methodology." }, { "from": 5, "to": 6, "label": "Problem & Complication", "description": "The deck presents data tables on use case prioritization and challenges." }, { "from": 8, "to": 9, "label": "Evidence & Proof", "description": "The deck includes case studies and data tables to support the findings." }, { "from": 15, "to": 17, "label": "Impact & Next Steps", "description": "The deck concludes with insights on key challenges and next steps." } ], "loops": [ { "from": 8, "to": 9, "label": "Zoom In", "description": "What are specific examples of GenAI use cases in credit?" }, { "from": 16, "to": 17, "label": "Cost Of Inaction", "description": "What are the consequences of not adopting GenAI use cases in credit?" } ] }