{ "kind": "all", "value": "all", "collectionKey": "slides:all:all:all-document-kinds:all-producers:all-orientations", "filters": { "documentKinds": [], "sourceTypes": [], "orientations": [] }, "total": 319975, "page": 923, "pageSize": 60, "pageCount": 5333, "rows": [ { "docId": "019dd923-5e88-73ef-bd5d-3d2fee959bc6", "docSlug": "8f735b0bda01b73e", "documentTitle": "2024 Executive Perspectives Unlocking Impact from AI", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 22, "slideType": "roadmap", "function": "plan_implementation", "notes": "The slide uses a sequential process flow with three distinct pillars.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-3d2fee959bc6/22", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-3d2fee959bc6", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-3d2fee959bc6#slide-22", "loopMatches": [ { "to": 22, "from": 19, "name": "Three Horizons", "slug": "49-three-horizons", "bestFor": "Innovation strategy, long-term planning, board presentations", "matchId": "019dd95a-088b-72c8-b7df-d4b9d8c681e5", "evidence": "p19 explicit 3 horizons (0-12 / 3-24 / 6-24+ months); p20-21 tech enablers; p22 3-phase action plan", "position": 5, "objective": "Sequence value realisation across short / medium / long horizons and land the way forward", "structure": "Horizon 1 (Core Business) -> Horizon 2 (Emerging Opportunities) -> Horizon 3 (Future Bets) -> Balanced Portfolio", "confidence": 80, "description": "Structure initiatives across time horizons: protect core, grow adjacencies, create futures" } ], "arcBeatMatches": [ { "to": 23, "from": 22, "beatId": "019dd95a-0682-776c-8e39-bbccb1071b66", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Impact & Next Steps", "beatSlug": "consultants-gambit-impact-next-steps", "evidence": "Way forward 3-phase plan + BCG expert contacts", "position": 5, "confidence": 90, "parentBeatName": "Resolution", "parentBeatSlug": "resolution" }, { "to": 22, "from": 14, "beatId": "019dd95a-0682-776c-8e39-c55880f77c2e", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Action (Now What)", "beatSlug": "triple-take-the-action-now-what", "evidence": "Transform all elements, sequenced horizons, way forward 3 phases", "position": 3, "confidence": 70, "parentBeatName": "Resolution", "parentBeatSlug": "resolution" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-3d2fee959bc6", "docSlug": "8f735b0bda01b73e", "documentTitle": "2024 Executive Perspectives Unlocking Impact from AI", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 23, "slideType": "team_bio", "function": "other", "notes": null, "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-3d2fee959bc6/23", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-3d2fee959bc6", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-3d2fee959bc6#slide-23", "loopMatches": [], "arcBeatMatches": [ { "to": 23, "from": 22, "beatId": "019dd95a-0682-776c-8e39-bbccb1071b66", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Impact & Next Steps", "beatSlug": "consultants-gambit-impact-next-steps", "evidence": "Way forward 3-phase plan + BCG expert contacts", "position": 5, "confidence": 90, "parentBeatName": "Resolution", "parentBeatSlug": "resolution" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-3d2fee959bc6", "docSlug": "8f735b0bda01b73e", "documentTitle": "2024 Executive Perspectives Unlocking Impact from AI", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 24, "slideType": "cover", "function": "front_matter", "notes": null, "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-3d2fee959bc6/24", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-3d2fee959bc6", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-3d2fee959bc6#slide-24", "loopMatches": [], "arcBeatMatches": [], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-42e9078455d0", "docSlug": "4bc8260cae8e26dd", "documentTitle": "2024 Executive Perspectives Unlocking potential from AI and GenAI", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 1, "slideType": "section_divider", "function": "transition", "notes": null, "imagePath": "https://imgproxy.kitesheet.com/Ph2in88mBlBqzkbsBSI0epoLtDH47jBwp1MC9oDwBUE/rs:fit:1200:1200:0/q:82/f:webp/czM6Ly9raXRlc2hlZXQvY29ycHVzL3NsaWRlcy80YmM4MjYwY2FlOGUyNmRkL3AwMDEuanBn", "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-42e9078455d0/1", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0#slide-1", "loopMatches": [], "arcBeatMatches": [], "imagePathAlt": "https://imgproxy.kitesheet.com/qXetlN1q_iALuVXpiJf3hWkYFDFPF0qHXp191FNxhDw/rs:fit:1200:1200:0/q:82/f:webp/czM6Ly9raXRlc2hlZXQvY29ycHVzL3NsaWRlcy80YmM4MjYwY2FlOGUyNmRkL3AwMDEucG5n", "thumbSrc": "https://imgproxy.kitesheet.com/dCvAOiog2wW9aHCtKxI5fhm_rYpdocmIuo9wrcRqXOU/rs:fit:480:480:0/q:78/f:webp/czM6Ly9raXRlc2hlZXQvY29ycHVzL3NsaWRlcy80YmM4MjYwY2FlOGUyNmRkL3AwMDEuanBn", "thumbSrcAlt": "https://imgproxy.kitesheet.com/7RUZwEoFNAgrvr_LP20hZhaecIgcUq6cL341WUkcVCs/rs:fit:480:480:0/q:78/f:webp/czM6Ly9raXRlc2hlZXQvY29ycHVzL3NsaWRlcy80YmM4MjYwY2FlOGUyNmRkL3AwMDEucG5n" }, { "docId": "019dd923-5e88-73ef-bd5d-42e9078455d0", "docSlug": "4bc8260cae8e26dd", "documentTitle": "2024 Executive Perspectives Unlocking potential from AI and GenAI", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 2, "slideType": "executive_summary", "function": "establish_context", "notes": "The slide serves as a front-matter or executive summary for a report on AI in finance.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-42e9078455d0/2", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0#slide-2", "loopMatches": [ { "to": 3, "from": 2, "name": "Golden Circle", "slug": "11-golden-circle", "bestFor": "Visionary leadership, brand positioning, mission statements", "matchId": "019dd95a-088b-72c8-b7df-a7c006637ebe", "evidence": "Three boxes 'Time to act on AI is now / AI unlocks many sources of value / Executing successfully' = why/what/how", "position": 1, "objective": "Frame why AI now, what it unlocks, how to execute", "structure": "The Why (Belief) -> The How (Process) -> The What (Result)", "confidence": 70, "description": "Invert the typical pitch by starting with why you exist, rather than what you do" } ], "arcBeatMatches": [ { "to": 3, "from": 2, "beatId": "019dd95a-0682-776c-8e39-979546d04613", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Situation & Context", "beatSlug": "consultants-gambit-situation-context", "evidence": "'Introduction' + executive summary frames AI in finance, time-to-act", "position": 1, "confidence": 90, "parentBeatName": "Setup", "parentBeatSlug": "setup" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-42e9078455d0", "docSlug": "4bc8260cae8e26dd", "documentTitle": "2024 Executive Perspectives Unlocking potential from AI and GenAI", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 3, "slideType": "executive_summary", "function": "summarize", "notes": "The slide uses a three-part structure to summarize the current state, value proposition, and implementation path for AI in finance.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-42e9078455d0/3", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0#slide-3", "loopMatches": [ { "to": 3, "from": 2, "name": "Golden Circle", "slug": "11-golden-circle", "bestFor": "Visionary leadership, brand positioning, mission statements", "matchId": "019dd95a-088b-72c8-b7df-a7c006637ebe", "evidence": "Three boxes 'Time to act on AI is now / AI unlocks many sources of value / Executing successfully' = why/what/how", "position": 1, "objective": "Frame why AI now, what it unlocks, how to execute", "structure": "The Why (Belief) -> The How (Process) -> The What (Result)", "confidence": 70, "description": "Invert the typical pitch by starting with why you exist, rather than what you do" } ], "arcBeatMatches": [ { "to": 3, "from": 2, "beatId": "019dd95a-0682-776c-8e39-979546d04613", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Situation & Context", "beatSlug": "consultants-gambit-situation-context", "evidence": "'Introduction' + executive summary frames AI in finance, time-to-act", "position": 1, "confidence": 90, "parentBeatName": "Setup", "parentBeatSlug": "setup" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-42e9078455d0", "docSlug": "4bc8260cae8e26dd", "documentTitle": "2024 Executive Perspectives Unlocking potential from AI and GenAI", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 4, "slideType": "key_takeaways", "function": "present_framework", "notes": "The slide uses a maturity model (staircase) and a 2x2 matrix (Efficiency vs Effectiveness) to illustrate the strategic shift.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-42e9078455d0/4", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0#slide-4", "loopMatches": [ { "to": 5, "from": 4, "name": "Tale Two Worlds", "slug": "04-tale-two-worlds", "bestFor": "Competitive analysis, benchmarking, case for change", "matchId": "019dd95a-088b-72c8-b7df-a9503191830a", "evidence": "p4 build-up Traditional bookkeeper to Value driver + 2x2; p5 Common starting points to AI-enabled", "position": 2, "objective": "Contrast current finance vs next-gen vision via maturity progression", "structure": "Current State -> Desired State / Benchmark -> The Gap & Implication", "confidence": 85, "description": "Show the gap between two states to drive urgency or highlight opportunity" } ], "arcBeatMatches": [ { "to": 7, "from": 4, "beatId": "019dd95a-0682-776c-8e39-9be527f95aff", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Problem & Complication", "beatSlug": "consultants-gambit-problem-complication", "evidence": "Compromise gap, maturity gap, GenAI still nascent (64% exploring)", "position": 2, "confidence": 90, "parentBeatName": "Complication", "parentBeatSlug": "complication" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-42e9078455d0", "docSlug": "4bc8260cae8e26dd", "documentTitle": "2024 Executive Perspectives Unlocking potential from AI and GenAI", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 5, "slideType": "comparison_table", "function": "present_framework", "notes": "Uses a maturity model framework to illustrate the progression of finance functions.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-42e9078455d0/5", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0#slide-5", "loopMatches": [ { "to": 5, "from": 4, "name": "Tale Two Worlds", "slug": "04-tale-two-worlds", "bestFor": "Competitive analysis, benchmarking, case for change", "matchId": "019dd95a-088b-72c8-b7df-a9503191830a", "evidence": "p4 build-up Traditional bookkeeper to Value driver + 2x2; p5 Common starting points to AI-enabled", "position": 2, "objective": "Contrast current finance vs next-gen vision via maturity progression", "structure": "Current State -> Desired State / Benchmark -> The Gap & Implication", "confidence": 85, "description": "Show the gap between two states to drive urgency or highlight opportunity" } ], "arcBeatMatches": [ { "to": 7, "from": 4, "beatId": "019dd95a-0682-776c-8e39-9be527f95aff", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Problem & Complication", "beatSlug": "consultants-gambit-problem-complication", "evidence": "Compromise gap, maturity gap, GenAI still nascent (64% exploring)", "position": 2, "confidence": 90, "parentBeatName": "Complication", "parentBeatSlug": "complication" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-42e9078455d0", "docSlug": "4bc8260cae8e26dd", "documentTitle": "2024 Executive Perspectives Unlocking potential from AI and GenAI", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 6, "slideType": "initiative_list", "function": "present_solution", "notes": "The chart on the right is a maturity model showing value creation vs. recency of application.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-42e9078455d0/6", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0#slide-6", "loopMatches": [ { "to": 9, "from": 6, "name": "So What Cascade", "slug": "41-so-what-cascade", "bestFor": "Data presentations, executive summaries, driving to recommendations", "matchId": "019dd95a-088b-72c8-b7df-aeee4afccbcb", "evidence": "Lever framework, adoption survey, transformation potential 20-35%, opportunities heatmap across 5 processes", "position": 3, "objective": "Build from AI as a lever to specific opportunity inventory", "structure": "The Data -> So What? (Insight 1) -> So What? (Insight 2) -> So What? (The Action)", "confidence": 75, "description": "Chain insights together, each answering 'so what?' until you reach the actionable conclusion" } ], "arcBeatMatches": [ { "to": 7, "from": 4, "beatId": "019dd95a-0682-776c-8e39-9be527f95aff", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Problem & Complication", "beatSlug": "consultants-gambit-problem-complication", "evidence": "Compromise gap, maturity gap, GenAI still nascent (64% exploring)", "position": 2, "confidence": 90, "parentBeatName": "Complication", "parentBeatSlug": "complication" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-42e9078455d0", "docSlug": "4bc8260cae8e26dd", "documentTitle": "2024 Executive Perspectives Unlocking potential from AI and GenAI", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 7, "slideType": "industry_trends", "function": "analyze_data", "notes": "The chart uses a stacked bar format to show maturity levels across different AI use cases.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-42e9078455d0/7", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0#slide-7", "loopMatches": [ { "to": 9, "from": 6, "name": "So What Cascade", "slug": "41-so-what-cascade", "bestFor": "Data presentations, executive summaries, driving to recommendations", "matchId": "019dd95a-088b-72c8-b7df-aeee4afccbcb", "evidence": "Lever framework, adoption survey, transformation potential 20-35%, opportunities heatmap across 5 processes", "position": 3, "objective": "Build from AI as a lever to specific opportunity inventory", "structure": "The Data -> So What? (Insight 1) -> So What? (Insight 2) -> So What? (The Action)", "confidence": 75, "description": "Chain insights together, each answering 'so what?' until you reach the actionable conclusion" } ], "arcBeatMatches": [ { "to": 7, "from": 4, "beatId": "019dd95a-0682-776c-8e39-9be527f95aff", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Problem & Complication", "beatSlug": "consultants-gambit-problem-complication", "evidence": "Compromise gap, maturity gap, GenAI still nascent (64% exploring)", "position": 2, "confidence": 90, "parentBeatName": "Complication", "parentBeatSlug": "complication" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-42e9078455d0", "docSlug": "4bc8260cae8e26dd", "documentTitle": "2024 Executive Perspectives Unlocking potential from AI and GenAI", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 8, "slideType": "customer_journey", "function": "present_solution", "notes": "Includes a legend for color-coded AI impact levels and a disclaimer about estimated impact.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-42e9078455d0/8", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0#slide-8", "loopMatches": [ { "to": 9, "from": 6, "name": "So What Cascade", "slug": "41-so-what-cascade", "bestFor": "Data presentations, executive summaries, driving to recommendations", "matchId": "019dd95a-088b-72c8-b7df-aeee4afccbcb", "evidence": "Lever framework, adoption survey, transformation potential 20-35%, opportunities heatmap across 5 processes", "position": 3, "objective": "Build from AI as a lever to specific opportunity inventory", "structure": "The Data -> So What? (Insight 1) -> So What? (Insight 2) -> So What? (The Action)", "confidence": 75, "description": "Chain insights together, each answering 'so what?' until you reach the actionable conclusion" } ], "arcBeatMatches": [ { "to": 9, "from": 8, "beatId": "019dd95a-0682-776c-8e39-9fc6688007ea", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Solution & Approach", "beatSlug": "consultants-gambit-solution-approach", "evidence": "AI lever transformation maps + opportunities matrix across 5 processes", "position": 3, "confidence": 90, "parentBeatName": "Turn", "parentBeatSlug": "turn" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-42e9078455d0", "docSlug": "4bc8260cae8e26dd", "documentTitle": "2024 Executive Perspectives Unlocking potential from AI and GenAI", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 9, "slideType": "initiative_list", "function": "present_solution", "notes": "The slide uses a color-coded legend (High, Medium, Low) to indicate the value unlocked by AI in specific finance processes.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-42e9078455d0/9", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0#slide-9", "loopMatches": [ { "to": 9, "from": 6, "name": "So What Cascade", "slug": "41-so-what-cascade", "bestFor": "Data presentations, executive summaries, driving to recommendations", "matchId": "019dd95a-088b-72c8-b7df-aeee4afccbcb", "evidence": "Lever framework, adoption survey, transformation potential 20-35%, opportunities heatmap across 5 processes", "position": 3, "objective": "Build from AI as a lever to specific opportunity inventory", "structure": "The Data -> So What? (Insight 1) -> So What? (Insight 2) -> So What? (The Action)", "confidence": 75, "description": "Chain insights together, each answering 'so what?' until you reach the actionable conclusion" } ], "arcBeatMatches": [ { "to": 9, "from": 8, "beatId": "019dd95a-0682-776c-8e39-9fc6688007ea", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Solution & Approach", "beatSlug": "consultants-gambit-solution-approach", "evidence": "AI lever transformation maps + opportunities matrix across 5 processes", "position": 3, "confidence": 90, "parentBeatName": "Turn", "parentBeatSlug": "turn" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-42e9078455d0", "docSlug": "4bc8260cae8e26dd", "documentTitle": "2024 Executive Perspectives Unlocking potential from AI and GenAI", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 10, "slideType": "case_study", "function": "illustrate_case", "notes": "Uses a classic Context-Solution-Impact structure.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-42e9078455d0/10", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0#slide-10", "loopMatches": [ { "to": 12, "from": 10, "name": "Before After", "slug": "21-before-after", "bestFor": "Product demos, process improvements, ROI justification", "matchId": "019dd95a-088b-72c8-b7df-b28c35ffd506", "evidence": "Case I: 17% forecast error to 50%+ accuracy / 80%+ variance reduction with driver tree", "position": 4, "objective": "Prove forecasting case with context/solution/impact + driver-tree deep dive", "structure": "The Old Way (Pain) -> The Moment of Change -> The New Way (Glory) -> The Measurable Delta", "confidence": 88, "description": "Show the dramatic contrast between the old way and the new way through side-by-side comparison" } ], "arcBeatMatches": [ { "to": 16, "from": 10, "beatId": "019dd95a-0682-776c-8e39-a2adf74a45d8", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Evidence & Proof", "beatSlug": "consultants-gambit-evidence-proof", "evidence": "Three AI-in-action client cases: forecasting, GenAI BI, annual report", "position": 4, "confidence": 90, "parentBeatName": "Evidence", "parentBeatSlug": "evidence" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-42e9078455d0", "docSlug": "4bc8260cae8e26dd", "documentTitle": "2024 Executive Perspectives Unlocking potential from AI and GenAI", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 11, "slideType": "case_study", "function": "present_solution", "notes": "The slide uses a additive framework to show how different components contribute to the final operational driver forecast.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-42e9078455d0/11", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0#slide-11", "loopMatches": [ { "to": 12, "from": 10, "name": "Before After", "slug": "21-before-after", "bestFor": "Product demos, process improvements, ROI justification", "matchId": "019dd95a-088b-72c8-b7df-b28c35ffd506", "evidence": "Case I: 17% forecast error to 50%+ accuracy / 80%+ variance reduction with driver tree", "position": 4, "objective": "Prove forecasting case with context/solution/impact + driver-tree deep dive", "structure": "The Old Way (Pain) -> The Moment of Change -> The New Way (Glory) -> The Measurable Delta", "confidence": 88, "description": "Show the dramatic contrast between the old way and the new way through side-by-side comparison" } ], "arcBeatMatches": [ { "to": 16, "from": 10, "beatId": "019dd95a-0682-776c-8e39-a2adf74a45d8", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Evidence & Proof", "beatSlug": "consultants-gambit-evidence-proof", "evidence": "Three AI-in-action client cases: forecasting, GenAI BI, annual report", "position": 4, "confidence": 90, "parentBeatName": "Evidence", "parentBeatSlug": "evidence" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-42e9078455d0", "docSlug": "4bc8260cae8e26dd", "documentTitle": "2024 Executive Perspectives Unlocking potential from AI and GenAI", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 12, "slideType": "other", "function": "present_framework", "notes": "The slide uses a hierarchical decomposition (driver tree) to map financial outcomes to operational inputs.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-42e9078455d0/12", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0#slide-12", "loopMatches": [ { "to": 12, "from": 10, "name": "Before After", "slug": "21-before-after", "bestFor": "Product demos, process improvements, ROI justification", "matchId": "019dd95a-088b-72c8-b7df-b28c35ffd506", "evidence": "Case I: 17% forecast error to 50%+ accuracy / 80%+ variance reduction with driver tree", "position": 4, "objective": "Prove forecasting case with context/solution/impact + driver-tree deep dive", "structure": "The Old Way (Pain) -> The Moment of Change -> The New Way (Glory) -> The Measurable Delta", "confidence": 88, "description": "Show the dramatic contrast between the old way and the new way through side-by-side comparison" } ], "arcBeatMatches": [ { "to": 16, "from": 10, "beatId": "019dd95a-0682-776c-8e39-a2adf74a45d8", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Evidence & Proof", "beatSlug": "consultants-gambit-evidence-proof", "evidence": "Three AI-in-action client cases: forecasting, GenAI BI, annual report", "position": 4, "confidence": 90, "parentBeatName": "Evidence", "parentBeatSlug": "evidence" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-42e9078455d0", "docSlug": "4bc8260cae8e26dd", "documentTitle": "2024 Executive Perspectives Unlocking potential from AI and GenAI", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 13, "slideType": "client_example", "function": "illustrate_case", "notes": "Uses a classic 'Context-Solution-Impact' structure.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-42e9078455d0/13", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0#slide-13", "loopMatches": [ { "to": 14, "from": 13, "name": "Before After", "slug": "21-before-after", "bestFor": "Product demos, process improvements, ROI justification", "matchId": "019dd95a-088b-72c8-b7df-b47fc0aaac12", "evidence": "Case II: 2-4x faster insight via GenAI chatbot drilldown into driver tree", "position": 5, "objective": "Prove GenAI BI case with context/solution/impact + chatbot deep dive", "structure": "The Old Way (Pain) -> The Moment of Change -> The New Way (Glory) -> The Measurable Delta", "confidence": 85, "description": "Show the dramatic contrast between the old way and the new way through side-by-side comparison" } ], "arcBeatMatches": [ { "to": 16, "from": 10, "beatId": "019dd95a-0682-776c-8e39-a2adf74a45d8", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Evidence & Proof", "beatSlug": "consultants-gambit-evidence-proof", "evidence": "Three AI-in-action client cases: forecasting, GenAI BI, annual report", "position": 4, "confidence": 90, "parentBeatName": "Evidence", "parentBeatSlug": "evidence" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-42e9078455d0", "docSlug": "4bc8260cae8e26dd", "documentTitle": "2024 Executive Perspectives Unlocking potential from AI and GenAI", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 14, "slideType": "client_example", "function": "illustrate_case", "notes": "The slide uses a conversational UI metaphor to show how GenAI can navigate complex driver trees to explain business performance.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-42e9078455d0/14", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0#slide-14", "loopMatches": [ { "to": 14, "from": 13, "name": "Before After", "slug": "21-before-after", "bestFor": "Product demos, process improvements, ROI justification", "matchId": "019dd95a-088b-72c8-b7df-b47fc0aaac12", "evidence": "Case II: 2-4x faster insight via GenAI chatbot drilldown into driver tree", "position": 5, "objective": "Prove GenAI BI case with context/solution/impact + chatbot deep dive", "structure": "The Old Way (Pain) -> The Moment of Change -> The New Way (Glory) -> The Measurable Delta", "confidence": 85, "description": "Show the dramatic contrast between the old way and the new way through side-by-side comparison" } ], "arcBeatMatches": [ { "to": 16, "from": 10, "beatId": "019dd95a-0682-776c-8e39-a2adf74a45d8", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Evidence & Proof", "beatSlug": "consultants-gambit-evidence-proof", "evidence": "Three AI-in-action client cases: forecasting, GenAI BI, annual report", "position": 4, "confidence": 90, "parentBeatName": "Evidence", "parentBeatSlug": "evidence" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-42e9078455d0", "docSlug": "4bc8260cae8e26dd", "documentTitle": "2024 Executive Perspectives Unlocking potential from AI and GenAI", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 15, "slideType": "case_study", "function": "illustrate_case", "notes": "Uses a classic Context-Solution-Impact structure.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-42e9078455d0/15", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0#slide-15", "loopMatches": [ { "to": 16, "from": 15, "name": "Before After", "slug": "21-before-after", "bestFor": "Product demos, process improvements, ROI justification", "matchId": "019dd95a-088b-72c8-b7df-ba67d1c4f826", "evidence": "Case III: 40-60% efficiency in annual report drafting via GenAI tool", "position": 6, "objective": "Prove GenAI annual-report case with context/solution/impact + tool deep dive", "structure": "The Old Way (Pain) -> The Moment of Change -> The New Way (Glory) -> The Measurable Delta", "confidence": 85, "description": "Show the dramatic contrast between the old way and the new way through side-by-side comparison" } ], "arcBeatMatches": [ { "to": 16, "from": 10, "beatId": "019dd95a-0682-776c-8e39-a2adf74a45d8", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Evidence & Proof", "beatSlug": "consultants-gambit-evidence-proof", "evidence": "Three AI-in-action client cases: forecasting, GenAI BI, annual report", "position": 4, "confidence": 90, "parentBeatName": "Evidence", "parentBeatSlug": "evidence" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-42e9078455d0", "docSlug": "4bc8260cae8e26dd", "documentTitle": "2024 Executive Perspectives Unlocking potential from AI and GenAI", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 16, "slideType": "client_example", "function": "illustrate_case", "notes": null, "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-42e9078455d0/16", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0#slide-16", "loopMatches": [ { "to": 16, "from": 15, "name": "Before After", "slug": "21-before-after", "bestFor": "Product demos, process improvements, ROI justification", "matchId": "019dd95a-088b-72c8-b7df-ba67d1c4f826", "evidence": "Case III: 40-60% efficiency in annual report drafting via GenAI tool", "position": 6, "objective": "Prove GenAI annual-report case with context/solution/impact + tool deep dive", "structure": "The Old Way (Pain) -> The Moment of Change -> The New Way (Glory) -> The Measurable Delta", "confidence": 85, "description": "Show the dramatic contrast between the old way and the new way through side-by-side comparison" } ], "arcBeatMatches": [ { "to": 16, "from": 10, "beatId": "019dd95a-0682-776c-8e39-a2adf74a45d8", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Evidence & Proof", "beatSlug": "consultants-gambit-evidence-proof", "evidence": "Three AI-in-action client cases: forecasting, GenAI BI, annual report", "position": 4, "confidence": 90, "parentBeatName": "Evidence", "parentBeatSlug": "evidence" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-42e9078455d0", "docSlug": "4bc8260cae8e26dd", "documentTitle": "2024 Executive Perspectives Unlocking potential from AI and GenAI", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 17, "slideType": "customer_journey", "function": "present_solution", "notes": "Uses a process flow framework to contrast manual vs. automated workflows.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-42e9078455d0/17", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0#slide-17", "loopMatches": [ { "to": 18, "from": 17, "name": "Ripple Effect", "slug": "26-ripple-effect", "bestFor": "Change management, scaling initiatives, demonstrating broad value", "matchId": "019dd95a-088b-72c8-b7df-be9294ee1630", "evidence": "p17 process before/after with 80% to 30% time on data; p18 mandates/engagement/talent reinvention", "position": 7, "objective": "Cascade AI's impact from process tasks to operating-model and roles", "structure": "Individual Impact -> Team Impact -> Organizational Impact -> Market/Industry Impact", "confidence": 78, "description": "Show how impact spreads from individual to team to organization to market" } ], "arcBeatMatches": [ { "to": 22, "from": 17, "beatId": "019dd95a-0682-776c-8e39-a4d2f8072a0e", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Impact & Next Steps", "beatSlug": "consultants-gambit-impact-next-steps", "evidence": "Process/operating-model/tech implications, vendor map, 6 CSFs, contacts", "position": 5, "confidence": 90, "parentBeatName": "Resolution", "parentBeatSlug": "resolution" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-42e9078455d0", "docSlug": "4bc8260cae8e26dd", "documentTitle": "2024 Executive Perspectives Unlocking potential from AI and GenAI", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 18, "slideType": "org_design", "function": "present_framework", "notes": null, "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-42e9078455d0/18", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0#slide-18", "loopMatches": [ { "to": 18, "from": 17, "name": "Ripple Effect", "slug": "26-ripple-effect", "bestFor": "Change management, scaling initiatives, demonstrating broad value", "matchId": "019dd95a-088b-72c8-b7df-be9294ee1630", "evidence": "p17 process before/after with 80% to 30% time on data; p18 mandates/engagement/talent reinvention", "position": 7, "objective": "Cascade AI's impact from process tasks to operating-model and roles", "structure": "Individual Impact -> Team Impact -> Organizational Impact -> Market/Industry Impact", "confidence": 78, "description": "Show how impact spreads from individual to team to organization to market" } ], "arcBeatMatches": [ { "to": 22, "from": 17, "beatId": "019dd95a-0682-776c-8e39-a4d2f8072a0e", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Impact & Next Steps", "beatSlug": "consultants-gambit-impact-next-steps", "evidence": "Process/operating-model/tech implications, vendor map, 6 CSFs, contacts", "position": 5, "confidence": 90, "parentBeatName": "Resolution", "parentBeatSlug": "resolution" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-42e9078455d0", "docSlug": "4bc8260cae8e26dd", "documentTitle": "2024 Executive Perspectives Unlocking potential from AI and GenAI", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 19, "slideType": "market_landscape", "function": "present_framework", "notes": "The slide uses a matrix-like structure to compare three vendor categories across approach, use cases, and sample vendors.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-42e9078455d0/19", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0#slide-19", "loopMatches": [ { "to": 21, "from": 19, "name": "Scenario Fork", "slug": "35-scenario-fork", "bestFor": "Strategic planning, risk analysis, board presentations", "matchId": "019dd95a-088b-72c8-b7df-c2d3f75df558", "evidence": "Three vendor archetypes (Enterprise / Point / Foundation), target architecture, 6 CSFs", "position": 8, "objective": "Equip CFO with vendor scenarios, target tech state, and CSFs", "structure": "The Decision Point -> Scenario A (If X) -> Scenario B (If Y) -> Scenario C (If Z) -> Recommendation", "confidence": 75, "description": "Present multiple futures based on different assumptions or decisions" } ], "arcBeatMatches": [ { "to": 22, "from": 17, "beatId": "019dd95a-0682-776c-8e39-a4d2f8072a0e", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Impact & Next Steps", "beatSlug": "consultants-gambit-impact-next-steps", "evidence": "Process/operating-model/tech implications, vendor map, 6 CSFs, contacts", "position": 5, "confidence": 90, "parentBeatName": "Resolution", "parentBeatSlug": "resolution" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-42e9078455d0", "docSlug": "4bc8260cae8e26dd", "documentTitle": "2024 Executive Perspectives Unlocking potential from AI and GenAI", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 20, "slideType": "roadmap", "function": "present_solution", "notes": "The slide uses a layered architecture diagram to represent the technology stack and a bulleted list for implementation priorities.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-42e9078455d0/20", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0#slide-20", "loopMatches": [ { "to": 21, "from": 19, "name": "Scenario Fork", "slug": "35-scenario-fork", "bestFor": "Strategic planning, risk analysis, board presentations", "matchId": "019dd95a-088b-72c8-b7df-c2d3f75df558", "evidence": "Three vendor archetypes (Enterprise / Point / Foundation), target architecture, 6 CSFs", "position": 8, "objective": "Equip CFO with vendor scenarios, target tech state, and CSFs", "structure": "The Decision Point -> Scenario A (If X) -> Scenario B (If Y) -> Scenario C (If Z) -> Recommendation", "confidence": 75, "description": "Present multiple futures based on different assumptions or decisions" } ], "arcBeatMatches": [ { "to": 22, "from": 17, "beatId": "019dd95a-0682-776c-8e39-a4d2f8072a0e", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Impact & Next Steps", "beatSlug": "consultants-gambit-impact-next-steps", "evidence": "Process/operating-model/tech implications, vendor map, 6 CSFs, contacts", "position": 5, "confidence": 90, "parentBeatName": "Resolution", "parentBeatSlug": "resolution" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-42e9078455d0", "docSlug": "4bc8260cae8e26dd", "documentTitle": "2024 Executive Perspectives Unlocking potential from AI and GenAI", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 21, "slideType": "initiative_list", "function": "present_solution", "notes": null, "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-42e9078455d0/21", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0#slide-21", "loopMatches": [ { "to": 21, "from": 19, "name": "Scenario Fork", "slug": "35-scenario-fork", "bestFor": "Strategic planning, risk analysis, board presentations", "matchId": "019dd95a-088b-72c8-b7df-c2d3f75df558", "evidence": "Three vendor archetypes (Enterprise / Point / Foundation), target architecture, 6 CSFs", "position": 8, "objective": "Equip CFO with vendor scenarios, target tech state, and CSFs", "structure": "The Decision Point -> Scenario A (If X) -> Scenario B (If Y) -> Scenario C (If Z) -> Recommendation", "confidence": 75, "description": "Present multiple futures based on different assumptions or decisions" } ], "arcBeatMatches": [ { "to": 22, "from": 17, "beatId": "019dd95a-0682-776c-8e39-a4d2f8072a0e", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Impact & Next Steps", "beatSlug": "consultants-gambit-impact-next-steps", "evidence": "Process/operating-model/tech implications, vendor map, 6 CSFs, contacts", "position": 5, "confidence": 90, "parentBeatName": "Resolution", "parentBeatSlug": "resolution" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-42e9078455d0", "docSlug": "4bc8260cae8e26dd", "documentTitle": "2024 Executive Perspectives Unlocking potential from AI and GenAI", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 22, "slideType": "team_bio", "function": "other", "notes": null, "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-42e9078455d0/22", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0#slide-22", "loopMatches": [], "arcBeatMatches": [ { "to": 22, "from": 17, "beatId": "019dd95a-0682-776c-8e39-a4d2f8072a0e", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Impact & Next Steps", "beatSlug": "consultants-gambit-impact-next-steps", "evidence": "Process/operating-model/tech implications, vendor map, 6 CSFs, contacts", "position": 5, "confidence": 90, "parentBeatName": "Resolution", "parentBeatSlug": "resolution" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-42e9078455d0", "docSlug": "4bc8260cae8e26dd", "documentTitle": "2024 Executive Perspectives Unlocking potential from AI and GenAI", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 23, "slideType": "cover", "function": "front_matter", "notes": "This is a standard BCG brand cover slide.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-42e9078455d0/23", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-42e9078455d0#slide-23", "loopMatches": [], "arcBeatMatches": [], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 1, "slideType": "cover", "function": "front_matter", "notes": null, "imagePath": "https://imgproxy.kitesheet.com/yiT87Wn-k7Ncimkc_foAXpE23dUXUSS9jCHpFqPzZnc/rs:fit:1200:1200:0/q:82/f:webp/czM6Ly9raXRlc2hlZXQvY29ycHVzL3NsaWRlcy80ODViMWU5OTZhNjM3YjQzL3AwMDEuanBn", "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/1", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-1", "loopMatches": [], "arcBeatMatches": [], "imagePathAlt": "https://imgproxy.kitesheet.com/kkEQbheAy4XCW-re-VM99Zq3l4gBds0W5toX4AH6A7M/rs:fit:1200:1200:0/q:82/f:webp/czM6Ly9raXRlc2hlZXQvY29ycHVzL3NsaWRlcy80ODViMWU5OTZhNjM3YjQzL3AwMDEucG5n", "thumbSrc": "https://imgproxy.kitesheet.com/uvyR-an73vmMFvIEKUTYkUqEVbfMqw6muqK0-We0U20/rs:fit:480:480:0/q:78/f:webp/czM6Ly9raXRlc2hlZXQvY29ycHVzL3NsaWRlcy80ODViMWU5OTZhNjM3YjQzL3AwMDEuanBn", "thumbSrcAlt": "https://imgproxy.kitesheet.com/sNAF03sUzfg06Xmnpp2Kk0X77muV1QKbfowwejgeczQ/rs:fit:480:480:0/q:78/f:webp/czM6Ly9raXRlc2hlZXQvY29ycHVzL3NsaWRlcy80ODViMWU5OTZhNjM3YjQzL3AwMDEucG5n" }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 2, "slideType": "situation_overview", "function": "establish_context", "notes": "The slide uses an illustration to explain the hub-and-spoke nature of the MCP protocol connecting a client to various data sources.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/2", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-2", "loopMatches": [], "arcBeatMatches": [ { "to": 9, "from": 2, "beatId": "019dd95a-0682-776c-8e39-eb31cb1a8081", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Situation & Context", "beatSlug": "consultants-gambit-situation-context", "evidence": "Section 01 'Agent evolution' establishes maturing field and multi-agent direction", "position": 1, "confidence": 82, "parentBeatName": "Setup", "parentBeatSlug": "setup" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 3, "slideType": "agenda", "function": "summarize", "notes": null, "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/3", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-3", "loopMatches": [], "arcBeatMatches": [ { "to": 9, "from": 2, "beatId": "019dd95a-0682-776c-8e39-eb31cb1a8081", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Situation & Context", "beatSlug": "consultants-gambit-situation-context", "evidence": "Section 01 'Agent evolution' establishes maturing field and multi-agent direction", "position": 1, "confidence": 82, "parentBeatName": "Setup", "parentBeatSlug": "setup" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 4, "slideType": "section_divider", "function": "transition", "notes": null, "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/4", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-4", "loopMatches": [], "arcBeatMatches": [ { "to": 9, "from": 2, "beatId": "019dd95a-0682-776c-8e39-eb31cb1a8081", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Situation & Context", "beatSlug": "consultants-gambit-situation-context", "evidence": "Section 01 'Agent evolution' establishes maturing field and multi-agent direction", "position": 1, "confidence": 82, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 12, "from": 4, "beatId": "019dd95a-0682-776c-8e39-fe448dff941c", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Sections 01-02 inventory agent evolution and PMF cases", "position": 1, "confidence": 55, "parentBeatName": "Setup", "parentBeatSlug": "setup" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 5, "slideType": "timeline", "function": "present_framework", "notes": "The slide uses a maturity model framework to show the progression of AI capabilities.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/5", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-5", "loopMatches": [ { "to": 9, "from": 5, "name": "Maturity Curve", "slug": "36-maturity-curve", "bestFor": "Digital transformation, capability building, benchmarking", "matchId": "019dd95a-088b-72c8-b7df-ef1b695c53c3", "evidence": "p5 five-stage horizontal progression with 'At scale / Still early / Very early' tags; pp6-9 unpack drivers and end-state", "position": 1, "objective": "Plot agent maturity from token predictors to multi-agent systems", "structure": "Current Maturity Level -> The Gap to Next Level -> Required Capabilities -> The Roadmap", "confidence": 80, "description": "Show where you are on a progression and what it takes to reach the next level" } ], "arcBeatMatches": [ { "to": 9, "from": 2, "beatId": "019dd95a-0682-776c-8e39-eb31cb1a8081", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Situation & Context", "beatSlug": "consultants-gambit-situation-context", "evidence": "Section 01 'Agent evolution' establishes maturing field and multi-agent direction", "position": 1, "confidence": 82, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 12, "from": 4, "beatId": "019dd95a-0682-776c-8e39-fe448dff941c", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Sections 01-02 inventory agent evolution and PMF cases", "position": 1, "confidence": 55, "parentBeatName": "Setup", "parentBeatSlug": "setup" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 6, "slideType": "industry_trends", "function": "summarize", "notes": "The slide uses a three-column layout to categorize the evolution of AI agents into community growth, framework development, and commercial proof-points.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/6", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-6", "loopMatches": [ { "to": 9, "from": 5, "name": "Maturity Curve", "slug": "36-maturity-curve", "bestFor": "Digital transformation, capability building, benchmarking", "matchId": "019dd95a-088b-72c8-b7df-ef1b695c53c3", "evidence": "p5 five-stage horizontal progression with 'At scale / Still early / Very early' tags; pp6-9 unpack drivers and end-state", "position": 1, "objective": "Plot agent maturity from token predictors to multi-agent systems", "structure": "Current Maturity Level -> The Gap to Next Level -> Required Capabilities -> The Roadmap", "confidence": 80, "description": "Show where you are on a progression and what it takes to reach the next level" } ], "arcBeatMatches": [ { "to": 9, "from": 2, "beatId": "019dd95a-0682-776c-8e39-eb31cb1a8081", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Situation & Context", "beatSlug": "consultants-gambit-situation-context", "evidence": "Section 01 'Agent evolution' establishes maturing field and multi-agent direction", "position": 1, "confidence": 82, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 12, "from": 4, "beatId": "019dd95a-0682-776c-8e39-fe448dff941c", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Sections 01-02 inventory agent evolution and PMF cases", "position": 1, "confidence": 55, "parentBeatName": "Setup", "parentBeatSlug": "setup" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 7, "slideType": "industry_trends", "function": "summarize", "notes": "The slide uses a three-column layout to present distinct trends, each supported by metrics and visual evidence (screenshots/charts).", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/7", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-7", "loopMatches": [ { "to": 9, "from": 5, "name": "Maturity Curve", "slug": "36-maturity-curve", "bestFor": "Digital transformation, capability building, benchmarking", "matchId": "019dd95a-088b-72c8-b7df-ef1b695c53c3", "evidence": "p5 five-stage horizontal progression with 'At scale / Still early / Very early' tags; pp6-9 unpack drivers and end-state", "position": 1, "objective": "Plot agent maturity from token predictors to multi-agent systems", "structure": "Current Maturity Level -> The Gap to Next Level -> Required Capabilities -> The Roadmap", "confidence": 80, "description": "Show where you are on a progression and what it takes to reach the next level" } ], "arcBeatMatches": [ { "to": 9, "from": 2, "beatId": "019dd95a-0682-776c-8e39-eb31cb1a8081", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Situation & Context", "beatSlug": "consultants-gambit-situation-context", "evidence": "Section 01 'Agent evolution' establishes maturing field and multi-agent direction", "position": 1, "confidence": 82, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 12, "from": 4, "beatId": "019dd95a-0682-776c-8e39-fe448dff941c", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Sections 01-02 inventory agent evolution and PMF cases", "position": 1, "confidence": 55, "parentBeatName": "Setup", "parentBeatSlug": "setup" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 8, "slideType": "comparison_table", "function": "compare_options", "notes": "The slide uses a process-flow diagram to contrast linear, gate-based workflows with iterative, feedback-loop-based autonomous agents.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/8", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-8", "loopMatches": [ { "to": 9, "from": 5, "name": "Maturity Curve", "slug": "36-maturity-curve", "bestFor": "Digital transformation, capability building, benchmarking", "matchId": "019dd95a-088b-72c8-b7df-ef1b695c53c3", "evidence": "p5 five-stage horizontal progression with 'At scale / Still early / Very early' tags; pp6-9 unpack drivers and end-state", "position": 1, "objective": "Plot agent maturity from token predictors to multi-agent systems", "structure": "Current Maturity Level -> The Gap to Next Level -> Required Capabilities -> The Roadmap", "confidence": 80, "description": "Show where you are on a progression and what it takes to reach the next level" } ], "arcBeatMatches": [ { "to": 9, "from": 2, "beatId": "019dd95a-0682-776c-8e39-eb31cb1a8081", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Situation & Context", "beatSlug": "consultants-gambit-situation-context", "evidence": "Section 01 'Agent evolution' establishes maturing field and multi-agent direction", "position": 1, "confidence": 82, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 12, "from": 4, "beatId": "019dd95a-0682-776c-8e39-fe448dff941c", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Sections 01-02 inventory agent evolution and PMF cases", "position": 1, "confidence": 55, "parentBeatName": "Setup", "parentBeatSlug": "setup" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 9, "slideType": "market_landscape", "function": "present_framework", "notes": "The slide uses a three-tier architecture: Orchestration Agent, Human-Agent SCM Team, and Data/Tool Landscape.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/9", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-9", "loopMatches": [ { "to": 9, "from": 5, "name": "Maturity Curve", "slug": "36-maturity-curve", "bestFor": "Digital transformation, capability building, benchmarking", "matchId": "019dd95a-088b-72c8-b7df-ef1b695c53c3", "evidence": "p5 five-stage horizontal progression with 'At scale / Still early / Very early' tags; pp6-9 unpack drivers and end-state", "position": 1, "objective": "Plot agent maturity from token predictors to multi-agent systems", "structure": "Current Maturity Level -> The Gap to Next Level -> Required Capabilities -> The Roadmap", "confidence": 80, "description": "Show where you are on a progression and what it takes to reach the next level" } ], "arcBeatMatches": [ { "to": 9, "from": 2, "beatId": "019dd95a-0682-776c-8e39-eb31cb1a8081", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Situation & Context", "beatSlug": "consultants-gambit-situation-context", "evidence": "Section 01 'Agent evolution' establishes maturing field and multi-agent direction", "position": 1, "confidence": 82, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 12, "from": 4, "beatId": "019dd95a-0682-776c-8e39-fe448dff941c", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Sections 01-02 inventory agent evolution and PMF cases", "position": 1, "confidence": 55, "parentBeatName": "Setup", "parentBeatSlug": "setup" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 10, "slideType": "section_divider", "function": "transition", "notes": null, "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/10", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-10", "loopMatches": [], "arcBeatMatches": [ { "to": 17, "from": 10, "beatId": "019dd95a-0682-776c-8e39-efdc0c3bfe92", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Problem & Complication", "beatSlug": "consultants-gambit-problem-complication", "evidence": "PMF reached only narrowly; 6-dim assessment exposes capability gaps", "position": 2, "confidence": 82, "parentBeatName": "Complication", "parentBeatSlug": "complication" }, { "to": 12, "from": 4, "beatId": "019dd95a-0682-776c-8e39-fe448dff941c", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Sections 01-02 inventory agent evolution and PMF cases", "position": 1, "confidence": 55, "parentBeatName": "Setup", "parentBeatSlug": "setup" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 11, "slideType": "market_landscape", "function": "analyze_data", "notes": "The slide uses a table to show growth metrics for specific companies and a process-oriented diagram to show maturity requirements.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/11", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-11", "loopMatches": [ { "to": 12, "from": 11, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-088b-72c8-b7df-f39f00ba41ef", "evidence": "Cursor, Replit, Bolt, Lovable, V0, Windsurf revenue+user stats stack to a single PMF claim", "position": 2, "objective": "Aggregate vendor evidence into a PMF pattern", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 72, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 17, "from": 10, "beatId": "019dd95a-0682-776c-8e39-efdc0c3bfe92", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Problem & Complication", "beatSlug": "consultants-gambit-problem-complication", "evidence": "PMF reached only narrowly; 6-dim assessment exposes capability gaps", "position": 2, "confidence": 82, "parentBeatName": "Complication", "parentBeatSlug": "complication" }, { "to": 12, "from": 4, "beatId": "019dd95a-0682-776c-8e39-fe448dff941c", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Sections 01-02 inventory agent evolution and PMF cases", "position": 1, "confidence": 55, "parentBeatName": "Setup", "parentBeatSlug": "setup" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 12, "slideType": "client_example", "function": "illustrate_case", "notes": "The slide uses a mix of specific company case studies and a summary of BCG's own client work to validate the thesis.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/12", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-12", "loopMatches": [ { "to": 12, "from": 11, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-088b-72c8-b7df-f39f00ba41ef", "evidence": "Cursor, Replit, Bolt, Lovable, V0, Windsurf revenue+user stats stack to a single PMF claim", "position": 2, "objective": "Aggregate vendor evidence into a PMF pattern", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 72, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 17, "from": 10, "beatId": "019dd95a-0682-776c-8e39-efdc0c3bfe92", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Problem & Complication", "beatSlug": "consultants-gambit-problem-complication", "evidence": "PMF reached only narrowly; 6-dim assessment exposes capability gaps", "position": 2, "confidence": 82, "parentBeatName": "Complication", "parentBeatSlug": "complication" }, { "to": 12, "from": 4, "beatId": "019dd95a-0682-776c-8e39-fe448dff941c", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Sections 01-02 inventory agent evolution and PMF cases", "position": 1, "confidence": 55, "parentBeatName": "Setup", "parentBeatSlug": "setup" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 13, "slideType": "section_divider", "function": "transition", "notes": null, "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/13", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-13", "loopMatches": [], "arcBeatMatches": [ { "to": 17, "from": 10, "beatId": "019dd95a-0682-776c-8e39-efdc0c3bfe92", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Problem & Complication", "beatSlug": "consultants-gambit-problem-complication", "evidence": "PMF reached only narrowly; 6-dim assessment exposes capability gaps", "position": 2, "confidence": 82, "parentBeatName": "Complication", "parentBeatSlug": "complication" }, { "to": 17, "from": 13, "beatId": "019dd95a-0682-776c-8e3a-022aa9a7736c", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Implications (So What)", "beatSlug": "triple-take-the-implications-so-what", "evidence": "Section 03 reliability benchmarks and BCG capability framework", "position": 2, "confidence": 55, "parentBeatName": "Reflection", "parentBeatSlug": "reflection" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 14, "slideType": "industry_trends", "function": "summarize", "notes": "The slide highlights the shift in AI benchmarking from simple tasks to complex, multi-turn, and robust agent evaluation.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/14", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-14", "loopMatches": [ { "to": 15, "from": 14, "name": "Aha Moment", "slug": "03-aha-moment", "bestFor": "Data-heavy sections, research findings, analytical arguments", "matchId": "019dd95a-088b-72c8-b7df-f64c44f43b3b", "evidence": "p14 sets up agent benchmarks; p15 reveals METR exponential with 'month-long projects' dark callout", "position": 3, "objective": "Reveal the 7-month doubling capability insight", "structure": "The Problem/Question -> What the Data Shows -> The Insight", "confidence": 80, "description": "Establish a tension, present data, then deliver the insight that changes everything" } ], "arcBeatMatches": [ { "to": 17, "from": 10, "beatId": "019dd95a-0682-776c-8e39-efdc0c3bfe92", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Problem & Complication", "beatSlug": "consultants-gambit-problem-complication", "evidence": "PMF reached only narrowly; 6-dim assessment exposes capability gaps", "position": 2, "confidence": 82, "parentBeatName": "Complication", "parentBeatSlug": "complication" }, { "to": 17, "from": 13, "beatId": "019dd95a-0682-776c-8e3a-022aa9a7736c", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Implications (So What)", "beatSlug": "triple-take-the-implications-so-what", "evidence": "Section 03 reliability benchmarks and BCG capability framework", "position": 2, "confidence": 55, "parentBeatName": "Reflection", "parentBeatSlug": "reflection" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 15, "slideType": "industry_trends", "function": "quantify_impact", "notes": "The chart uses a logarithmic scale for task duration over time.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/15", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-15", "loopMatches": [ { "to": 15, "from": 14, "name": "Aha Moment", "slug": "03-aha-moment", "bestFor": "Data-heavy sections, research findings, analytical arguments", "matchId": "019dd95a-088b-72c8-b7df-f64c44f43b3b", "evidence": "p14 sets up agent benchmarks; p15 reveals METR exponential with 'month-long projects' dark callout", "position": 3, "objective": "Reveal the 7-month doubling capability insight", "structure": "The Problem/Question -> What the Data Shows -> The Insight", "confidence": 80, "description": "Establish a tension, present data, then deliver the insight that changes everything" } ], "arcBeatMatches": [ { "to": 17, "from": 10, "beatId": "019dd95a-0682-776c-8e39-efdc0c3bfe92", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Problem & Complication", "beatSlug": "consultants-gambit-problem-complication", "evidence": "PMF reached only narrowly; 6-dim assessment exposes capability gaps", "position": 2, "confidence": 82, "parentBeatName": "Complication", "parentBeatSlug": "complication" }, { "to": 17, "from": 13, "beatId": "019dd95a-0682-776c-8e3a-022aa9a7736c", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Implications (So What)", "beatSlug": "triple-take-the-implications-so-what", "evidence": "Section 03 reliability benchmarks and BCG capability framework", "position": 2, "confidence": 55, "parentBeatName": "Reflection", "parentBeatSlug": "reflection" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 16, "slideType": "other", "function": "present_framework", "notes": "The slide presents a proprietary BCG framework for AI agent evaluation.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/16", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-16", "loopMatches": [ { "to": 17, "from": 16, "name": "Capability Gap", "slug": "48-capability-gap", "bestFor": "M&A strategy, digital transformation, organizational design", "matchId": "019dd95a-088b-72c8-b7df-fbe911d5d520", "evidence": "p16 defines 6 dimensions; p17 plots Low/Med/High maturity per dimension with limitations and what-needs-to-happen", "position": 4, "objective": "Diagnose where agents fall short across 6 capabilities", "structure": "Required Capabilities -> Current State -> The Gap -> Build vs Buy vs Partner Decision", "confidence": 78, "description": "Compare required capabilities vs. current capabilities to define the build/buy/partner decision" } ], "arcBeatMatches": [ { "to": 17, "from": 10, "beatId": "019dd95a-0682-776c-8e39-efdc0c3bfe92", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Problem & Complication", "beatSlug": "consultants-gambit-problem-complication", "evidence": "PMF reached only narrowly; 6-dim assessment exposes capability gaps", "position": 2, "confidence": 82, "parentBeatName": "Complication", "parentBeatSlug": "complication" }, { "to": 17, "from": 13, "beatId": "019dd95a-0682-776c-8e3a-022aa9a7736c", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Implications (So What)", "beatSlug": "triple-take-the-implications-so-what", "evidence": "Section 03 reliability benchmarks and BCG capability framework", "position": 2, "confidence": 55, "parentBeatName": "Reflection", "parentBeatSlug": "reflection" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 17, "slideType": "maturity_model", "function": "diagnose", "notes": "The slide uses a maturity model framework to map current AI agent capabilities against future requirements.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/17", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-17", "loopMatches": [ { "to": 17, "from": 16, "name": "Capability Gap", "slug": "48-capability-gap", "bestFor": "M&A strategy, digital transformation, organizational design", "matchId": "019dd95a-088b-72c8-b7df-fbe911d5d520", "evidence": "p16 defines 6 dimensions; p17 plots Low/Med/High maturity per dimension with limitations and what-needs-to-happen", "position": 4, "objective": "Diagnose where agents fall short across 6 capabilities", "structure": "Required Capabilities -> Current State -> The Gap -> Build vs Buy vs Partner Decision", "confidence": 78, "description": "Compare required capabilities vs. current capabilities to define the build/buy/partner decision" } ], "arcBeatMatches": [ { "to": 17, "from": 10, "beatId": "019dd95a-0682-776c-8e39-efdc0c3bfe92", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Problem & Complication", "beatSlug": "consultants-gambit-problem-complication", "evidence": "PMF reached only narrowly; 6-dim assessment exposes capability gaps", "position": 2, "confidence": 82, "parentBeatName": "Complication", "parentBeatSlug": "complication" }, { "to": 17, "from": 13, "beatId": "019dd95a-0682-776c-8e3a-022aa9a7736c", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Implications (So What)", "beatSlug": "triple-take-the-implications-so-what", "evidence": "Section 03 reliability benchmarks and BCG capability framework", "position": 2, "confidence": 55, "parentBeatName": "Reflection", "parentBeatSlug": "reflection" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 18, "slideType": "section_divider", "function": "transition", "notes": null, "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/18", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-18", "loopMatches": [], "arcBeatMatches": [ { "to": 23, "from": 18, "beatId": "019dd95a-0682-776c-8e39-f35192ff20f5", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Solution & Approach", "beatSlug": "consultants-gambit-solution-approach", "evidence": "Section 04 introduces MCP and how it slots into enterprise architecture", "position": 3, "confidence": 82, "parentBeatName": "Turn", "parentBeatSlug": "turn" }, { "to": 27, "from": 18, "beatId": "019dd95a-0682-776c-8e3a-0662ddf93f56", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Action (Now What)", "beatSlug": "triple-take-the-action-now-what", "evidence": "Section 04 + key points prescribe MCP-based enterprise build", "position": 3, "confidence": 55, "parentBeatName": "Resolution", "parentBeatSlug": "resolution" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 19, "slideType": "other", "function": "present_framework", "notes": "The slide uses a layered architecture diagram to explain the MCP protocol stack.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/19", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-19", "loopMatches": [ { "to": 21, "from": 19, "name": "Zoom In", "slug": "06-zoom-in", "bestFor": "Technical deep-dives, case studies, detailed analysis", "matchId": "019dd95a-088b-72c8-b7df-fc112746e8be", "evidence": "p19 protocol primitives, p20 unified flow, p21 overlays MCP onto 4 of 6 capability gaps", "position": 5, "objective": "Zoom from MCP overview into mechanics and gap-fit", "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication", "confidence": 75, "description": "Start broad, then progressively focus on specific details that prove your point" } ], "arcBeatMatches": [ { "to": 23, "from": 18, "beatId": "019dd95a-0682-776c-8e39-f35192ff20f5", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Solution & Approach", "beatSlug": "consultants-gambit-solution-approach", "evidence": "Section 04 introduces MCP and how it slots into enterprise architecture", "position": 3, "confidence": 82, "parentBeatName": "Turn", "parentBeatSlug": "turn" }, { "to": 27, "from": 18, "beatId": "019dd95a-0682-776c-8e3a-0662ddf93f56", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Action (Now What)", "beatSlug": "triple-take-the-action-now-what", "evidence": "Section 04 + key points prescribe MCP-based enterprise build", "position": 3, "confidence": 55, "parentBeatName": "Resolution", "parentBeatSlug": "resolution" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 20, "slideType": "other", "function": "present_framework", "notes": "The slide uses a process flow to explain the technical architecture of MCP.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/20", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-20", "loopMatches": [ { "to": 21, "from": 19, "name": "Zoom In", "slug": "06-zoom-in", "bestFor": "Technical deep-dives, case studies, detailed analysis", "matchId": "019dd95a-088b-72c8-b7df-fc112746e8be", "evidence": "p19 protocol primitives, p20 unified flow, p21 overlays MCP onto 4 of 6 capability gaps", "position": 5, "objective": "Zoom from MCP overview into mechanics and gap-fit", "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication", "confidence": 75, "description": "Start broad, then progressively focus on specific details that prove your point" } ], "arcBeatMatches": [ { "to": 23, "from": 18, "beatId": "019dd95a-0682-776c-8e39-f35192ff20f5", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Solution & Approach", "beatSlug": "consultants-gambit-solution-approach", "evidence": "Section 04 introduces MCP and how it slots into enterprise architecture", "position": 3, "confidence": 82, "parentBeatName": "Turn", "parentBeatSlug": "turn" }, { "to": 27, "from": 18, "beatId": "019dd95a-0682-776c-8e3a-0662ddf93f56", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Action (Now What)", "beatSlug": "triple-take-the-action-now-what", "evidence": "Section 04 + key points prescribe MCP-based enterprise build", "position": 3, "confidence": 55, "parentBeatName": "Resolution", "parentBeatSlug": "resolution" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 21, "slideType": "diagnosis", "function": "diagnose", "notes": "The slide uses a numbered list format to map MCP's role against six identified agent capability gaps.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/21", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-21", "loopMatches": [ { "to": 21, "from": 19, "name": "Zoom In", "slug": "06-zoom-in", "bestFor": "Technical deep-dives, case studies, detailed analysis", "matchId": "019dd95a-088b-72c8-b7df-fc112746e8be", "evidence": "p19 protocol primitives, p20 unified flow, p21 overlays MCP onto 4 of 6 capability gaps", "position": 5, "objective": "Zoom from MCP overview into mechanics and gap-fit", "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication", "confidence": 75, "description": "Start broad, then progressively focus on specific details that prove your point" } ], "arcBeatMatches": [ { "to": 23, "from": 18, "beatId": "019dd95a-0682-776c-8e39-f35192ff20f5", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Solution & Approach", "beatSlug": "consultants-gambit-solution-approach", "evidence": "Section 04 introduces MCP and how it slots into enterprise architecture", "position": 3, "confidence": 82, "parentBeatName": "Turn", "parentBeatSlug": "turn" }, { "to": 27, "from": 18, "beatId": "019dd95a-0682-776c-8e3a-0662ddf93f56", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Action (Now What)", "beatSlug": "triple-take-the-action-now-what", "evidence": "Section 04 + key points prescribe MCP-based enterprise build", "position": 3, "confidence": 55, "parentBeatName": "Resolution", "parentBeatSlug": "resolution" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 22, "slideType": "industry_trends", "function": "summarize", "notes": "The chart uses a line graph to compare Github stars over time for various AI frameworks, highlighting MCP's steep growth trajectory.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/22", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-22", "loopMatches": [ { "to": 23, "from": 22, "name": "Why Now", "slug": "15-why-now", "bestFor": "Sales pitches, fundraising, requesting immediate budget approval", "matchId": "019dd95a-088b-72c8-b7e0-0004a5637af7", "evidence": "p22 'de facto standard' momentum callout; p23 'how to think about MCP in your architecture'", "position": 6, "objective": "Justify why MCP adoption window is open now", "structure": "The Context (Trends) -> The Trigger Event -> The Window of Opportunity", "confidence": 65, "description": "Create temporal urgency by proving that the window of opportunity is opening or closing" } ], "arcBeatMatches": [ { "to": 23, "from": 18, "beatId": "019dd95a-0682-776c-8e39-f35192ff20f5", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Solution & Approach", "beatSlug": "consultants-gambit-solution-approach", "evidence": "Section 04 introduces MCP and how it slots into enterprise architecture", "position": 3, "confidence": 82, "parentBeatName": "Turn", "parentBeatSlug": "turn" }, { "to": 27, "from": 18, "beatId": "019dd95a-0682-776c-8e3a-0662ddf93f56", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Action (Now What)", "beatSlug": "triple-take-the-action-now-what", "evidence": "Section 04 + key points prescribe MCP-based enterprise build", "position": 3, "confidence": 55, "parentBeatName": "Resolution", "parentBeatSlug": "resolution" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 23, "slideType": "diagnosis", "function": "diagnose", "notes": "The slide uses a before-and-after framing to illustrate the architectural benefit of Model Context Protocol (MCP).", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/23", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-23", "loopMatches": [ { "to": 23, "from": 22, "name": "Why Now", "slug": "15-why-now", "bestFor": "Sales pitches, fundraising, requesting immediate budget approval", "matchId": "019dd95a-088b-72c8-b7e0-0004a5637af7", "evidence": "p22 'de facto standard' momentum callout; p23 'how to think about MCP in your architecture'", "position": 6, "objective": "Justify why MCP adoption window is open now", "structure": "The Context (Trends) -> The Trigger Event -> The Window of Opportunity", "confidence": 65, "description": "Create temporal urgency by proving that the window of opportunity is opening or closing" } ], "arcBeatMatches": [ { "to": 23, "from": 18, "beatId": "019dd95a-0682-776c-8e39-f35192ff20f5", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Solution & Approach", "beatSlug": "consultants-gambit-solution-approach", "evidence": "Section 04 introduces MCP and how it slots into enterprise architecture", "position": 3, "confidence": 82, "parentBeatName": "Turn", "parentBeatSlug": "turn" }, { "to": 27, "from": 18, "beatId": "019dd95a-0682-776c-8e3a-0662ddf93f56", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Action (Now What)", "beatSlug": "triple-take-the-action-now-what", "evidence": "Section 04 + key points prescribe MCP-based enterprise build", "position": 3, "confidence": 55, "parentBeatName": "Resolution", "parentBeatSlug": "resolution" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 24, "slideType": "risk_register", "function": "diagnose", "notes": "The slide uses a numbered list to map specific risks to a system architecture diagram.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/24", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-24", "loopMatches": [ { "to": 26, "from": 24, "name": "Pre Mortem", "slug": "19-pre-mortem", "bestFor": "Project kick-offs, risk management, building trust with skeptical stakeholders", "matchId": "019dd95a-088b-72c8-b7e0-041d413f6f30", "evidence": "p24 security risks, p25 A2A protocol fragmentation warning, p26 future architecture mitigation", "position": 7, "objective": "Surface failure modes and target-state guardrails", "structure": "The Future Disaster (Hypothetical) -> What Went Wrong -> The Prevention Plan", "confidence": 62, "description": "Build trust by anticipating failure modes before they happen and solving them in advance" } ], "arcBeatMatches": [ { "to": 26, "from": 24, "beatId": "019dd95a-0682-776c-8e39-f422f49a28f7", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Evidence & Proof", "beatSlug": "consultants-gambit-evidence-proof", "evidence": "Security treatment, A2A coexistence, target-state platform diagram", "position": 4, "confidence": 82, "parentBeatName": "Evidence", "parentBeatSlug": "evidence" }, { "to": 27, "from": 18, "beatId": "019dd95a-0682-776c-8e3a-0662ddf93f56", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Action (Now What)", "beatSlug": "triple-take-the-action-now-what", "evidence": "Section 04 + key points prescribe MCP-based enterprise build", "position": 3, "confidence": 55, "parentBeatName": "Resolution", "parentBeatSlug": "resolution" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 25, "slideType": "industry_trends", "function": "establish_context", "notes": "The slide uses a layered architecture diagram to show the relationship between agents, A2A, and MCP servers.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/25", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-25", "loopMatches": [ { "to": 26, "from": 24, "name": "Pre Mortem", "slug": "19-pre-mortem", "bestFor": "Project kick-offs, risk management, building trust with skeptical stakeholders", "matchId": "019dd95a-088b-72c8-b7e0-041d413f6f30", "evidence": "p24 security risks, p25 A2A protocol fragmentation warning, p26 future architecture mitigation", "position": 7, "objective": "Surface failure modes and target-state guardrails", "structure": "The Future Disaster (Hypothetical) -> What Went Wrong -> The Prevention Plan", "confidence": 62, "description": "Build trust by anticipating failure modes before they happen and solving them in advance" } ], "arcBeatMatches": [ { "to": 26, "from": 24, "beatId": "019dd95a-0682-776c-8e39-f422f49a28f7", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Evidence & Proof", "beatSlug": "consultants-gambit-evidence-proof", "evidence": "Security treatment, A2A coexistence, target-state platform diagram", "position": 4, "confidence": 82, "parentBeatName": "Evidence", "parentBeatSlug": "evidence" }, { "to": 27, "from": 18, "beatId": "019dd95a-0682-776c-8e3a-0662ddf93f56", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Action (Now What)", "beatSlug": "triple-take-the-action-now-what", "evidence": "Section 04 + key points prescribe MCP-based enterprise build", "position": 3, "confidence": 55, "parentBeatName": "Resolution", "parentBeatSlug": "resolution" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 26, "slideType": "market_landscape", "function": "present_framework", "notes": "The slide uses a layered architecture diagram to illustrate the relationship between Agent Apps, the AI Platform, and LLM Providers.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/26", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-26", "loopMatches": [ { "to": 26, "from": 24, "name": "Pre Mortem", "slug": "19-pre-mortem", "bestFor": "Project kick-offs, risk management, building trust with skeptical stakeholders", "matchId": "019dd95a-088b-72c8-b7e0-041d413f6f30", "evidence": "p24 security risks, p25 A2A protocol fragmentation warning, p26 future architecture mitigation", "position": 7, "objective": "Surface failure modes and target-state guardrails", "structure": "The Future Disaster (Hypothetical) -> What Went Wrong -> The Prevention Plan", "confidence": 62, "description": "Build trust by anticipating failure modes before they happen and solving them in advance" } ], "arcBeatMatches": [ { "to": 26, "from": 24, "beatId": "019dd95a-0682-776c-8e39-f422f49a28f7", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Evidence & Proof", "beatSlug": "consultants-gambit-evidence-proof", "evidence": "Security treatment, A2A coexistence, target-state platform diagram", "position": 4, "confidence": 82, "parentBeatName": "Evidence", "parentBeatSlug": "evidence" }, { "to": 27, "from": 18, "beatId": "019dd95a-0682-776c-8e3a-0662ddf93f56", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Action (Now What)", "beatSlug": "triple-take-the-action-now-what", "evidence": "Section 04 + key points prescribe MCP-based enterprise build", "position": 3, "confidence": 55, "parentBeatName": "Resolution", "parentBeatSlug": "resolution" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 27, "slideType": "initiative_list", "function": "present_solution", "notes": "The slide uses a visual equation (Agents + MCP) and lists five key implementation steps, concluding with a reference to the appendix.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/27", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-27", "loopMatches": [ { "to": 27, "from": 27, "name": "So What Cascade", "slug": "41-so-what-cascade", "bestFor": "Data presentations, executive summaries, driving to recommendations", "matchId": "019dd95a-088b-72c8-b7e0-081a7cfc3d57", "evidence": "Numbered 1-5 actions: evals, MCP plan, orchestration platform, legal review, technical appendix", "position": 8, "objective": "Cascade analysis into 5 enterprise recommendations", "structure": "The Data -> So What? (Insight 1) -> So What? (Insight 2) -> So What? (The Action)", "confidence": 75, "description": "Chain insights together, each answering 'so what?' until you reach the actionable conclusion" } ], "arcBeatMatches": [ { "to": 29, "from": 27, "beatId": "019dd95a-0682-776c-8e39-f846edbd06d7", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Impact & Next Steps", "beatSlug": "consultants-gambit-impact-next-steps", "evidence": "Five numbered enterprise recommendations and team contact page", "position": 5, "confidence": 82, "parentBeatName": "Resolution", "parentBeatSlug": "resolution" }, { "to": 27, "from": 18, "beatId": "019dd95a-0682-776c-8e3a-0662ddf93f56", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Action (Now What)", "beatSlug": "triple-take-the-action-now-what", "evidence": "Section 04 + key points prescribe MCP-based enterprise build", "position": 3, "confidence": 55, "parentBeatName": "Resolution", "parentBeatSlug": "resolution" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 28, "slideType": "appendix", "function": "other", "notes": null, "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/28", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-28", "loopMatches": [], "arcBeatMatches": [ { "to": 29, "from": 27, "beatId": "019dd95a-0682-776c-8e39-f846edbd06d7", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Impact & Next Steps", "beatSlug": "consultants-gambit-impact-next-steps", "evidence": "Five numbered enterprise recommendations and team contact page", "position": 5, "confidence": 82, "parentBeatName": "Resolution", "parentBeatSlug": "resolution" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 29, "slideType": "team_bio", "function": "other", "notes": "Includes a list of co-authors and a footer showing the AI Platforms Group.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/29", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-29", "loopMatches": [], "arcBeatMatches": [ { "to": 29, "from": 27, "beatId": "019dd95a-0682-776c-8e39-f846edbd06d7", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Impact & Next Steps", "beatSlug": "consultants-gambit-impact-next-steps", "evidence": "Five numbered enterprise recommendations and team contact page", "position": 5, "confidence": 82, "parentBeatName": "Resolution", "parentBeatSlug": "resolution" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 30, "slideType": "appendix", "function": "appendix", "notes": null, "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/30", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-30", "loopMatches": [], "arcBeatMatches": [], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 31, "slideType": "key_messages", "function": "summarize", "notes": "The slide uses a three-column layout to explain the accessibility and ecosystem of the Model Context Protocol.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/31", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-31", "loopMatches": [], "arcBeatMatches": [], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 32, "slideType": "market_landscape", "function": "present_solution", "notes": "The slide uses a grid layout to categorize MCP servers into four columns: Enterprise software, Desktop automation, Productivity tools, and Software Dev/Ops tools. A legend at the bottom defines the origin of the servers (Opensource, Vendor, Community).", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/32", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-32", "loopMatches": [], "arcBeatMatches": [], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 33, "slideType": "initiative_list", "function": "present_framework", "notes": "The diagram illustrates the interaction between Application Front-end, MCP Client, LLM, and MCP Servers/Systems, with numbered callouts corresponding to the list items.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/33", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-33", "loopMatches": [ { "to": 35, "from": 33, "name": "Paradox Resolver", "slug": "20-paradox-resolver", "bestFor": "Strategic pivots, innovation pitches, challenging conventional wisdom", "matchId": "019dd95a-088b-72c8-b7e0-0f0030d8184b", "evidence": "p33 best-practices framing, p34 contrasts both extremes, p35 prescribes 'lean MCP servers' middle path", "position": 9, "objective": "Reconcile monolith vs microservice MCP extremes", "structure": "The Apparent Contradiction -> Why Both Seem True -> The Deeper Truth That Reconciles", "confidence": 70, "description": "Introduce a seeming contradiction that captures attention, then resolve it with a deeper truth" } ], "arcBeatMatches": [], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-47815abb602b", "docSlug": "485b1e996a637b43", "documentTitle": "2025 AI Agents and the Model Context Protocol", "authorId": "BCG", "authorName": "BCG", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 34, "slideType": "comparison_table", "function": "compare_options", "notes": "The slide uses a visual comparison to illustrate architectural anti-patterns for MCP (Model Context Protocol) servers.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-47815abb602b/34", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-47815abb602b#slide-34", "loopMatches": [ { "to": 35, "from": 33, "name": "Paradox Resolver", "slug": "20-paradox-resolver", "bestFor": "Strategic pivots, innovation pitches, challenging conventional wisdom", "matchId": "019dd95a-088b-72c8-b7e0-0f0030d8184b", "evidence": "p33 best-practices framing, p34 contrasts both extremes, p35 prescribes 'lean MCP servers' middle path", "position": 9, "objective": "Reconcile monolith vs microservice MCP extremes", "structure": "The Apparent Contradiction -> Why Both Seem True -> The Deeper Truth That Reconciles", "confidence": 70, "description": "Introduce a seeming contradiction that captures attention, then resolve it with a deeper truth" } ], "arcBeatMatches": [], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true } ] }