{
  "docId": "019dd923-5e88-73ef-bd5d-11ca0e4babc6",
  "docSlug": "341e745702a30aa1",
  "documentTitle": "2024 Executive Perspectives AI Powered R D",
  "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,
  "pageCount": 24,
  "prevPage": 6,
  "nextPage": 8,
  "slideType": "client_example",
  "function": "illustrate_case",
  "density": "balanced",
  "nDataPoints": 8,
  "notes": "The slide uses a process-oriented layout to categorize AI applications in R&D.",
  "elementsJson": [
    "process_diagram",
    "infographic"
  ],
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5e88-73ef-bd5d-11ca0e4babc6/7",
  "deckHref": "/decks/019dd923-5e88-73ef-bd5d-11ca0e4babc6",
  "deckJsonHref": "/decks/019dd923-5e88-73ef-bd5d-11ca0e4babc6.json",
  "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-11ca0e4babc6#slide-7",
  "components": [
    {
      "bbox": {
        "h": 0.1,
        "w": 0.9,
        "x": 0.05,
        "y": 0.2
      },
      "kind": "diagram",
      "text": "Research and define concept -> Develop and industrialize product -> Evolve product",
      "attrs": null,
      "subkind": "process",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "f88ed2e1-9a88-40f3-83e6-536087c02c66",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.55,
        "w": 0.9,
        "x": 0.05,
        "y": 0.35
      },
      "kind": "list",
      "text": "Biopharma: 30-40% reduction of cycle time; Automotive: 15% lighter/stronger parts; Industrial goods: 30-40% acceleration; Biopharma: 3 months acceleration; Consumer goods: 50% reduction; Industrial goods: 30% avoidance; Automotive: 50% cost reduction",
      "attrs": null,
      "subkind": "bullet",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "36c64180-f5b0-42a5-a096-4e06fb1590a2",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": null,
      "kind": "metric",
      "text": "Performance improvement: 50%",
      "attrs": null,
      "subkind": "primary",
      "toolName": "Quantification",
      "toolSlug": "quantification",
      "confidence": null,
      "componentId": "019dd952-47c6-7671-887c-52eba0720679",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.05,
        "w": 0.9,
        "x": 0.05,
        "y": 0.05
      },
      "kind": "title",
      "text": "Transforming R&D with AI is already in progress across many industries",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "5aa02ac6-504e-45b9-a0b9-e81c163ffb8d",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [
    {
      "name": "Evidence Matrix",
      "slug": "evidence-matrix",
      "agent": "Designer",
      "layer": "block",
      "matchId": "019dd95a-10d5-72cc-9320-92253fda9ee5",
      "evidence": "Each card pairs claim, sector, and quantified evidence",
      "confidence": 70
    },
    {
      "name": "Inductive Reasoning",
      "slug": "inductive-reasoning",
      "agent": "Architect",
      "layer": "loop",
      "matchId": "019dd95a-10d5-72cc-9320-958255076e28",
      "evidence": "Many concrete cases ladder to the pattern: AI works across sectors",
      "confidence": 80
    },
    {
      "name": "Action Titles",
      "slug": "action-titles",
      "agent": "Architect",
      "layer": "slide",
      "matchId": "019dd95a-10d5-72cc-9320-7d1a02d43b47",
      "evidence": "'Transforming R&D with AI is already in progress across many industries'",
      "confidence": 85
    },
    {
      "name": "Bandwagon Effect",
      "slug": "bandwagon-effect",
      "agent": "Storyteller",
      "layer": "slide",
      "matchId": "019dd95a-10d5-72cc-9320-8fc754ec5911",
      "evidence": "Biopharma, auto, industrial, CPG all shown adopting",
      "confidence": 70
    },
    {
      "name": "Concrete Language",
      "slug": "concrete-language",
      "agent": "Storyteller",
      "layer": "slide",
      "matchId": "019dd95a-10d5-72cc-9320-81c965f12d99",
      "evidence": "Specific deltas: 30-40% cycle time, 15% lighter parts, up to 50% cost reduction",
      "confidence": 90
    },
    {
      "name": "Credibility Transfer",
      "slug": "credibility-transfer",
      "agent": "Storyteller",
      "layer": "slide",
      "matchId": "019dd95a-10d5-72cc-9320-874ca3d4a92d",
      "evidence": "'Top-5 auto OEM', 'Top-10 global pharma' borrow authority",
      "confidence": 80
    },
    {
      "name": "Small Multiples",
      "slug": "small-multiples",
      "agent": "Designer",
      "layer": "slide",
      "matchId": "019dd95a-10d5-72cc-9320-88dc3eaf18e2",
      "evidence": "Repeated case-study cards across the R&D process flow",
      "confidence": 85
    }
  ],
  "frameworks": [
    {
      "name": "R&D Process Stages (Research-Develop-Evolve)",
      "slug": null,
      "matchId": "019dd95a-1ca5-70bb-bba1-e85995938852",
      "evidence": "Labeled chevron arrows: Research and define concept / Develop and industrialize product / Evolve product",
      "confidence": 75
    }
  ],
  "arcBeats": [
    {
      "to": 8,
      "from": 7,
      "beatId": "019dd95a-0682-776c-8e38-5ea0430c90e9",
      "arcName": "The Consultant's Gambit",
      "arcSlug": "consultants-gambit",
      "beatName": "Evidence & Proof",
      "beatSlug": "consultants-gambit-evidence-proof",
      "evidence": "Cross-industry case studies with specific KPI gains",
      "position": 4,
      "confidence": 75,
      "parentBeatName": "Evidence",
      "parentBeatSlug": "evidence"
    },
    {
      "to": 8,
      "from": 1,
      "beatId": "019dd95a-0682-776c-8e38-6636536cc2cb",
      "arcName": "The Triple Take",
      "arcSlug": "triple-take",
      "beatName": "The Facts (What)",
      "beatSlug": "triple-take-the-facts-what",
      "evidence": "R&D context + AI capabilities + industry evidence",
      "position": 1,
      "confidence": 60,
      "parentBeatName": "Setup",
      "parentBeatSlug": "setup"
    }
  ],
  "loops": [
    {
      "to": 8,
      "from": 7,
      "name": "Pattern Hunter",
      "slug": "02-pattern-hunter",
      "bestFor": "Time-pressed audiences, building consensus, when data is strong",
      "matchId": "019dd95a-07fe-70ce-8d40-6ecdab58f83b",
      "evidence": "Multiple sector cases (biopharma, auto, industrial, CPG) with KPI deltas converging on AI = impact",
      "position": 2,
      "objective": "Aggregate cross-industry evidence to prove AI in R&D works",
      "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion",
      "confidence": 85,
      "description": "Group multiple pieces of evidence that together point to a pattern or conclusion"
    }
  ],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}