{
  "docId": "019dd923-5e88-73ef-bd5d-544caffa879c",
  "docSlug": "05c71e6a3ed5c6d0",
  "documentTitle": "2025 Executive Perspectives Unlocking Impact from AI Driving Sustaingable Cost Advantage with 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": 6,
  "pageCount": 20,
  "prevPage": 5,
  "nextPage": 7,
  "slideType": "market_landscape",
  "function": "present_framework",
  "density": "balanced",
  "nDataPoints": 0,
  "notes": "The slide uses a Venn-like diagram to categorize AI technologies based on data type and modality.",
  "elementsJson": [
    "headline_text",
    "callout_box",
    "infographic",
    "numbered_list"
  ],
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5e88-73ef-bd5d-544caffa879c/6",
  "deckHref": "/decks/019dd923-5e88-73ef-bd5d-544caffa879c",
  "deckJsonHref": "/decks/019dd923-5e88-73ef-bd5d-544caffa879c.json",
  "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-544caffa879c#slide-6",
  "components": [
    {
      "bbox": {
        "h": 0.08,
        "w": 0.3,
        "x": 0.65,
        "y": 0.18
      },
      "kind": "callout",
      "text": "Significant applicability in manufacturing; please stay tuned for our perspective on AI in manufacturing, expected to be published in June 2025",
      "attrs": null,
      "subkind": "primary",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "31a897ab-3f8c-43fd-8620-5dda3ceab721",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.5,
        "w": 0.75,
        "x": 0.15,
        "y": 0.25
      },
      "kind": "diagram",
      "text": "AI technology ecosystem map",
      "attrs": null,
      "subkind": "other",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "f534fed9-9bd1-4655-ac7a-d03defba079e",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.05,
        "w": 0.8,
        "x": 0.05,
        "y": 0.9
      },
      "kind": "disclaimer",
      "text": "1. For the purposes of this work, we have included deep learning (DL) as part of the computer vision solution cluster as the primary space where DL technologies achieved practical breakthroughs and widespread adoption. However, as noted in our solution cluster deep dives and trends, DL is increasingly applied in previous NLP and standard ML domains as well",
      "attrs": null,
      "subkind": null,
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "adfdfa75-1dfe-4671-bd1e-29b7de218d03",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.1,
        "w": 0.9,
        "x": 0.05,
        "y": 0.8
      },
      "kind": "list",
      "text": "1. Traditional machine learning techniques primary applied to structure data\n2. Processing, analysis, and generation of textual data and cognitive interactions\n3. Solutions for leveraging processing, analysis, and generation of image and video data\n4. Specialized chips and hardware tailored to advanced/high-volume AI workloads",
      "attrs": null,
      "subkind": "numbered",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "732fb2e3-3f62-4c47-a532-2d5f30a60d77",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.08,
        "w": 0.9,
        "x": 0.05,
        "y": 0.03
      },
      "kind": "title",
      "text": "Companies can apply different technologies in the AI ecosystem to deliver sustainable cost advantage",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "e6c321d5-7a5f-4126-8624-e5635c3711c2",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [
    {
      "name": "Chunking",
      "slug": "chunking",
      "agent": "Architect",
      "layer": "loop",
      "matchId": "019dd95a-121f-71e6-8e8b-7aed814a2438",
      "evidence": "Loop chunks the AI ecosystem into four numbered clusters.",
      "confidence": 75
    },
    {
      "name": "Action Titles",
      "slug": "action-titles",
      "agent": "Architect",
      "layer": "slide",
      "matchId": "019dd95a-121f-71e6-8e8b-69e82a4c4711",
      "evidence": "Title states the framework's purpose: deliver sustainable cost advantage.",
      "confidence": 85
    },
    {
      "name": "Annotation",
      "slug": "annotation",
      "agent": "Designer",
      "layer": "slide",
      "matchId": "019dd95a-121f-71e6-8e8b-75620d7caef1",
      "evidence": "Numbered legend (1-4) explains each AI tech cluster.",
      "confidence": 70
    },
    {
      "name": "Gestalt Principles",
      "slug": "gestalt-principles",
      "agent": "Designer",
      "layer": "slide",
      "matchId": "019dd95a-121f-71e6-8e8b-6eee758240a3",
      "evidence": "Four overlapping coloured Venn-style bubbles signal relatedness.",
      "confidence": 80
    },
    {
      "name": "Grid System",
      "slug": "grid-system",
      "agent": "Designer",
      "layer": "slide",
      "matchId": "019dd95a-121f-71e6-8e8b-70a510ef4663",
      "evidence": "Two labelled axes ('Modality of data' x 'Data type') anchor a 2x2 plot.",
      "confidence": 70
    }
  ],
  "frameworks": [
    {
      "name": "2x2 Matrix",
      "slug": null,
      "matchId": "019dd95a-1ca6-71be-9e55-e4635ae0bd2a",
      "evidence": "Labelled axes 'Modality of data' (distributed/batch) x 'Data type' (relational/unstructured).",
      "confidence": 75
    },
    {
      "name": "AI Technology Ecosystem Map",
      "slug": null,
      "matchId": "7d56fee6-ac76-4319-9db7-464fdde567a8",
      "evidence": "Visual categorization of AI technologies by data type and modality",
      "confidence": 0.8
    }
  ],
  "arcBeats": [
    {
      "to": 7,
      "from": 6,
      "beatId": "019dd95a-0701-77fe-ae97-aa51f29e2ba5",
      "arcName": "The Consultant's Gambit",
      "arcSlug": "consultants-gambit",
      "beatName": "Solution & Approach",
      "beatSlug": "consultants-gambit-solution-approach",
      "evidence": "AI tech ecosystem mapped to four cost-structure patterns.",
      "position": 3,
      "confidence": 92,
      "parentBeatName": "Turn",
      "parentBeatSlug": "turn"
    },
    {
      "to": 9,
      "from": 6,
      "beatId": "019dd95a-0701-77fe-ae97-b57ff0dae61c",
      "arcName": "The Triple Take",
      "arcSlug": "triple-take",
      "beatName": "The Facts (What)",
      "beatSlug": "triple-take-the-facts-what",
      "evidence": "Four AI patterns + cross-industry impact table.",
      "position": 1,
      "confidence": 55,
      "parentBeatName": "Setup",
      "parentBeatSlug": "setup"
    }
  ],
  "loops": [
    {
      "to": 7,
      "from": 6,
      "name": "Mece Breakdown",
      "slug": "40-mece-breakdown",
      "bestFor": "Problem structuring, ensuring completeness, strategic analysis",
      "matchId": "019dd95a-088b-72c8-b7e0-83c2a144453e",
      "evidence": "p.6 maps AI tech to data dimensions; p.7 distills four distinct, non-overlapping cost patterns.",
      "position": 2,
      "objective": "Carve the AI cost-impact space into 4 MECE patterns",
      "structure": "The Whole -> Category A (distinct) -> Category B (distinct) -> Category C (distinct) -> Complete Coverage",
      "confidence": 78,
      "description": "Divide a complex topic into mutually exclusive, collectively exhaustive categories"
    }
  ],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}