{
  "docId": "019dd923-5ca1-7489-b633-7a814d47b8dd",
  "docSlug": "4d3687fa691fcd8b",
  "documentTitle": "The art of AI maturity Advancing from practice to performance",
  "authorId": "Accenture",
  "authorName": "Accenture",
  "documentKindSlug": "consulting-deck",
  "documentKindLabel": "Consulting deck",
  "sourceTypeSlug": "strategy_consulting",
  "sourceTypeLabel": "Strategy consulting",
  "presentationDate": null,
  "orientation": "landscape",
  "aspectRatio": 1.778,
  "pageNumber": 23,
  "pageCount": 40,
  "prevPage": 22,
  "nextPage": 24,
  "slideType": "key_messages",
  "function": "summarize",
  "density": "overcrowded",
  "nDataPoints": 1,
  "notes": "Part of a series on AI maturity; mentions 'Achievers' as a specific segment.",
  "elementsJson": [
    "headline_text",
    "action_title",
    "subtitle_text",
    "paragraph",
    "photo"
  ],
  "metadataConfidence": 0.95,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5ca1-7489-b633-7a814d47b8dd/23",
  "deckHref": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd",
  "deckJsonHref": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd.json",
  "deckAnchorHref": "/decks/019dd923-5ca1-7489-b633-7a814d47b8dd#slide-23",
  "components": [
    {
      "bbox": null,
      "kind": "callout",
      "text": "To extract value from their data quickly and effectively, Achievers are also 32% more likely, on average, than Experimenters to either develop custom-built machine learning applications or work with a partner that offers solutions-as-a-service.",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd951-9f64-72ba-b402-97105ee49377",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.868,
        "w": 0.431,
        "x": 0.512,
        "y": 0.057
      },
      "kind": "image",
      "text": "Two people working at a computer",
      "attrs": null,
      "subkind": "photo",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "3d347fff-bd00-4c83-959e-323f53a9c8a0",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": null,
      "kind": "metric",
      "text": "Achievers more likely than Experimenters to productize AI: 32%",
      "attrs": null,
      "subkind": "primary",
      "toolName": "Quantification",
      "toolSlug": "quantification",
      "confidence": null,
      "componentId": "019dd951-9f64-72ba-b402-9811a752c8d9",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.25,
        "w": 0.205,
        "x": 0.285,
        "y": 0.45
      },
      "kind": "paragraph",
      "text": "To extract value from their data quickly and effectively, Achievers are also 32% more likely, on average, than Experimenters to either develop custom-built machine learning applications or work with a partner that offers solutions-as-a-service. Achievers are also more likely than Innovators to use AI for innovation, tapping into readily available developer networks that can swiftly productionize and scale successful pilots.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "1145479d-c0b5-4f59-914b-2fc835b49b51",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.18,
        "w": 0.25,
        "x": 0.057,
        "y": 0.321
      },
      "kind": "paragraph",
      "text": "Another priority for Achievers involves building an AI core: an operational data and AI platform that taps into companies’ talent, technology and data ecosystems, allowing firms to balance experimentation and execution. An AI core helps organizations productize their AI applications and integrate AI into other applications, which makes differentiation with AI more seamless.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "13f06001-d607-4154-a5a8-76285761f86c",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.02,
        "w": 0.267,
        "x": 0.029,
        "y": 0.94
      },
      "kind": "paragraph",
      "text": "The art of AI maturity—Advancing from practice to performance",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "45172b44-fbfe-4c1c-bc62-74e7b918a8af",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.12,
        "w": 0.205,
        "x": 0.285,
        "y": 0.321
      },
      "kind": "paragraph",
      "text": "To build AI cores, Achievers harness the power of internal and external data, making that data trustworthy and storing it in a single enterprise-grade cloud platform—complete with appropriate usage, monitoring and security policies.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "5a78da8c-81db-432f-8686-71a1faa3c38b",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.25,
        "w": 0.25,
        "x": 0.057,
        "y": 0.515
      },
      "kind": "paragraph",
      "text": "An AI core also works across the cloud continuum (e.g. from migration to innovation), provides end-to-end data capabilities (foundation, management and governance), manages the machine learning lifecycle (workflow, model training, model deployment) and provides self-service capabilities. AI cores are, in turn, managed by dedicated interdisciplinary teams of machine learning engineers, data scientists, data-domain experts and systems engineers.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "6fb8d635-9202-4f6d-8a1e-a12ba7e7c6cc",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.075,
        "w": 0.405,
        "x": 0.057,
        "y": 0.175
      },
      "kind": "title",
      "text": "Industrialize AI tools and teams to create an AI core",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "a0d62642-bdf7-42fd-9594-5854c802d915",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.032,
        "w": 0.205,
        "x": 0.057,
        "y": 0.103
      },
      "kind": "title",
      "text": "Success Factor 03",
      "attrs": null,
      "subkind": "subtitle",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "fdc19a21-b1d4-4ba7-8762-0a0edb842474",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [
    {
      "metricRaw": "Achievers more likely than Experimenters to productize AI",
      "numberRaw": "32%",
      "numberKind": "percent",
      "actionTitle": "Industrialize AI tools and teams to create an AI core",
      "calloutText": "To extract value from their data quickly and effectively, Achievers are also 32% more likely, on average, than Experimenters to either develop custom-built machine learning applications or work with a partner that offers solutions-as-a-service.",
      "numberScale": null,
      "numberValue": 32,
      "metricFamily": "other",
      "numberCurrency": null
    }
  ],
  "tools": [
    {
      "name": "Action Titles",
      "slug": "action-titles",
      "agent": "Architect",
      "layer": "slide",
      "matchId": "019dd95a-0f08-73d3-987b-e82258f4cc15",
      "evidence": "Directive: 'Industrialize AI tools and teams to create an AI core'",
      "confidence": 90
    },
    {
      "name": "Concrete Language",
      "slug": "concrete-language",
      "agent": "Storyteller",
      "layer": "slide",
      "matchId": "019dd95a-0f08-73d3-987b-ec93e02c70c3",
      "evidence": "32% more likely to build custom ML applications",
      "confidence": 80
    },
    {
      "name": "Logical chain",
      "slug": "logical-chain",
      "agent": null,
      "layer": "slide",
      "matchId": "cc6b62bb-040a-4cb6-873b-560641c7acb5",
      "evidence": "To extract value from their data quickly and effectively, Achievers are also 32% more likely, on average, than Experimenters to either develop custom-built machine learning applications or work with a partner",
      "confidence": 0.7
    }
  ],
  "frameworks": [],
  "arcBeats": [
    {
      "to": 27,
      "from": 17,
      "beatId": "019dd95a-0682-776c-8e32-7e8c761f9fc5",
      "arcName": "The Consultant's Gambit",
      "arcSlug": "consultants-gambit",
      "beatName": "Solution & Approach",
      "beatSlug": "consultants-gambit-solution-approach",
      "evidence": "Five recommendations to master AI maturity, each with case study",
      "position": 3,
      "confidence": 90,
      "parentBeatName": "Turn",
      "parentBeatSlug": "turn"
    }
  ],
  "loops": [
    {
      "to": 27,
      "from": 17,
      "name": "Mece Breakdown",
      "slug": "40-mece-breakdown",
      "bestFor": "Problem structuring, ensuring completeness, strategic analysis",
      "matchId": "019dd95a-07fe-70ce-8d3a-483bc3c6386e",
      "evidence": "Five distinct recommendation categories (sponsor / talent / AI core / responsible / invest) each with case study",
      "position": 5,
      "objective": "Five MECE recommendations to become an Achiever",
      "structure": "The Whole -> Category A (distinct) -> Category B (distinct) -> Category C (distinct) -> Complete Coverage",
      "confidence": 85,
      "description": "Divide a complex topic into mutually exclusive, collectively exhaustive categories"
    }
  ],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}