{
  "kind": "tool",
  "value": "curse-of-knowledge-antidote",
  "collectionKey": "slides:tool:curse-of-knowledge-antidote:all-document-kinds:all-producers:all-orientations",
  "filters": {
    "documentKinds": [],
    "sourceTypes": [],
    "orientations": []
  },
  "total": 2,
  "page": 1,
  "pageSize": 24,
  "pageCount": 1,
  "rows": [
    {
      "docId": "019dd923-5ca1-7489-b635-564114f9d8e5",
      "docSlug": "6343adff3aaa1ea3",
      "documentTitle": "AI Radar 2025",
      "authorId": "BCG",
      "authorName": "BCG",
      "documentKindSlug": "consulting-deck",
      "documentKindLabel": "Consulting deck",
      "sourceTypeSlug": "strategy_consulting",
      "sourceTypeLabel": "Strategy consulting",
      "presentationDate": null,
      "orientation": "landscape",
      "aspectRatio": 1.91,
      "pageNumber": 17,
      "slideType": "other",
      "function": "present_framework",
      "notes": "The slide uses a two-part categorization to define AI agents.",
      "imagePath": null,
      "matchCount": 1,
      "evidence": "Simplifies a technical concept for non-experts.",
      "slideHref": "/slides/019dd923-5ca1-7489-b635-564114f9d8e5/17",
      "deckHref": "/decks/019dd923-5ca1-7489-b635-564114f9d8e5",
      "deckAnchorHref": "/decks/019dd923-5ca1-7489-b635-564114f9d8e5#slide-17",
      "loopMatches": [
        {
          "to": 20,
          "from": 16,
          "name": "Zoom In",
          "slug": "06-zoom-in",
          "bestFor": "Technical deep-dives, case studies, detailed analysis",
          "matchId": "019dd95a-088b-72c8-b7e0-d4df648fd095",
          "evidence": "Divider 'Year of AI agents?' then definition, 67% considering, geo data, 5 leadership priorities.",
          "position": 4,
          "objective": "Zoom into AI agents as the next frontier",
          "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication",
          "confidence": 72,
          "description": "Start broad, then progressively focus on specific details that prove your point"
        }
      ],
      "arcBeatMatches": [
        {
          "to": 25,
          "from": 14,
          "beatId": "019dd95a-0701-77fe-ae98-33649f64918f",
          "arcName": "The Consultant's Gambit",
          "arcSlug": "consultants-gambit",
          "beatName": "Evidence & Proof",
          "beatSlug": "consultants-gambit-evidence-proof",
          "evidence": "Risk data, agent adoption stats, geographic benchmarks, talent/upskilling proof.",
          "position": 4,
          "confidence": 82,
          "parentBeatName": "Evidence",
          "parentBeatSlug": "evidence"
        }
      ],
      "imagePathAlt": null,
      "thumbSrc": null,
      "thumbSrcAlt": null,
      "locked": true
    },
    {
      "docId": "019dd923-5de0-76bd-a16b-0ea49d021bbe",
      "docSlug": "148a062f7e47b0b5",
      "documentTitle": "Trends &amp; AI in the Contact Center",
      "authorId": "Deloitte",
      "authorName": "Deloitte",
      "documentKindSlug": "consulting-deck",
      "documentKindLabel": "Consulting deck",
      "sourceTypeSlug": "strategy_consulting",
      "sourceTypeLabel": "Strategy consulting",
      "presentationDate": null,
      "orientation": "landscape",
      "aspectRatio": 1.778,
      "pageNumber": 21,
      "slideType": "other",
      "function": "establish_context",
      "notes": null,
      "imagePath": null,
      "matchCount": 1,
      "evidence": "Side-by-side definitions decode GenAI vs LLM jargon.",
      "slideHref": "/slides/019dd923-5de0-76bd-a16b-0ea49d021bbe/21",
      "deckHref": "/decks/019dd923-5de0-76bd-a16b-0ea49d021bbe",
      "deckAnchorHref": "/decks/019dd923-5de0-76bd-a16b-0ea49d021bbe#slide-21",
      "loopMatches": [
        {
          "to": 23,
          "from": 19,
          "name": "Zoom In",
          "slug": "06-zoom-in",
          "bestFor": "Technical deep-dives, case studies, detailed analysis",
          "matchId": "019deab8-552d-7483-8368-36887106dcf8",
          "evidence": "Solution framework (p19) -> nuts-and-bolts flow (p20) -> GenAI def (p21) -> applicability (p22) -> architecture (p23).",
          "position": 4,
          "objective": "Drill from Deloitte assets down to GenAI applicability and architecture",
          "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication",
          "confidence": 72,
          "description": "Start broad, then progressively focus on specific details that prove your point"
        }
      ],
      "arcBeatMatches": [
        {
          "to": 23,
          "from": 14,
          "beatId": "019deab8-53af-7352-a807-e611713aa078",
          "arcName": "The Consultant's Gambit",
          "arcSlug": "consultants-gambit",
          "beatName": "Solution & Approach",
          "beatSlug": null,
          "evidence": "CAI use cases, value, TrueServe framework, GenAI fit, architecture (p16-23).",
          "position": 3,
          "confidence": 88,
          "parentBeatName": null,
          "parentBeatSlug": null
        },
        {
          "to": 23,
          "from": 16,
          "beatId": "019deab8-546c-7543-b0a1-9efb0c50aa46",
          "arcName": "The Triple Take",
          "arcSlug": "triple-take",
          "beatName": "The Implications (So What)",
          "beatSlug": null,
          "evidence": "AI use cases, NPS impact, solution framework.",
          "position": 2,
          "confidence": 55,
          "parentBeatName": null,
          "parentBeatSlug": null
        }
      ],
      "imagePathAlt": null,
      "thumbSrc": null,
      "thumbSrcAlt": null,
      "locked": true
    }
  ]
}