{
  "docId": "019dd923-5de0-76bd-a167-dc33bed363eb",
  "docSlug": "42b983acd15c7ada",
  "documentTitle": "Elevating the Exchange",
  "authorId": "Accenture",
  "authorName": "Accenture",
  "documentKindSlug": "consulting-deck",
  "documentKindLabel": "Consulting deck",
  "sourceTypeSlug": "strategy_consulting",
  "sourceTypeLabel": "Strategy consulting",
  "presentationDate": null,
  "orientation": "landscape",
  "aspectRatio": 1.778,
  "pageNumber": 20,
  "pageCount": 36,
  "prevPage": 19,
  "nextPage": 21,
  "slideType": "case_study",
  "function": "illustrate_case",
  "density": "overcrowded",
  "nDataPoints": 4,
  "notes": "The slide uses a pull-quote style headline to emphasize the impact of Gen AI.",
  "elementsJson": [
    "paragraph",
    "callout_box",
    "photo"
  ],
  "metadataConfidence": 0.95,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5de0-76bd-a167-dc33bed363eb/20",
  "deckHref": "/decks/019dd923-5de0-76bd-a167-dc33bed363eb",
  "deckJsonHref": "/decks/019dd923-5de0-76bd-a167-dc33bed363eb.json",
  "deckAnchorHref": "/decks/019dd923-5de0-76bd-a167-dc33bed363eb#slide-20",
  "components": [
    {
      "bbox": null,
      "kind": "callout",
      "text": "With the help of the bot, the exchange's employees were able to reduce lead times by over 90%—with a 95% response accuracy rate.",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd951-a672-7739-9cec-0340f5ba67b7",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.25,
        "w": 0.18,
        "x": 0.724,
        "y": 0.538
      },
      "kind": "image",
      "text": null,
      "attrs": null,
      "subkind": "photo",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "9c6a3d46-6ad9-4399-9563-f6e5f2d06128",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": null,
      "kind": "metric",
      "text": "Lead time reduction: 90%",
      "attrs": null,
      "subkind": "primary",
      "toolName": "Quantification",
      "toolSlug": "quantification",
      "confidence": null,
      "componentId": "019dd951-a672-7739-9cec-0792438706c9",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.25,
        "w": 0.18,
        "x": 0.298,
        "y": 0.338
      },
      "kind": "paragraph",
      "text": "As gen AI helps exchange workers become more creative and efficient, such outcomes will become increasingly common. Take developers: Gen AI could enable them to speed up coding and testing, improve code quality, convert code into other languages and prevent, predict and fix bugs.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "1b137e20-cf2b-4295-82e5-3ab96f52ba59",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.35,
        "w": 0.18,
        "x": 0.511,
        "y": 0.158
      },
      "kind": "paragraph",
      "text": "Consider the experience of a market infrastructure company that we also worked with. The company sought to explore whether a gen AI-powered programming tool could enhance the productivity of its developers by 10%, but without undermining the quality of their coding. The answer: Developers in a pilot initiative saw their throughput increase by 40%, on average, with no adverse effects on code quality.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "788f8ff9-698e-4246-9f0c-8d11185d134e",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.25,
        "w": 0.18,
        "x": 0.083,
        "y": 0.158
      },
      "kind": "paragraph",
      "text": "Al technologies aren’t new. Nasdaq, for instance, introduced an “agent-based” AI model in 1997. But recent advances, especially in generative AI’s capabilities, have the potential to transform both how exchanges work and where they can make money.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "b4f28e0d-24a4-468a-8f9a-291a29d071e3",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.45,
        "w": 0.18,
        "x": 0.724,
        "y": 0.158
      },
      "kind": "paragraph",
      "text": "In the front office, gen AI will increasingly allow exchanges to use their data to personalize services for customers in real-time... Gen AI can, likewise, also help detect money laundering and other financial crimes by supplementing exchanges’ traditional AI models.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "d4e9045e-6923-45f2-bbb9-ec5a4cb6bff2",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.12,
        "w": 0.18,
        "x": 0.298,
        "y": 0.148
      },
      "kind": "title",
      "text": "With the help of the bot, the exchange’s employees were able to reduce lead times by over 90%—with a 95% response accuracy rate.",
      "attrs": null,
      "subkind": "action-title",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "3f63f4c7-30d7-4d27-9e0d-d677c61ec04a",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [
    {
      "metricRaw": "Lead time reduction",
      "numberRaw": "90%",
      "numberKind": "percent",
      "actionTitle": "With the help of the bot, the exchange's employees were able to reduce lead times by over 90%—with a 95% response accuracy rate.",
      "calloutText": "With the help of the bot, the exchange's employees were able to reduce lead times by over 90%—with a 95% response accuracy rate.",
      "numberScale": null,
      "numberValue": 90,
      "metricFamily": "other",
      "numberCurrency": null
    }
  ],
  "tools": [
    {
      "name": "Concrete Language",
      "slug": "concrete-language",
      "agent": "Storyteller",
      "layer": "slide",
      "matchId": "019dd95a-0c43-7422-b2f6-a03c86aa0a15",
      "evidence": "'Lead times reduced by over 90% with 95% accuracy'",
      "confidence": 85
    },
    {
      "name": "Credibility Transfer",
      "slug": "credibility-transfer",
      "agent": "Storyteller",
      "layer": "slide",
      "matchId": "019dd95a-0c43-7422-b2f6-a9a949d89573",
      "evidence": "Quotes a real exchange's measured AI outcome",
      "confidence": 65
    },
    {
      "name": "Sinatra Test",
      "slug": "sinatra-test",
      "agent": "Storyteller",
      "layer": "slide",
      "matchId": "019dd95a-0c43-7422-b2f6-a4f83f6932f4",
      "evidence": "Single flagship AI-bot case used to prove the AI step",
      "confidence": 65
    }
  ],
  "frameworks": [],
  "arcBeats": [
    {
      "to": 22,
      "from": 10,
      "beatId": "019dd95a-0680-7418-820a-5fa4131fdf65",
      "arcName": "The Consultant's Gambit",
      "arcSlug": "consultants-gambit",
      "beatName": "Solution & Approach",
      "beatSlug": "consultants-gambit-solution-approach",
      "evidence": "Four numbered 'Steps' with digital-core framework and tech enablers",
      "position": 3,
      "confidence": 90,
      "parentBeatName": "Turn",
      "parentBeatSlug": "turn"
    },
    {
      "to": 31,
      "from": 10,
      "beatId": "019dd95a-0680-7418-820a-7161d37dbb94",
      "arcName": "The Transformation Tale",
      "arcSlug": "transformation-tale",
      "beatName": "The Bridge",
      "beatSlug": "transformation-tale-the-bridge",
      "evidence": "Four-step reinvention path with proof points",
      "position": 3,
      "confidence": 60,
      "parentBeatName": "Resolution",
      "parentBeatSlug": "resolution"
    }
  ],
  "loops": [
    {
      "to": 22,
      "from": 20,
      "name": "Precedent",
      "slug": "09-precedent",
      "bestFor": "Risk mitigation, strategy validation, building confidence in new approaches",
      "matchId": "019dd95a-07fd-712f-b773-d848e190f73f",
      "evidence": "p.20 AI bot cut lead times 90%, p.21 DLT trade-cycle case, p.22 smart-contracts compliance precedent.",
      "position": 4,
      "objective": "Use AI bot and DLT smart-contract precedents to validate Step 3",
      "structure": "The Precedent Case -> What Happened -> The Parallel -> Applied Learning",
      "confidence": 78,
      "description": "Use historical or external examples to validate your approach"
    }
  ],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}