{
  "docId": "019dd923-5ca1-7489-b637-2c79eb4da4f7",
  "docSlug": "fd1a22c5f6d95a06",
  "documentTitle": "TEI Microsoft Agentic AI",
  "authorId": "Forrester",
  "authorName": "Forrester",
  "documentKindSlug": "consulting-deck",
  "documentKindLabel": "Consulting deck",
  "sourceTypeSlug": "industry_analyst",
  "sourceTypeLabel": "Industry analyst",
  "presentationDate": null,
  "orientation": "portrait",
  "aspectRatio": 0.773,
  "pageNumber": 18,
  "pageCount": 46,
  "prevPage": 17,
  "nextPage": 19,
  "slideType": "appendix_data",
  "function": "quantify_impact",
  "density": "overcrowded",
  "nDataPoints": 11,
  "notes": "Page 18 of a Forrester Consulting study.",
  "elementsJson": [
    "bullet_list",
    "numbered_list",
    "big_number"
  ],
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5ca1-7489-b637-2c79eb4da4f7/18",
  "deckHref": "/decks/019dd923-5ca1-7489-b637-2c79eb4da4f7",
  "deckJsonHref": "/decks/019dd923-5ca1-7489-b637-2c79eb4da4f7.json",
  "deckAnchorHref": "/decks/019dd923-5ca1-7489-b637-2c79eb4da4f7#slide-18",
  "components": [
    {
      "bbox": null,
      "kind": "callout",
      "text": "To account for these risks, Forrester adjusted this benefit downward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $16.2 million.",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd951-c23e-726c-a79b-323a791ac9ae",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.15,
        "w": 0.9,
        "x": 0.05,
        "y": 0.55
      },
      "kind": "list",
      "text": "Modeling and assumptions used by Forrester to estimate external spend reduction.",
      "attrs": null,
      "subkind": "bullet",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "243828ef-8cbe-41fb-9aef-2c7d4e9523f0",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.15,
        "w": 0.9,
        "x": 0.05,
        "y": 0.4
      },
      "kind": "list",
      "text": "Survey results showing percentage reductions in various external spend categories (marketing, finance, IT, etc.).",
      "attrs": null,
      "subkind": "bullet",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "27a09f50-162f-4767-a972-1e0bae727a03",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.1,
        "w": 0.9,
        "x": 0.05,
        "y": 0.7
      },
      "kind": "list",
      "text": "Risks and adjustments applied to the final benefit calculation.",
      "attrs": null,
      "subkind": "bullet",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "33b877e5-85d2-4892-8bf7-14fd72da22ea",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.35,
        "w": 0.9,
        "x": 0.05,
        "y": 0.05
      },
      "kind": "list",
      "text": "Qualitative insights from energy and professional services organizations regarding external spend reduction.",
      "attrs": null,
      "subkind": "bullet",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "f32f71b9-12fe-4d52-b590-3209144e2542",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.05,
        "w": 0.1,
        "x": 0.05,
        "y": 0.85
      },
      "kind": "metric",
      "text": "3.2%",
      "attrs": null,
      "subkind": "big-number",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "990b881b-0af5-49fd-bc63-715a0556077f",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": null,
      "kind": "metric",
      "text": "Reduction in non-COGS external spend: 3.2%",
      "attrs": null,
      "subkind": "primary",
      "toolName": "Quantification",
      "toolSlug": "quantification",
      "confidence": null,
      "componentId": "019dd951-c23e-726c-a79b-3ba2efca34ab",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.02,
        "w": 0.4,
        "x": 0.05,
        "y": 0.9
      },
      "kind": "paragraph",
      "text": "Reduction in non-COGS external spend over three years",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "ddbbc0da-6f37-463b-abe0-2147de33980b",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": null,
      "kind": "quote",
      "text": "The primary differentiating factor in the energy sector is efficiently getting oil out of the ground and refining it. The goal is to make oil and gas affordable, reliable, and ever cleaner. We are looking to use agentic AI to lead in this space.",
      "attrs": null,
      "subkind": null,
      "toolName": "Authority citation",
      "toolSlug": "authority-citation",
      "confidence": null,
      "componentId": "019dd951-c23e-726c-a79b-344da095e8c2",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.02,
        "w": 0.4,
        "x": 0.05,
        "y": 0.02
      },
      "kind": "title",
      "text": "The Total Economic Impact™ Of Microsoft's Agentic AI Solutions",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "b7f2835b-1a3b-4c17-af34-d431b2e76af7",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [
    {
      "metricRaw": "Reduction in non-COGS external spend",
      "numberRaw": "3.2%",
      "numberKind": "percent",
      "actionTitle": null,
      "calloutText": "To account for these risks, Forrester adjusted this benefit downward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $16.2 million.",
      "numberScale": null,
      "numberValue": 3.2,
      "metricFamily": "cost_savings",
      "numberCurrency": null
    }
  ],
  "tools": [
    {
      "name": "Authority Bias",
      "slug": "authority-bias",
      "agent": "Storyteller",
      "layer": "slide",
      "matchId": "019de8ca-06fb-76cc-82b5-5811f8338b6b",
      "evidence": "Energy-sector executive quote.",
      "confidence": 70
    },
    {
      "name": "Chart Selection Guide",
      "slug": "chart-selection-guide",
      "agent": "Designer",
      "layer": "slide",
      "matchId": "2035deda-705b-462a-a651-18de955c1fb6",
      "evidence": "list/bullet: Survey results showing percentage reductions in various external spend categories (marketing, finance, IT, etc.).",
      "confidence": 0.6
    },
    {
      "name": "Concrete Language",
      "slug": "concrete-language",
      "agent": "Storyteller",
      "layer": "slide",
      "matchId": "019de8ca-06d7-70bf-b6ee-498ede3f3f4b",
      "evidence": "Specific 3.2% non-COGS reduction with 10% risk haircut.",
      "confidence": 78
    }
  ],
  "frameworks": [],
  "arcBeats": [
    {
      "to": 31,
      "from": 10,
      "beatId": "019de8c9-fdf5-7511-a886-09c1ac2e7bff",
      "arcName": "The Consultant's Gambit",
      "arcSlug": "consultants-gambit",
      "beatName": "Evidence & Proof",
      "beatSlug": null,
      "evidence": "Detailed quantified benefits and costs with risk-adjusted tables and quotes.",
      "position": 4,
      "confidence": 88,
      "parentBeatName": null,
      "parentBeatSlug": null
    },
    {
      "to": 31,
      "from": 10,
      "beatId": "019de8c9-fe8a-778d-ae50-87ab43e9ed55",
      "arcName": "The Triple Take",
      "arcSlug": "triple-take",
      "beatName": "The Implications (So What)",
      "beatSlug": null,
      "evidence": "Per-category benefit and cost analyses with risk-adjusted PV.",
      "position": 2,
      "confidence": 60,
      "parentBeatName": null,
      "parentBeatSlug": null
    }
  ],
  "loops": [
    {
      "to": 19,
      "from": 16,
      "name": "Waterfall Value",
      "slug": "31-waterfall-value",
      "bestFor": "Financial analysis, value bridges, variance explanations",
      "matchId": "019de8c9-ff70-7189-a529-ade0d65ed5b7",
      "evidence": "p16-17 labor efficiencies; p18-19 external spend reduction with risk-adjusted PV per driver.",
      "position": 5,
      "objective": "Quantify Operations transformation benefits",
      "structure": "The Total -> Driver 1 Impact -> Driver 2 Impact -> Driver 3 Impact -> The Remainder",
      "confidence": 84,
      "description": "Break down a big number into its component drivers to show where value is created or lost"
    }
  ],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}