{
  "docId": "019dd923-5e88-73ef-bd5d-d76a2779de1a",
  "docSlug": "0251578dbff75a2b",
  "documentTitle": "2025 The AI Dossier",
  "authorId": "Deloitte",
  "authorName": "Deloitte",
  "documentKindSlug": "consulting-deck",
  "documentKindLabel": "Consulting deck",
  "sourceTypeSlug": "strategy_consulting",
  "sourceTypeLabel": "Strategy consulting",
  "presentationDate": null,
  "orientation": "landscape",
  "aspectRatio": 1.778,
  "pageNumber": 11,
  "pageCount": 190,
  "prevPage": 10,
  "nextPage": 12,
  "slideType": "problem_statement",
  "function": "frame_problem",
  "density": "overcrowded",
  "nDataPoints": 0,
  "notes": "The slide uses a two-column layout: the left side defines the problem (reactive retail management) and the right side provides the solution (four specific AI agent capabilities).",
  "elementsJson": [
    "headline_text",
    "action_title",
    "paragraph",
    "callout_box",
    "icon_grid"
  ],
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5e88-73ef-bd5d-d76a2779de1a/11",
  "deckHref": "/decks/019dd923-5e88-73ef-bd5d-d76a2779de1a",
  "deckJsonHref": "/decks/019dd923-5e88-73ef-bd5d-d76a2779de1a.json",
  "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-d76a2779de1a#slide-11",
  "components": [
    {
      "bbox": {
        "h": 0.03,
        "w": 0.15,
        "x": 0.07,
        "y": 0.43
      },
      "kind": "callout",
      "text": "ISSUE/OPPORTUNITY",
      "attrs": null,
      "subkind": "primary",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "8ac8eed3-a62e-4ce3-9ff7-3dcf76d68f9f",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": null,
      "kind": "callout",
      "text": "Agentic AI can help stores become highly efficient, semi-autonomous systems—where human associates focus on value-added service and strategic priorities while AI handles routine operational tasks.",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd952-5642-7795-a9f8-29d079f585f1",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.06,
        "w": 0.38,
        "x": 0.54,
        "y": 0.15
      },
      "kind": "framework",
      "text": "HOW AI CAN HELP",
      "attrs": null,
      "subkind": "instance",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "3e35feee-10ee-46d0-8f13-881207da9915",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.15,
        "w": 0.18,
        "x": 0.75,
        "y": 0.23
      },
      "kind": "list",
      "text": "Automated compliance monitoring: A compliance agent can use computer vision and sensor data to monitor planogram adherence, promotion execution, and safety hazards, triggering immediate corrective actions as needed.",
      "attrs": null,
      "subkind": "bullet",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "02a633e1-5aa3-46c6-bd2d-a5ad800a5bc1",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.15,
        "w": 0.18,
        "x": 0.55,
        "y": 0.23
      },
      "kind": "list",
      "text": "Continuous store sensing: A store sensing agent can monitor real-time data streams from cameras, IoT sensors, POS systems, and digital twins to track foot traffic, queue lengths, inventory levels, associate availability, and local events.",
      "attrs": null,
      "subkind": "bullet",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "22341528-c031-4fe3-a5fb-de975e706739",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.15,
        "w": 0.18,
        "x": 0.75,
        "y": 0.43
      },
      "kind": "list",
      "text": "Coordinated multi-agent oversight: A store manager agent can oversee all other agents, resolving conflicts, optimizing labor deployment, and coordinating with upstream systems such as workforce management, ERP, and order management platforms.",
      "attrs": null,
      "subkind": "bullet",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "4ffc67c1-d398-4c27-8b95-c46074afa7ba",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.15,
        "w": 0.18,
        "x": 0.55,
        "y": 0.43
      },
      "kind": "list",
      "text": "Dynamic task allocation: A task management agent can reprioritize and assign tasks such as restocking, returns processing, online order pickup, or promotional setup based on current demand and available labor.",
      "attrs": null,
      "subkind": "bullet",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "c32db4b2-4f23-43e5-a245-be3ada823551",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.06,
        "w": 0.4,
        "x": 0.07,
        "y": 0.33
      },
      "kind": "paragraph",
      "text": "Agentic AI systems can coordinate in-store activities by continuously monitoring conditions and taking automated actions to achieve smooth, efficient, customer-responsive operations.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "14bb91df-8edf-497b-a96f-d7c021083522",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.25,
        "w": 0.17,
        "x": 0.3,
        "y": 0.46
      },
      "kind": "paragraph",
      "text": "This reactive approach can lead to problems such as stock-outs, bottlenecks at checkout, haphazard execution of merchandising plans, and missed sales opportunities—operational frictions that can quickly erode revenue, profitability, and customer satisfaction. Agentic AI can help stores become highly efficient, semi-autonomous systems—where human associates focus on value-added service and strategic priorities while AI handles routine operational tasks.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "94e9864c-1d36-4cc3-86cd-d7dcc466f573",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.25,
        "w": 0.22,
        "x": 0.07,
        "y": 0.46
      },
      "kind": "paragraph",
      "text": "Running a high-performing retail store involves hundreds of large and small decisions each day: allocating staff to handle peak traffic, restocking shelves when inventory runs low, responding to customer requests, and ensuring that promotional displays are set up correctly. In many cases, these actions are handled reactively, based on direct observation by a manager or sales associate, rather than being driven by data in real time.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "bfd7b18c-48e1-4e2a-aae1-455c4e891e24",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.03,
        "w": 0.4,
        "x": 0.07,
        "y": 0.28
      },
      "kind": "title",
      "text": "Autonomous in-store coordination to optimize retail execution",
      "attrs": null,
      "subkind": "action-title",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "78dc9b1c-cbe4-4c93-9714-c085e491b0ce",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.08,
        "w": 0.35,
        "x": 0.07,
        "y": 0.14
      },
      "kind": "title",
      "text": "Next-generation store operations",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "2f0a41df-7299-47cd-a976-74af06728178",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [],
  "frameworks": [
    {
      "name": "list-presentation",
      "slug": null,
      "matchId": "77cc903d-af44-46c7-a564-07573c0175f5",
      "evidence": "Structured list of capabilities under a header",
      "confidence": 0.8
    }
  ],
  "arcBeats": [
    {
      "to": 16,
      "from": 10,
      "beatId": "6771310d-2b44-4a3a-8cf9-82ad3cdccd54",
      "arcName": "The Sequoia Pitch",
      "arcSlug": "sequoia-pitch",
      "beatName": "Solution",
      "beatSlug": "sequoia-pitch-solution",
      "evidence": "The document then presents solutions to these problems, showcasing AI-driven approaches to address business challenges.",
      "position": 1,
      "confidence": 0.8,
      "parentBeatName": "Turn",
      "parentBeatSlug": "turn"
    }
  ],
  "loops": [
    {
      "to": 16,
      "from": 7,
      "name": "Cost Of Inaction",
      "slug": "27-cost-of-inaction",
      "bestFor": "Urgent budget requests, compliance, risk mitigation",
      "matchId": "fdbfb761-8041-4565-bf06-c7a52cd6fcab",
      "evidence": "The document presents problem statements and solutions, implicitly highlighting the costs of not adopting AI-driven approaches.",
      "position": 0,
      "objective": "Highlighting the costs of inaction in adopting AI solutions",
      "structure": "The Status Quo -> The Hidden Costs Accumulating -> The Future State of Inaction -> The Tipping Point",
      "confidence": 0.6,
      "description": "Quantify what happens if the audience does nothing"
    }
  ],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}