{
  "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": 185,
  "pageCount": 190,
  "prevPage": 184,
  "nextPage": 186,
  "slideType": "problem_statement",
  "function": "frame_problem",
  "density": "overcrowded",
  "nDataPoints": 0,
  "notes": "The slide uses a two-column layout: the left side defines the problem/opportunity, and the right side (in a callout box) explains the technical solution.",
  "elementsJson": [
    "paragraph",
    "callout_box"
  ],
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5e88-73ef-bd5d-d76a2779de1a/185",
  "deckHref": "/decks/019dd923-5e88-73ef-bd5d-d76a2779de1a",
  "deckJsonHref": "/decks/019dd923-5e88-73ef-bd5d-d76a2779de1a.json",
  "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-d76a2779de1a#slide-185",
  "components": [
    {
      "bbox": {
        "h": 0.73,
        "w": 0.39,
        "x": 0.54,
        "y": 0.15
      },
      "kind": "callout",
      "text": "HOW AI CAN HELP",
      "attrs": null,
      "subkind": "primary",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "e6fe0820-02dd-4f2d-9204-92c63da9ebd8",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": null,
      "kind": "callout",
      "text": "AI can separate vocals or instruments from mixed audio tracks even when the original files are not available, opening up new opportunities for licensing, remixing, archival restoration, and monetization.",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd952-5643-744f-8949-b74584a073ea",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.3,
        "w": 0.18,
        "x": 0.75,
        "y": 0.24
      },
      "kind": "paragraph",
      "text": "Leveraging Software-as-a-Service: Most deployments today use AI-powered SaaS platforms that allow internal teams to process catalog tracks quickly and securely. Internal quality control—along with artist or management approval—is then layered on to ensure that the extracted stems meet the creative and technical expectations of the project.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "43d315d9-e9e1-4d06-a532-c316edd3066f",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.35,
        "w": 0.4,
        "x": 0.07,
        "y": 0.55
      },
      "kind": "paragraph",
      "text": "Many recordings in music labels' back catalogs were produced at a time when multitrack preservation practices were inconsistent, and, in many cases, the original recordings have been lost, damaged, or never existed in isolated formats. This limits the ability to fulfill requests for custom edits—such as instrumentals, a cappella songs, or remixes—thereby stalling lucrative licensing deals, particularly for synchronization (music in film, television, and advertising) and derivative content creation. Manual audio reconstruction is costly, time-consuming, and often technically infeasible at scale. Yet demand for high-quality, tailored audio continues to grow, especially with the global expansion of streaming and sync opportunities.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "556aa0f1-1df5-4970-9592-951338de7284",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.45,
        "w": 0.18,
        "x": 0.56,
        "y": 0.24
      },
      "kind": "paragraph",
      "text": "Separating music into its component parts: AI, particularly deep learning-based source separation models, can analyze a fully mixed audio file and isolate its constituent elements—vocals, guitar, bass, drums, ambient noise, etc.—into discrete audio tracks with high fidelity. These models have matured significantly in recent years and can now perform at a level sufficient for commercial use in many scenarios. Rather than depending on traditional DSP (digital signal processing) or manual studio methods, the AI learns from large datasets of music to \"de-mix\" the sound using learned patterns of frequency and structure.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "cf58a16b-fa83-435e-a078-6f429316862d",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.1,
        "w": 0.4,
        "x": 0.07,
        "y": 0.35
      },
      "kind": "paragraph",
      "text": "Separating mixed audio tracks into their component parts using AI. AI can separate vocals or instruments from mixed audio tracks even when the original files are not available, opening up new opportunities for licensing, remixing, archival restoration, and monetization.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "d3922534-7ad1-4c00-b912-456b90717233",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.12,
        "w": 0.4,
        "x": 0.07,
        "y": 0.14
      },
      "kind": "title",
      "text": "AI-powered source separation for music remastering",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "6f142882-7788-4960-95a0-a715ef71ac8c",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.03,
        "w": 0.2,
        "x": 0.07,
        "y": 0.51
      },
      "kind": "title",
      "text": "ISSUE/OPPORTUNITY",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "7b174544-3e7c-47b7-a2ed-1f71345ac8ed",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [],
  "frameworks": [],
  "arcBeats": [],
  "loops": [],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}