PLAYBOOK ATLASPlaybook Atlas

Entertainment

AI in entertainment: explore 30 applications.

Support creative production and audience discovery through distinct, reviewable workflows.

Explore writing, storyboards, localization, production assets and recommendations for creators, studios and entertainment platforms.

Plan your first AI move See the 30 applications

Where the 30 applications sit

Explore the domains
  1. 16Content Creation
  2. 9Audience Engagement
  3. 4Music Production
  4. 2Business Operations
  5. 2Content Distribution

01Why AI now

Why entertainment is moving on AI, in three figures.

Each figure links to its source.

  1. GenAI music/audiovisual market: €64B in 2028

    A global economic study publicized by CIAM projected that the market for generative-AI music and audiovisual content would grow from €3 billion to €64 billion by 2028.

    Source: Teosto

  2. Netflix: 80% of watched content AI-recommended

    Recommendation engines drive $1B+ annual value

    Source: SAGE Journals

  3. 80% say GenAI is critical to compete

    A 2024 survey of 204 North American media and entertainment decision-makers found that 80% considered integrating generative AI into products and services critical to competitiveness.

    Source: Forrester Consulting

How to evaluate a first project

Specify the accepted output and the rights available for its inputs. For creative work, review continuity, voice, editability and delivery quality with the production team. For recommendations, evaluate audience experience against a baseline. Keep those two evaluations separate when deciding whether a workflow is useful.

02Capability map

Where entertainment puts AI to work, domain by domain.

Pick a domain to see its applications. Each one opens a full report.

Inside Content Creation

Applications in Content Creation

What the use cases ask AI to do

Share of the entertainment use cases, by the kind of work the AI does.

  1. Reasoning

    Analyzes, predicts and decides: scoring, forecasting, classifying.

    43%

  2. Generation

    Creates: text, images, code and other content.

    36%

  3. Agentic

    Acts: plans, uses tools and completes multi-step tasks.

    12%

  4. Perception

    Sees, hears and reads: documents, images, audio and video.

    9%

Under 1% each: optimization.

03Workflow ideas

Ways AI could change the work, in entertainment.

Atlas groups these applications by the changes they propose to a workflow. These preliminary classifications help you explore ideas; each application still needs a review of its sources and operating requirements.

Workflow categories in this catalog

Share of the 27 applications assigned a category.

How to evaluate an application

  1. Start with a bounded task

    Identify the inputs, the person responsible, and the decision or output the workflow must produce.

  2. Check the support for each method

    Look for source-backed details about the specific implementation, including its limits and human review.

  3. Measure the pilot

    Record the current baseline, agree on acceptance criteria, and test on representative cases before expanding.

Explore applications

04What gets built

The approaches entertainment uses most, and when to skip them.

Knowing when not to use one matters as much as knowing when to.

  1. 01 · 12 applications

    Generative AI

    Models that learn data distributions to generate new text, images, code, and media.

    Use it when

    • When you need to generate natural language, code, or media that would be expensive or slow for humans to produce manually.
    • When tasks involve ambiguity, open-ended questions, or creative exploration rather than strict right-or-wrong answers.
    • When you can ground the model on reliable internal or external data sources to improve factuality.

    Skip it when

    • When the task requires strict determinism, formal guarantees, or regulatory-grade traceability (e.g., certain financial calculations, safety-critical control systems) without a robust verification layer.
    • When you lack access to high-quality, authoritative data or subject-matter expertise to validate outputs in a high-risk domain.
    • When a simple rules engine, search, or template-based system can solve the problem more reliably and cheaply.
    Generative AI, in depth

  2. 02 · 4 applications

    RecSys

    AI pattern for predicting and ranking items a user is likely to prefer.

    Use it when

    • You have a large or growing catalog of items (products, content, ads, jobs, etc.) and users struggle to manually discover relevant options.
    • You can reliably log user interactions with items (views, clicks, purchases, likes, completions) and maintain a stable user/item identity.
    • Personalization or relevance is a key driver of business value (engagement, revenue, retention, satisfaction).

    Skip it when

    • Your catalog is very small or static, and users can easily browse all options without assistance.
    • You lack sufficient interaction data or cannot reliably track user behavior due to privacy, technical, or regulatory constraints.
    • User identity is highly transient or anonymous, and you cannot build meaningful histories or segments.
    RecSys, in depth

  3. 03 · 3 applications

    RAG-Standard

    LLM + retrieval layer that injects relevant documents into the prompt at query time.

    Use it when

    • You need an LLM to answer questions based on proprietary or private documents that are not part of its pretraining data.
    • Your knowledge changes frequently (e.g., policies, product docs, pricing) and you want updates to be reflected without re-training the model.
    • You have a large corpus of unstructured or semi-structured text (wikis, PDFs, tickets, emails, manuals) that users struggle to search effectively.

    Skip it when

    • Your task is primarily structured data querying or analytics (e.g., reporting over a relational database) where SQL or BI tools are more accurate and efficient.
    • You have very small, static knowledge (e.g., a short FAQ) that can fit directly into a prompt or be encoded as rules without a retrieval layer.
    • You require strict, deterministic behavior with no tolerance for probabilistic errors (e.g., certain financial calculations, safety-critical controls).
    RAG-Standard, in depth

05The applications

30 entertainment applications, each with a full report.

The widest first, by the number of use cases each covers. Every report shows what changed, how it works, who has done it and what can go wrong.

  1. Entertainment Experience RecommendersAI systems that learn each viewer’s tastes to deliver highly personalized movies, shows, music, and interactive content across streaming and entertainment apps. By fusing foundation models, behavioral signals, and on-device or federated recommenders, they surface the right content at the right moment to boost engagement and viewing time. This drives higher subscription retention, ad revenue, and content ROI while reducing user churn and choice fatigue.Gate → Open22 use cases
  2. Screenplay Drafting WorkbenchAutomated Screenplay Development refers to using advanced language models and creative tooling to accelerate the end‑to‑end process of turning an idea into a production-ready script. It supports ideation, outlining, character development, scene breakdowns, dialogue drafting, and iterative revisions, all within structured workflows tailored to screenwriting formats and conventions. Writers remain in creative control, while the system handles repetitive, exploratory, and formatting-heavy tasks. This application matters because traditional script development cycles are slow, expensive, and resource-intensive, especially for individual writers, small studios, and fast-moving content teams. By leveraging AI co-writing and structured prompt workflows, organizations can dramatically shorten time-to-first-draft, explore more story options in parallel, and iterate faster with fewer resources. The result is lower development costs, higher creative throughput, and a greater likelihood of discovering commercially viable stories in competitive entertainment markets.Human Creative → Augmented8 use cases
  3. Film and Media Music StudioThis AI solution uses generative AI to compose, arrange, and enhance original music and soundscapes tailored to films, videos, and virtual performers. By automating soundtrack creation, improving audio quality, and assisting composers, it cuts production time and costs while enabling highly customized, on-demand scores for entertainment content at scale.Human Creative → Augmented6 use cases
  4. Entertainment AI Strategy InsightsThis AI solution is focused on providing structured, market-level insight into how artificial intelligence is reshaping the entertainment and media value chain, so executives can make informed strategic decisions. Rather than executing production tasks directly, these tools and analyses map where AI is impacting content creation, distribution, monetization, and IP control, and quantify adoption across film, TV, streaming, music, gaming, and advertising. It matters because major media conglomerates sit on large, high-value content libraries and complex production ecosystems that are being disrupted by generative models, automation, and new intermediaries. Strategy insight products in this AI solution help leaders understand where to cut costs and speed up production, how to protect and monetize IP, and how to prioritize AI investments while managing risks to jobs, bargaining power, and long-term franchise value.Expert → AI5 use cases
  5. Video Soundtrack SynchronizerAutomated Video Soundtracking refers to tools that analyze a video’s content, pacing, and emotional arc to automatically select, edit, and synchronize music and sound effects. Instead of manually searching royalty‑free libraries, checking licensing, trimming tracks, and aligning transitions, creators upload or edit a video and receive a tailored, ready‑to‑use soundtrack that fits length, mood shifts, and key moments. This matters because audio quality and fit have a disproportionate impact on viewer engagement, but most creators and marketing teams lack the time, budget, or expertise for professional sound design. By automating track selection, mixing, and timing, these applications reduce friction in the production workflow, enable non‑experts to get professional results, and allow studios, brands, and individual creators to scale video content production with consistent, on‑brand soundscapes.Human Creative → Augmented5 use cases
  6. Writers Room Story DevelopmentThis AI solution uses generative AI to help entertainment teams ideate, outline, and refine stories—supporting everything from loglines and character arcs to full scripts and episodic structures. By automating routine writing tasks and accelerating revisions, it shortens development cycles, reduces creative bottlenecks, and enables studios and writers’ rooms to explore more concepts with the same resources.Human Creative → Augmented4 use cases
All 30 applications, A to Z

Work with us

The catalog shows what is possible. A sprint decides what you should fund.

Reading every report still leaves the hardest question open: which move is worth funding first, here, given your constraints. That is the sprint: two weeks, fixed scope, and a candid answer either way.

Book a 30-minute callHow the sprint works

Two weeks Fixed scope A candid fit-or-no-fit answer

  1. 01

    Three ranked opportunities

    A decision-ready shortlist scored against value, feasibility, evidence, risk, and time to proof.

  2. 02

    One pilot-ready workflow

    A bounded operating design with actors, handoffs, data needs, controls, and success measures.

  3. 03

    A defensible 90-day plan

    Build-versus-buy guidance, ROI assumptions, risk treatment, sequencing, and executive decisions.

06Rules & standards

Rules and standards to check, with their scope and sources.

Selected US and EU references for these workflows. Applicability depends on jurisdiction, organization, data and intended use. The planning notes are Atlas editorial guidance; they are not official implementation-time estimates or an exhaustive list of obligations.

  1. Copyright and AI-assisted outputs

    Agency guidance

    United States · copyrightability of outputs

    The Copyright Office’s report distinguishes human-authored expression from material generated by AI. Copyrightability of an output and permission to use input material are separate questions.

    US Copyright Office: AI report, Part 2

    Source checked

    Project planning note

    Track human authorship and separately clear source material, performer permissions and applicable contract terms.

  2. EU AI Act · interaction and content transparency

    Law or regulation

    European Union · covered providers and deployers

    Article 50 assigns different transparency duties for AI interactions and certain generated or manipulated content. Applicable exceptions and the provider/deployer role matter; these duties are separate from high-risk classification.

    European Commission: Article 50 transparency guidelines

    Source checked

    Project planning note

    Determine which synthetic media outputs need disclosure and how the label survives distribution.

Your next step

Find your first move in entertainment.

Ask the workspace which move fits your company. Add your size, systems and constraints, and it works from these 30 applications to a plan your team can act on.

Plan your first AI move

Rather talk it through? Book a 30-minute call