PLAYBOOK ATLASPlaybook Atlas

Media

AI in media: explore 34 applications.

Make media assets discoverable and editorial decisions easier to verify.

Explore transcription, video indexing, newsroom assistance, recommendations and audience analysis for publishers and media teams.

Plan your first AI move See the 34 applications

Where the 34 applications sit

Explore the domains
  1. 15Audience Engagement
  2. 12Content Creation
  3. 8Content Management
  4. 1Content Distribution
  5. 1Market Analysis

01Why AI now

Why media is moving on AI, in three figures.

Each figure links to its source.

  1. Social media moderation: $8.3B in 2024

    Grand View Research valued the global social-media moderation market at $8.3 billion in 2024, including both automated and human-led moderation.

    Source: Grand View Research

  2. 97% of publishers prioritize AI back-end automation

    In a 2026 survey of 280 digital news leaders across 51 countries, 97% said back-end automation would be important that year.

    Source: Reuters Institute for the Study of Journalism

  3. 74% expect GenAI to boost newsroom productivity

    A survey of 135 senior newsroom leaders across 40 countries found that 74% expect generative AI to increase productivity and improve workflows.

    Source: Reuters Institute for the Study of Journalism

How to evaluate a first project

Choose an editorial or audience task with a specific accepted output. Preserve timestamps, source passages, rights and editorial changes. Evaluate retrieval accuracy or recommendation quality against the task’s baseline, and check published assets for factual accuracy, accessibility and delivery requirements.

02Capability map

Where media puts AI to work, domain by domain.

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

Inside Audience Engagement

Applications in Audience Engagement

What the use cases ask AI to do

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

  1. Reasoning

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

    52%

  2. Generation

    Creates: text, images, code and other content.

    36%

  3. Perception

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

    8%

  4. Agentic

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

    4%

Under 1% each: optimization.

03Workflow ideas

Ways AI could change the work, in media.

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 30 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 media uses most, and when to skip them.

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

  1. 01 · 9 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

  2. 02 · 7 applications

    API-Wrapper

    Thin application layer over a managed AI API.

    API-Wrapper, in depth

  3. 03 · 5 applications

    Computer-Vision

    AI pattern for interpreting and acting on images and video data.

    Use it when

    • When you need to automate or augment tasks that currently rely on human visual inspection (e.g., quality control, surveillance).
    • When visual information (images, video, documents) is the primary or richest source of signal for the problem.
    • When real-time or near-real-time decisions based on camera feeds are required (e.g., robotics, autonomous systems, safety monitoring).

    Skip it when

    • When the problem can be solved more simply and robustly with non-visual sensors or structured data (e.g., barcodes, RFID, logs).
    • When you lack access to sufficient, representative visual data and cannot feasibly collect or label it.
    • When privacy, legal, or ethical constraints prohibit capturing or processing the necessary images or video.
    Computer-Vision, in depth

05The applications

34 media 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. Audience Preference Modeling EngineThis AI solution analyzes viewing, reading, and interaction patterns to infer granular audience preferences across news, entertainment, and streaming platforms. It powers personalized recommendations, content tagging, and adaptive experiences that increase engagement, session length, and subscription retention while reducing content discovery friction.Gate → Open10 use cases
  2. News Feed Personalization EngineAI-Powered Media Personalization uses large language models and advanced recommendation algorithms to tailor news, articles, and media feeds to each user’s interests, reading history, and intent. By dynamically profiling audiences and optimizing content, tags, and search results in real time, it boosts engagement, increases session length, and drives higher subscription and ad revenues for media companies.Batch → RT10 use cases
  3. Media Experience Personalization EngineThis AI solution powers hyper-personalized media experiences across news, entertainment, and social platforms by using machine learning and large language models to tailor content, recommendations, and interfaces to each user. It optimizes engagement through real-time behavior analysis, content relevance scoring, and A/B-tested recommendation strategies while enforcing intelligent moderation to maintain brand safety. The result is higher viewer retention, increased content consumption, and improved monetization through more relevant experiences and ads.Batch → RT7 use cases
  4. Automated Video Content ProductionThis application area focuses on using generative tools to plan, create, and finish short- and mid‑form video content with far less time, cost, and specialist expertise than traditional production. Instead of requiring cameras, studios, actors, editors, and visual effects teams for each asset, users can go from script or text prompt to finished videos, complete with avatars, voiceovers, sound, and effects, largely within software. It spans marketing, social media, explainer, training, and brand storytelling videos. It matters because media and brand teams now need a continuous, high-volume stream of video tailored to multiple platforms, languages, and audiences—something that conventional workflows cannot deliver economically. Generative models automate storyboard creation, scene generation, visual effects, localization, and post‑production steps, enabling rapid iteration and large-scale personalization while maintaining acceptable quality. This shifts video from a high-friction, project-based activity into an always-on, scalable content channel that non‑experts can manage.Human Creative → Augmented6 use cases
  5. Automated News Production WorkflowAutomated News Content Production refers to the use of software to assist or partially automate core newsroom tasks such as research, drafting, summarization, editing, tagging, and multi‑channel distribution of news stories. These systems ingest large volumes of information—from wires, social media, public data, and archives—then generate briefs, first drafts, headlines, and SEO‑optimized variants, while also handling repetitive production work like formatting, metadata creation, and channel‑specific packaging. This application matters because news organizations face intense pressure to publish more content, faster, across more platforms, while operating with shrinking budgets and staff. By offloading low‑value, time‑consuming tasks to automation, journalists can concentrate on investigation, judgment, and storytelling quality. When implemented with clear governance and transparency, this improves newsroom throughput and consistency without proportionally increasing headcount and while helping maintain audience trust in the integrity of the final product.Human Creative → Augmented5 use cases
  6. News Content Personalization and PackagingThis application area focuses on using automation to personalise, package, and distribute news and media content at scale across channels. It covers drafting and re‑drafting articles, summaries, headlines, and snippets; translating and localising stories; tagging and structuring archives; and dynamically tailoring what each reader sees based on interests, behaviour, and context. The goal is to serve more audiences—niche, global, and multi‑platform—without requiring proportional increases in newsroom staff. It matters because media organisations face flat or shrinking newsrooms while audience expectations have shifted toward highly personalised, always‑on, multi‑format content. By offloading repetitive editorial tasks and enabling targeted recommendations and interactive experiences (such as chat‑like Q&A on news topics), these systems help journalists focus on original reporting and analysis, while improving reader engagement, loyalty, and time on site. They also unlock more value from existing content archives by continually repackaging and resurfacing relevant material for each audience segment.Human Creative → Augmented5 use cases
All 34 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

    Record human editorial contributions and separately clear rights for source assets and distribution.

  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

    Assess deepfake and public-interest content duties for each output and publishing role.

07The AI graveyard

Two failures, and the lesson each one paid for.

  1. CNET AI Content Scandal

    2023 · Source

    Editorial credibility damaged

    What went wrong

    AI-generated financial advice articles contained errors and were published without clear disclosure. Corrections required across dozens of articles.

    The lesson

    AI content requires human editorial oversight and clear labeling

  2. Sports Illustrated AI Authors

    2023 · Source

    Staff layoffs, trust crisis

    What went wrong

    Created fake AI author personas with generated headshots writing AI content. Revealed by external investigation.

    The lesson

    AI content deception destroys brand credibility when exposed

Your next step

Find your first move in media.

Ask the workspace which move fits your company. Add your size, systems and constraints, and it works from these 34 applications to a plan your team can act on.

Plan your first AI move

Rather talk it through? Book a 30-minute call