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Media & entertainment

AI use cases

AI use cases in media and entertainment

19 AI agents that media and entertainment companies run today, grouped by the team they work for. For each one: the process, what the AI does, where people step in and the result the company reported.

Customer service

In production · reported 2025

~40,000 messages/day

Player chatbot with safety classification

Game developer with child players

What the AI does: Labels each player message for safety and jailbreak risk, answers safe questions from game data and checks responses before they go out.
Where people step in: Messages flagged as safety issues go to a human; red-team findings are fed back into classifiers.

In production · reported 2026

37,000 fan interactions in 12 days

Festival-goer guide and schedule planning

US live events producer

What the AI does: Routes fan questions to subagents, answers from live festival data and the festival website, and helps fans plan schedules.
Where people step in: When sources lack the answer, it declines and directs fans to guest services.
Systems: CRM · Database · Mobile app

In production · reported 2025

233,000 conversations per season (expected)

Fan game-day support and football ops insights

US professional spring football league

What the AI does: Answers fan game-day and ticketing questions, while a second agent pulls player stats to compare players and teams for staff.
Where people step in: Complex questions go to a service rep with the conversation history logged.
Systems: CRM · Ticketing · Data warehouse · Sports data API

In production

~80% deflection of peak-volume queries

Peak-event player support

US fantasy sports operator

What the AI does: Deflects peak-volume player questions triggered by live game moments, within regulatory guardrails.
Where people step in: The team keeps real-time control during live events, following payment, identity and problem-gaming guidelines.
Systems: Helpdesk

In production · reported 2026

+40% containment

Ticket location and delivery support

US online ticket marketplace

What the AI does: Recognizes logged-in fans, pulls their order details, helps locate tickets, explains delivery timelines and troubleshoots issues.
Where people step in: The CX team focuses on root-cause fixes and programs.
Systems: Order system

Sales

In production · reported 2025

+22% subscription sales via WhatsApp

WhatsApp support and subscription upsell

Brazilian streaming service

What the AI does: Answers subscriber questions, guides signup, account creation and login recovery, and nudges free users toward paid plans.
Where people step in: Out-of-scope questions are declined or escalated to a live rep.
Systems: WhatsApp · Knowledge base · CRM

In production · reported 2026

~10% greater identity match accuracy

Patron data unification and identity resolution

US orchestra nonprofit

What the AI does: Ingests patron data from three systems and uses LLM-based identity matching to build one golden record per patron for profiles and segmentation lists.
Where people step in: Staff review and approve record merges; every match is traceable.
Systems: Ticketing · Fundraising · CRM

In production · reported 2026

$2.7M additional closed revenue

Outreach to dormant warm leads

US SaaS community and events company

What the AI does: Sends personalized outreach to overlooked leads, follows up, re-identifies contacts after team turnover and nurtures leads until they are ready for a person.
Where people step in: It cannot book meetings or promise speaking slots; escalations go to the Chief AI Officer in Slack.
Systems: CRM · Slack/Teams

In production · reported 2025

Up to 3M automated calls/year

Telephone ordering with cross-sell

German live TV commerce retailer

What the AI does: Takes telephone orders within minutes, handles up to 600 simultaneous calls and offers cross-sell recommendations.
Where people step in: It supports up to 1,300 external customer advisors who enter orders.
Systems: CRM · Merchandise management · Payments · Recommendation engine

In production · reported 2026

65% of inquiries handled independently

Phone ticket reservations and cancellations

German cinema ticketing platform

What the AI does: Answers movie and showtime questions, guides reservations, texts a booking link and cancels booked tickets.
Systems: Cinema data API · SMS · Phone

Operations

In production

80%+ recommendations actionable

Daily story distribution recommendations

US local news network, 8 stations

What the AI does: Builds a daily Top 10 story list before editorial meetings from audience analytics and CMS inventory, flags competitor coverage gaps and suggests platform formats.
Where people step in: Editors approve or adjust recommendations in the tool.
Systems: Web analytics · CMS

In production · reported 2026

5,000-case backlog cleared in 3 weeks

Journalist FAQs and media accreditation processing

Global sports federation media team

What the AI does: Answers journalist FAQs around the clock from approved knowledge, and screens, translates and drafts responses to accreditation applications.
Where people step in: A team member reviews each accreditation application and makes the final decision.
Systems: CRM · Data warehouse · Media portal

In production

>85% categorization accuracy

Ad-segment and product record classification

US digital advertising metadata company

What the AI does: Classifies each record as brand, segment or uncategorizable, enriches it, retrieves candidate categories via vector search, picks the best one and scores confidence.
Systems: Vector search

In production

Result described without a number

Newsroom proofreading and early fact checks

Swiss newspaper publisher

What the AI does: Scans copy for style, language, punctuation, foreign-name transcription and location issues, suggests fixes in context and learns from editor decisions.
Where people step in: Editors accept or override each suggestion.
Systems: Stylebook

Finance

In production · reported 2025

64% case resolution rate

Billing inquiries and account troubleshooting

US internet and media brand

What the AI does: Updates credit cards end to end and verifies accuracy, explains billing breakdowns and troubleshoots login and account settings issues.
Where people step in: Escalates technical or complex issues to a live agent when needed.

In production · reported 2026

>90% of inquiries resolved without human intervention

Tier 1 support, refunds and bug reporting

US newsletter publishing platform

What the AI does: Handles refunds and cancellations end to end through APIs, tags conversations and reports bugs and feature requests automatically.
Systems: Platform APIs · Support tools

IT

In production

57% autonomous resolution

Employee IT support in chat

US diversified media company

What the AI does: Resolves common IT issues, unlocks accounts, finds knowledge articles and routes tickets in Slack and Teams.
Where people step in: Support staff handle more complex requests.
Systems: Slack/Teams · Helpdesk · Identity management · Knowledge base

Marketing

In production

80%+ recommendations actionable

Daily story distribution recommendations

US local news network, 8 stations

What the AI does: Builds a daily Top 10 story list before editorial meetings from audience analytics and CMS inventory, flags competitor coverage gaps and suggests platform formats.
Where people step in: Editors approve or adjust recommendations in the tool.
Systems: Web analytics · CMS

In production · reported 2026

6K+ interactions first race

Fan questions on technical regulations

Global motorsport series

What the AI does: Explains the 2026 technical regulations in plain language, retrieving answers only from approved rules and series content.
Where people step in: Guardrails restrict answers to approved content, with a factual tone and no speculation.
Systems: Data platform · Commerce platform · CRM

In production · reported 2026

~10% greater identity match accuracy

Patron data unification and identity resolution

US orchestra nonprofit

What the AI does: Ingests patron data from three systems and uses LLM-based identity matching to build one golden record per patron for profiles and segmentation lists.
Where people step in: Staff review and approve record merges; every match is traceable.
Systems: Ticketing · Fundraising · CRM

In production · reported 2026

$2.7M additional closed revenue

Outreach to dormant warm leads

US SaaS community and events company

What the AI does: Sends personalized outreach to overlooked leads, follows up, re-identifies contacts after team turnover and nurtures leads until they are ready for a person.
Where people step in: It cannot book meetings or promise speaking slots; escalations go to the Chief AI Officer in Slack.
Systems: CRM · Slack/Teams

In production

45% interactions resolved

Payment questions and targeted promotions

Online gaming operator

What the AI does: Answers deposit and withdrawal questions, delivers targeted promotions and translates replies into 6 languages.
Where people step in: On request, customers move to live chat and the agent gets the bot transcript.
Systems: Web chat · Back-end systems

Anonymised summaries of public case studies published from 2023 to 2026. Company and vendor names are withheld. Figures are as the companies reported them; they are not Retexa results.

Questions

AI in media and entertainment

What are the most common AI use cases in media and entertainment?

In the 19 public cases we collected, AI agents in media and entertainment work mostly in customer service, sales and marketing. Examples: player chatbot with safety classification; festival-goer guide and schedule planning; fan game-day support and football ops insights.

Where do people stay in the loop?

Each card says where people step in. In this industry, for example: messages flagged as safety issues go to a human; red-team findings are fed back into classifiers; when sources lack the answer, it declines and directs fans to guest services.

Are these Retexa results?

No. These are anonymised summaries of public case studies from 2023 to 2026, with company and vendor names withheld. Figures are as the companies reported them. Retexa builds the same kind of agents on top of the systems you already run.

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