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How media and entertainment can transcend AI experimentation for value

Authors

  • Rahul Gautam

    EY Global Technology, Media & Entertainment and Telecommunications (TMT) Consulting Leader; EY Americas Media & Entertainment Consulting Leader

  • Hugh Burgin

    EY Americas AI, Automation and Analytics Leader

7 minute read 20 Jun 2025

Companies that embrace AI and data sooner can break away from rivals in innovation, audience engagement, operational efficiency and more.

In brief

  • Traditional media industries can confront competition from digital platforms by embracing new technologies and understanding evolving audience preferences.
  • AI and data analytics can cut the cost of producing hyper-personalized content and boost the impact of marketing investments to engage viewers effectively.
  • AI can drive less edgy but highly valuable impacts to operational efficiency, risk mitigation and marketing compliance.

Today, films and TV compete with vast multiplayer online video games, ecosystems of podcasts and viral sensations from social media influencers, as well as endless entertainment and vacation options. Who is more likely to thrive: established media giants with extensive content creation experience and libraries of rich intellectual property (IP) — or tech-savvy newcomers that harness artificial intelligence (AI) and analytics to produce snackable content for niche audiences across where and when they want to engage? What if a media and entertainment giant became a tech-savvy, industry-transforming icon?

But after dabbling in AI for the past year — by testing tools, piloting workflows and chasing headlines — media companies face the challenge of breaking free of experimentation and entering the next phase: seizing measurable impact. To stay competitive, how can media leaders move beyond proofs-of-concept and start deploying AI where it drives real value?

Recent innovations have transformed every step of the content supply chain, affecting how entertainment and promotional materials are developed, produced, delivered and analyzed for effectiveness. Such dramatic opportunities can be captured in audience engagement and growth, content monetization, risk mitigation, marketing compliance and operational efficiency.

Data and AI are eliminating uncertainty in understanding viewer personas — not as a faceless audience, but as a diverse array of individuals with overlapping demographics and geographies. Additionally, automation is streamlining the creation of hyper-personalized content on an unprecedented scale. Insights from enhanced data and improved processing capabilities can support new business models and revenue streams, enabling businesses to capitalize on trends like experiential events.

Traditional media companies can augment their valuable IP and sophisticated creative expertise for the next generation by embracing innovations and establishing the necessary data and AI infrastructure for scalable results. But the challenges can run deep in operations: lacking a systematic method for tracking content throughout the development cycle, and often discovering that vital data is fragmented across systems or completely missing. To effectively handle complex tasks, including the scalable processing of large video and audio datasets, these technologies must be fully integrated and seamlessly connected.

of senior leaders in the 2024 EY AI Pulse Survey¹ said AI adoption would be faster if they had a stronger data infrastructure.

To drive the next wave of growth, media and entertainment companies need to be both content-centric and data-driven, recognizing how each aspect informs the other for improved outcomes.

1

Chapter 1

The role of data and its critical importance for AI success

The most impactful and innovative AI solutions require investments in AI-ready data with the specificity and completeness needed to drive personalization and automation effectively.

In the streaming era, companies have gained a deeper understanding of their viewers as individual households, enabling them to track not only whether viewers remained engaged with a show until the end but also the exact moment they lost interest. This innovation allows forward-thinking companies to explore critical questions: What specific elements within the content caused a viewer to tune out? Was it a scene that became too explicit or too dull, or was there a character or plot line that failed to resonate? Companies are gaining insights from enhanced data and improved processing capabilities to improve engagement.

By analyzing these factors, companies can effectively tailor their content to meet the diverse needs and preferences of their audience. But this effort relies on a combination of first- and third-party data and metadata, which together enable AI-driven solutions. While first-party data forms the backbone of personalization, metadata — such as tags that describe specific moments in a piece of content — plays a crucial role as well.

Traditionally, content creators have included basic metadata such as title, language, rating, cast and genre used in recommendation engines. However, companies have found that capturing metadata at a scene-by-scene level was cost-prohibitive and time-intensive. Today, innovations in AI tools, cloud and GPU-powered computing, and automation have drastically lowered these costs, making it feasible to create detailed metadata, significantly enhancing the business case and revenue potential. Imagine a shift from one or two pieces of metadata per minute of content toward dozens of metadata per second automatically created. The possibilities and value are endless.

When combined with consumer responses and demographic information, this data helps companies gain a more commercial understanding of individual preferences. And in turn it significantly impacts content production, personalized or contextual ads and promotions, and content sales and licensing, all of which rely on the interconnectedness of data.

For instance, we developed a model that can generate multiple 20-second clips from a multi-series program, specifically tailored for targeted marketing campaigns. By inputting all relevant episodes into the model and leveraging AI to assign specific data points to each moment — such as thrilling encounters, scene locations and character dynamics — we can effectively align these clips with viewer insights. This ensures that our promotional materials resonate with audiences in specific markets. Ultimately, this strategy not only streamlines content creation but also enhances our return on investment by aligning our efforts with viewer preferences and emerging trends.

2

Chapter 2

Unlocking new possibilities with data and AI

Here are several focus areas where media and entertainment companies can secure a competitive advantage with AI and data.

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    • Accelerating content development with generative AI (GenAI): AI provides initial storyboard drafts and content concepts tailored to audience preferences, including genres and themes. It also facilitates real-time subtitles and dubbing in multiple languages, as well as cultural changes (such as swapping in local billboards), ensuring global accessibility and relevance.
       
    • Innovating new forms of content: Digitally native production houses are already experimenting with new forms of AI-generated content to test the appetite for consumers globally and find the next content format that will resonate with audiences.
       
    • Engagement tracking: AI assesses audience reactions to programs and marketing campaigns in real time. By analyzing viewership patterns and social media interactions, it identifies which content resonates most, helping media companies optimize programming and marketing strategies.
       
    • Targeted promotions: AI creates customized promos for TV shows or films aimed at specific demographics, such as older males who enjoy similar programming. Marketers can easily request tailored content, like posters or short episode cutdowns.
       
    • Personalized advertising: By analyzing consumer purchasing patterns, AI determines effective pricing strategies and adjusts ad placements in real time based on user interactions, ultimately driving higher sales and subscriptions.
    3

    Chapter 3

    Five essential steps for media industry transformation

    Here’s how media and entertainment companies can accelerate their journeys beyond AI experimentation into actual differentiating value.

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      1.  Invest in AI-ready data foundations: Media and entertainment companies should allocate at least 80% of their AI and data investments to building infrastructure that makes data AI-ready and gives people the tools needed for successful AI adoption. Key characteristics to aim for include accessibility at scale, visibility, recency, trustworthiness, security, openness, reliability and global reach.

      2. Update data strategy and governance: Establish a clear data strategy for responsible use of AI including how data is collected, stored, processed and utilized, including first- and third-party data. Ensure compliance with regulations like the EU General Data Protection Regulation and the California Consumer Privacy Act to avoid misleading insights from poor-quality data.

      3. View data as a commercial asset: Recognize the intrinsic value of data in driving business decisions and enhancing customer experiences. Forming data-sharing partnerships can enrich insights, while selling anonymized audience data to advertisers or exploring targeted marketing campaigns can create new revenue streams.

      4. Cultivate a culture of AI adoption: Reimagine AI in everything you do. Foster a data-driven workforce by ensuring employees understand the value of AI and how it enhances their work. Provide training and support to help them embrace AI as a tool for achieving better outcomes, rather than viewing it as a threat. Upskill and train enabling functions like human resources and legal to ensure they stay true to their respective missions and while keeping pace with innovation.

      5. Build an ecosystem of partners: Continuously evaluate partnerships and data sources to maintain a near-real-time focus on how various aspects of the value chain can be enhanced through targeted investments. AI and data innovation require collaboration beyond individual organizations, so leveraging third-party knowledge can help navigate the rapidly changing market landscape.

      Special thanks to Sarah Rosso, Senior Consultant, Ernst & Young LLP, and Shivang Rao, Manager, Ernst & Young LLP, for their contributions to this content.


      Summary 

      Success in today’s media landscape requires a dual focus on being content-centric and data-driven. Companies that evolve beyond experimentation in AI, automation and analytics — into audience engagement and growth, combined with operational efficiency — are best-positioned to leap ahead of the competition. For instance, investing in AI-ready data, particularly automated metadata generation now produced cost-effectively, can help achieve hyper-personalization and tap into new revenue streams. Companies can now understand viewer preferences with unprecedented relevance, allowing them to tailor content and marketing strategies effectively. This integration of data and creativity is crucial for not just surviving but thriving in the digital future.

      About this article

      Authors

      • Rahul Gautam
        EY Global Technology, Media & Entertainment and Telecommunications (TMT) Consulting Leader; EY Americas Media & Entertainment Consulting Leader
      • Hugh Burgin
        EY Americas AI, Automation and Analytics Leader
      Related topics
      Emerging technology
      Consulting
      Data and decision intelligence
      AI

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