AI and Universal Content IDs are ending OTT chaos, says Nielsen-owned Gracenote's CTO

In an increasingly fragmented digital landscape, the challenge for content platforms is no longer access—it’s discovery. As OTT and Connected TV consumption explode in India, content overload, inconsistent metadata, and fragmented audience measurement are creating serious roadblocks for both viewers and advertisers.

Enter media intelligence platform Gracenote- Nielsen’s content data business unit, which is turning to AI, universal content IDs, and real-time semantic analysis to solve this complexity.

In an interview with Storyboard18, Phanimohan Kalagara, CTO of Gracenote, discusses how the company is using a three-layered metadata approach, human-in-the-loop AI systems, and strategic partnerships to power smarter recommendations, precise ad targeting, and unified measurement across platforms like Netflix, JioCinema, YouTube, and more.

Read more: Only 32% marketers able to measure media effectiveness across traditional, digital platforms: Nielsen

He also outlines how Gracenote is addressing copyright challenges in the generative AI era, boosting regional content discovery in India, and safeguarding advertisers from digital ad fraud, forecasted to bleed over $71 billion globally in 2024.

Edited excerpts:

As OTT and CTV consumption explodes in India, how is Gracenote leveraging AI to not just personalize content discovery but also address the challenges of content overload in such a fragmented media ecosystem?

As OTT and connected TV consumption continues to grow in India, Gracenote, (a content data business unit of Nielsen) as a media intelligence platform, is leveraging AI to manage both personalization and content overload in a fragmented media ecosystem. AI plays a central role in generating rich metadata that goes beyond basic attributes like title or genre to include mood, scene context, themes, and cast relationships. This depth of metadata enables more relevant and intent-driven recommendations, improving the discovery experience for users who are often overwhelmed by the volume of content available.

To unify content across multiple platforms and reduce fragmentation, Gracenote employs universal content identifiers. These act as a consistent reference point for content, regardless of how it is categorized or labeled by different platforms. This standardization is essential for maintaining consistent metadata across OTT apps and connected devices, especially in a country as linguistically and culturally diverse as India. This shift is influencing content distribution strategies in several ways. First, platforms are moving towards hyper-personalized presentation of content. Second, the use of universal metadata allows content owners to implement platform-agnostic strategies, promoting shows across different services with consistency.

Read more: 72.4% of TV viewing on ad-supported platforms in Q1 2025: Nielsen

However, challenges persist. Data silos remain a major barrier. Most platforms retain control over their own user data, which makes it difficult to build a unified view of audience behavior and affects the accuracy of both targeting and measurement.

Audience measurement itself is also becoming more complex. Advertisers and platforms now demand outcome-based metrics such as viewing time or content engagement, not just impressions or reach. Without access to reliable cross-platform data, delivering on these expectations becomes difficult.

India is an exciting market to be in, and we are expanding on the current customer base. We are proud to be serving marquee customers like Tata Play, JioHotStar, Dish TV, Hathway, Viacom, Google and Samsung.

As the open internet begins to outperform social media in user engagement—especially among younger audiences—how should media companies rethink their content and ad strategies to stay relevant in this new digital attention economy?

As younger audiences shift from social media to the open internet, media companies need to adapt by focusing on intent-based engagement and discoverability. Content should be optimized for search and contextual relevance, not just viral potential.

Formats like long-form video and explainers perform better when they align with user intent.

Ad strategies must move from audience-based targeting to contextual targeting. With cookies fading and privacy laws tightening, understanding the content's tone and context becomes critical. This makes AI-driven metadata and semantic analysis, like Gracenote’s approach, more valuable for precise ad placements.

Distribution also needs to expand beyond social platforms. Media companies should integrate with smart TVs, content aggregators, and streaming interfaces where younger audiences spend time. Success will depend on being discoverable, context-aware, and aligned with how users actively seek content, not just passively consume it.

Given the surge in AI-generated media and content, how should the industry approach copyright protection and content authenticity in an age where recommendation engines and algorithms are increasingly influencing what people consume?

The rise of AI-generated content intensifies the need for trusted, structured metadata to preserve content authenticity and integrity across platforms. As recommendation engines increasingly influence what users watch, Gracenote’s role is to ensure that the content being surfaced is accurately described, correctly attributed, and contextually understood.

Rather than generating new content summaries or media, AI is used to enrich and organize existing content through structured metadata. By focusing on semantic understanding rather than content generation, Gracenote avoids the pitfalls of AI hallucinations or copyright infringement. We maintain a strong editorial foundation supported by AI, with human-in-the-loop validation to ensure accuracy and cultural relevance.

By working directly with licensed content providers and broadcasters, it also ensures that the metadata it produces is based on legitimate, authorized inputs. The metadata does not repurpose or reinterpret content in a generative way, but rather clarifies what the content is, how it should be discovered, and whom it is relevant for. In a noisy ecosystem increasingly shaped by synthetic content, this kind of grounded metadata becomes essential for both discoverability and trust.

With India projected to cross 65 million CTV households by 2025, what unique measurement and content indexing challenges does this fragmented digital+linear environment pose—and how is Gracenote solving for it?

As Connected TV adoption accelerates in India, the explosion of platforms, devices, and languages is creating a tangled web of viewing experiences that’s difficult to measure or index reliably. Audiences are scattered across countless streaming apps and linear TV channels, consuming content in multiple languages and on various devices, making it nearly impossible to track what’s being watched, where, and by whom with consistency. This fragmentation leads to major challenges in both audience measurement and content discovery, as identical programs often appear under different titles or metadata across services, further muddying the waters.

Accurately identifying, classifying, and indexing this vast and diverse content library is crucial for helping viewers find what they want, enabling precise audience targeting, and ensuring effective ad delivery. Yet the inconsistent presentation of shows and movies across linear and digital environments hampers these efforts, impacting everyone from broadcasters and platforms to advertisers and, ultimately, viewers themselves.

Gracenote provides advanced metadata and content identification solutions designed to bring order and transparency to the fragmented CTV environment. We offer industry-standard metadata and unique content IDs that enable accurate identification and classification of TV shows, movies, and sports programming across platforms. We also power contextual CTV advertising by creating detailed content categories and signals that allow advertisers to target programming aligned with brand values and exclude unsuitable content. Additionally, it assists content publishers and distributors from the planning phase through launch and distribution by ensuring metadata quality and consistency.

Gracenote is expanding its expertise in content discovery to enable real-time contextual ad targeting in programmatic and CTV environments.

Content overload has become a critical issue across OTT platforms. In your view, what role should AI and metadata enrichment play in helping users navigate this abundance and make meaningful viewing choices?

In today’s digital-first world, where audiences have many choices, the real challenge lies not in accessing content but in discovering the right content. That’s where AI and metadata enrichment are proving to be game-changers for the media and entertainment industry. AI algorithms are transforming how users navigate vast content libraries. By analyzing user behavior, preferences, viewing history, and engagement patterns, AI delivers highly personalized content recommendations.

This not only reduces decision fatigue but also improves user satisfaction and boosts retention rates. As a result, viewers are more likely to stay engaged with platforms that consistently serve up content aligned with their preferences.

The global AI in media and entertainment market was valued at USD 15.11 billion in 2023, and is projected to grow to USD 121.99 billion by 2032, expanding at a CAGR of 26.12% from 2024 to 2032.

Complementing AI is metadata enrichment - adding rich, descriptive context to content in the form of titles, cast, synopsis, genres, release dates, and thematic tags. This metadata is essential for organizing content and making it easily searchable and discoverable. In a market like India, where audiences span dozens of languages and platforms, enriched metadata is vital for enabling personalized discovery and navigation.

This innovation will also come with challenges. AI can sometimes struggle with scalability, especially for new users with limited data, or it may limit content exposure by overly personalizing recommendations.

However, with growing user awareness and better algorithmic transparency, these challenges are increasingly being managed. In the evolving OTT landscape, AI and metadata aren’t just supporting tools—they’re fast becoming the pillars of how content is consumed, discovered, and monetized.