The media and entertainment (M&E) industry thrives on content, but managing and retrieving vast amounts of media assets remains a significant challenge. The sector is witnessing a rapid growth in content archives. The global entertainment and media market is projected to expand from $2.67 trillion in 2024 to $2.87 trillion in 2025, reflecting a compound annual growth rate (CAGR) of 7.5% [1]. With millions of hours of video, audio, and image assets, traditional search methods are proving inefficient and time consuming. The advent of AI search and metadata agents is transforming content discovery, reducing turnaround time, and enabling new monetization strategies. This demand for fast, intelligent, and contextual search will only keep growing.
The Evolution of AI search in M&E
While many advanced search solutions have hit the market over the years, content retrieval relied on automated logging of basic metadata which had to be augmented by manual logging of deep metadata. The teams that were involved with it on a daily basis (Digital archivists, metadata experts and content operations managers) spent most of their time ensuring that the ingested content was tagged accurately. Inspire of that, marketing and business teams looking for specific content often had to spend hours before finding relevant assets.
With the onset of AI and its subsequent evolution, this process has seen a drastic shift. Today, we have:
- Automated Metadata Tagging: AI metadata agents can tag content with attributes like faces, objects, emotions, and brand placements at scale.
- Conversational AI: Enables users to interact with search interfaces using natural language.
- Semantic Search & Matching: AI search agents understand the intent behind queries, delivering more accurate results.
- Knowledge Base & Database Integration: AI search agents can now discover content across documents, archives, and media asset management (MAM) systems.
These advancements are driving faster, more intuitive media workflows, letting teams not only find relevant content, but drive a higher level of personalization into their campaigns. Another benefit that emerges from this is that each content piece now has a longer lifetime value, increasing its ROI.
How to leverage these AI search and metadata agents in your organization
Several AI-agentic solutions in the search and metadata realms have made for interesting use cases:
1. Conversational AI for Intuitive Search
AI-powered search now understands complex, context-driven queries, making it easier for users to retrieve specific content. For example, a sports broadcaster can ask, “Show me all clips from the last World Cup where Lionel Messi scored a goal,” and get instant results.
2. Contextual Metadata Tagging for Deeper Discovery
AI agents for M&E can auto-tag vast media libraries with attributes like facial recognition, scene segmentation, emotion analysis, and even OCR-based text extraction. A film studio repurposing classic movies for streaming can now identify scenes featuring specific actors or themes, increasing content discoverability and enabling custom marketing campaigns for fans.
3. AI-Driven Video Compilation for Social Media & Monetization
Brands and studios are using AI to create automated thematic compilations to boost engagement. AI search can generate:
- Festive Highlights: “Find all Thanksgiving scenes from the show Upper West Side.”
- Thematic Supercuts: “Create a montage of the most emotional moments from Season 1 of Stranger Things.”
- Brand-Driven Edits: “Compile all product placements of a beverage brand across five Hollywood films.”
Being able to search through multiple episodes and seasons for such content has proved to save up to 70% time during search operations, letting editorial teams focus on creating an engaging promo instead of digging through multiple archives.
4. Intuitive Search across MAM, Documents & Archives
Media companies often store essential production guidelines, contracts, and reference documents in scattered repositories. AI-powered knowledge base search enables teams to retrieve documents, PDFs, and legacy contracts across multiple MAM systems, improving operational efficiency by at least 30% both in terms of time saved as well as ensuring relevant content retrieval.
5. AI-Powered Localization and Metadata-Driven Dubbing
With increasing demand for localized content, AI is automating subtitle generation, cultural adaptation, and dubbing workflows. Deep metadata tagging identifies culturally sensitive scenes and provides real-time insights for adaptation.
So, what next?
The next frontier for AI search in M&E will focus on hyper-personalized search experiences, multimodal AI processing, and real-time indexing of live events. As AI models continue to improve, the industry will see even more seamless and scalable content management solutions.
Prime Focus Technologies is among the pioneers leveraging AI search and metadata solutions to help media enterprises improve content discoverability using CLEAR® AI, an in house platform combining the best of breed LLMs, SLMs and agents from across the industry today. CLEAR® AI’s unified solution combines metadata tagging and intelligent, contextual search, optimizing workflows and reducing the turnaround time for your content operations. Our ability to integrate with any MAM system eliminates manual bottlenecks, allowing enterprises to achieve a higher content ROI.