Broadcasters look to AI to unlock value from content archives

For many broadcast and media organizations, one of the largest untapped assets is already in their possession. Content libraries built over years or decades remain difficult to find, harder to reuse and largely invisible to the AI systems now being deployed across workflows.

That is beginning to change. As artificial intelligence tools mature in areas like metadata generation, content indexing and automated search, organizations are applying those capabilities directly to archive exploitation – using AI to surface existing footage, assess rights, identify reuse opportunities and connect older material to new distribution channels.

Archive exploitation is one of the areas where AI’s connection to business outcomes is most direct, and where the returns on metadata investment are most clearly visible.

The archive problem

The challenge most organizations face with their archives is not that the content doesn’t exist. It is that the content can’t be found efficiently enough to be useful.

Legacy archives were built under different operational assumptions. Content was logged manually, often inconsistently and catalogued using taxonomies that reflected the technology and workflow priorities of the time.

The result, for many organizations, is a library that is technically accessible but practically opaque – footage that exists somewhere in a storage system but cannot be surfaced quickly enough to justify the effort of looking.

“Many AI initiatives are focused on improving metadata quality, particularly for archives that lack the thorough descriptive information needed to improve content discoverability and enhance viewing experiences,” said Geoff Stedman, chief marketing officer, SDVI, in the Industry Insights roundtable on AI and media workflows.

“The success or failure of these initiatives is largely based on how accurate the AI tools are at extracting descriptive information and associated timecode so that the metadata is usable and searchable,” said Stedman.

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An archive search that can return a timestamp, not just a file or a programme, changes the economics of reuse. Editors and producers looking for a specific moment, a particular speaker or a scene with defined characteristics can locate it in seconds rather than hours. That compression of search time is where the operational value most directly appears.

Metadata enrichment at scale

The process of improving archive metadata through AI typically involves analyzing existing content such as video, audio, transcripts (where they exist), and generating descriptive records that did not previously exist or were too sparse to support reliable search.

“Video understanding and the metadata it extracts are becoming increasingly prevalent. It’s an area where AI is being used to automate processes that traditionally required hours of manual review, like celebrity detection, quality control (QC), compliance verification and ad break detection. AI can process content in minutes, allowing organizations to refocus talent on more creative endeavors,” said Steph Lone, global leader, solutions architecture, media and entertainment, games and sports, Amazon Web Services, described AI being used to automate processes that traditionally required hours of manual review.

Agentic AI systems, she added, could extract technical specifications, summaries and both chapter- and shot-level detail from large content libraries.

The scale at which that enrichment can now be applied is a meaningful shift. Content owners often manage petabytes of video, Lone noted, a volume that made comprehensive manual cataloguing impractical regardless of intent.

Phil Petitpont, co-founder and CEO, Moments Lab, described multimodal AI as capable of comprehensively indexing video by breaking it into scenes and enriching each with the detailed metadata needed for agentic AI to locate exact clips. For organizations with large unscripted libraries, that capability changes how archive value is assessed.

“Banijay has seen an 80% reduction in the time it takes to locate the right clips. This efficiency has lowered production costs from about $800 per clip to just $10 per clip and has put them on a path to ‘100X’-ing their social media output, driving more revenue from platforms like YouTube,” Petitpont said.

From search to reuse

The business case for archive AI extends beyond search.

Once content is properly indexed, it becomes a source of material for new productions, social distribution, rights-aware licensing and platform-specific versioning, workflows that were previously limited by the difficulty of finding the right footage quickly enough.

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“AI has moved from pilot projects into everyday newsroom workflows, where it now helps with footage review and archive search. It’s being used both upstream and downstream in production: upstream to build new shows from unscripted footage and unlock more value from archives, and downstream to recycle content for new audiences by quickly finding key moments for social clips and short-form video,” Petitpont said.

Content intelligence is one of the most mature areas of AI application according to contributors in the roundtable, with significant upside in understanding asset libraries more thoroughly.

Beyond search and retrieval, this opens up new monetization of the catalog and opportunities in hyper-localization and personalization.

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Simon Wocka, regional sales manager, Pixitmedia, described growth in archive exploitation, compliance checking and rights management as areas where AI was expected to have meaningful near-term impact. Those use cases, he noted, rely less on creative generation and more on how well data is organized, moved and surfaced across systems.

“The strongest impact will keep coming where AI connects tightly to data management and workflow foundations, not where it operates in isolation,” Wocka said.

Archive exploitation does not operate independently of rights management, however.

Reusing footage requires confirming what rights exist, across which platforms and within what time windows — a process that has historically been as much a bottleneck as the search problem itself. For organizations applying AI to large libraries, rights-aware asset management is a prerequisite, not an add-on.

The compounding return on metadata investment

Archive exploitation was one of the clearest demonstrations of a principle that ran throughout the roundtable: the returns on AI investment compound in proportion to the quality of the underlying data infrastructure. 

Metadata is crucial to broadcast workflows, enabling a world of automation

Organizations that have invested in taxonomy design, metadata governance and archive cleanup find that each AI-enabled workflow benefits from those foundations.

Search improves. Reuse rates increase. Rights clearance accelerates. Compliance workflows become less labor-intensive. Each gain reinforces the next.

The inverse is equally consistent.

Organizations attempting to apply AI to archives with inconsistent or sparse metadata find the tools operating below their potential – surfacing incomplete results, missing relevant material and generating records that require significant manual correction before they are operationally useful.

For broadcast and media organizations evaluating where to direct AI investment, the archive represents both an opportunity and a test. The content is there. Whether AI can make it work depends largely on the data foundations that were, or were not, built around it.

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