Every newsroom and sports media organization sits on top of a goldmine of content including decades of footage, thousands of hours of game tape, interviews, B-roll, and breaking news coverage. The problem isn’t whether the content exists. It’s finding it.
Ask any archivist, producer, or content licensing manager how they locate a specific moment, a quote, a goal, a reaction shot, in a library with 50,000+ hours of footage, and you’ll hear some version of the same answer: “I remember it’s from a game a few years back… I’ll have to dig.”
That “digging” is expensive. It’s also avoidable.
The old way: manual review, multiplied by everyone who needs footage
Traditional archive search relies on manually logged metadata such as file names, loose tags, and maybe a short list (if you’re lucky). When metadata is incomplete or inconsistent (which it almost always is at scale), the fallback is manual scrubbing. That requires an archivist or associate producer watching footage in real time or 2x speed, hoping to land on the right ten seconds.
This isn’t a fringe problem, and it isn’t going away. A 2023 industry survey of 375+ media and entertainment IT professionals found that 52 percent reported ongoing problems locating assets in their own archives, and nearly 30 percent were still managing search with a combination of spreadsheets and manual labels, even for archives exceeding 500TB. A separate 2018 survey of Australian advertising and media professionals found the average time overhead of managing content was five working weeks a year.
And the problem hasn’t resolved with time: a more recent 2025 global survey of 330 media and entertainment professionals found that automation, metadata enrichment, and third-party integration remain the top workflow challenge for nearly half of respondents (45 percent), with three-quarters of organizations now raising technology budgets specifically to address it.
That’s not a one-time cost. It’s a daily tax on every team that touches the archive: producers building packages, licensing teams fulfilling third-party requests, digital teams repurposing content for social, and compliance teams pulling footage for legal review.
The math: what faster search can mean
Organizations that move from manual footage review to AI-assisted, multimodal search often report significant reductions in discovery time, in some cases from hours to minutes, and catalog coverage, the share of an archive that’s actually described and searchable, can expand once AI-assisted indexing supplements manual tagging.
Put simply: every hour spent scrubbing footage is an hour of a genuinely skilled team member’s time.
Putting hypothetical, illustrative numbers to a mid-size newsroom or regional sports network:
- 3 staff members who regularly pull archival footage
- An assumed search overhead of approximately 200 hours/year per person (a hypothetical figure for illustration; the survey finding above measured overall content-management time per organization)
In this example, that’s roughly 600 hours a year, across just three people, that could be spent scrubbing footage instead of doing the work they were actually hired for: producing, licensing, reporting, editing. Reclaiming even a portion of that time doesn’t mean a headcount to cut. It’s time that can shift from manual search back to the archivist who can now build the “on this day” package instead of hunting for the clip, the producer who can take on a second story instead of waiting on footage, or the licensing lead who can chase more deals instead of fielding search requests.
This example is illustrative only. Actual time savings will vary depending on archive size, workflow configuration, data quality, and implementation approach.
And that’s before accounting for the revenue side: every hour of footage that sits undiscovered is an hour it can’t be licensed, repurposed, or monetized. Archives that are easy to search get used more, and archives that get used more earn more. Faster search doesn’t just save time; it expands what the same team can produce, license, and publish with the hours they already have.
Why this matters more for newsrooms and sports specifically
Two things make speed-to-footage especially high-stakes in these verticals:
Newsrooms live and die by the news cycle. When a story breaks, the window to publish contextual footage such as “Here’s what this senator said five years ago on this exact issue” is measured in minutes, not hours. An archivist who can search visually and by transcript simultaneously (not just keyword-tagged clips) can turn breaking news around before the cycle moves on.
Sports media runs on granular, visual moments. “Tennis player red shirt,” “goal celebration, blue jersey, second half,” “coach gesture after the call” — these are inherently visual queries that transcript-only or manually tagged systems simply can’t answer well. A missed highlight isn’t just a workflow delay; it’s a missed social clip, a missed licensing opportunity, a missed fan engagement moment while the game is still trending.
What faster looks like in practice
This is where the shift from manual review to AI-powered discovery changes the operating model, not just the search bar:
- Producers self-serve instead of filing requests and waiting on an archivist
- Licensing teams fulfill footage requests same-day instead of over multiple days
- Compliance and legal teams can search the archive more broadly for relevant statements and moments, beyond the clips someone remembered to tag
- Digital and social teams turn archive footage into same-day content instead of letting relevant moments go stale
What’s the common threat? Search stops being a bottleneck owned by one team and becomes a capability everyone can use.
The takeaway
For newsrooms and sports media organizations, the archive isn’t just a storage cost — it’s a revenue-generating, storytelling asset that’s only as valuable as it is findable. Manual review scales linearly with headcount. Natural-language, multimodal search is designed to scale with the size of the library itself, helping turn hours of scrubbing into faster, more targeted searching, and turning archives from a cost center into an active part of the content pipeline.
Curious what this would look like against your own archive size and team structure? Reach out today to learn more.
Sources
https://cloudian.com/blog/media-archive-storage-hybrid-cloud.md
https://www.bandt.com.au/media/moving-images-thing-time-take-2019-state-play-industry-survey
https://www.pixitmedia.com/pages/the-2025-state-of-media-archiving-report/
