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October 18, 2024

Industry perspectives: a media giant's metadata challenge and new AI solutions

Verbatim insights from our conversation with the director of a US-based global media business. Hear how their organization is facing legacy metadata challenges, and how multimodal AI can monetize those stranded media assets.
Sales exec weighing up new asset creation for NYC skyline footage vs using Coactive
"There’s no way in 2024... an archive as rich and vast as ours should be losing money. We should be making a lot of money."

We recently spoke with the director of a US-based global media organization. Like many media organizations, they are sitting on goldmines of content. But they’re struggling to efficiently monetize their vast archives due to outdated systems and poor metadata.

This blog is part of our Industry Perspectives series, featuring insights and real-world experiences from current business leaders. All interviewee quotes are presented without attribution, to protect sensitive business practices. This piece explores how multimodal AI can help media companies better monetize their archives.  

The challenge: poor metadata and old systems are preventing archive monetization

Media businesses know their archives represent a huge financial opportunity. It’s why we’ve seen a surge in digital transformation projects across the sector. But as our source indicated, it’s a steep learning curve:

“Content and broadcasting is our bread and butter, so [monetizing] the archives… is almost like we're starting a whole new business.”

They cited three core issues hindering content discoverability:

  • Poor or non-existent metadata
  • Outdated archiving systems
  • Inefficient search and retrieval processes
“Our metadata is terrible or non-existent… and that is across millions of assets. [It’s] very difficult to monetize assets when you can’t see what’s on them.”

For instance, our source recently discovered a huge flaw in their legacy system. For decades, keyword metadata was entered as phrases. So a tag like “man on the street interview” was entered as “manonthestreetinterview”, making that information completely invisible to traditional search tools. 

The impact: missed opportunities and financial drain

The consequences of these challenges range from delayed licensing deals, to higher operational costs, frustrated clients, and untapped revenue streams.

“I'm working on a deal right now... but if I had better metadata, that deal might be done. Just getting through this metadata mess is preventing us from even arriving at a valuation. If we had what we're aspiring to, I would know everything within an hour, but right now it’s a major undertaking.”

Without clear metadata, media companies struggle to license content – whether it’s for B-roll, documentaries, advertising, or AI model training, the obstacle is the same. Inaccurate or missing metadata slows deals and forces teams to manually sift through archives, wasting time and resources.

As our source explained, their organization is currently having to deploy loss-making work-arounds, just to maintain market relevance. “We do charge a fee for screeners*, but it's [still] financially a loss for us,” they explained. They’re in a race against time to overhaul legacy systems and turn their loss-making archive sales into a profit center. 

*Screeners review, categorize, and log content within media archives. Their role is highly manual, typically  involving tedious scrubbing and trawling through images and videos in the hope of finding relevant content for an internal or external client.

Example: analytics required to value a content licensing deal

When we spoke, our source was in the middle of a high-stakes deal. “I'm trying to create a valuation for a tranche of assets. And I just can't do it yet because of the metadata. If I had better metadata, that deal might be done already.” 

They gave us a fictional scenario: “What if Ken Burns is doing a documentary on Vietnam, and he wants to license footage? It’s very hard for us to tell him what we actually have. We know we have a lot of footage. But if he wants, say, a speech that Richard Nixon gave about Vietnam… maybe we get lucky, and there's a transcript – because it's a presidential speech. But if someone else was giving a speech [how would we find it]?” 

From our source’s perspective, their ability to find relevant content is a total lottery. This makes it impossible for their archive sales team to efficiently arrive at accurate valuations for licensing deals.

“We can make a lot of money with B roll,” our source explained, but legacy methods don’t allow them to meet clients’ granular needs. “If somebody wants poinsettias, or World War II tanks, or [footage of] HIV-prevention syringe programs… How the hell are we going to find that? It's a major undertaking on our part. [Often] we just can’t.”

The solution: multimodal AI

With AI-powered metadata enrichment and multimodal search capabilities, Coactive accelerates content discovery, improving both the quality and quantity of licensable assets. 

Illustration showing Coactive enabling sales teams to connect archive teams with requested content

Our platform integrates with your existing MAM/DAM, streamlining the entire content licensing process.

“Media businesses no longer need to let yesterday’s metadata be today’s barrier. With multimodal AI, you can finally monetize archive content efficiently.” – Will Gaviria Rojas, co-founder and Field CTO at Coactive AI

The Coactive platform offers:

  1. Content Enrichment: Create customized metadata using multimodal inputs, allowing your archives to remain relevant and easy to monetize
  2. Content Discovery: Search across images, videos, and audio using a combination of text, image, and keyword prompts for faster, more accurate retrieval (check out our intelligent search feature)
  3. Content Analytics: Leverage powerful AI to extract insights from your visual datasets and unlock untapped value (natural language prompts, SQL, and data visualizations available)
  4. APIs and SDKs to integrate with your existing DAM, making it easy to apply Coactive’s AI-driven tools without disrupting your current workflows

Summary

Media and entertainment businesses no longer need to deploy costly, ineffective workarounds to monetize their archives. Multimodal AI transforms your archive through scalable, detailed metadata enrichment, powerful search capabilities, and detailed data analytics. All of which accelerates your content discovery and licensing capabilities. 

For the first time, businesses can efficiently capitalize on their legacy images, video, and audio, to create diverse and scalable revenue streams.

Don’t let legacy issues stop you from monetizing your media archives. Request a demo at www.coactive.ai.

P.S. A huge thanks to our industry expert for sharing their experiences with us!