Finding the perfect clip shouldn't feel like going on an archaeological dig. With the right AI in the driver’s seat, you can transform your media archive from a storage mess into an accessible creative resource. 

But with so many options out there, how do you pick the system that actually fits your needs? How can you find an AI-driven video discovery platform that works with you, rather than creates more work? 

Before you invest in AI for your production asset management, you need to better understand your own production processes. Here are some important questions to ask to get you ready to deploy AI.

Where should we start deploying AI in our media production pipeline?

To understand where AI could have the most impact, first you need to establish which part of your workflow could do with more efficiency. Document the complete journey, including the archiving and video story process, and analyze each milestone for resource needs. 

Then, look at how you can deploy AI to take care of the work that wastes time and resources without adding value. Things like creating metadata and video chaptering can be handled by AI instead of relying on manual human input, which can free staff to focus on more valuable tasks.

Up to 50 percent of a creative team's hours go to searching for usable clips. One media company we work with calculated that finding a single moment in their system takes five minutes—and that's when you know what you're looking for. Manually transcribing one hour of a video interview? Eight hours of someone's day.

AI helps to combat this time drain. A search capability that understands meaning and context, not just precise keywords, lets you input "crowd celebrates after goal" and retrieve that exact sequence from your archive, including timecodes. AI that can analyze both what it sees and hears can generate transcripts, titles, descriptions, and tags in minutes, not hours. For example, French daily Le Parisien cut the time spent on writing and publishing editorial titles, descriptions and chaptering from 15 minutes per video to three minutes—and this saved four hours daily across the team. Imagine the annual impact of that kind of time saving.

When documenting and analyzing your process, look for patterns like these in your workflows:

• Teams searching by filename because metadata is incomplete—which means they need to know the filename they seek
• Journalists under tight deadlines waiting for archive retrieval
• Valuable footage going unused because no one can find it fast enough
• The need for manual tagging and other transcription bottlenecks

Be smart about the end goal: Target one pain point first. Deploy your AI solution, prove the ROI, and then expand. Always have one eye on what could be next in this search for efficiency and better use of your media assets.

Who needs to be involved in evaluating potential video discovery platforms?

There will be plenty of stakeholders wanting to be involved in evaluation, but the most important ones to listen to are the people who feel the pain the most—those who will use the system daily.

Skip the generic pilot where IT picks random clips to get to a proof of concept. Instead, involve the video editors who race deadlines, the archivists managing requests, the producers researching angles, the social teams tasked with repurposing content. These stakeholders are best placed to evaluate the platforms under consideration, because they intimately know where help is most needed.

Then, give them real work: "Find all shots of the CEO speaking about innovation from Q3" or "Pull together a highlight reel of game-winning goals from this season." Track how long it takes for them to find the right shot, and how accurate the search results are. This will give you the all-important data to inform your evaluation, and make the case for investment.

For example, TF1, a major French media group, tested Moments Lab’s video discovery platform and multimodal AI MXT-2 for indexing content to create trailers and generate text summaries efficiently. "Moments Lab's AI models provide us with an efficient way to index content, generate tags and create content summaries," says Olivier Penin, Director of Innovation at TF1. "This opens up opportunities to improve searchability within our archives."

Ready to evaluate video discovery platforms? Try running your trial for two weeks using 200 to 500 files, with multiple people across different needs using the platform. This approach can help to surface edge cases without overwhelming your team, arming you with the data to move to the next phase of evaluation and, ultimately, investment.

What questions should we ask to understand our team's needs?

There’s no point investing in an expensive video discovery platform if it’s not going to suit the daily needs of your team. Here are four questions that can help to expose whether an AI-driven media solution will actually work for your organization.

Who is searching?

Journalists need speed when under deadline. Archivists need precision across decades of footage. Brand teams need rights-cleared social clips. With the right customization, one AI can serve all three.

What are they searching for?

In the moment, a user could be searching for anything: Specific people, key soundbites, shot types, emotional moments, and more. You’ll want an AI solution that analyzes both visuals and audio, that recognizes faces, actions, locations, shot composition, and compelling quotes—all of the elements that matter when you're building a story.

Where does the content live?

Consider where you’re storing this all-important content: is it on-premises, in the cloud, or maybe a hybrid option split between both? Some AI solutions can force you to chunk files into smaller segments for storage, which can create encoding work and metadata headaches at scale. Cloud-native tools can handle full-length content and offer hybrid options, analyzing low-bitrate proxies in the cloud that link to high-resolution originals stored locally. There are also applications that bridge the gap between on-premise servers and tapes, and AI-powered video discovery. 

How do they currently search?

The way we search can impact the results we get. Advances in search and the advent of semantic search are helping drive more precise and nuanced surfacing. Natural language queries—meaning a search term that mimics the way we speak normally, such as "athletes celebrating in locker room"—demand a search capability that understands meaning and context, not just exact keyword matches. Browsing by theme or emotion? Maybe opt for an AI trained to classify and surface those patterns instead.

What technical details matter most?

Now you’ve established the basics of your user needs, it’s time to go beyond the basics. Get into the details and verify these capabilities before you commit resources and investment.

File length limits

Be clear about the maximum file size the platform can handle. If the system you’re evaluating caps analysis at 20 minutes, your engineers will spend time chunking and re-encoding files. That's high-maintenance at scale.

What this means for you: Don’t assume all platforms are one size fits all. Get into the weeds and understand how the solution will ingest, analyze, and archive your files—or risk investing in a platform that doesn’t fit your needs, and may even add complexity to your workflows. 

Customization options

Look for solutions that let you tailor AI output to match your content type—without rebuilding the entire model from scratch. This means adjusting how the AI interprets your specific footage, terminology and workflows. Don’t just go out of the box; consider how you can train it on your own style, your own processes, your own internal data needs.

What this means for you: A news organization can train the AI to recognize their anchors and reporters, and identify and describe interview settings such as vox pops. A sports broadcaster can customize it to understand their specific league terminology and player names. You adapt the AI to your content, not the other way around.

Learning from corrections

Can you flag inaccuracies so the AI gets smarter? Systems that learn from your corrections are better able to adapt to how you actually work, and the large language models of generative AI are built to learn and adapt. Tools that only connect through rigid external bridges often can't support this kind of ongoing improvement.

What this means for you: If the AI misidentifies a player or misses an important moment, you can correct it. This helps it to learn and get better at recognizing similar situations in future footage, helping search become ever more precise.

Search architecture built for AI

Any platform under consideration needs a database designed to handle AI-powered search at scale. The way AI works is by converting your search queries and video content into mathematical representations that can be compared for meaning—it’s an algorithm comparing numbers, not just exact word matches. This is a process known as embedding, and it helps to represent high-dimensional data in a low-dimensional space. Legacy systems built before AI often can't keep up with the search format or data volume modern search requires.

What this means for you: You can search for "emotional celebration moment" and find relevant clips even if those exact words were never spoken or written in the metadata. The system understands the concept, not just the keywords, thanks to the way it encodes and labels the files. 

Data portability

Don’t end up in data prison: Make sure you ask if the solution’s internal data format is open or locked to one vendor. Open formats give you control and flexibility to switch tools later or use your data for other purposes like recommendation engines. Proprietary formats mean every search calls only the vendor's system—and you can't change providers without starting over, seeking a new solution and evaluating all over again.

What this means for you: With data portability, you own the data and can move it freely. You're not locked into one vendor forever, which is essential in case you decide to switch platforms or use your metadata with other tools.

How will we know it's working?

Like any good business process, you’ll need to track and measure the system to be able to analyze its progress. But it’s not just about the system itself—measure what really matters, which is things like time saved, content found, and revenue generated.

AI-enabled video discovery platforms can be transformative. Teams using AI for video discovery report:

• 70 percent reduction in search time
• 8x productivity increase for video ingest teams
• 50 percent boost in archive marketability
• $1 million potential annual revenue per 10,000 hours of archived footage

When building your proof of concept, be sure to track these metrics to help your evaluation:

• Time spent searching (before versus after)
• Number of relevant clips retrieved per query
• Team adoption rate (who uses it daily after training? Is there any drop off?)
• Editorial consistency (if generating text automatically)

Choosing the right AI for video search and discovery

Great AI can't rescue messy input—but the right AI applied to the right workflow has the potential to change how fast you create. It’s important to put the time in and evaluate various video discovery platforms to make sure you choose something that isn’t just within budget, but that also solves your users’ pain points and can add distinct value.

To do this, start small: one pain point, involving people who live it, with two weeks of real scenarios. Prove value with this, and then scale. See how multimodal AI technology can not only transform your workflows and maximize the benefit you get from your archives, but seek potential new revenue streams, too.

Ready to see if Moments Lab is the right fit for you? Contact us for a demo.