Article overview:

• Traditional search is broken: Manual tagging doesn't scale, consistency is impossible, and valuable moments inside videos go undiscovered
• Instead of matching keywords, multimodal AI analyzes visual, audio, and speech signals simultaneously—surfacing relevant content through natural language queries 
• Organizations report 100x faster cataloging, and 72% of media executives using generative AI in production already see ROI

Finding the right archive clip while on a tight deadline can be frustrating—a proverbial search for the needle in a haystack—and every delay costs production money. There is help on the horizon, though, with organizations increasingly turning to multimodal AI to address their media asset management challenges.

And the market reflects this shift. Valued at roughly $2 billion in 2025, media asset management is projected to reach $10 billion by 2035. The value driver isn't just more content; it's the opportunities inherent in smarter content infrastructure powered by AI that actually understands what's inside your videos.

What is multimodal AI in media asset management?

A producer at a major sports network recently described her workflow: "I know the perfect shot exists somewhere in our archive. I can picture it—the crowd erupting, confetti falling, the team rushing the field. But finding it means guessing keywords someone used eight years ago and scrubbing through hours of footage." 

That frustration is shared across organizations, from news broadcasters to corporate marketing teams. Enter: Multimodal AI. It’s here to reshape how organizations manage, search and monetize video content. 

Using the power of large language models and generative AI, the technology understands video by analyzing what's seen, heard and known simultaneously—rewriting the rules of media asset management. Rather than tackle tagging in levels one by one, multimodal AI can parse it all at once and generate the tags, chapters, metadata and more, all within a fraction of the time.

The hidden cost of "good enough" search

Most media teams have adapted to working around their archives rather than with them. They've built elaborate folder structures. Created naming conventions. Hired people to tag footage manually.

It works, until suddenly it doesn't.

Say a broadcast network needs b-roll of a specific athlete from five years ago. Or a brand team wants every mention of a competitor across 10,000 hours of earnings calls. Or a news organization needs to surface relevant historical footage within minutes of a breaking story.

Traditional media asset management systems struggle here because they rely on metadata that humans created—metadata that's incomplete, inconsistent or simply missing. When you search for "CEO announcement," you only find clips someone thought to tag with those exact words. Here’s why that matters:

• Manual tagging doesn't scale
  
  • One study found organizations with large archives would need decades of human effort to fully annotate their backlog.
  • Meanwhile, new content arrives daily. A mid-sized broadcaster might ingest 50 hours of new footage per week; that's 2,600 hours annually, on top of whatever already sits untagged in the archive.
• Consistency is nearly impossible
  
  • Different people tag differently—One editor's "interview" is another's "talking head" is another's "vox pop." 
  • Without controlled vocabularies and enforcement, search results vary wildly based on who created the tags.
  • Even well-intentioned teams drift over time as staff changes and priorities shift.
• Context gets lost 
  
  • A 45-minute video might receive five tags describing its overall topic.
  • But what about the 30-second segment where the CEO mentions a competitor? The cutaway showing the prototype? The unscripted moment that would make perfect social content? 
  • Traditional tagging captures the container, not the moments inside it.

MAM search issues cost time and money: Every hour spent searching is an hour not spent creating. 

What makes multimodal AI different to traditional search?

Multimodal AI processes video the way humans experience it: by analyzing what's seen, heard and known simultaneously. Rather than relying on pre-assigned tags, these systems generate rich, time-coded descriptions of visual content, speech, on-screen text and audio cues—all indexed and searchable.

Let’s look at the difference between the old keyword search and new multimodal AI search processes.

This isn't incremental improvement. It's a category shift.

How does multimodal AI actually work for archive search?

Multimodal AI systems analyze assets using four main steps: visual analysis, audio processing, semantic integration, and time-coded indexing. To unlock the value of multimodal AI for production asset management, it helps to understand how the technology works and set realistic expectations before evaluating vendors. 

Visual analysis

Computer vision models identify objects, faces, actions, text on screen, scene composition and emotional tone from video frames. Modern systems process multiple frames per second, creating dense visual understanding. This means the AI agent knows when someone walks into frame, when a logo appears, when the scene shifts from indoor to outdoor—all automatically.

Audio processing 

Speech-to-text captures dialogue with increasing accuracy across accents and domain-specific vocabulary. Audio classification detects music, ambient sound, applause, silence and other non-speech elements. Speaker diarization identifies who's talking when—distinguishing between the interviewer and guest, or tracking multiple speakers in a panel discussion.

Semantic integration

The breakthrough isn't with any single modality; it's combining them. A multimodal system understands that the person speaking (audio) while pointing at a chart (visual) during the word "revenue" (speech) creates meaning none of those signals can convey alone. This cross-modal understanding enables queries that would be impossible with single-modality systems.

Time-coded indexing

Results point to specific moments, not just files. Search for "tense negotiation" and get the 47-second segment starting at 23:14, not a 90-minute meeting you'll need to scrub through. This precision transforms how teams work—you get the exact clip, not the haystack containing the needle somewhere.

Potential challenges with multimodal AI search

However, there are some potential challenges to overcome when deploying multimodal AI search in your media asset management, including:

• Model performance varies with content type 
• Systems trained primarily on broadcast footage may struggle with user-generated content, security cameras or specialized industrial video
• Audio quality matters significantly for speech recognition accuracy

Mitigate the risk: Make sure to ask vendors about training data, domain-specific performance, and how they’ll handle edge cases in your content library.

Why is the media asset search market shifting?

The business case for multimodal AI rests on measurable outcomes:

• Tagging speed: Organizations report 100x faster content cataloging compared to manual methods. What previously would take a team several weeks can now be completed in mere hours. This means a 10,000-hour archive that would have required many years of manual annotation can be indexed by multimodal AI in days.
• Search effectiveness: Natural language queries surface relevant content that keyword search would’ve missed entirely. Teams report finding "lost" footage they'd forgotten existed—historical interviews, event coverage, product demos buried in archives unused and forgotten.
• Production efficiency: AI-suggested clip boundaries and automatic rough cuts can reduce editing prep time significantly. One production team reported cutting an entire day from its post-match highlights creation process.
• ROI reality: Industry research found 72 percent of media and entertainment executives using generative AI in production already see ROI on at least one use case. This isn't future potential—it's current performance measured across hundreds of organizations.

Evaluating multimodal AI for media asset management: 5 capabilities to look for

Multimodal AI isn't a single feature. It's a foundation that enables several interconnected capabilities. Here's what to evaluate when assessing video discovery platforms.

1. Semantic search across visual, audio and text

Traditional search matches keywords. Semantic search can understand the intent behind the query.

For example, if you’re looking for "tense moments in the fourth quarter", a multimodal system can surface clips showing close scores, crowd reactions and commentator urgency—even if no one ever labeled them that way. The AI interprets context across modalities: the visual intensity, the audio energy, the spoken commentary.

Consider the difference:

• Legacy approach: Guess keywords, browse results, scrub through clips, repeat. 
• Semantic approach: Describe what you're picturing, review time-coded results, select and export. 

The second workflow respects how creative professionals actually think about content.

What this means for you: Your archive becomes a conversation. Non-technical users—producers, marketers, executives—can find what they need without learning complex filters or relying on librarians. You just need to ask the system the same way you’d ask a colleague: "Show me clips of executives discussing sustainability."

Question to ask: Does semantic search actually work for your content? Generic demos on curated content prove little; make sure you run pilots with real queries on real footage. You need to know how the system will handle your specific domain, whether that’s scripted or unscripted television, sports broadcasting, news, corporate video, user-generated content, or something else entirely.

2. Automated metadata enrichment

With multimodal AI, the production workflow transforms from reactive to proactive. Instead of tagging after the fact (or not at all), content arrives in the database pre-enriched. Editors can search immediately. Compliance teams can screen automatically. Licensing teams can discover inventory they didn't know they had.

Every frame can now generate its own description: 

• Face recognition identifies speakers
• Speech-to-text captures dialogue
• Scene detection marks transitions
• Object recognition catalogs what appears on screen

But make sure this metadata isn't trapped in a proprietary format. Seek a video discovery platform that lets you export it as portable text—meaning you own the intelligence layer even if you switch providers in the future.

What this means for you: New content gets indexed automatically on ingest. Legacy archives can be enriched in bulk. That metadata debt you've accumulated over years? Multimodal AI can help close that gap in weeks rather than decades.

Evaluation question: What's the metadata export story? Can you extract AI-generated descriptions in standard formats (XML, JSON, CSV)? Portable metadata protects your investment regardless of future platform decisions.

3. Intelligent clip generation

Finding the right moment is only step one. Extracting that clip used to get very hands-on, requiring manual trimming, format conversion and export workflows.

AI-assisted systems can now identify natural clip boundaries—complete thoughts, scene changes, speaker transitions—and suggest optimal cut points. Some platforms can generate rough cuts automatically based on search results. Others will export to editing timelines with markers at AI-suggested cut points.

The practical impact of this automation compounds across teams. A social media manager can pull 10 clips for the week's posts in minutes instead of hours. A producer can assemble interview highlights without scrubbing through raw footage. A sales team can find and share relevant customer testimonials same-day. All the while, your archive is getting worked harder and smarter.

What this means for you: A task that took 20 minutes per clip (find, review, mark in/out, export) could shrink to seconds. Production teams can review AI-suggested edits rather than starting from scratch.

Evaluation question: Ask what integrations exist within this platform. Check it can connect to your editing tools, storage systems and distribution platforms. Roadmap promises matter less than the ability to ship right now.

4. Content compliance and rights management

Multimodal AI can flag potential issues before they become expensive problems: unlicensed music in the background, brand logos that shouldn't appear, faces requiring consent.

This screening can run automatically on ingest, flagging issues before content enters production workflows. This is especially important for organizations distributing content across multiple markets with varying rights agreements—what's cleared for broadcast in one region might require different licensing in another.

The shift from reactive to proactive compliance changes risk profiles significantly. Instead of discovering a rights violation after broadcast—when takedowns are embarrassing and settlements expensive—teams have the opportunity to catch issues during production when fixes are straightforward.

What this means for you: Compliance moves from reactive (fixing problems after broadcast) to proactive (catching issues during production). One misused clip can cost more in legal fees than an entire year of AI tooling.

Evaluation question: What does the system detect—music, logos, faces, text? How does it integrate with your existing rights management workflows?

5. Archive monetization

Media companies could be sitting on decades of footage with value potential. The challenge in realizing this potential revenue stream has always been discoverability—potential buyers can't license what they can't find.

AI-powered search can transform archives from cost centers to revenue opportunities. Sports leagues can surface historical highlights for anniversary coverage. News organizations can package archival footage for documentary producers. Brands can identify user-generated content featuring their products.

The economics start to shift when search friction disappears. A licensing inquiry that required a researcher spending hours locating relevant clips could now be completed in minutes. Response times drop. Deal velocity increases. Content that was effectively invisible becomes valuable inventory.

What this means for you: Your archive's value isn't just preservation—it's potential revenue. 

Evaluation question: How do external partners currently access your searchable archive? What permissions and preview capabilities exist in this new system for potential licensees?

The challenges of integrating multimodal AI into your video discovery workflows

Adopting multimodal AI isn't plug-and-play. Decision-makers should plan for these very real challenges when integrating multimodal AI into existing media asset management and production asset management processes. 

Infrastructure requirements

Cloud-based deployment dominates the market (with 64 percent share), largely because AI processing demands significant compute resources. Hybrid models work well for organizations with on-premises archives and security requirements—they can ingest footage locally, process in the cloud, and store metadata wherever they choose. The key to setting your infrastructure requirements is understanding how data flows and ensuring this aligns with your security posture.

Data quality 

AI performs best with consistent, well-organized source material. Archives with mixed formats, damaged files or inconsistent audio can present real challenges to data quality. Make sure you budget for preprocessing and normalization. Organizations often underestimate this step; plan around 10–20 percent of your project budget to go towards data preparation.

Careful change management

Teams accustomed to keyword search will need time to adapt to semantic queries. The good news: natural language interfaces can lower the learning curve significantly. But don't underestimate the human side of technology adoption. Build in training time, gather feedback, and expect a ramp-up period before productivity gains start to materialize.

Varying cost structures 

There’s no industry-standard way to pay for multimodal AI—some vendors charge per hour of video processed, others use subscription models, still more have a hybrid approach. Make sure you understand whether you're paying for storage, processing, by the volume of search queries, or all three. The cost of cloud computing can surprise organizations that process large archives quickly. Model your expected usage and get it all documented before signing contracts.

The risk of vendor lock-in 

It’s essential to clarify metadata portability. Can you export AI-generated descriptions in standard formats? What happens to your enriched metadata if you switch platforms? Some vendors offer portable metadata; others trap intelligence in proprietary systems meaning you’re either stuck with them or you lose your data. Your enrichment investment should remain yours.

Security and ownership

Clarify where your media goes during processing and who can access the AI-generated metadata. Seek bring-your-own-storage options and ensure you retain full ownership of both source files and derived intelligence. For sensitive content, confirm data residency options and compliance certifications. And remember, the more connections in the workflow, the bigger the potential security attack surface.

Building your business case for multimodal AI video discovery platforms

Focus on three proof points when evaluating multimodal AI: Time savings, risk reduction, and revenue potential. 

• Time savings: Calculate current hours spent searching, tagging and preparing clips. Even conservative 50 percent reductions could translate to significant productivity gains. One editor saving 10 hours per week represents more than 500 hours annually. Multiply across your team for the full picture.
• Risk reduction: Quantify the cost of compliance failures, missed deadlines due to search friction, and opportunities lost when content couldn't be found in time. One rights violation settlement could exceed annual platform costs.
• Revenue potential: Estimate licensing revenue from archival content that’s currently invisible to potential buyers. If 5 percent of your archive could generate $100 per clip annually, what's the total opportunity? Even modest improvements in discoverability can shift archives from cost centers to profit contributors.

Get ready to build your business case by starting with a focused pilot. Select a contained archive, e.g. one show's footage, one year's marketing content, one product line's media, and measure results against these metrics before scaling. 

And make sure you define success criteria upfront: search time reduction targets, tagging accuracy thresholds, user adoption milestones.

The future of multimodal AI in production asset management

Several trends look set to shape multimodal AI in media asset management over the next three to five years—and understanding them can help organizations to make platform decisions that won't require replacement in 24 months.

Look for AI agents, not just AI tools

The industry is shifting from passive analysis to active task execution. Future systems won't just tag content—they'll enrich it, transform it for different channels, check compliance, and suggest distribution strategies, all autonomously. The shift from "tool you use" to "agent that works alongside you" is already underway. Early implementations can handle routine tasks like format conversion and basic compliance screening without human intervention—though it always pays to maintain human oversight of any AI workflows.

Deeper workflow integration

Expect tighter connections between MAM systems and creative tools. Plugins can already pull from AI-indexed archives directly into editing software. This integration is likely to extend across the post-production landscape, making AI-powered search invisible—it’ll just be part of how editors work. The goal: zero context-switching between finding content and using it.

Real-time processing for live content

Sports broadcasters and news organizations need AI analysis during events, not after. Processing latency is likely to continue dropping, helping to enable highlight generation and clip extraction while cameras are still rolling. The gap between capture and searchability could shrink from hours to minutes to seconds as live events become instantly searchable archives.

Multimodal generation, not just analysis

Today's systems understand existing content. Tomorrow's will create it. Generating rough cuts, suggesting b-roll, even producing content for social platforms based on archive material. The line between finding content and creating content will blur. Expect AI-assembled highlight reels, auto-generated social clips and intelligent content repurposing at scale.

Cross-archive search

As metadata becomes portable and standards emerge, look for federated search across multiple archives and organizations. Finding the right footage won't be limited to what you own. Licensing marketplaces, content partnerships and cross-organization collaboration could all benefit from interoperable search.

Standards and interoperability

As the market matures, expect pressure for portable metadata formats and API standardization. Organizations don't want intelligence locked in proprietary silos. The vendors who embrace openness are best placed; those who don't may find customers increasingly unwilling to accept lock-in.

Unlocking the future of multimodal AI in PAM and MAM

Multimodal AI represents a fundamental shift in how organizations relate to their video archives. The question isn't whether this technology will transform media asset management—it already is.

The shift from guessing to describing represents the future of how organizations can relate to their media assets. Early adopters can build competitive advantage through faster production, better content utilization and new revenue streams. Those that wait are likely to accumulate more metadata debt while competitors make their archives work harder.

Multimodal AI in media asset management isn't about replacing human creativity. It's about removing the friction that prevents creative teams from accessing and using their own content. For organizations ready to move beyond keyword search and manual tagging, the opportunity has never been clearer.

The technology is ready. The market is moving. Your archive is waiting to become useful again. 

Key takeaways: Prepare your content assets for multimodal AI

• The market is growing fast: Media asset management is valued at $2 billion in 2025 and projected to reach $10 billion by 2035—driven by AI that understands what's inside video, not just metadata.
• Traditional search is broken: Editors spend up to 30% of their time locating footage. Manual tagging doesn't scale, consistency is impossible, and valuable moments inside videos go undiscovered.
• Multimodal AI changes the game: Instead of matching keywords, it analyzes visual, audio, and speech signals simultaneously—surfacing relevant content through natural language queries like "CEO announcing product on stage."
• The results are measurable: Organizations report 100x faster cataloging, and 72% of media executives using generative AI in production already see ROI.
• Five capabilities to evaluate: Semantic search, automated metadata enrichment, intelligent clip generation, compliance screening, and archive monetization potential.
• Implementation isn't plug-and-play: Plan for infrastructure requirements (cloud dominates at 64% share), data quality prep (10-20% of budget), change management, and vendor lock-in risks.
• The future is agentic: Expect AI that doesn't just tag content but enriches, transforms, and distributes it autonomously—plus real-time processing and cross-archive federated search.

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

Frequently asked questions about multimodal AI in media asset management

What is multimodal AI in media asset management?

Multimodal AI analyzes video by processing visual, audio, and text signals simultaneously. It automatically generates time-coded metadata, descriptions, and tags—making content searchable through natural language queries instead of manual keyword matching.

How is multimodal search different from keyword search?

Keyword search matches exact terms in metadata. Multimodal semantic search understands meaning and context. Search for "CEO announcing new product on stage" and find every relevant scene—time-coded to the exact moment—regardless of how it was originally tagged.

What does multimodal AI analyze in video content?

• Visual: Objects, faces, actions, on-screen text, scene composition, emotional tone
• Audio: Speech-to-text, music detection, ambient sound, speaker identification
• Semantic: Cross-modal meaning (e.g., person speaking + pointing at chart + saying "revenue")
• Temporal: Time-coded indexing to specific moments, not just files

How does multimodal AI help with compliance?

AI automatically flags potential issues before broadcast: unlicensed music, brand logos requiring clearance, faces needing consent. Screening runs on ingest, catching problems during production when fixes are simple—not after broadcast when they're expensive.

What infrastructure do I need for multimodal AI?

Cloud-based deployment dominates (64% market share) due to compute demands. Hybrid models work for organizations with on-premises archives and security requirements—ingest locally, process in cloud, store metadata wherever needed.

How much does multimodal AI cost?

Pricing models vary: per hour of video processed, subscription-based, or hybrid. Budget 10-20% of project costs for data preparation and normalization. Model your expected usage before signing contracts.

Can I export AI-generated metadata?

Look for platforms that export metadata in standard formats (XML, JSON, CSV). Portable metadata protects your investment if you switch vendors. Some platforms trap intelligence in proprietary systems—clarify portability before committing.