Somewhere in your content archive, there’s a video that answers the question your team spent last week trying to resolve. It might be an inspection recording, a product demonstration, a training session, or decades of broadcast footage. The answer exists. The problem is that your search bar has no idea the video does.
For organizations that run on rich media—manufacturers with engineering inspection footage, broadcasters with years of archived programming, life sciences firms with imaging libraries, energy companies with field documentation—this is a daily frustration with real consequences. Content that costs time and money to produce sits completely dark: inaccessible to search, invisible to AI, and ungoverned by records management policies.
This post explains why that happens, what AI actually needs to change it, and how OpenText™ Knowledge Discovery—paired with OpenText Document Management solutions or OpenText™ Digital Asset Management—solves the problem at the source.
The archive that nobody can search
Most content management conversations focus on documents: contracts, invoices, policies, and records. Rich media tends to live in a different category, often managed separately, often not managed at all.
For many organizations, though, rich media is not a secondary concern. It’s central to how work gets done. A manufacturing plant may capture hundreds of hours of inspection video each year. A broadcast company may hold decades of archived programming. An energy company may rely on visual documentation to manage asset maintenance across thousands of field sites. A life sciences firm may hold product and process images that carry regulatory significance.
In every case, the same problem exists: that content arrived without meaningful metadata. No descriptions, no tags, no searchable text. It was stored, and it became dark data the moment it landed.
Why traditional search can’t see your visual content
Search works on text. It indexes words, matches queries to keywords, and surfaces results. That logic works well for documents. It breaks entirely for images and video.
A folder of product images has no words. A 45-minute inspection video has no index. Without descriptive metadata, text that explains what the content contains, these assets are invisible to any search or AI system, no matter how capable that system is. You can type a precise query and get nothing back, not because the content doesn’t exist, but because the system has nothing to match against.
Manual tagging sounds like the answer. In practice, it doesn’t scale. For organizations with large or growing visual content estates, assigning descriptive metadata to thousands of assets by hand is slow, inconsistent, expensive, and always behind. Teams either don’t start, or they start and don’t finish, leaving the bulk of the archive untouched and unsearchable.
The result: a content estate that is stored but not usable—not searchable, not classifiable, and not ready to support AI.
What AI actually needs from rich media
Here’s the gap that most AI readiness conversations miss: AI systems don’t consume video frames or raw image files the way people do. They work on text—descriptions, entities, attributes, and context. Before any downstream AI system can classify, surface, recommend, or act on visual content, that content needs to be translated into a language the system can read.
That translation is the missing step for most organizations. They’ve invested in AI content management tools and governance frameworks. But the visual content sitting in their archives was never prepared to participate. It arrived without the metadata layer that AI depends on, and nothing has created it since.
This is an upstream problem. It has to be solved at the point where content enters your environment—not after the fact, and not manually.
How OpenText Knowledge Discovery changes the equation
OpenText Knowledge Discovery addresses this problem with built-in integration to multimodal GenAI. When images or video arrive, at the point of ingestion or as part of a bulk enrichment run across an existing archive, AI generates descriptive text metadata for each asset. For video, it works by inspecting frames in the video, producing descriptions that capture what is actually happening in the content over time.
No manual tagging. No backlog. No dependency on the person who captured the footage to remember to add a description.
The result: visual content enters your content management environment already enriched. It’s searchable. It’s classifiable. It carries the metadata that records management policies can act on and that downstream AI systems, including AI-powered search, can actually use.
This is generative AI applied upstream, enriching content before it reaches any search or AI system. That’s a fundamentally different approach from indexing text that already exists. If descriptive metadata isn’t attached to visual content, there’s nothing to index, no matter how sophisticated the search layer above it is.
For organizations that manage their visual content in OpenText Content Management, OpenText Documentum Content Management, or OpenText Digital Asset Management, OpenText Knowledge Discovery integrates into the environment where those assets already live. Content arrives enriched and ready, without requiring a separate workflow or a separate team to manage it.
Governed, scalable, and built for the enterprise
The value of enriched metadata isn’t only findability. It’s governance.
When a video asset carries descriptive metadata—subject matter, location, content type, entities—records management policies can apply to it the same way they apply to a document. Retention schedules become enforceable. Access controls become meaningful. Compliance obligations become manageable.
For IT, knowledge management, and records management leaders, this matters as much as search. A rich media estate that AI can read is also a rich media estate that governance frameworks can act on. That’s what makes AI knowledge discovery sustainable at enterprise scale, not just technically interesting, but operationally sound.
OpenText Knowledge Discovery works as the intelligence layer that prepares content for the broader environment around it. When that environment includes OpenTextTM Content Aviator for AI-powered content interaction, the enriched metadata OpenText Knowledge Discovery generates becomes the foundation that makes those experiences reliable and trustworthy.
Get more from the visual content you already have
Your organization has likely spent years capturing rich media content. The recordings exist. The images are stored. The value is there. What’s been missing is the metadata layer that makes any of it usable.
OpenText Knowledge Discovery generates that layer automatically—at ingestion, at scale, and within a governed content management environment.
Explore OpenText Knowledge Discovery to see how your visual content estate can become searchable, classifiable, and AI-ready.
