How AI is revolutionising metadata management

Effectively managing metadata has always been the number one challenge for DAM Managers, but artificial intelligence has had a big impact on DAM workflows and processes.

From automated metadata tagging at scale and advanced prompt engineering, there are few innovations that have had such a big effect.

In this article we’re going to take a closer look at how AI metadata management is revolutionising this essential element of Digital Asset Management.

Why traditional metadata management no longer scales

When digital asset libraries were smaller and handled by a single team, manually tagging files and maintaining basic schema was manageable. However, that approach simply doesn’t scale in 2026.

Modern organisations are creating thousands of new assets every month, particularly images and video, and those assets are used by multiple teams across marketing, communications, fundraising and digital channels. Manually applying metadata at this volume can be slow and inconsistent, and under pressure, teams might apply minimal tags, use different terminology or skip metadata tagging entirely, which makes search unreliable and undermines trust in the system.

What AI changes about metadata

AI metadata management changes the game for DAM Managers in three key ways:

1. AI-powered metadata tagging
2. Enhanced digital asset discovery
3. Advanced functionality

AI-powered metadata tagging

AI changes metadata management by taking on the heavy lifting that would otherwise consume hours of manual effort. Instead of relying solely on users to describe every image or video, AI can interpret visual content directly, identifying objects, people, locations and activities within an asset. This means a single upload can be automatically tagged with rich, descriptive metadata the moment it enters the system.

The benefit for DAM Managers is obvious, with time spent on repetitive tagging tasks dramatically reduced, while consistency improves because the same logic is applied across the entire library. 

AI can also handle more complex information processing, such as populating one metadata field based on the content of another or applying conditional rules at scale. For example, an image recognised as containing people could automatically trigger consent-related fields or usage flags.

By generating the metadata teams actually need, rather than relying on what users remember to add, AI helps ensure assets are searchable, compliant and ready for reuse from day one.

Enhanced digital asset discovery

AI does not just change how metadata is created, it also transforms how assets are found. Traditional search relies heavily on exact matches to tags or filenames, which means results are only as good as the metadata applied to digital assets. 

AI-powered discovery removes this limitation by allowing users to search using natural language.

Phrases such as “a classic red car” or “a fundraising event” can return relevant assets even if those exact words never appear in the metadata, which is especially powerful in large libraries where tagging may be incomplete or inconsistent. AI can also surface visually similar assets based on a single reference image, helping users explore alternatives quickly without starting from scratch.

For DAM Managers, this reduces pressure on perfect metadata coverage while still improving access. For users, it means faster discovery, less frustration and greater confidence that they are seeing the best available content—not just the easiest to find.

Advanced functionality

Beyond tagging and search, AI enables more advanced metadata workflows that were previously impractical at scale. Modern Digital Asset Management systems can use AI prompts to generate structured outputs and write them directly into dedicated metadata fields, turning unstructured media into usable, searchable information.

Common examples include automated transcription and subtitling of audio and video files, which makes spoken content searchable and accessible without manual intervention. AI can also detect objects, places or specific people, supporting more detailed classification and compliance workflows, while facial recognition and detection can help flag assets that include identifiable individuals, prompting review of consent, permissions or usage restrictions.

These capabilities allow DAM Managers to manage complex media types more confidently, without losing control over how AI outputs are applied. Rather than replacing strong governance, AI supports it by making it easier to scale and manage complexity that manual processes can no longer manage effectively.

READ MORE: 6 metadata tasks automated with AI in ResourceSpace

Ready to find out more about how ResourceSpace leverages AI to enhance metadata management and streamline your workflows? Book your free demo below.