AI tagging uses image and video recognition to suggest keywords and attributes for assets automatically. It is not a replacement for governed metadata: AI proposes tags fast and at scale, but a person and a controlled vocabulary still decide what is correct and consistent.
Why it matters
Enriching large libraries by hand is slow. AI tagging removes much of that load, especially for objective attributes like objects and colors, freeing people to handle the context and judgment machines miss.
How it shows up in practice
A retailer runs thousands of product shots through AI tagging to capture color, object, and scene, then has a librarian confirm campaign, rights, and brand fields. The best results come from mapping AI output to the existing controlled vocabulary rather than letting it invent free-form tags.
Common mistakes
- Trusting AI tags wholesale with no human quality-control pass.
- Letting AI generate free-text tags outside the controlled vocabulary.
- Expecting AI to capture context, brand nuance, or intent it cannot see.
Stacks covers this in artificial intelligence in DAM.