Taxonomy is the backbone of a DAM system. Get it right and your users find what they need. Get it wrong and they give up and ask someone else, or worse, they recreate assets that already exist. This article walks through how to build a taxonomy that works — and how AI, agents, and agentic workflows fit into that picture.
A taxonomy is how you classify assets inside your digital asset management (DAM) system. It's the structure that makes search work.
Here's a simple way to think about it. Imagine three roommates who inventory everything they own together:
| Item | Owner | Location |
|---|---|---|
| Mixer | Pat | Kitchen |
| Jewelry Box | Alex | Alex's room |
| Weights | Chris | Basement |
That list is a taxonomy. The mixer is the asset. The owner and location are the metadata — information that describes it. In a DAM, a photograph is the asset, and the photographer's name, shoot location, creation date, and subject matter are all metadata. The taxonomy defines how all of that is structured and labeled.
What makes enterprise taxonomy hard isn't the concept. It's the scale, the competing vocabularies across departments, and the fact that it has to keep working as the organization changes. That's what the rest of this article is about.
Before you build anything, do the research. You'll make better decisions with real data behind you.
Survey people across departments to understand what assets they work with and how they currently find things. Look at your existing file-naming conventions — some of those terms will translate directly into taxonomy terms. Pull your search logs and look for two things: what people search for most, and what searches come back empty. The empty ones are the most useful. They tell you either that the asset doesn't exist, or that it exists but isn't tagged in a way users can find.
Most organizations have more taxonomy infrastructure than they realize — it's just scattered. Department lists, product naming guides, style guides, and internal glossaries all contain terms your users already know and use. Start there rather than inventing new terminology.
Depending on your industry, there may also be established thesauri worth building from. The SEC publishes standard financial taxonomies. ERIC covers education. The Healthcare Provider Taxonomy Code Set covers healthcare providers. BARTOC.org indexes hundreds of controlled vocabularies across fields. If someone has already done the work for your industry, use it.
A taxonomy built for the marketing team won't necessarily work for legal, IT, or product. Talk to different user types about how they search — not just what they search for, but how they think about assets. What attributes matter to them? Do they search by date, by campaign, by product line, by region? Do they use internal shorthand that doesn't match formal terminology?
Those conversations will surface conflicts early and help you design a taxonomy that covers more than one way of thinking about the same asset.
Back to the roommates: one calls it the basement, the other calls it the cellar. In a small household, no one gets confused. In an enterprise with hundreds of users, someone will search "cellar," get zero results, and assume the asset doesn't exist.
Set preferred terms and map synonyms to them. When a user searches "TV," the system returns results for "television." The preferred term stays clean, and users don't hit dead ends. Some DAM managers also let users add their own tags, which get reviewed periodically. Done well, this lets the taxonomy evolve with how the organization actually speaks.
Card sorting is a quick and useful exercise here. Write terms on cards, ask users to group them, and watch what happens. You'll learn what categories feel natural, what groupings don't make sense, and where your assumptions about user behavior were wrong.
Once you have a draft taxonomy, ask real users to complete real search tasks in the system and tell you what's missing or confusing. Create a simple form for reporting gaps. Ask users to include their department — if one team is consistently struggling, that's a targeted problem you can fix. Plan for a few rounds. The first version won't be the final one.
A taxonomy that worked two years ago may not work today. New products, new markets, new asset types — the organization changes, and the taxonomy has to change with it.
Set a review schedule and stick to it. Check your search logs regularly for new terms that are getting traction or categories that are being ignored. Look at usage data — if one department's assets dominate the DAM, it's probably harder for everyone else to find their own content. And watch asset engagement: which assets get used and which don't often reveals as much about tagging quality as anything else.
Manual tagging doesn't scale. Most teams know this. When asset libraries grow fast, metadata quality drops — not because people stop caring, but because there isn't enough time to tag everything properly. AI autotagging solves the volume problem.
Upload an image of a mountain range and the system tags it: mountain, outdoor, nature, landscape. Upload a product video and the AI recognizes logos, product names, and campaign themes, applying tags accordingly. The result is richer metadata, applied faster, with more consistency than manual workflows can realistically achieve.
According to Forrester research, 40% of DAM leaders say automated metadata tagging with brand vocabulary support is a top priority for the next two years. The emphasis on brand vocabulary matters — generic computer vision labels aren't enough. The AI needs to recognize your terminology, not just what's in the image.
A few things to keep in mind:
Autotagging generates metadata. AI agents act on it.
An AI agent can observe context, make a decision, and take an action — without waiting for someone to click a button. In a DAM, that looks like this: a newly uploaded asset gets tagged, an agent reads those tags, routes the asset to the right review queue, notifies the right team, and checks rights expiration, all before anyone has opened their inbox. The human doesn't initiate each step. The system handles it.
36% of DAM leaders say AI agents for task and workflow automation will be among the most important DAM capabilities within two years, per Forrester. That number reflects where teams already lose time — not in finding assets, but in the manual handoffs between finding an asset and doing something with it. Agents close that gap.
But they only work if the underlying metadata is reliable. An agent reading inconsistent or incomplete tags can't make good decisions. This is why the taxonomy work in steps 1 through 6 isn't just about human search — it's the foundation that determines how much AI can actually do.
Agentic workflows are what happen when multiple agents coordinate across systems without humans managing each handoff. Think about what that means in practice for a team publishing content across 20 channels and 6 markets. Right now, that probably involves someone manually routing assets through a chain of approvals, channel-specific resizing, rights checks, and distribution steps. An agentic workflow handles all of it: the asset moves from DAM through compliance, adaptation, and distribution based on its metadata and predefined rules, with humans reviewing exceptions rather than executing every step.
31% of DAM leaders say they plan to use their DAM to orchestrate workflows across multiple enterprise systems — connecting CMS, PIM, CRM, and creative tools into one content operation. That shift is already underway. The question is whether your taxonomy is ready for it.
Inconsistent tags create ambiguity. Ambiguity breaks automation. An agent that can't reliably interpret metadata can't make reliable decisions. Clean, well-governed taxonomy is what makes agentic workflows actually work at scale.
The way to think about taxonomy now isn't just "how do users find assets." It's "what does this metadata need to support." Human search is one answer. AI autotagging is another. Agents acting on that metadata is a third. Agentic workflows coordinating across systems is a fourth.
67% of DAM leaders expect their use of AI to grow significantly within two years. The teams that benefit most from that growth won't be the ones who buy the best AI — they'll be the ones whose taxonomy was ready for it.
OrangeDAM lets you build, audit, and evolve taxonomies that work for your team today and the workflows you'll be running tomorrow. See how it works.