How GenAI Is Transforming DAM

This feature article has been contributed by Christopher McLaughlin, CMO at Vertesia, as part of our editorial series on ‘Will Generative AI Make DAM Obsolete?’

 

Digital asset management tools will continue to have a role going forward, but GenAI is poised to dramatically change how these platforms work, how they’re used, and where their value will truly lie in the years ahead.

Over the past several decades, DAM solutions have opened up new possibilities in fields like advertising, media, and retail. Luxury fashion brands use the tools to manage their seasonal photography across global markets, employees at pharmaceutical companies use them to quickly access FDA-compliant medical imagery relevant to specific conditions, and broadcasters leverage DAM to rapidly hunt down archival clips for reuse in news segments.

Still, DAM has never completely lived up to its potential. That’s because DAM tools are only as good as the metadata behind them—and tagging digital assets has historically been a laborious, costly, and time-consuming process.

The creatives who produce the content stored by DAM systems typically resent the administrative burden of tagging their work with metadata, often viewing the process as tedious busywork that takes them away from what they do best. And many organizations can’t afford to hire dedicated librarians to spend 40 hours a week applying metadata labels to digital objects. On top of that, it’s hard to be consistent with that type of manual, detail-oriented work. It’s inherently error-prone, and tagging often ends up being applied inconsistently across different users or even the same person on different days, weakening the overall value of DAM platforms.

Generative AI (GenAI) tools are about to solve this problem for good.

For the first time, we have a technology capable of accurately applying virtually unlimited metadata tags to digital assets. Unlike humans, GenAI tools never get bored, tired, or frustrated with repetitive tasks. They can analyze images, videos, and documents to generate rich, consistent metadata at a scale that would be impractical for even the most diligent humans.

Is This the End of DAM as We Know It?

It’s a question we’re hearing more and more—and it’s not without merit. As GenAI continues to advance, many teams are starting to wonder if traditional digital asset management systems are still worth the investment. The answer? It depends. Like any tool, it comes down to what you’re trying to accomplish.

If your only use case is storing and retrieving completed assets, you probably don’t need a DAM platform anymore. DAM was once indispensable because it gave teams a secure, centralized place to store assets, and these repositories were the only place metadata lived to support search and discovery. But now, there are countless secure storage options, and creative teams can simply pair GenAI with a database and a secure binary store like Amazon S3, and get a smarter, leaner, and more scalable solution. Not only can GenAI generate the tags, but it can also outperform traditional systems when it comes to finding and retrieving assets through natural language search.

But DAM isn’t dead just yet. For work-in-progress assets, DAM systems remain essential. GenAI alone can’t yet replicate the structure, process, and guardrails required to manage assets through their full creative life cycle. This is where DAM solutions still hold their value. They facilitate creative workflows—from scheduling and collaboration on asset development to review and approval processes and even downstream asset distribution. In addition, these platforms offer version control, access controls and permissioning, brand governance, and audit trails that creative teams rely on to keep projects moving and compliant.

The Road Ahead

The GenAI movement continues to gain steam, and we’re seeing more and more software vendors advertising AI as a key feature of their platforms. DAM vendors are no exception. However, software vendors tend to think of AI as simply another feature rather than using the technology to fundamentally reshape how their customers use their products. In the DAM space, for example, this might mean that existing vendors solve for isolated use cases, such as automated tagging during ingestion, without offering the more comprehensive capabilities needed to transform how organizations work with their digital assets.

Looking ahead, the real opportunity isn’t just layering AI on top of legacy systems—it’s reimagining how digital asset workflows should work in a GenAI-powered world. That means moving beyond incremental improvements to fundamentally new ways of working.

In the near future, I believe we’ll see creative teams pair GenAI with existing DAM systems in ways that reshape the entire lifecycle of an asset. A designer will be able to create a new asset and simply drop it into a repository, where a GenAI agent then tags it with rich, consistent metadata based on the organization’s taxonomy and ontology. A human reviewer will be pulled into the process automatically, where necessary, to review and revise machine-generated outcomes. Retrieval becomes instant and intuitive via natural language prompts.

Beyond ingestion and retrieval, GenAI will increasingly play an active role in shaping the creative process itself. For instance, teams with access to both DAM and GenAI tools will be able to generate concept variations for new creative assets based on existing brand guidelines, color palettes, and even the success rates of previous campaigns. These AI-generated concepts won’t be finished products, but they can serve as a powerful starting point for creative teams, allowing them to repurpose their past thinking. As teams upload new assets, GenAI can automatically evaluate them against established brand guidelines, flagging issues before they cause problems with clients or compliance teams. AI tools will also help teams identify conceptual relationships across assets, adding a new layer of creative potential to the search process.

The future of DAM isn’t about choosing between old and new—it’s about combining structure with intelligence. GenAI won’t just redefine digital asset management; it will reshape what it’s used for and where creative teams find value. As GenAI takes over tagging, search, and retrieval, DAM’s role will shift from static storage to a dynamic engine for managing creative workflows. It’s this pairing—DAM combined with GenAI platforms—that will streamline collaboration, unlock new levels of creative efficiency, and power the next evolution of creative work.

About Chris McLaughlin

Chris McLaughlin is Chief Marketing Officer at Vertesia, where he leads the company’s global go-to-market strategy and helps customers rapidly build and intelligently operate GenAI solutions. He brings more than 25 years of experience in enterprise software, with leadership roles spanning high-growth startups and large global organizations. Prior to Vertesia, Chris served as Chief Revenue Officer at Hyland Software, a $1.2 billion leader in content services, and as Chief Product and Marketing Officer at Nuxeo, where he helped increase revenue by over 500%. He has also held senior executive roles at LumApps, Dell EMC, and Thunderhead.

You can connect with Chris via his LinkedIn profile.

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