3 myths about AI in DAM: What enterprise brands need to know

Digital Asset Management (DAM) Specialist and Consultant Kristina Huddart has over a decade of experience consulting with global brands on how to source, implement, and fully harness DAM in their business. 

We asked Kristina to join us for a ‘myth-busting’ webinar session where she unpacked the three most common myths associated with AI in DAM. 

Here we’ve compiled the key highlights from the session, but you can catch up with the full recording on demand.

Myth 1: “AI in DAM is just about auto-tagging”

One of the most significant challenges in uploading and managing assets to the DAM is the application of metadata. Auto-tagging has emerged as a key solution in this area, and it remains a top priority for most DAM managers seeking to leverage AI.

But the potential of AI in DAM extends well beyond metadata tagging. Increasingly, brands are recognizing that AI can address needs across the entire asset lifecycle. 

AI is also being used to help brands evaluate the performance of their content; enabling them to gain deeper insight into which assets are delivering ROI and make more informed decisions that ultimately power better content experiences for their customers.

In short, AI capabilities in DAM are transforming what once was a simple organizational tool into a powerful, end-to-end solution for content strategy, creation, and management.

Read more about the increasing role of AI in DAM in our 2025 State of DAM Report.

Myth 2: “AI will replace human DAM managers”

The rapid pace of AI can be daunting, even for those who are already quite familiar with the technology. Its swift evolution continues to reshape the way people work; particularly in areas like digital asset management.

In many roles today, AI is no longer being thought of as ‘a support tool,’ it’s capable of the full handling of certain tasks that were once very manual and time-consuming. 

In the context of DAM and content workflows, this shift is already happening, and it means that human involvement is shifting toward higher-level responsibilities like approvals and quality checks. 

As a result, some managers are not only managing teams of people, but overseeing AI systems, and this reflects a fundamental shift in the workplace dynamic.

So, despite the ongoing debate about whether AI may eventually replace certain roles, AI is more likely to redefine the existing roles held by humans. 

For DAM managers specifically, this means evolving responsibilities rather than redundancy. And with many enterprise organizations operating with lean DAM teams that are often overstretched by incoming requests and continuous workload, AI offers promising new levels of support through its ability to, for example, help teams work more efficiently and save time.

Myth 3: “You need to be a tech expert to use AI in DAM”

While deep technical expertise isn’t mandatory to start using AI within a DAM platform, a problem-solving mindset as well as the willingness to experiment are essential. 

However, it does create something of a ‘chicken or egg’ dynamic. While trying out ideas with AI should be encouraged sooner rather than later, there still needs to be clarity on the exact problems that need solving with AI. With thousands of AI tools on the market already, brands need to keep their focus on identifying the ‘why’ behind their AI adoption. 

The increasing accessibility of AI-powered tools means that technical experience is no longer a barrier to entry. But again, before investing any significant time or money into AI, it’s critical to have a well-considered strategy in place, and to fully understand the risks and implications of introducing AI — particularly regarding ethics and responsible usage. 

Developing a clear AI policy or framework can ensure its safe and effective adoption within an organization, while also providing brand teams with the guardrails, guidance, and space they need to try things out and unleash its full potential.

Watch the full webinar recording on demand