When AI is discussed in the context of digital asset management, the conversation often jumps straight to automation and creativity. Smart tagging. Content recommendations. Predictive search. But a recent thesis based on real user insight points in a different direction. One that has less to do with content generation and more to do with content governance.
Users want help managing the clutter.
The study set out to explore how a user-centered design process could shape AI in Enterpise DAM. It didn’t start with assumptions about what AI should do. Instead, it asked users what they needed. These users came from large organizations with complex content environments. What emerged was not a call for more innovation in creation, but a strong demand for support. Specifically, for help keeping their DAM clean and under control.
It was about governance. Cleaning up unused assets. Finding duplicates. Understanding what was outdated or irrelevant. The kind of work that rarely gets attention, yet directly affects system usability. Users described frustration with the time it takes to manage old content and the lack of visibility into what could be archived or removed. They weren’t asking for AI to take over. They were asking for a way to stay ahead of the mess.
The AI concept that came out of the study focused on exactly that. It was not autonomous. It didn’t delete files or act without permission. Instead, it flagged content for review, suggested clean-up actions, and explained why certain files were selected. It made the process visible and kept the user in control.
This reflects a key principle in designing AI for DAM. It must be transparent. It must respect user workflows. And it must enhance decision-making rather than override it.
At QBank, this aligns with what we see in the field. Our customers work across departments, brands, and markets. Their needs vary. But a shared challenge is the pressure to maintain clarity and control in systems that never stop growing. AI can absolutely play a role here. Not by automating everything, but by reducing the effort it takes to manage complexity.
This approach was shaped by the human-centered AI principles defined by researchers like Shneiderman and Wärnestål. It’s not about what the tech can do. It’s about what the user needs. That shift in perspective changes everything.
This blog follows the thinking we introduced in our first post on why AI needs UX. And it leads into our next post, which looks at the future of AI in DAM through a different lens. One where design, not code, becomes the key to trust and usability.
Because the goal isn’t just to make AI smarter. It’s to make DAM systems more usable, more trustworthy, and more human.