by Veronika Altenbach
Updated in 2026 – with a view to current AI developments in DAM.
Digital content is everywhere today.
Images, videos, graphics, presentations, and documents are created in large volumes every single day. For companies, this makes it increasingly difficult to maintain an overview.
This is exactly where Digital Asset Management (DAM) comes into play.
A DAM system helps store, organize, and retrieve digital content.
But the more content there is, the greater the effort becomes.
Metadata has to be maintained, files need to be stored correctly, and content must be found quickly.
Artificial intelligence (AI) helps significantly reduce this effort.
In DAM, AI primarily takes over recurring tasks that would otherwise consume a lot of time.
It works in the background and supports employees – it does not replace them.
The AI functions described here are realistic, reliable, and proven today. They are rule-based and support clearly defined tasks.
It is important to understand:
These functions are not autonomous AI agents that make independent decisions. They are the foundation for them. Only when data, rules, and processes are clearly defined does more advanced automation make sense.
AI in DAM does not mean giving up control. It means handing off routine tasks.
From automated metadata creation to improved findability: AI helps increase the value of digital content while reducing effort at the same time.
AI is evolving rapidly.
Many of the functions described here form the basis for what is now being discussed as AI agents – systems that can gradually take on more tasks. However, such autonomous solutions do not emerge automatically. They require clear rules, clean data, defined standards, and an appropriate level of maturity.
What is already realistically possible today – and where current limits lie – is explored in the follow-up article:
AI Agents in Digital Asset Management: Between vision, maturity, and reality
Related article: AI Agents in Digital Asset Management
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