Would you guess that this image contains an invisible watermark?
Original 2018 blog updated in 2026.
There is a vast amount of imagery available online. With Google Search, websites and social media, a suitable image can often be found in just a few seconds. However, the fact that you can download an image to your own computer does not mean that you are free to use it.
Was this image created by AI? This article has been edited with AI.
Artificial intelligence has quickly become a normal part of communications and marketing work. It is used to create images, edit photographs, produce text, create videos, generate subtitles and search for content. At the same time, a new question arises:
How can an organisation later know which assets have been created or processed using AI – and how can that information be preserved with the asset all the way through to publication?
In a world where technology is entwined with our daily lives, museums too have the opportunity to step into the digital era. Digital Asset Management (DAM) systems are like a treasure chest that can bring immense value to museums. DAM refers to the organization, storage, and retrieval of digital files, including images, documents, and multimedia content. By leveraging DAM, museums can not only preserve the rich tapestry of cultural heritage but also engage with audiences in novel and meaningful ways.
Museums manage large collections of images, videos, documents, and other digital assets that need to be accessible to multiple departments, including collections management, communications, education, and external media.
One common challenge is managing rights for temporary exhibitions and loaned objects. While museums may have permission to display an artwork, the rights to use images for marketing, publications, websites, or press materials are often limited and subject to specific agreements.
From answer to insight: When LLM search feeds the metadata loop
One of the most powerful—but still underexplored—opportunities in AI-enabled DAMs is not just that an LLM can return the right asset, but that it can explain why that asset was returned.
If an LLM-driven search surfaces the correct image and shows how it maps to the DAM’s controlled vocabulary—concepts, taxonomies, relationships, and business terms—the search result becomes more than an answer. It becomes insight.
Suddenly, search is no longer a terminal action. It becomes part of a feedback loop.