AI, Machine Learning, and Your DAM

This week, we highlight a couple of great articles on how Artificial Intelligence is affecting Digital Asset Management systems, and discuss AI’s strengths and weaknesses.

Digital Asset Management News has an original, lengthy and meaty article by Ralph Windsor, DAM News Editor and Project Director of DAM Consultants, Daydream.  It is titled Subconscious and Conscious Data: Where AI & Machine Learning Could Create Genuine Value for DAM.

He notes that AI isn’t a magic bullet, and that human conscious and unconscious data is necessary to realize effective tagging and metadata to deliver an optimal DAM experience; and that sales teams are likely to over-promote AI’s ability to deliver on these needs.

He also spends valuable time on Machine Learning:

“While Machine Learning is frequently mentioned by vendor marketing materials, they rarely implement any form of it themselves. The reason is because getting useful results from ML necessitates custom development work and frequently their core architecture was never built with this kind of use-case in mind.”

He makes the point that DAM vendors will likely need to get more involved in the AI/ML custom development work, or face disintermediation and end up merely reselling the technology of others; and then goes into an excellent amount of detail about how this can be avoided, something DAM vendors should heed.

Mindy Carner of Metashop has written (another) fine article, this one also on AI, with some great citations, titled Artificial Intelligence (AI) is Not an End-All to Metadata, it is Built From it.

It makes some similar points, basically that AI and ML are not going to make taxonomy, tagging and metadata work by librarians obsolete any time soon, as the business changes faster than these systems can be reprogrammed to adjust to contextual changes.

One of the major points of these essays is that while AI and ML will be useful, but be wary of the effort it will take to make them work well for your DAM system.

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