Using Digital Asset Supply Chains And AI To Generate Superior Automated Metadata

I have written a feature article for DAM News on the use of AI in combination with digital asset supply chains (which seems to be the hot topic of the day at present).

“DAM vendors who are seeking to gain some kind of competitive advantage with their use of AI (and more specifically with solving the relevant metadata challenge) need to provide these missing contextual elements themselves by leveraging the digital asset supply chains which they service on-behalf of their clients.  It is not sufficient to just treat these tools like a black box, hope they work and blame a third party when they don’t.  The ‘machine learning’ must take place further up the stack in the DAM application layer itself.   The methods I have described are more complex than just connecting to a commodity image recognition toolkit, but they are still not especially hard to implement, especially for those who have invested into a well-designed systems architecture that supports efficient and flexible metadata ( and the workflows which accompany them).” [Read More]

The article is less of a critique of the AI tools and more an analysis of how to get more value from them, in particular by using both the metadata generated on digital asset supply chains and AI text analysis (rather than just image recognition).  I believe that (in this case) vendors and their developers are best placed to come up with some better tools to achieve this.  I am willing to collaborate and share some ideas with any that might share my opinion on this subject.  Read the article and if you think that might be of interest, contact me and we will continue the conversation.

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