There is a significant disconnect between the perception and reality of how enterprise AI products are built.

The narrative seems to be that given a business problem, the data science team sets about gathering a training dataset (for supervised problems) that reflects the desired output; when the dataset has been built, the team switches to the modelling phase during which various networks are experimented with, at the conclusion of which the network with the best metrics is deployed to production … job done, give it enough time and watch the money roll in.

It’s Never That Simple

My experience building enterprise B2B products has been that this is only where the work begins. Freshly minted models rarely satisfy customers’ needs, out of the box, for two common reasons.

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