In an enterprise, data typically belongs to a particular business domain, and is generated by the interaction of customers with specific business products or services. Unlike software engineering projects, the fundamental unit of AI is not lines of code, but code and data. It is therefore paramount to decode the business problem first and ask whether an AI approach is the only and best way forward. More often than not, throwing a complex AI-based solution at a problem is not the right approach, where a simpler analytical or rule-based solution is sufficient to have things up and running.
Part 1: Intuition (Why) Commercial AI projects often fail due to a lack of organizational understanding of the utility of AI vis-a-vis the business problem(s) to be solved. I have classified these under four broad areas and will tackle each of these themes individually in future blog posts:
