[gk1]

One way to deal with AI/ML shortcomings I've seen is to require end-user intervention for edge cases; such as a support chatbot that transfers the customer to a human rep if it can't understand the issue. Human intervention isn't mentioned in the article but maybe they'd put that under "narrow the problem," or they may not consider that a solution since human involvement eats into margins.

I believe all software companies will be AI companies in <5 years. By then, not having AI/ML would be like not having a database today. There will be no choice but to deal with the long tail, and the competitive advantage will go to the company that does it better. That makes this advice all the more timely and important, and it also means opportunities for startups to innovate in this space. Eg, better model optimization, low-cost operations without regressing to colocated GPUs, etc.

"The critical design element is that each model addresses a global slice of data... There is no substitute, it turns out, for deep domain expertise." Totally true for marketing as well. Much more effective to define audience segments and tailor the messaging and marketing for each.