This doesn't directly answer your question, but, there's an important distinction to draw between two use-cases for "ML": 1. ML is used to improve UX / ops 2.

This doesn't directly answer your question, but, there's an important distinction to draw between two use-cases for "ML": 1. ML is used to improve UX / ops 2. Other people's jobs depend on the quality of the ML output 1 can encompass things like offering recommendations or optimizing click-through. 2 is things like analytics-as-a-product in smart manufacturing. I assume 1 is a lot easier to "succeed" at: if I receive a bad music recommendation, I just move onto the next one. 2 is harder to do well, and the output quickly loses credibility when it's bad. That said, bad ML in high-stakes contexts (by which I mean, someone else is using the ML to do their job better) should get weeded out pretty quickly...so for the total effort and care taken to address 2, they may be more successful on average.