I've seen similar patterns with clients and companies I've worked at as well. My experience was less that ML wasn't useful, it's just that no organization I wo

I've seen similar patterns with clients and companies I've worked at as well. My experience was less that ML wasn't useful, it's just that no organization I worked with could really break down the silos in order for it to work. Especially in ML, the entire process from data collection to the final product and feedback loop needs to be integrated. This is _really_ difficult for most companies. Many data scientists I knew were either sitting on their hands waiting for data or working on problems that the downstream teams had no intention of implementing (even if they were improvements). I still really believe that ML (be it fancy deep learning or just evidence driven rules-based models) will effectively be table stakes for most industries in the upcoming decade. However, it'll take more leadership than just hiring a bunch of smart folks out of a PhD program.