ML is great if you already have scale, data quality, and problem sets that match ML approaches.

But often the bulk of the work is getting or cleaning data to make ML work in the first place. And even after that, the results need to be better or cheaper than having an employee personally look at the same data sometimes (i.e. a low tech expert system).

If you have a thousand emails and you want an emotion score assigned to the contents, it's often cheaper to use temp workers or something like Mechanical Turk.

It's also worth noting that many interesting automation tasks don't have room for 99% confidence intervals. It's fine if Google flips it's search result rankings rarely. It's not OK to only miscalculate 1% of account balances. And predicting which balances are bad might be useful, but the bulk of the work is the boring enterprise work of getting the concerns in front of a human or something.

That's why ML seems like a solution in search of a problem. Unless your problem is getting people to click on ads.