We use ML to model complex interactions in electrical grids in order to make decisions that improve grid efficiency, which has been (at least in the short term) more effective than using an optimizer and trying to iterate on problem specification to get better results.

Generally speaking, I think if you know your data relationships you don't need ML. If you don't, it can be especially useful.