This is of course a kind of a toy counterexample. But still, it does seem to illustrate the fundamental problem with a lot of ML-based solutions:

"Just because you can, doesn't mean you should"

I mean yeah - it is kind of neat (and interesting, in a fundamental way) that we can just "throw" a learning algorithm at an ever more, and ever broadening class of problems. But from a basic engineering perspective - it doesn't mean you want to. For a whole bunch of obvious reasons.

Then again, if your company leadership has gurgled the kool-aid, and decided that "our customers want an AI solution, because it's like, new and shiny" ...