As someone with a similar background, I believe some of the confusion is because there is a lot of overlap. System identification is very similar to supervised learning, however there are other learning "methods" that still fall under the umbrella of ML/AI. For example, unsupervised learning doesn't really have a good controls analog (as far as I know). Reinforcement learning on the other hand is somewhat analogous to model predictive control.

A better way of phrasing your point is that ML/AI is "just" optimization.