It’s because the ML solutions to optimization problems that are relevant to most businesses are garbage. Look at the attempts to solve the traveling salesman problem with just 100 nodes (a problem pretty much solved for the operations research community) with RL [1].

In other applications like forecasting, there is so much hype, and when you put the (non-interpretable) solutions to the test you end up disappointed (versus a pure stats approach).

There are of course fields where ML is pretty much the only viable solution, but trying to blame corporate bureaucracy to explain its failures in other fields does not help.

[1] https://research.google/pubs/pub45821/