You are right and I get the point (I have also worked on some solutions, though it's not my expertise), it's just that usually the people creating the ML model are supposed to substitute the creativity that the model itself does not offer. You run the model, see the false negatives and start thinking of what is missing, if there is also information that can be represented differently or can be compressed, if there is some other approach entirely. Good ML ultimately requires having deep expertise of your domain and can sometimes lead to deeper insight.

With LLM you unlock a whole new world of possibilities regarding the speed and result but we are also finding ourselves in a position where "asking the LLM" because the cheat for everything, no reason to dig further