The most expensive mistake in AI isn't a bad model. It's solving the wrong problem.

I've watched teams spend six months building a sophisticated deep learning pipeline when a simple rules-based system would have worked fine. I've also seen the reverse — engineers insisting "we don't need ML" when the problem clearly needed it.

The gap isn't technical. It's product thinking.

Before writing a single line of code, I now ask: What decision does this model support? What happens when the model is wrong? Can we start with a rule-based system and only add ML if we need to? What's the actual cost of NOT having this model?

These questions have saved me months of wasted work on multiple occasions.

The evolution I've observed in senior AI engineers: they start as model builders ("give me a problem and I'll train a model"), then become systems thinkers ("let me design the whole pipeline"), and eventually become problem framers ("are we even asking the right question?").

That last stage is where the highest impact lives. And the highest salaries.