Every delivery manager I've worked with runs a prediction model on Monday morning. It's called dread.
Open the portfolio, scan 30 projects, and your gut sorts them: these five worry me, those twenty are fine, and that one — that one is going to blow up this month. No math. But it's a real model: inputs (velocity, escalations, that PM who's gone quiet), weights (learned from years of pain), and an output (who gets your attention today).
I spent 10 years in SRE and DevOps building that kind of gut. Now I'm transitioning into AI engineering, and the first thing I forced myself to do was NOT train a model. I wrote thinking documents instead. Problem framing before code. It felt like a waste of build time.
It turned out to be the point.
The hard part isn't the math.
