The smartest AI team I ever worked with shipped nothing for 18 months.

Four PhDs. Brilliant researchers. Could discuss the latest papers for hours. Could not deploy a model to save their lives.

Meanwhile, a team down the hall — an ML engineer, a data engineer, an MLOps engineer, and a product manager — shipped three products in the same period.

The difference wasn't intelligence. It was balance.

The minimum viable AI team I'd build: one ML engineer (builds models), one data engineer (builds reliable pipelines), one MLOps engineer (deploys and monitors), and one product manager (defines the right problems to solve). That's four people who can go from idea to production.

The most common mistakes I see in AI team building: all researchers with no engineers, no MLOps consideration from day one, no clear problem statement (just "do AI stuff"), and chasing state-of-the-art instead of solving business problems.

The best AI team isn't the smartest one. It's the most balanced one — with a clear problem to solve and the complementary skills to solve it end-to-end.