20 June 20264 min read
How to actually make AI work for your dev team
The research is clear that AI underdelivers by default. It's just as clear about why, and that points straight at the fix. Five operating levers separate the teams that get real gains from the ones that get rework. This is the model we install, and none of it is about a better model.
Short version: if AI is underwhelming on your team, the instinct is to wait for a smarter model. Don't. The independent research that found AI slowing teams down was already using frontier models. The problem was never intelligence. It was everything around the model: what it works on, how its output gets reviewed, what context it sees, and the state of the code it touches. Fix those and the same model that disappointed you starts paying off. That set of fixes has a name. It's an operating model, and it's the actual product.
We laid out the problem, with sources, in what the research says about AI coding agents. This is the other half: what the teams getting real value do differently. Five levers.
Lever 1: Point AI at the right work#
Stanford's data is blunt about this: AI delivered 35-40% gains on greenfield, low-complexity work and single digits on the complex, brownfield code most teams live in. So the first decision is Boilerplate, tests, migrations, scaffolding, the high-volume low-context work, is where the gains are real. The gnarly core a senior holds in their head is where AI burns time you thought you saved.