Earlier this year I wrote about the AI Engineering Maturity Model — a five-level framework for understanding how engineers actually work with AI, from ad-hoc chatbot use all the way through to bounded autonomous workflows. This post is the first set of results.

We all feel that the use of AI in software engineering will be transformative, and there are a lot of enthusiastic evangelists around. Going behind the hype reveals a much more mixed picture of adoption. My goal was to get behind the great anecdotes and get an honest, org-wide picture of how engineers are really using these new tools.

For those who haven’t seen the original post: the AIMM is a five-level maturity scale scored from 1.0 to 4.0. L1 (Explorer) is where engineers are experimenting individually with AI tools. L2 (Tool Adopter) is active, habitual use — IDE plugins installed, regular AI interaction — but practice is still individual.