Show HN: I Built an AI Maturity Model for Software Engineers (and No One Cared) (github.com)

A software engineer released the AI Maturity Model for Software Engineering Teams (AI‑MM SET), an open, role-aware framework to help engineering teams assess and evolve AI adoption. Structured as a three‑axis maturity matrix, it maps five maturity levels (Exploratory → Transformational) across six core dimensions—AI literacy, SDLC/workflow integration, tooling integration, trust/safety & governance, AI‑augmented collaboration, and business impact—and ties expectations to roles from Junior to Distinguished Engineer. The project includes detailed level-by-dimension criteria (e.g., IDE/CI/CD integration, automated compliance checks, AI‑first workflow design) plus methodology, motivation, and contribution docs; it’s CC BY 4.0 and open for community input. For the AI/ML community this matters because tooling adoption outpaces governance and skills alignment, creating maintainability, safety, and trust risks. AI‑MM SET gives teams a practical roadmap to standardize review practices for AI outputs, embed models into CI/CD and internal platforms, establish automated governance and risk assessment, and measure business impact. By making role‑specific expectations explicit, it helps organizations plan investments in training, platform work, and policies while aligning engineering career progression with responsible, scalable AI use.