fabricaAI maturity models
A short list of AI maturity models and scorecards I found useful while working on an evidence-first agentic engineering scorecard.
Most are survey-first or enterprise-oriented. Mine is meant to start by inspecting the repo and workflow evidence.
Closest To Agentic Engineering
- Coder AI Maturity Self-Assessment
Closest in positioning for engineering teams adopting coding agents. Useful for the adoption-stage framing.
- Coder: What 100 Engineering Teams Revealed About AI Maturity
Useful follow-up data point: agent adoption is ahead of environment standardization, governance, and measurement.
- Defra AI SDLC Maturity Assessment Framework
Broad SDLC framework with technical and cultural dimensions. More complete than what most small teams need day to day.
- AI-MM SET
Open software-engineering-team model with maturity levels, dimensions, and role progression.
- Thoughtworks: AI and software delivery
Good consulting-adjacent view of AI across the delivery lifecycle, with strong engineering oversight.
Broader AI Maturity Context
- Microsoft Copilot Studio maturity model
Enterprise-agent maturity, especially platform, process, governance, and data readiness.
- OWASP AI Maturity Assessment
Responsible AI, governance, security, trustworthiness, and compliance framing.
- CMU SEI + Accenture AI Adoption Maturity Model
Broader organizational adoption model. Useful context, but less targeted at hands-on agentic engineering.
- MITRE AI Maturity Model
Enterprise-oriented organizational assessment for incorporating AI capabilities and practices.
How I Read These
The useful pattern across these models is that maturity is not just tool adoption. It is repeatability, governance, measurement, delivery coverage, and the operating system around the work.
The gap I wanted to explore is narrower: can an AI agent inspect the working system first, then give a small team a useful score and concrete next steps?