AI governance and engineering quality, with evidence.
Practical guides for engineering leaders measuring the quality, cost, risk, and readiness of AI-assisted software delivery.
Shadow AI in Engineering Teams: How to Build an AI Tool Inventory Without Surveillance
Shadow AI is the AI your teams already use without a record. How to build an AI tool inventory that satisfies auditors without watching anyone's screen.
SQCM v2.0: a repository is not the whole artifact
ScaleQuality Code Maturity Model v2.0 adds Supply Chain as a fifth domain and states the measurement boundary: measure each dimension where its artifact exists. Published methodology, 34 references, new DOI.
Code Maturity Model: why we built SQCM (and published the methodology)
Introducing the ScaleQuality Code Maturity Model (SQCM) v1.0: a model that scores a repository's maturity from observable evidence, in minutes, with AI code durability as a first-class domain. Published methodology with 27 references.
AI Governance Definition: What It Means for Engineering Leaders
What an AI governance definition actually means for engineering leaders: an operating model with evidence, enforcement, and measurable outcomes, not just a policy document.
Agile Maturity Score: How Engineering Leaders Should Measure Real Delivery Health
Learn how to build an agile maturity score that measures delivery predictability, quality, flow, governance, learning, and AI-enabled engineering readiness.
What Is AI Governance in Software Engineering? A Practical Guide for Engineering Leaders
Learn how AI governance helps engineering leaders measure quality, risk, cost, and accountability across AI-assisted software delivery.