AI governance and engineering quality, with evidence.
Practical guides for engineering leaders measuring the quality, cost, risk, and readiness of AI-assisted software delivery.
SQCM v2.1: a coverage number needs a measurement story
Measured coverage now contributes continuously to SQCM. The revised methodology explains where the number came from, what it includes, and when two scores can be compared.
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.