What is AI Adoption Score?
A composite measure of how well AI coding tools are being adopted across a team.
Definition
An AI adoption score summarizes how effectively an organization is taking up AI coding tools into a single number. It blends breadth (how much work is AI-assisted), coverage (how many teams have adopted), and engagement (how often suggestions are accepted and how many licensed seats are active) — separating real adoption from a tool that was bought but never used.
How it’s measured
Combine signals you already collect: the share of pull requests showing AI assistance, the share of teams with active usage, suggestion-acceptance rate, and active users versus licensed seats. Weighted together and tracked over time, they show whether a team is maturing from experimentation to scaled adoption.
What good looks like
Read it as a maturity arc: experimenting (low breadth, sporadic use), adopting (growing breadth and engagement), and scaled (most work is AI-assisted with high acceptance). Rising acceptance over time is the strongest signal that engineers are learning to use the tools well.
Why it matters
Leaders are accountable for AI spend but rarely know if it is landing. An adoption score turns a vague "are people using Copilot?" into a tracked number with clear friction points to fix — the first step before you can measure AI impact at all.
Related terms
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