What is AI Impact Measurement?
Measuring whether AI coding tools actually improve delivery — causally, not just by usage.
Definition
AI impact measurement is the practice of determining whether AI coding tools are genuinely improving software delivery, rather than just being used. The common mistake is reporting adoption ("51% of PRs used AI") and calling it impact. Real measurement isolates the effect of AI on outcomes like lead time, throughput, and quality.
How it’s measured
The credible approach is a difference-in-differences design: compare how delivery metrics change for teams that adopted AI against a control of teams that did not, over the same window. That estimates the counterfactual — what would have happened without AI — and attributes the difference, on speed and quality together.
What good looks like
A trustworthy result shows the size of the effect with an honest evidence base (sample sizes, confidence) and frames it as association, not proof. Speed gains that come with rising rework or review load are flagged, not celebrated.
Why it matters
Boards and budgets ask "is the AI investment working?". Usage percentages cannot answer that; causal measurement can. It is the difference between a number that looks good in a slide and one you can defend.
Related terms
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