Measuring adoption
Measure usage and business outcomes, not installations. Adoption rate, active usage, and the outcome the tool was meant to improve reveal whether behavior changed, deployment counts hide quiet non-use until the ROI review.
Usage and outcomes over installs
Track how many people actively use the tool for real work and whether the target outcome improved. These expose the gap between deployment and adoption that vanity install metrics conceal.
What you measure as success is what your program optimizes for. Count installs and you will get installs; measure active usage and outcome improvement and you will get adoption. Because resistance is quiet, usage data is often the only early warning that a rollout is stalling, the training attendance looked fine, but nobody changed their workflow.
Frequently asked questions.
What metrics show real AI adoption?
Active usage for real tasks, share of the team using the tool weekly, and improvement in the outcome the tool targets. Combine them, usage without outcome improvement means the tool is used but not effective.