How do you measure AI adoption in a company?
Measure AI adoption by depth of use, not licences issued. The honest signals are how many people use AI on real work each week, whether it has changed specific workflows, and whether the output actually improved. Logins and seat counts flatter the numbers without proving anything changed.
Seat-based metrics are popular because they are easy, and misleading for the same reason. A thousand licences and a burst of first-week logins can sit on top of a workforce that has changed nothing about how it works.
Better measures look at behaviour and outcome: recurring use tied to real tasks, workflows that have genuinely been rebuilt around AI, and evidence that the work is better than before. Those are harder to game and far more useful to a leader deciding where to invest next.
Weekly active use on real tasks, number of workflows genuinely rebuilt with AI, verified before-and-after improvements, and capability growth over time. Avoid leaning on licences sold or one-off logins.