Measuring the Outcomes

Measuring What Matters 

As you execute your plan, here are three metrics to repeatedly collect and evaluate:

1. 01

   Adoption breadth: The percentage of your experienced workers who have completed at least one structured AI experiment.

   Gallup’s ongoing tracking shows nearly half of U.S. workers still report never using AI in their role. [01] Your target isn’t a benchmark borrowed from a survey; it’s meaningful forward movement in your own organization.

2. 02

   Confidence, not just usage: The answer to the short pulse question “How comfortable do you feel experimenting with AI in your role?”

   Adoption without confidence is fragile and unlikely to deepen, and this question tracks what usage numbers miss. For experienced workers especially, hesitation is more often about psychological safety than technical inability. Confidence moves first. Behavior follows.

3. 03

   Workflow changes: The real indicator of progress is whether people have changed how they work, not just whether they’ve opened a new application.

   Even a handful of documented workflow changes across your cohort in 90 days represents genuine transformation. It’s a leading indicator of the deeper integration that produces lasting organizational value.