You measure an AI strategy the way you measure any investment: against a baseline, on outcomes the business cares about. The trap is measuring activity - logins, prompts, "engagement" - which tells you people are curious, not that value is being created.
Metrics that actually matter
- Time saved on the target task, versus the before
- Cost per outcome - cheaper to serve a customer, close a ticket, produce a report
- Quality - fewer errors, higher satisfaction, faster turnaround
- Adoption that sticks - are people still using it after the novelty fades?
Set the baseline before you start, or none of these mean anything - the discipline we set out in measuring success from an AI rollout.
Vanity vs value
Usage counts are a vanity metric. A team can generate thousands of prompts and save no time. Always trace the tool back to a business number, not a dashboard of activity.
The bottom line
Measure AI on time, cost, quality and lasting adoption against a baseline - never on usage. Building that measurement discipline is a core outcome of the AI Strategy for Executives course at London School of Business UK. Enquire today.