You measure AI success the same way you measure any investment: against a baseline, in numbers that matter to the business. The trap is measuring activity - tools bought, prompts run, people trained - instead of outcomes. Activity feels like progress; only outcomes justify the spend.
Metrics that prove value
- Time saved - hours per week returned to higher-value work
- Cost reduced - lower cost per task, ticket or output
- Quality improved - fewer errors, faster response, higher satisfaction
- Revenue enabled - new capability that drives sales or retention
Each needs a before figure. No baseline, no proof.
Vanity metrics to ignore
Number of tools adopted, licences bought, or "AI initiatives launched" measure effort, not value. They are easy to grow and prove nothing - the same illusion behind several big AI mistakes businesses make.
Build measurement in from the start
Decide the metric before the pilot, not after. Capture the baseline, run for a fixed period, compare like-for-like, and be willing to kill what does not deliver. This discipline is what separates a real strategy from a hopeful one, and it underpins how to build an AI strategy for your company.
The bottom line
Measure outcomes against a baseline, ignore vanity metrics, and decide the measure before you start. Building that evidence discipline is a core outcome of the Strategic Application of AI in Business course at London School of Business UK. Enquire today.