The short answer
Measure ROI from AI by capturing a baseline before implementation and tracking the same measures afterwards: time per task, response and resolution times, volume handled per person, error and rework rates, cost per transaction, revenue influenced and adoption. Compare the gains with the full cost of the solution, including implementation, licences, oversight and change management.
Key takeaways
- No baseline, no ROI: measure before you build.
- Combine operational, financial and adoption measures.
- Count the full cost, including human oversight and change management.
Start with a baseline
The single most important step in measuring AI ROI happens before any AI is deployed: recording how the workflow performs today. Without it, results become anecdotes.
Operational measures
- Time per task or per case
- First response and resolution times
- Volume handled per person or team
- Share of routine requests completed without handover
- Error, rework and escalation rates
Financial measures
- Cost per transaction or enquiry
- Capacity released — hours redirected to higher-value work
- Revenue influenced — for example, faster lead response and more follow-ups completed
Adoption and quality measures
A solution nobody uses has no return. Track active use, customer satisfaction and review outcomes — for example, how often reviewers approve AI-drafted work without changes.
Count the full cost
Include implementation, integration, licences, ongoing oversight, knowledge maintenance and training. Honest ROI accounts for the people who keep the system accurate.
Attribute carefully
Other things change at the same time as an AI deployment — seasonality, staffing, pricing. Where possible, compare against a control group or a comparable period.
Every A.C.T. Programme engagement agrees its success measures in the Assess stage, and our case studies report results against the baseline.