Many generative-AI pilots stall not because they fail, but because nobody can show clearly what they achieved. Measuring return on investment from the start turns a promising demo into a funded programme.
Start with a baseline
Before building anything, measure the current process: how long the task takes, how many are handled per week, error and rework rates, cost per transaction and satisfaction scores. Without a baseline, any improvement is anecdotal.
The four types of value
- Productivity — time saved per task multiplied by volume. The most common and easiest to measure.
- Quality — fewer errors, more consistent outputs, better compliance.
- Speed — shorter cycle times, such as faster claim decisions or quote turnaround.
- Revenue and experience — higher conversion, retention or satisfaction. Valuable, but harder to attribute.
Count the full cost
- Model and platform usage (tokens, search, storage, compute).
- Build costs: engineering, data preparation, integration and testing.
- Run costs: monitoring, evaluation, support, content upkeep and model updates.
- Change management: training, communication and process redesign.
Measure quality and risk, not just savings
An assistant that saves time but introduces errors destroys value. Track accuracy on an evaluation set, the share of outputs accepted without edits, escalation rates and incidents. Report these alongside productivity numbers so leaders see the whole picture.
Be realistic about time saved
Time saved only becomes value when it is redeployed — to more cases, faster service or higher-value work. Agree up front how freed-up capacity will be used, and measure that outcome rather than assuming it.
A simple scorecard
| Metric | Baseline | Target | Actual |
|---|---|---|---|
| Minutes per task | — | — | — |
| Tasks per person per week | — | — | — |
| Outputs accepted without edits | n/a | — | — |
| Error / rework rate | — | — | — |
| Monthly run cost | n/a | — | — |
| User satisfaction | — | — | — |
Report early and often
Share results monthly in the first quarter: what improved, what didn’t, and what you changed. Honest reporting — including use cases you stopped — builds the credibility needed to scale the ones that work.
Our Enterprise AI practice helps organisations build value cases, measure outcomes and decide where to scale.
Frequently asked questions
How do you calculate generative AI ROI?
Compare the measured value (time saved and redeployed, quality improvements, faster cycle times, revenue impact) against the full cost of building, running and supporting the solution, using a pre-AI baseline.
How long does it take to see ROI from generative AI?
Well-chosen use cases can show measurable productivity gains within weeks of launch, but full ROI depends on adoption, process changes and how freed-up time is used.
Why do generative AI pilots fail to scale?
Common reasons are no baseline or success metrics, unclear ownership, poor data quality, missing integration with real workflows and underestimated running costs.