Many AI pilots end with a vague sense that “people liked it.” That isn’t enough to justify scaling. A simple measurement plan, set up before the pilot starts, tells you what worked and what it was worth.
Key takeaways
- Measure the baseline before the pilot starts.
- Track time, quality, cost and business outcomes.
- Use conservative assumptions and real data.
- Decide in advance what results justify scaling.
1. Pick a specific workflow
Measure workflows, not tools. “Drafting first-response customer emails” is measurable; “using AI in support” is not.
2. Capture a baseline
Before the pilot, record how long the task takes, how often it happens, error or rework rates and any related business metrics, such as response time or conversion rate. Even a week of rough timing is better than guessing.
3. Choose your metrics
- Time: minutes saved per task multiplied by volume.
- Quality: error rates, rework, review scores or customer satisfaction.
- Speed: turnaround and response times.
- Outcomes: leads, conversion, revenue or cost per transaction where AI plausibly affects them.
- Adoption: how many people use the workflow regularly.
4. Calculate value conservatively
A simple formula: (time saved per task × tasks per month × loaded hourly cost) + measurable outcome improvements − (licenses + setup + training + ongoing support). Not all saved time turns into savings, so be explicit about how the time is reused.
5. Account for costs
Include software, implementation, training, review time and the effort to maintain prompts, skills and integrations.
6. Decide what success means
Set a threshold before you start, such as a minimum time saving with no drop in quality. At the end of the pilot, scale, adjust or stop based on that threshold rather than enthusiasm.
7. Report honestly
Share results, assumptions and limitations with leadership. Credible, modest numbers build more support than inflated claims.