How to measure the ROI of a first AI pilot
An AI pilot can look impressive.
But if it measures nothing, it is difficult to know whether it creates real value.
Return on investment therefore begins before the pilot.
Measure the current situation
Before the experiment, the business should establish a baseline.
Examples:
- 45 minutes to prepare a file;
- 12% error rate;
- three days to respond to a customer;
- eight hours per week spent on one task;
- 25% of prospects without follow-up.
Without a baseline, the pilot result becomes difficult to interpret.
Different forms of value
ROI should not be limited to direct financial savings.
It may include:
Time
How many hours are saved?
Quality
Are errors reduced?
Capacity
Can the team handle more work?
Speed
Has response time improved?
Revenue
Does the system increase opportunities or conversion?
Satisfaction
Do employees or customers perceive an improvement?
Risk
Does the system reduce some risks or create new ones?
Full cost
The business should also measure:
- licenses;
- API costs;
- infrastructure;
- integration;
- training;
- support;
- governance;
- maintenance.
A technically strong system can still be economically weak.
A simple formula
The business can begin with:
OBSERVED VALUE – FULL COST = NET VALUE
Then add non-financial dimensions.
The “time saved” trap
Time saved creates value only if it is reused productively.
If a team saves five hours per week but nothing changes in output, the theoretical gain is not fully realized.
The business should therefore measure what the released time actually enables.
The final decision
At the end of the pilot, the business should be able to answer:
- is value demonstrated?
- is cost acceptable?
- is risk manageable?
- are users adopting?
- can the system be maintained?
Then decide:
STOP
REDESIGN
EXTEND
SCALE
INDUSTRIALIZE
A pilot is useful if it enables a better decision.
Not only if it leads to deployment.