Measurement guide
Measuring AI means comparing how the process works before and after.
We define a baseline and a small set of indicators connected to real work. We then measure improvement, costs and side effects from the implementation.
Direct answer
Usage metrics do not replace outcome metrics.
The number of prompts, users or answers describes activity. Impact is demonstrated when time, quality, errors, cost or decision-making capacity improve.
- Baseline before implementation
- Process and outcome indicators
- Adoption, cost and side effects
01 / Metrics
A useful metric must be connected to the process.
The number of prompts or answers is not enough. The real question is whether work is completed better, faster or with fewer errors.
FAQ
Frequently asked questions
Which metrics are useful for evaluating AI?
They depend on the process, but often include time, quality, errors, adoption, cost, satisfaction and improved decisions.
When should measurement be defined?
Before implementation, so a baseline exists and metrics are not chosen only after the outcome is known.
Content reviewed: 29 July 2026