How do you measure whether an AI feature is actually working?

Measure the workflow

Start from the task, not the model. Record whether people complete the task, how often they correct the output, how often they escalate, and how long the task takes with and without the feature. Use the same definitions before and after release.

• Completion: the task reaches an agreed finished state.
• Correction: a person changes a material part of the output.
• Escalation: the person routes the case to another owner or process.
• Time: elapsed work time with the same start and end definitions.

Set a baseline and sample failures

Measure the current process before the feature ships. Then review a sample of failures on a fixed rhythm and group them by cause: missing data, unclear instructions, a permission gap or a genuine model limitation. The grouping determines the next fix.

Report operating evidence, not model scores

A model benchmark can help select an approach, but it does not show whether the feature is useful in daily work. Report completion, correction and escalation rates with their definitions, the period covered and the population measured.