Evaluate the Decision, Not Just the Model

Accuracy is a useful model metric. It is not a product strategy. A team needs to know whether the system helps people make better decisions in the conditions where the product will actually operate.

This is an original companion to Part 7 of the AI & PM Insights ML strategy series on evaluation. The central idea is to connect every technical measure to a meaningful outcome.

Begin with the consequence of an error

False positives and false negatives do not have the same cost in every workflow. In some settings, missing a rare event is unacceptable. In others, repeated unnecessary reviews make the system unusable. Describe those costs with the people responsible for the work before selecting a threshold or primary metric.

This turns evaluation into a shared product decision rather than an isolated ML task.

Use more than one kind of evidence

An evaluation plan should include offline test data, but it should not stop there. Review performance across meaningful slices such as location, device, environment, customer segment, and time. Test the product experience with representative users. Where risk is meaningful, introduce the capability gradually and monitor outcomes closely.

Each source of evidence reveals a different failure mode.

Keep the evaluation tied to action

A useful evaluation dashboard answers practical questions. Is performance good enough for the defined use? Where does it fail? What has changed? Which person or team decides the response?

The goal is not to collect every metric. It is to provide enough evidence for responsible product decisions throughout the model lifecycle.