AI uncertainty should be tested early
A model may look useful in a demo but fail under real data, permissions, latency, cost or workflow constraints.
Validation converts assumptions into testable conditions, so the client can decide the next step based on evidence.
What should be validated
- Whether available data is sufficient for the target task.
- Whether outputs can be evaluated with samples, metrics or user workflows.
- Whether deployment, latency, cost and security constraints are acceptable.
What a validation result should contain
A useful validation result should include the tested scope, sample conditions, observed behavior, risks, limitations and recommended next action.
A conclusion that the project should be adjusted or stopped is also a valid engineering result.