Practitioners · Applied AI
What should an AI pilot prove?
Value, feasibility and risk need to be tested together; model accuracy is only one measure. An AI pilot must show how its output can be used in a real workflow. This article examines baseline selection, test scenarios and the recording of limitations. Evaluating answer quality, operating cost and human-review needs grounds continue-or-stop decisions in evidence and distinguishes a convincing demonstration from readiness for everyday operation.
Model accuracy is only one measure. A pilot must show that an AI output is useful inside a real workflow
Three tests must run together
- Value: does a business measure, cycle time or service quality improve?
- Feasibility: are data, integration and technical operations reliable?
- Risk: are error, security, privacy and accountability controlled?
Put the pilot inside real work
A laboratory demonstration can look convincing while hiding process constraints, input-data quality and user decisions. Run the pilot in a bounded environment with real users and clear operational safeguards
Define stop criteria before starting
Record acceptable thresholds for quality, cost, response time and risk at the outset. If the pilot does not cross them, stopping or redesigning is a valid outcome
The goal is not to prove that the technology is exciting; it is to create enough evidence to scale, revise or stop
The expected decision package
The final output should give reviewers a clear view of value, architecture, data quality, risks, operating cost and the next step. Only after all three tests pass is a scale decision defensible