AI pilot scorecard — a decision template

Use one scorecard from problem selection to the decision to scale. Record observations separately from assumptions.

1. Define the service

Write: task; user; accountable owner; volume; current handling time; current error rate; cost of failure; and the person authorised to accept the result. If the owner cannot describe the existing process, establish that baseline first.

2. Set the acceptance criteria

Choose a primary measure and quality guardrails. State what a correct result means and which errors are unacceptable. Define the evaluation population, sample selection and conditions under which results will be reviewed. Avoid inventing universal percentage targets.

3. Compare complete costs

List preparation, integration, model use, review, support, training and exit costs. Separate one-off spend from continuing service cost. Show the sensitivity of the business case to volume, error rate and review effort.

4. Document the experiment

Keep model version, prompts, retrieval sources, data version and configuration. Record who reviewed the outputs and how disagreements were resolved. If the workflow changes halfway through, mark the break in the evaluation.

5. Make an explicit decision

Choose scale, revise, stop or extend. Record the evidence supporting the decision, remaining uncertainties, owner and review date. A successful demonstration without an operating owner should remain an experiment.

AIStrategy.gr working resource · 6 November 2026. Adapt this template to the actual workflow and responsible teams.