How to evaluate a first AI pilot

Small scope, clear acceptance criteria

1. 1

   Choose one task

   Instead of “adding AI to the company”, describe a bounded task, such as preparing dates and amounts from authorized documents for review. Specify inputs, outputs, the responsible person and actions outside the scope.

2. 2

   Record the baseline

   Record current processing time, errors and correction effort. Do not select only the easiest examples. Include unreadable documents and similar-looking products where relevant.

3. 3

   Separate the data

   Use only data you have permission to use. Keep evaluation examples separate from those used during development. A small sample cannot establish results for an entire business.

4. 4

   Measure quality and time together

   Alongside correct fields or matches, record missing outputs, false matches and manual corrections. Compare the same inputs under similar working conditions.

5. 5

   Build in approval

   An uncertain result is not an approved result. Decide who compares outputs with the source and which actions must never start automatically.

6. 6

   Decide whether to continue

   Define acceptance criteria before the trial. Review error types and additional maintenance effort. Even a successful small pilot does not automatically justify new data or broader access.

Include the time spent reviewing

The numbers below are an illustrative calculation only, not a customer measurement or project result.

Existing workflow

12 min

Pilot processing

4 min

Human review and correction

5 min

Pilot total

9 min

Net time difference

3 min

Counting only automated processing overstates the benefit. If quality falls, a time difference alone does not make the pilot successful.

Pilot checklist

• The task, inputs and expected output are defined.
• Permission to use the data and access boundaries are clear.
• Baseline times and error examples are recorded.
• Separate evaluation examples are ready.
• Success and stop criteria are written in advance.
• A person is responsible for reviewing results.
• Review and correction time are included in measurements.
• A safe stop and a way to undo incorrect actions are planned.
• Public recordings and results have separate permission.

Further reading

The NIST AI Risk Management Framework is a general reference for identifying and assessing risks. This guide is not a NIST certification or a statement of compliance.