What determines the scope and cost of an AI pilot?

1. One clearly bounded task

A pilot should test a specific assumption. Each additional user group, document type or process adds variants that need development and evaluation.

2. Data availability and condition

Existing, documented access makes a starting point clearer. Missing interfaces, inconsistent data or unresolved usage rights add work and should appear in the plan.

3. Connections to existing systems

An isolated demonstration has a different scope from an application that reads data, writes results back and respects company permissions. Define the necessary connections explicitly.

4. Business evaluation and approval

Plan appropriate test cases, business experts and time to evaluate results. Agree on how the pilot will be assessed and which failures are acceptable or disqualifying.

5. Running costs and support

Hosting, model usage, monitoring and maintenance can create ongoing costs beyond development. Usage, data volume and operating requirements affect the scope. A one-off pilot price does not describe ongoing responsibility.

6. Make proposals comparable

Ask for a clear description of deliverables and assumptions, so proposals can be compared against the same task.

• Which application and systems are included?
• What input is expected from your team?
• What will be delivered?
• How will the work be evaluated?
• Which usage costs and operating services are included?
• How are additional requirements handled?

Before commissioning

A short assessment can reveal the largest uncertainties. Then agree on the pilot’s scope and the decision its results should support.