How to structure an AI pilot that proves value (and doesn't become an endless project)

Many AI projects fail not because the technology doesn't work, but because it was never clear what 'working' meant. A well-structured pilot solves that before it starts.

Choose a small, representative slice

The pilot should cover a subset of the portfolio — one platform, one asset category, one workflow — small enough to run quickly and representative enough for the result to serve as the basis for expansion.

Define measurable success criteria

Before starting, agree with everyone involved on how success will be measured. Examples we use frequently:

• Percentage of tasks executed by the co-pilot without human intervention
• Escalation rate (how many actions needed approval) and its accuracy
• Time saved by the team on specific routines
• Discrepancies or risks identified that previously went unnoticed

Set a deadline and a decision

A pilot needs to end. Define the end date and the decision that will be made at the end: expand, adjust and repeat, or stop. Without that explicit decision, the pilot becomes an informal operation without governance.

Involve the people who will operate it

The team that will live with the co-pilot day to day should participate from the design stage. They know where the exceptions are, what the systems really do and which rules aren't written down anywhere.

Document everything

At the end, the pilot should produce a report with results against the criteria, learnings, necessary adjustments and a recommendation. That document is the basis for the expansion decision — and for the next pilot.