Define the decision before you build the AI pilot

AI pilots are easy to start because “Can we build it?” feels like a concrete question. It is also rarely the most important one.

A team can prove that a model produces plausible output, an agent completes a workflow, or a prototype delights a room. None of those observations necessarily answer whether the product creates durable value, can operate responsibly, or deserves the next dollar of investment.

A pilot should resolve a decision

Before implementation begins, write the decision the pilot is intended to support.

Examples:

• Should we invest in moving this capability into production?
• Which user segment experiences enough value to justify a focused release?
• Can this workflow meet the quality, latency, and cost constraints of real use?
• Does the capability change behavior or merely generate positive reactions?

If the team cannot name the decision, the pilot is likely becoming activity rather than evidence.

Write both success and failure criteria

Success criteria are common. Failure criteria are more valuable because they create permission to stop.

A useful criterion is observable and tied to the decision. “Users like it” is weak. “Six of ten target users complete the task without expert intervention, with no critical accuracy failure” is stronger.

Failure criteria should identify evidence that would make further investment irresponsible. They prevent teams from redefining success after seeing disappointing results.

Evaluate the production path early

Prototype quality can hide operational reality. Include data access, privacy, security, evaluation, latency, cost, monitoring, and human fallback in the assessment before the prototype earns a roadmap.

The goal is not to solve every production concern during the pilot. The goal is to know whether those concerns change the investment decision.

End with a recommendation

Every pilot should conclude with one of four recommendations:

1. Continue toward production.
2. Modify the opportunity or implementation.
3. Delay until a named condition changes.
4. Kill the initiative and preserve the learning.

“The pilot was interesting” is not a decision.

The practical move

For the next AI initiative, create the decision memo before the project plan. Define the question, evidence standard, investment limit, and stopping rule. Then build only what is required to make the decision.

Planning an AI investment in the next six months? The AI Product Decision Sprint helps a leadership team reach a defensible continue, modify, delay, or kill recommendation before making a large commitment.