Inteq's Agentic AI Q&A Series
Question: How should you design an AI pilot so it exposes readiness gaps instead of hiding them?
Answer: You design an AI pilot to find problems, not to look good. The pilot is a diagnostic instrument, not a demonstration. That single reframe changes every design choice that follows.
In practice, that means deliberately including the messy parts: real exception cases, representative data quality, and realistic volume, rather than a sanitized slice that flatters the agent. Define success criteria up front that include behavior on edge cases and escalation, not just happy-path accuracy. Run the pilot long enough and broad enough to surface real variance, and instrument it to capture every instance where the agent was uncertain or wrong, because those are the data points that tell you whether it is safe to scale.
There is also a cultural shift required, and I name it explicitly with leadership: teams must be rewarded for surfacing gaps during the pilot rather than for declaring premature success. A pilot that reports no problems usually means the pilot was designed to avoid finding them - which is the most expensive kind of false confidence in an agentic AI program.
Designing pilots as diagnostic instruments is a discipline Inteq builds with clients through our Agentic AI Consulting engagements.
