Field guide
Why AI pilots fail—and how to move them into production.
A pilot can demonstrate that an AI model performs a task and still leave the organization unable to use it. This guide brings together six Using AI at Work conversations about the gap between a promising experiment and an operating workflow.
A practical guide to choosing an AI pilot, designing the production workflow, and measuring whether it creates business value.
The common failure: the demo has no destination
Ronnie Kwesi Coleman describes AI pilot purgatory as a problem of organizational readiness. The pilot may work, but nobody has designed the owner, integrations, expert review, and business metric needed for daily use. Jim Spignardo similarly sees companies move from purchased access to experiments without a structured adoption path.
Hear the conversations: Ronnie Kwesi Coleman · Jim Spignardo
Choose the pilot from the work backward
Eddie Irvin recommends clarifying a process before choosing a tool. Write down the current workflow, where it slows down, what information the task requires, and who can judge a good result. Darren Ward's experience suggests that a practical use case tied to a business priority gains more traction than mandatory tool training.
Hear the conversations: Eddie Irvin · Darren Ward
Design the production handoff on day one
Decide where the AI output goes, who reviews it, how exceptions are handled, and what happens when a tool is unavailable. Ronnie's conversation emphasizes expert judgment and integration planning. Markus Schmidberger describes moving the strongest use cases into production rather than letting experimentation grow without a destination.
Hear the conversations: Ronnie Kwesi Coleman · Markus Schmidberger
Measure the result, not the excitement
Denis Romanovskiy discusses economic effect; Ronnie cautions against treating faster output as sufficient evidence of ROI. Record the baseline, the changed cycle time and quality, the review effort, and the business result before calling the pilot successful. Then decide whether to scale, change, or stop it.
Hear the conversations: Denis Romanovskiy · Ronnie Kwesi Coleman
Questions answered
What leaders ask next.
Why do AI pilots get stuck?
They often prove a technical possibility without preparing ownership, integration, expert review, and an operating measure for production.
Hear the conversations: Ronnie Kwesi Coleman
What is a good first AI pilot?
A defined workflow with an owner, accessible context, a clear review step, and a measurable business problem is a stronger start than a tool demonstration.
Hear the conversations: Eddie Irvin · Jim Spignardo
When is an AI pilot ready to scale?
Scale after the workflow reliably reaches the intended result, its exceptions and review are manageable, and the value still holds after implementation and operating costs.
Hear the conversations: Ronnie Kwesi Coleman · Markus Schmidberger
Should every successful pilot go into production?
No. A useful pilot can reveal that the value is too small, the review burden too high, or the workflow too difficult to integrate. Those are decisions the pilot should make visible.
Hear the conversations: Ronnie Kwesi Coleman · Eddie Irvin
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