AI Pilot (Proof of Concept)

A pilot proves a model can do the task once, in controlled conditions, for a few users. It is the right first step, and a misleading one, because the gap between a working pilot and a production system is where the cost and difficulty sit: integration, data cleanup, evaluation, governance, and the ongoing expense of running it.

Analysts have put numbers on that gap. Gartner predicted in July 2024 that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025, citing poor data quality, inadequate risk controls, escalating costs or unclear business value, and put the cost of approaches that aim to transform a business model at $5 million to $20 million (Gartner, July 29, 2024). A year later it predicted that over 40% of agentic AI projects would be canceled by the end of 2027 for similar reasons, described most current agentic projects as early-stage experiments or proofs of concept driven by hype, and estimated that only about 130 of the thousands of vendors marketing agentic AI offer real agentic capabilities (Gartner, June 25, 2025). Both are analyst predictions, not measured outcomes.

Treat the pilot as the start of a path to production, with the hard parts scoped in from day one. Before a pilot starts, decide the business metric it has to move and how you will measure it, the data it will need in production rather than a cleaned sample, the evaluation that will tell you it still works after launch, and who will own and pay for it afterward. A pilot that cannot answer those questions is a demo.

Why enterprise AI fails to show ROI covers the pattern in more detail, how to measure if AI is paying off covers the metric side, and the enterprise AI topic collects the episodes with leaders who have moved AI past the pilot.