Most AI pilots don't fail because the AI didn't work. They fail because the pilot was set up to be unable to scale even if it did.
Anti-pattern 1: Scoped to be successful, not useful. The success criteria are technical (accuracy, latency) rather than business (revenue, cost, time saved). The pilot wins on a metric nobody outside the project cares about. Nothing gets funded for production.
Fix: Define success in P&L terms upfront. If "what would make this worth scaling" is unanswerable, the pilot isn't ready to run.
Anti-pattern 2: Pilot data isn't production data. The clean curated dataset behaves nothing like the messy stream of real inputs. Pilot metrics look great. Production metrics look like a different system, because they are.
Fix: Run the pilot on a slice of production data, even if it's smaller. The accuracy you measure must be the accuracy you'd get if you turned it on.
Anti-pattern 3: No production integration plan. The pilot lives in a notebook. Production lives in a Java monolith. The translation work is "TBD," which means the pilot can succeed brilliantly and still not reach users.
Fix: Architect the integration before the pilot starts. Build the pilot through the integration path, not around it.
Anti-pattern 4: Pilot owns no business unit. The pilot is run by Innovation or IT. The business unit it would serve hasn't agreed to take it on. When it works, nobody owns scaling it.
Fix: Co-own the pilot with the eventual operator from day one. They should be in design reviews.
Anti-pattern 5: No exit criteria. The pilot has a start date and a budget. It does not have a "what we learn here that ends the pilot" definition. So it runs forever, or it ends arbitrarily.
Fix: Write the decision the pilot is meant to inform. "Decide whether to fund a Q3 production build based on X, Y, Z observable in pilot data."
Anti-pattern 6: Theatre vs experiment. The pilot exists to be presented to the board. The metrics it produces will support whatever conclusion the sponsor wants. There's no falsifiable hypothesis.
Fix: Write down what would convince you the idea is wrong. If nothing would, you don't have an experiment; you have a roadshow.