Why AI Pilots Fail—and How to Save Them

AI pilots are failing at significantly higher rates than traditional IT projects, but the technology itself often isn’t the root cause. Research points to three deeper problems: poorly defined business problems, existing processes that were never redesigned for AI, and critical business rules that were never codified for machines to execute.

This article examines why even well-planned AI initiatives can stall when agents are forced to interpret fragmented policies, undocumented institutional knowledge, and conflicting sources of truth. It explores how a governed enterprise knowledge layer and machine-readable rules can give AI agents consistent, executable guidance instead of requiring them to infer the answer.