Most AI projects encounter the same obstacles, from undefined success metrics to data and integration issues. Crucially, teams discover these problems in the reverse order they should have been addressed, starting with the pilot's performance and only later dealing with fundamental business alignment.
Before going live, top teams run the AI system in parallel with existing workflows, processing real production traffic without exposing the output. This "shadow mode" provides an honest accuracy benchmark on unfiltered data and is treated as a non-negotiable step to de-risk the launch.
Teams mistakenly equate a successful pilot with a viable production system. However, the operating model required to scale is entirely different. Production costs for compute, monitoring, and human review average 380% more than pilot-stage estimates, causing projects to fail due to lack of budget and patience.
A common failure is defining an AI pilot's success with engineering metrics like accuracy or latency. True success is a business outcome, such as the finance team trusting the AI's output enough to stop manually double-checking it. Success metrics must be framed in terms a CFO would accept.