Why AI Projects Fail — 7 Patterns We See Repeatedly | KORIX

KORIX says 87% of AI projects never reach production, mostly because of operational gaps rather than model quality.

Common failure points include vague business goals, poor data readiness, missing governance, weak team design, and scaling too fast.

The article argues that success requires clear objectives, clean data, a governance framework, and a structure built for deployment.

It warns that many enterprises waste time and money by treating AI as a pilot exercise instead of a managed business program.