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

Short summary

87% of AI projects never reach production due to five common failures: unclear objectives, poor data quality, missing governance, wrong team structure, and premature scaling. Each failure has a specific prevention strategy—define measurable outcomes before starting, budget 40-60% of time for data prep, embed compliance constraints from day one, assign an operational owner, and pilot narrowly first. The author shares real-world examples and specific remedies from 19 years building software systems.

• •87% of AI projects fail due to five recurring patterns, each with specific prevention strategies
• •Budget 40-60% of project time for data preparation—data quality is the constraint, not model building
• •Embed governance and compliance requirements into architecture from day one; retrofitting is expensive

Generated with AI, which can make mistakes.