Originally published at korixinc.com . Why do AI projects fail? 87% of AI projects never reach production because of five recurring mistakes: unclear business objectives, poor data quality, no governance framework, wrong team structure, and scaling too fast. That number — from Gartner’s ongoing research into enterprise AI adoption — hasn’t improved much since 2020. If you’re planning an AI investment, understanding these failure modes is the single most important step you can take to protect your budget and timeline. I’ve spent 19 years building software systems, and the last several focused specifically on AI implementation . I’ve seen projects fail for all five of these reasons — including some I was brought in to rescue. Here’s what actually goes wrong, and more importantly, how to prevent each one.…