Why Most AI Implementations Fail
After 17 years building enterprise systems, I've watched companies pour millions into AI initiatives only to see them crumble. The pattern is always the same.
The Problem Isn't the Technology
Everyone blames the AI. "The model wasn't accurate enough." "The data was bad." "We needed more training."
Wrong.
The problem is almost never technical. It's architectural.
Three Patterns That Kill AI Projects
1. The Pilot Trap
Companies run a successful pilot, declare victory, then try to scale it. But pilots are designed to succeed. They have dedicated resources, hand-picked data, and motivated teams.
Production is different. Production is chaos.
2. The Integration Nightmare
AI doesn't exist in a vacuum. It needs to talk to your CRM, your ERP, your legacy systems from 2003 that nobody understands anymore.
Most teams underestimate this by 10x.
3. The Maintenance Blind Spot
You built it. It works. You move on.
Six months later, the model has drifted, the data pipeline is broken, and nobody knows how to fix it because the original team is gone.
What Actually Works
The companies that succeed treat AI like infrastructure, not a project.
This isn't sexy. It doesn't make good demos. But it's the difference between a proof-of-concept and a production system that generates millions.