Why AI projects fail before they ever reach production
Most AI initiatives die in the gap between a working demo and a system that actually runs the business. Here's where they break.
The pattern is familiar. A team builds something impressive in a notebook. Stakeholders are excited. Six months later, nothing is in production. The model didn't fail — the operating model around it did.
Most AI projects collapse on four predictable points: undefined ownership, no integration with the actual workflow, no observability and no plan for handover. The model is the easy part. The system around it is the work.
The fix is not more sophisticated models. It is treating AI as a system delivery problem from day one: who owns it, where it sits in the workflow, how it's monitored, how it's improved, who fixes it when it breaks.
When a project starts with those questions answered, models become almost a commodity choice. When it starts without them, no model is good enough.