Why Enterprise AI Fails in Production

Enterprise AI rarely fails because teams lack model capability. It fails because deployment architecture is treated as an afterthought.

A production system has to survive data drift, ownership ambiguity, and operational change. Without explicit control points for evaluation, rollback, and observability, each release increases uncertainty instead of trust.

This note outlines a practical architecture discipline for moving from pilot outcomes to durable operational performance.