AI products do not fail because they lack intelligence. They fail because they lack unit economics. At demo stage, AI is impressive. At scale, it becomes expensive — and if inference cost, infrastructure overhead, and support complexity are not engineered into the business model, margins collapse precisely when usage grows. This article examines the five failure patterns that break AI economics in production, the architectural decisions that build structural profitability, the unit economics framework for AI inference at scale, the governance structures that manage risk, and the five executive metrics — including revenue per token consumed and infrastructure utilization rate — that reveal whether an AI investment is delivering lasting business value or just impressive demos. Written as a strategic conversation for leadership, not a technical manual.