Why Your AI Agent Can't Make Money: The Real Reason AI Cost-Cutting Fails

AI adoption has reached 78% across major organizations, yet over 80% report no measurable performance improvement (McKinsey, 2025). This paper argues that the failure is not a product quality problem. It is a structural design problem. The Task Assignment Paradox (TAP) identifies the mechanism: when AI is inserted into a human-centered workflow, AI capability and human cognitive load scale in the same direction. The more capable the AI, the more decisions, exceptions, and coordination tasks flow back to the human bottleneck. Performance does not improve because the binding constraint — human cognition — has not changed. The 6% of organizations that report measurable AI gains share one structural feature: they redesigned workflows around AI capability rather than inserting AI into existing human-centered processes. TAP is not a new problem. Bainbridge (1983) identified the same mechanism in industrial automation forty years ago. AI has reproduced it at civilizational scale. This paper defines TAP, documents its evidence base, identifies the structural condition for escaping it, and argues that organizations failing to escape it are not making a technology error. They are making a workflow error — one with compounding costs. This paper is derived from Paper Five of the Decalogy on Artificial Intelligence (Ahn, 2026e).