The Enterprise AI Execution Problem - Turning AI Capability into Enterprise Outcomes examines why enterprise AI deployments frequently fail to produce durable performance gains despite rapid adoption and demonstrable model capability. Drawing on empirical studies, field experiments, and standards guidance, it argues that execution failures are not primarily technical but organizational and cognitive in nature. The analysis shows that productivity gains from AI are highly conditional, dependent on verification practices, workflow design, accountability structures, and human oversight mechanisms rather than model sophistication alone. The paper introduces orchestration as the missing execution layer that aligns AI systems with human decision making, quality control, and institutional responsibility. It concludes that without explicit orchestration, enterprises risk amplifying error, degrading judgment, and mistaking activity for progress, even as AI usage scales.