Despite enterprise spending on generative artificial intelligence reaching an estimated USD 30–40 billion in 2024, 95% of organizations report no measurable profit-and-loss impact from their pilots, and the broader RAND Corporation analysis of AI projects finds a failure rate exceeding 80% — twice the rate of non-AI information-technology initiatives. This paper examines the dominant explanatory hypothesis embedded in the trade press — that artificial-intelligence adoption fails because of insufficient breadth — and argues, on the basis of converging evidence from three independent research traditions, that the prevailing diagnosis is structurally inverted. Drawing on Goldratt's Theory of Constraints (1984), Pareto-Juran prioritization theory, the Lean Production System taxonomy of muda, and contemporary empirical reports from RAND, the MIT Project NANDA initiative, the Boston Consulting Group, McKinsey & Company, and the OECD, this review synthesizes 23 sources across six thematic clusters. The analysis demonstrates that the documented failure pattern is mechanistically predicted by Theory of Constraints: optimization of non-bottleneck resources cannot increase system throughput. In owner-operated small and medium businesses, the binding constraint is empirically the owner, identified as the principal organizational risk in over 95% of middle-market assessments. The paper proposes a bottleneck-first implementation framework — operationalized as Agentes Para Tu Negocio — in which Goldratt's Five Focusing Steps are mapped onto the deployment of AI agents in owner-operated firms.
Humberto Inciarte (2026) studied this question.