Abstract: Despite substantial organizational investment in generative artificial intelligence, measurable productivity improvements remain difficult to capture. This article examines why individual-focused GenAI deployment strategies consistently underdeliver on business value, drawing on organizational research, implementation evidence, and economic data. Analysis reveals that achieving meaningful productivity gains requires fundamentally different approaches than simply distributing AI tools to employees. Key barriers include inadequate training segmentation, measurement complexity, unaccounted token costs, quality degradation ("workslop"), and the absence of end-to-end process redesign. The evidence suggests that organizations pursuing transformational value from GenAI must shift from individual productivity narratives to comprehensive organizational redesign, embedding AI capabilities within reimagined workflows while maintaining rigorous quality standards. This synthesis offers practitioners a research-informed framework for realistic expectations and strategic investment decisions in generative AI adoption.
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