Companies investing billions in generative AI deployments are facing an uncomfortable reality: initial productivity gains are evaporating as organizations fail to redesign workflows around their new capabilities. According to MIT Sloan Management Review research cited by analyst Carolyn Geason-Beissel, generative AI has compressed traditionally expensive work across multiple domains—from code generation to legal analysis to marketing research—collapsing marginal costs for first attempts. Yet the research identifies a critical inflection point: what remains expensive is everything that happens after that first draft. Without organizational restructuring to handle downstream validation, review, and iteration at scale, companies cannot realize the compounding benefits that justify their AI investments. This gap between deployment and value capture represents one of the most significant strategic challenges facing enterprise technology leadership in 2024.