Generative AI has achieved something remarkable: it has collapsed the marginal cost of a first attempt. Marketing teams that once spent tens of thousands of dollars and months on consumer research can now run initial analyses in days. Software engineers generate code drafts in minutes instead of hours. Consultants prototype analyses and reports at a fraction of previous costs. Yet this efficiency windfall is masking a painful truth emerging across enterprises: organizations excel at automating individual tasks but falter when attempting to operationalize those gains at scale. The compression of early-stage work has revealed that what remains genuinely expensive—and organizationally disruptive—is everything that comes after the initial output: validation, integration, decision-making authority, and the human restructuring required to capitalize on freed-up capacity.