Enterprise investment in generative artificial intelligence (AI) tripled in a single year to roughly US$37 billion, yet independent field research finds that about 95% of enterprise generative-AI pilots deliver no measurable profit-and-loss impact. We argue that the dominant explanation--that models are not yet capable enough--is mistaken, and that enterprise AI has entered a Deployment Era in which advantage derives not from model intelligence but from the removal of the organizational and architectural friction that prevents a capable model from reaching production. Building on the software-engineering literature on technical debt and machine-learning deployment, and on a structured synthesis of independent field studies, we make the diagnosis operational. We introduce three linked constructs and one measurement instrument: the Deployment Wall, a six-stage value-leak model that mechanically reproduces observed survival rates; the Seam Index, a reproducible 0-12 diagnostic that scores any platform by how many of six recurring friction "seams" it removes natively rather than leaving to the adopter; and Deployment Debt, a construct that reframes unresolved friction as a compounding, quantifiable liability. We specify a scoring protocol with evidence anchors so the instrument can be applied consistently, illustrate it on a worked platform-selection example, and derive six falsifiable propositions with a research agenda for validation. The framework converts an eight-figure platform decision from a benchmark comparison into an architecture comparison.

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