The Procurement-To-Architecture Translation Gap In Public Sector AI

Abstract

Public sector artificial intelligence engagements fail in a structural way that current literature has not addressed directly. Procurement language designed to produce competitive bidding and contractor accountability does not translate cleanly into the architectural decisions that determine whether an implementation succeeds. Vendors win contracts on the basis of procurement-shaped proposals and then encounter architectural realities the contract language did not anticipate. The gap between the contract and the architecture is where many deployments appear to stall. This paper analyzes that gap from a vendor-side solution architecture perspective, drawing on practitioner experience translating public-sector procurement vehicles into deliverable technical scope. It identifies three recurring patterns of mistranslation, examines the structural causes that reproduce them, and proposes a reduction framework of three practices applicable at different stages of the procurement lifecycle. Sector-specific considerations for federal, state, and local vehicles are discussed, along with limitations and broader implications for AI governance. The argument suggests that procurement language shapes implementation outcomes in ways that warrant structural attention rather than ad hoc adjustment, and that modest changes on both buyer and seller sides may reduce avoidable failure.