Information architecture debt: Why legacy platform schema decisions constrain enterprise AI capability

Abstract

Enterprise platforms accumulate a specific category of technical debt this paper terms information architecture debt. Schema decisions optimized for transactional efficiency in earlier computing eras produce structural constraints that limit what artificial intelligence can accomplish on those platforms, largely independent of which models are selected or how they are orchestrated. This paper positions information architecture debt as a category distinct from code debt and infrastructure debt, and it argues that the distinction matters because remediation locality and coordination cost differ sharply. A five-indicator diagnostic framework is proposed, covering duplicate canonical entities, broken semantic chains, provenance gaps, enforcement asymmetry, and consumer assumption divergence. The framework supports a capability ceiling hypothesis: substrate defects impose an upper bound on AI outcomes that no model choice appears able to exceed. A sequenced remediation approach follows, prioritizing entity resolution, semantic alignment, and provenance instrumentation by AI-capability impact rather than ease of fix. Grounded in practitioner experience structuring enterprise information architecture across multi-language, multi-system platforms, the framework offers leaders diagnostic and sequencing tools, and it establishes a conceptual foundation for later quantitative and sector-specific refinement.