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

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Bibliographic Details
Title:	Information architecture debt: Why legacy platform schema decisions constrain enterprise AI capability.
Authors:	Shah, Mihir mihirshahconnect@gmail.com
Source:	International Journal of Electrical & Computer Engineering (2088-8708). Oct2026, Vol. 16 Issue 5, p2473-2482. 10p.
Subjects:	Artificial intelligence, Semantic integration (Computer systems), Constraint satisfaction, Data structures, Provenance (Geology)
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 offers a qualitative, conceptual framework, grounded in practitioner experience rather than an empirical study, and positions information architecture debt as a category distinct from code debt and infrastructure debt, arguing 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, synthesized through a structured qualitative process described in a dedicated methodology section. The framework supports a capability ceiling hypothesis, presented as a theoretical proposition rather than an experimentally validated claim: substrate defects impose an upper bound on artificial intelligence (AI) outcomes, most directly observable in retrieval-augmented and reasoning-based architectures, that model selection alone does not appear 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, while acknowledging that severe cases can require the sequence to be adapted. Grounded in practitioner experience structuring enterprise information architecture across multilanguage, multi-system platforms, the framework offers leaders diagnostic and sequencing tools and establishes a conceptual foundation for later empirical and quantitative work. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
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 offers a qualitative, conceptual framework, grounded in practitioner experience rather than an empirical study, and positions information architecture debt as a category distinct from code debt and infrastructure debt, arguing 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, synthesized through a structured qualitative process described in a dedicated methodology section. The framework supports a capability ceiling hypothesis, presented as a theoretical proposition rather than an experimentally validated claim: substrate defects impose an upper bound on artificial intelligence (AI) outcomes, most directly observable in retrieval-augmented and reasoning-based architectures, that model selection alone does not appear 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, while acknowledging that severe cases can require the sequence to be adapted. Grounded in practitioner experience structuring enterprise information architecture across multilanguage, multi-system platforms, the framework offers leaders diagnostic and sequencing tools and establishes a conceptual foundation for later empirical and quantitative work. [ABSTRACT FROM AUTHOR]
ISSN:	20888708
DOI:	10.11591/ijece.v16i5.pp2473-2482