Artificial intelligence (AI) has become central to enterprise transformation initiatives, yet enterprise-scale adoption remains uneven. Many AI initiatives succeed as local demonstrations, pilots, or productivity tools but struggle when extended into governed, cross-domain, production enterprise environments. A common explanation is model weakness, including poor accuracy, insufficient training data, hallucination, or inadequate orchestration. These explanations are valid but incomplete. In enterprise settings, AI operates within systems of data definitions, semantic assumptions, governance rules, decision rights, execution paths, audit requirements, and operational constraints. If these foundations are inconsistent, AI does not remove the inconsistency; it accelerates and amplifies it. This paper argues that enterprise AI adoption should be treated as an architectural design problemratherthanamodelselectionproblem. ItproposesanAI-enabledEnterpriseArchitecture (EA) framework that positions AI analysis and planning after purpose, domain invariants, data, semantics, and governance, and before decision systems, execution systems, Operational Intelligence, and the control plane. The paper contributes four elements: (1) a coherence- centered extension of Enterprise Architecture for AI-enabled systems; (2) a formal construct of EA Coherence grounded in strategic alignment, enterprise architecture, IT governance, semantic interoperability, and systems thinking; (3) Operational Intelligence as the runtime observability layer for AI-enabled enterprises; and (4) a mathematical framing that measures coherence through expected-versus-observed behavior, deviation variance, exception-rate classification, variance attribution, and ex-ante/ex-post coherence assessment. The framework reframes enterprise AI from a model-centric problem to a systems problem of coherence, observability, governance, and measurable alignment.
Gavara Haranadh (2026) studied this question.