AI-Enabled Enterprise Architecture: A Systems Framework for Coherence, Observability, and Measurable Alignment
Reframing enterprise AI adoption from an isolated model-selection problem into an architectural systems problem of structural coherence, runtime observability, and mathematical deviation variance.
1. The Enterprise AI Dilemma
Across enterprise organizations, generative AI initiatives frequently succeed as local pilots, conversational assistants, and productivity demonstrators, yet stall when extended into regulated, cross-domain production pipelines.
The typical diagnosis blames model capabilities: hallucination rates, context size limits, or prompt fragility. While valid, this paper demonstrates that this diagnosis is fundamentally incomplete. In enterprise environments, AI models do not operate in a vacuum—they operate inside existing webs of data definitions, semantic assumptions, governance policies, decision rights, and audit trails.
"If foundational data semantics and governance rules are inconsistent, AI does not remove that inconsistency; it accelerates and amplifies it across business operations."
2. The Four Architectural Contributions
- Coherence-Centered Extension of Enterprise Architecture: Positions AI analysis and execution strictly downstream of domain invariants, enterprise data contracts, and semantic ontologies.
- Formal Construct of EA Coherence: Unifies strategic alignment, IT governance, semantic interoperability, and systems thinking into a quantifiable metric.
- Operational Intelligence (OI) as Runtime Observability: Replaces post-hoc monitoring dashboards with active telemetry interceptors that evaluate expected-versus-observed behavior across execution paths.
- Mathematical Formulation of Coherence: Formalizes deviation variance, exception-rate classification, variance attribution, and ex-ante vs ex-post auditability.