LEAIM: A Layered Enterprise Architecture Model for Scalable and Governed Integration of Artificial Intelligence in Distributed Systems

The integration of Artificial Intelligence (AI) models into enterprise-scale distributed systems introduces architectural challenges that extend beyond model training and algorithmic optimization. While contemporary research emphasizes model accuracy and computational efficiency, system-level integration within complex enterprise environments remains insufficiently formalized. Existing approaches frequently embed AI components directly within business services or centralize inference without enforcing architectural separation, resulting in tight coupling, limited scalability, and governance vulnerabilities. This paper introduces the Layered Enterprise Artificial Intelligence Integration Model (LEAIM), a structured enterprise architecture framework for scalable and governed AI integration in distributed systems. LEAIM defines five formally separated layersdata acquisition, model lifecycle management, model serving, orchestration, and governanceand enforces explicit dependency constraints to prevent cross-layer coupling. The model incorporates scalability modeling, latency decomposition, security isolation, and lifecycle governance as primary architectural properties. Comparative evaluation demonstrates that LEAIM improves modularity, resilience, governance coverage, and fault containment relative to embedded and centralized integration patterns. By elevating AI deployment from ad-hoc implementation to formal architectural design, LEAIM contributes a reusable enterprise framework for sustainable AI system integration.