The Algorithmic Enterprise: Formalizing the Role of AI in Enterprise Architecture Governance

Abstract

The fast pace of digital transformation has shown that traditional Enterprise Architecture governance frameworks have serious flaws that make it hard to keep strategic alignment in agile, cloud-native settings.  The Algorithmic Enterprise introduces Computational Governance Agents as self-sufficient, AI-powered systems that keep an eye on, analyze, and enforce architectural policies across a wide range of technology landscapes.  These smart agents change governance from checking for compliance after the fact to enforcing policies before they happen by analyzing code repositories, runtime telemetry, and system metrics in real time.  CGAs put federated governance models into action that strike a balance between centralized policy definition and distributed enforcement. This lets domain teams work independently within set rules.  Advanced features include using machine learning to predict technical debt by looking at code complexity and dependency networks, automatically scoring architectural compliance across quality dimensions, and proactively assessing risk by mapping dependencies and simulating the effects of changes.  Generative AI takes these features even further by adding automated documentation creation, context-aware design pattern suggestions, and help with conversational architecture.  Implementation necessitates meticulous incorporation into CI/CD pipelines, resilient explainability frameworks to combat model opacity, extensive bias identification and alleviation strategies, and tiered human oversight structures that reconcile autonomous efficiency with accountability obligations.