Comparative Analysis of Centralized vs. Decentralized Governance Models for AI-BI in Multi-Cloud Enterprises

Abstract

Multi-cloud strategies offer organizations flexibility and vendor diversification but introduce complex governance challenges for AI-powered business intelligence initiatives. These environments demand sophisticated approaches to maintain consistent security, compliance, and operational controls across disparate cloud platforms. Organizations must navigate between centralized models, which establish unified authority and standardization, and decentralized frameworks that distribute responsibilities with shared principles. Both approaches present distinct advantages and implementation considerations for policy enforcement, operational agility, cost management, and regulatory compliance. Centralized governance provides stronger control and standardization but may create bottlenecks, while decentralized models enhance innovation and responsiveness but increase coordination complexity. The optimal governance structure depends on organizational characteristics, regulatory requirements, and technical maturity. Effective governance frameworks must balance standardized controls with operational flexibility, integrate cloud-native capabilities, and maintain consistent visibility across environments. As cloud technologies evolve, governance approaches must adapt to emerging capabilities while ensuring robust oversight for AI-BI workloads, where data privacy and model governance add additional complexit.