Defining the governed AI-BI cloud ecosystem: An integrated framework for enterprise adoption

This article proposes a comprehensive conceptual framework defining the "Governed AI-BI Cloud Ecosystem" at the intersection of enterprise cloud technologies, AI-driven Business Intelligence (BI), and regulatory governance. The framework dissects three core components: scalable cloud infrastructure tailored for AI workloads, sophisticated AI/ML models for business intelligence, and overarching governance mechanisms ensuring compliance and ethical AI use. By emphasizing critical interdependencies, such as how cloud-native services facilitate data lineage tracking for GDPR compliance or how containerization impacts security governance for AI models, the article demonstrates that viewing these domains in isolation leads to inefficiencies and risks. Architectural patterns like data lakes versus lakehouses in regulated environments are explored alongside implementation considerations including API-driven integration and cross-functional team structures. This foundational work provides practitioners with a common vocabulary and conceptual map for navigating this intricate technological and regulatory intersection, identifying key considerations for strategy, architecture, and implementation within large-scale enterprise contexts.