Regulatory Intelligence Engineering for Global Enterprises: Governance Architectures for AI-Enabled Compliance Operations

Global enterprises operate within an increasingly complex regulatory landscape shaped by cross jurisdictional legislation, evolving compliance standards, and heightened expectations for transparency and accountability in digital operations. Traditional compliance management approaches that rely primarily on manual monitoring, static policy documentation, and fragmented governance processes are becoming insufficient for managing regulatory obligations at enterprise scale. The rapid integration of artificial intelligence into enterprise information systems has created new opportunities to transform compliance operations through intelligent monitoring, automated risk detection, and adaptive governance architectures. This study introduces the concept of regulatory intelligence engineering as a systematic approach for designing enterprise governance architectures that enable AI enabled compliance operations across global organizations. The research examines how regulatory intelligence capabilities can be embedded within enterprise governance frameworks to continuously interpret regulatory requirements, monitor operational data streams, detect compliance deviations, and support decision making processes for regulatory risk mitigation. A conceptual architecture is developed to illustrate how AI driven analytics, policy knowledge repositories, governance control mechanisms, and human oversight interfaces can operate together as an integrated regulatory intelligence layer within enterprise systems. The proposed governance architecture emphasizes explainable decision processes, audit ready compliance workflows, and scalable regulatory monitoring across diverse regulatory environments. The study also presents operational models demonstrating how enterprises can integrate regulatory intelligence capabilities with existing enterprise resource planning systems, human capital management platforms, and financial governance infrastructures to enable continuous compliance management. By transforming compliance from reactive documentation based activities into proactive intelligence driven operations, the proposed framework enables organizations to achieve higher levels of regulatory visibility, operational accountability, and governance maturity. The findings contribute to emerging research on enterprise governance in the era of AI enabled decision systems and provide a structured foundation for organizations seeking to operationalize regulatory intelligence as a core component of enterprise compliance strategy.