AI-driven agile governance in enterprise SaaS: A scalable framework for no-code intelligence and continuous compliance

Das, Ullas (2025) AI-driven agile governance in enterprise SaaS: A scalable framework for no-code intelligence and continuous compliance. World Journal of Advanced Engineering Technology and Sciences, 15 (3). pp. 2466-2492. ISSN 2582-8266

The increasing complexity and speed of digital transformation have challenged traditional governance models in enterprise software-as-a-service (SaaS) environments. Simultaneously, the proliferation of no-code development and the adoption of artificial intelligence (AI) across business processes have created both new opportunities and governance risks. This review presents a comprehensive theoretical framework for AI-driven agile governance—a model that integrates autonomous AI agents with no-code platforms to enable scalable, adaptive, and continuously compliant enterprise operations. The paper outlines the architecture, input features, and training methodologies of the proposed system, demonstrating how it surpasses traditional rule-based and manual governance models in accuracy, responsiveness, and auditability. Drawing from case studies, industry implementations, and comparative evaluations, we show how AI can augment governance by automating compliance enforcement, optimizing decision-making, and empowering citizen developers through secure and intelligent orchestration. The review also offers targeted recommendations for practitioners, CTOs, and policymakers, while identifying future research directions in human-AI collaboration, governance benchmarking, and cross-domain scalability. Our findings suggest that the convergence of AI and no-code platforms, under an agile governance paradigm, represents a fundamental shift in how enterprises can innovate responsibly and govern intelligently at scale.