Abstract

The rapid evolution of artificial intelligence (AI) has significantly reshaped data governance practices, offering new opportunities to optimize data management, security, and compliance within organizations. AI-driven data governance is critical in aligning business intelligence (BI) with organizational goals, enabling enterprises to make data-driven decisions that drive operational efficiency, innovation, and competitive advantage. However, challenges such as data silos, regulatory constraints, AI model bias, and integration complexities continue to hinder the seamless adoption of AI in governance frameworks. This paper explores the core principles of AI-driven data governance, discusses its alignment with BI, and proposes a strategic roadmap for implementation. The roadmap focuses on a phased approach, including assessing data governance maturity, developing AI-driven policies, automating compliance and security measures, and establishing continuous monitoring systems. The paper concludes by offering recommendations for organizations to successfully adopt AI-driven data governance and scale its benefits to foster sustainable growth and regulatory compliance. As AI continues to evolve, it will be crucial for organizations to stay agile, adapting their data governance frameworks to incorporate emerging capabilities while adhering to evolving regulatory standards.