Abstract

The rapid proliferation of Generative Artificial Intelligence (GenAI) has fundamentally altered operational paradigms across various industries, enhancing efficiencies in domains such as software development, document summarization, and content creation. While AI-driven automation has been integral to manufacturing and supply chain industries for decades, the advent of large language models has catalyzed the widespread adoption of AI-driven decision-making. This accelerated integration necessitates a critical examination of governance structures to address transparency, accountability, and ethical concerns.Traditional data governance frameworks, designed to ensure data integrity, consistency, and compliance, provide a foundational model for AI governance. Given AI’s reliance on extensive datasets, expanding governance principles to encompass AI-specific challenges—such as algorithmic bias, ethical oversight, and regulatory compliance—is imperative. A structured AI governance framework should integrate established data governance capabilities, including data lineage documentation, quality monitoring, and role-based accountability structures, while addressing AI-specific risks related to bias mitigation, transparency, and ethical deployment.Furthermore, organizations must adapt risk management frameworks, particularly the “three lines of defense” model, to incorporate AI-specific oversight mechanisms. As global regulatory landscapes evolve, proactive governance strategies are essential to ensuring AI systems align with ethical standards and legal requirements. This paper argues that by extending well-established data governance principles to AI, enterprises can enhance trust, mitigate risks, and foster responsible AI adoption. The application of rigorous governance methodologies will be crucial in shaping the future trajectory of AI, ensuring its responsible deployment while safeguarding societal and organizational interests.