Data Democratization Through Auto-BI: Using Generative AI For Self-Service Analytics

As organizations generate increasingly large and complex datasets, traditional business intelligence (BI) models struggle to keep pace with the growing demand for timely, accessible insights. Most BI systems remain dependent on specialized technical teams, creating bottlenecks that limit broader data access and slow decision-making. This paper explores how Generative AI enabled Auto-BI systems can address these challenges by enabling self-service analytics through natural language interaction. I present an enterprise-ready Auto-BI framework that integrates Large Language Models, automated data preparation, explainability, and governance into a unified architecture. Drawing on real-world enterprise contexts, including large-scale operational and marketing analytics environments, the study examines how Auto-BI reduces decision latency, improves reporting efficiency, and expands analytics adoption among non-technical users. The findings demonstrate that when paired with built-in trust, auditability, and human-in-the-loop validation, Generative AI can meaningfully democratize analytics while preserving data integrity and organizational control.