AI Driven BI: How Large Language Models Transform Enterprise Analytics

Abstract

Large Language Models (LLMs) are reshaping how organizations interact with data by enabling more intuitive, conversational forms of analysis. Traditional Business Intelligence (BI) tools depend on predefined dashboards and structured queries, which often limit flexibility and slow down decision‑making in fast‑changing environments. In contrast, LLMs introduce a more adaptive analytical experience by interpreting natural‑language questions, generating contextual explanations, and guiding users through iterative exploration. This paper examines the emerging role of LLMs within enterprise analytics, focusing on how semantic understanding, conversational interfaces, automated insight generation, and AI‑assisted modeling collectively transform BI workflows. Through an analysis of current architectures and industry practices, the study highlights how LLM‑driven BI reduces time to insight, broadens access to analytical capabilities, and supports more informed and responsive decision processes. The findings point to a shift away from dashboard‑centric reporting toward intelligent, AI‑enabled analytical ecosystems that evolve with organizational needs.