Large Language Models to Analyze Business Intelligence User Narratives

Enterprises increasingly recognize the value of Business Intelligence (BI) systems in enabling data-informed decision-making. However, prior studies using structured data, surveys, and case studies often lack the granularity to capture deployment dynamics across diverse organizational settings. This study addresses that gap by analyzing customer testimonials from eight leading BI platforms. We propose a Large Language Model (LLM)-based framework for systematically processing and extracting insights from unstructured narratives. Using GPT-4, we analyzed approximately 2800 testimonials scraped from vendor websites to identify adoption drivers, implementation strategies, challenges, collaboration dynamics, and cross-platform trends. Our approach enables large-scale, structured insight extraction, transforming fragmented narratives into organized knowledge that informs both research and practice. The analysis also reflects how user perspectives and vendor framings shape BI platform narratives. This study demonstrates the potential of LLMs for scalable, automated qualitative analysis and contributes to understanding how generative AI can uncover actionable insights from unstructured enterprise data.