Transforming Self-Service Analytics with AI for Accelerated Business Intelligence

Abstract

Integrating Large Language Models (LLMs) into self-service analytics revolutionizes how organizations interact with business intelligence. This article examines the transformation from traditional self-service reporting, which often required technical expertise, to AI-enhanced systems that enable natural language querying and automated insights. This article illustrates how LLMs bridge the longstanding gap between business users and complex data structures by exploring the development of AI-driven semantic layers, conversational business intelligence, and generative analytics capabilities. It covers the technical mechanisms behind LLM-powered SQL generation, the architecture of modern semantic models, and strategies for ensuring governance while expanding data accessibility. As organizations continue to adopt these technologies, this article considers both immediate implementation challenges and the future trajectory toward autonomous decision intelligence platforms that fundamentally reshape the business analytics landscape.