The exponential growth of enterprise data has created significant challenges for organizations seeking to derive timely and actionable insights from complex datasets. The conventional Business Intelligence (BI) systems are typically based on expert analysts, formal query languages, and ad hoc dashboard development, which may reduce accessibility and reduce the speed of decision-making. The current developments in Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) offer fresh possibilities to make the traditional BI systems automated, intelligent, and easy to use. The technologies make it possible to interact with data naturally, create insights automatically and dynamically visualizing data, which considerably enhance analytical efficiency. The proposed research paper suggests a Self-service Data analytics-driven Auto-BI framework, which is driven by Generative Artificial Intelligence, to assist in scaling self-service data analytics in large organizations. The proposed architecture will incorporate several layers, such as data sources, data integration pipelines, generative AI analytics engine, BI service elements, and user interaction interfaces. Through
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