From Business Intelligence to Agentic Intelligence: A Framework for Autonomous Enterprise Analytics

Abstract

Business Intelligence (BI) transformed enterprise decision-making by converting operational data into reports, dashboards, key performance indicators, and analytical insights. Yet most BI systems remain fundamentally human-driven: users identify a question, locate relevant information, interpret the results, determine a response, and initiate action. The emergence of generative AI and intelligent agents introduces a new analytical paradigm—Agentic Intelligence—in which systems can interpret business objectives, determine analytical steps, retrieve governed enterprise data, reason across multiple domains, recommend actions, and, within defined controls, initiate business processes. This article proposes the Autonomous Enterprise Analytics Framework (AEAF), a five-layer architecture consisting of a trusted data foundation, semantic business context, agentic reasoning and orchestration, governed decision and action, and continuous learning and observability. The framework is illustrated through healthcare operational analytics and retail decision-intelligence scenarios. The central argument is that agentic analytics should extend, not bypass, established enterprise data and governance investments. Reliable autonomy depends less on model sophistication alone and more on trustworthy data, explicit business semantics, risk-based controls, and measurable decision quality.