<![CDATA[General Background: Business Intelligence (BI) systems have evolved from static dashboards to dynamic tools for data-driven decision-making. Specific Background: However, traditional BI remains reactive and limited in autonomy, creating a performance bottleneck in rapidly changing enterprise environments. Knowledge Gap: Current literature lacks a comprehensive model that integrates agentic AI—AI systems capable of autonomous planning, execution, and adaptation—into existing BI frameworks. Aims: This study introduces the "BI-Agentic Decision Loop" framework to operationalize agentic AI within BI systems and assess its transformative impact. Results: Empirical findings from financial, ESG, and operational domains show improvements including a 45% rise in decision accuracy, 75% reduction in ESG reporting time, and 60% gain in real-time responsiveness. Novelty: Unlike prior models, the proposed framework emphasizes closed-loop autonomy with contextual perception, adaptive reasoning, and continuous learning while ensuring human oversight. Implications: The integration of agentic AI into BI signifies not merely a technological upgrade but a paradigm shift in enterprise strategy, requiring new governance models, organizational change, and ethical safeguards to fully harness its potential.Highlights: BI-Agentic Decision Loop – A new framework enabling autonomous, proactive, and adaptive decision-making in business systems. Performance Boost – Real-world cases show 45% improved forecast accuracy, 75% faster ESG reporting, and 60% better operational response. Strategic Shift – Adoption requires addressing governance, trust, and organizational change to fully leverage agentic AI capabilities Keywords: Agentic AI, Business Intelligence, Decision-Making, Autonomous Systems, Enterprise Transformation]]>
