Applied Machine Learning in Business Intelligence: Opportunities and Emerging Paradigms

Abstract The integration of Machine Learning (ML) into Business Intelligence (BI) systems has redefined how enterprises harness data for strategic advantage. This research paper examines the current applications of ML within BI, with a special focus on forecasting, classification, and anomaly detection. It evaluates the role of Automated Machine Learning (AutoML) in lowering entry barriers for model deployment and explores the potential of Reinforcement Learning (RL) in optimizing dynamic business strategies. Through real-world use cases and a proposed integration framework, this paper provides both academic insight and practical guidance for organizations aiming to advance their BI capabilities. Future directions such as Explainable AI and Federated Learning are also discussed to contextualize the ongoing evolution of intelligent decision support systems.