Advancing Business Intelligence Capabilities Through the Integration of Machine Learning Driven Predictive Models in Dynamic Market Environments

Abstract

Business Intelligence (BI) systems are increasingly under pressure to adapt to rapidly evolving markets. Traditional descriptive analytics are no longer sufficient; instead, organizations are transitioning to predictive analytics powered by machine learning (ML) algorithms. This paper explores the role of ML in enhancing BI systems’ forecasting accuracy, real-time adaptability, and strategic agility. Drawing on prior research and an analysis of market-responsive ML models, we examine the implementation framework for integrating ML within BI pipelines in volatile business contexts. Our study finds that dynamic ML-BI integrations significantly improve decision-making under uncertainty and reduce reaction time to market shifts.