AI-Enhanced Analytical Processing in Data Warehouses: Methods, Tools, and Decision Support

Abstract

Abstract; The convergence of artificial intelligence (AI) and data warehousing is revolutionizing analytical processing and decision support. By integrating machine learning (ML), predictive models, and automated intelligence into traditional online analytical processing (OLAP) systems, organizations gain deeper insights and faster, more accurate forecasts. This review examines current methods for embedding AI in data warehouses, surveys tools and platforms that support AI-driven analytics, and evaluates the impact on decision support. We describe data integration processes (ETL/ELT), common AI/ML techniques (supervised/unsupervised learning, NLP, predictive analytics), and emerging capabilities such as automated query optimization and anomaly detection. Case studies show that AI-enabled warehouses improve forecasting accuracy and data exploration, though challenges remain in data quality, cost, and workforce skills. Comparative analysis of recent literature confirms that advanced analytical algorithms and big-data technologies significantly enhance managerial decision-making by consolidating disparate data and enabling real-time, predictive insights (Ismaili & Besimi, 2024; Kopczewski et al., 2025). We conclude by discussing best practices and future trends – including cloud-based AI services and self-optimizing architectures – that will further empower business intelligence (BI) in the era of data-driven decision support.