Optimizing Business Decision-Making Through Ai-Enhanced Business Intelligence Systems: A Systematic Review Of Data-Driven Insights In Financial And Strategic Planning

Abstract

This study systematically examines the role of AI-driven Business Intelligence (BI) systems in enhancing financial decision-making, fraud detection, customer segmentation, supply chain optimization, and strategic business planning. By following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this review ensures a transparent and rigorous analysis of existing literature. A total of 98 high-quality peer-reviewed articles were selected from an initial pool of 2,450 studies, spanning major academic databases such as Scopus, Web of Science, IEEE Xplore, ScienceDirect, and Google Scholar. The findings reveal that AI-powered BI improves forecasting accuracy by 32-45%, reduces fraudulent transactions by 47%, enhances customer engagement by 38%, and optimizes supply chain efficiency by 23%, demonstrating AI’s significant impact across industries. Furthermore, the study identifies major challenges, including data governance complexities, algorithmic transparency issues, and bias in AI models, which hinder widespread adoption. While previous research has primarily focused on the technical capabilities of AI-driven BI, this study extends the discussion by emphasizing its strategic and operational implications in modern business environments. The review underscores the need for explainable AI, fairness-aware algorithms, and improved data integration frameworks to ensure ethical, reliable, and effective AI-driven decision-making. These insights contribute to both academic discourse and industry practice, providing a comprehensive foundation for future research and practical advancements in AI-enhanced BI systems.