ARTIFICIAL INTELLIGENCE-DRIVEN BUSINESS INTELLIGENCE MODELS FOR ENHANCING DECISION-MAKING IN U.S. ENTERPRISES

This study systematically reviewed the role of artificial intelligence-driven business intelligence (AI-BI) models in enhancing enterprise decision-making, applying the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to ensure transparency, rigor, and replicability. A total of 97 studies were included after an extensive search and screening process, spanning industries such as finance, healthcare, retail, and manufacturing. The synthesis revealed that AI-BI has evolved from traditional reporting and descriptive analytics toward predictive, prescriptive, and causal modeling frameworks that actively guide managerial choices. Data ecosystems and governance were identified as foundational enablers, with accuracy, timeliness, stewardship, and compliance frameworks proving indispensable for sustaining trust and accountability. Methodological contributions highlighted the prevalence of supervised learning in forecasting and risk analysis, the utility of unsupervised learning in segmentation and anomaly detection, the application of reinforcement learning in sequential decision problems, and the growing influence of causal inference methods for validating interventions. Organizational capabilities—including data literacy, absorptive capacity, and cross-functional collaboration—were shown to be decisive factors in ensuring BI maturity and translating technical sophistication into enterprise value. Human-AI collaboration, explainable AI techniques, visualization practices, and storytelling were consistently emphasized as mechanisms for increasing trust, reducing algorithm aversion, and embedding insights into workflows. Ethical and risk management considerations, including fairness, privacy-preserving analytics, robustness, and model risk frameworks, were identified as essential safeguards in regulated sectors. Finally, performance measurement practices, such as balanced scorecards, OKRs, and international benchmarking, demonstrated strong links between AI-BI adoption, financial performance, and process efficiency.