A SYSTEMATIC REVIEW OF AI-ENHANCED DECISION SUPPORT TOOLS IN INFORMATION SYSTEMS: STRATEGIC APPLICATIONS IN SERVICE-ORIENTED ENTERPRISES AND ENTERPRISE PLANNING

Abstract

This systematic review investigates the integration of artificial intelligence (AI) into decision support tools (DSTs) within enterprise information systems, with a particular focus on their strategic deployment in service-oriented enterprises and enterprise planning environments. Drawing on a meta-analytical synthesis of 175 peer-reviewed academic articles, industry white papers, and empirical case studies published between 2010 and 2023, the study evaluates how AI-driven capabilities—such as machine learning algorithms, natural language processing (NLP), deep learning, and predictive analytics—transform traditional decision-making mechanisms. These AI technologies are analyzed for their contributions to improving the accuracy, scalability, adaptability, and responsiveness of decision support systems across operational domains including finance, marketing, logistics, production, and customer relationship management. The review demonstrates that AI-enhanced DSTs significantly support dynamic resource allocation, multi-scenario modeling, anomaly detection, and real-time decision-making, thus elevating enterprise responsiveness and agility in volatile environments. Moreover, it identifies how AI-enabled decision systems align with enterprise goals such as customer-centricity, operational efficiency, innovation enablement, and strategic scalability. Special attention is given to AI integration in ERP and CRM platforms, where intelligent forecasting, customer segmentation, service personalization, and cross-functional coordination have shown measurable performance gains. The review also outlines the role of AI in enabling data fusion from disparate sources, building adaptive learning loops, and supporting explainable decision pipelines to foster trust and interpretability among stakeholders. At the same time, the study acknowledges critical challenges associated with AI adoption in decision systems, including data silos, algorithmic opacity, limited digital maturity, and the complexities of human-AI collaboration in hybrid decision environments. Implementation success is shown to hinge on robust data infrastructure, cross-functional governance, stakeholder buy-in, and continuous performance monitoring. The findings offer a comprehensive framework for both scholars and practitioners, detailing the enablers, inhibitors, and strategic impacts of AI-driven decision systems.