Explainable Artificial Intelligence–Driven Decision Support Systems for Customer Retention, Risk Management, and Trustworthy Managerial Intelligence

Abstract

Explainable Artificial Intelligence (XAI) has emerged as a critical paradigm in the evolution of intelligent decision support systems, particularly in high-stakes domains where trust, accountability, and transparency are essential. As machine learning models become increasingly complex and pervasive across sectors such as telecommunications, healthcare, finance, and service management, their opacity poses significant challenges for organizational adoption, regulatory compliance, and user confidence. This research presents a comprehensive and theory-driven examination of XAI-enabled decision support systems, focusing on their role in customer retention and churn prediction, managerial decision-making, and risk management. Drawing strictly on contemporary scholarly literature, the study integrates perspectives from explainable machine learning, human–computer interaction, customer participation risk theory, and intelligent decision support frameworks. The article advances a unified conceptual understanding of how explainability enhances trust, improves cognitive alignment between humans and AI systems, and supports more informed, ethical, and defensible decisions. Through an extensive qualitative methodological synthesis, the research analyzes explainability techniques such as feature attribution, model transparency, and relevance-based explanations, alongside their implications for system usability, organizational learning, and strategic governance. The findings reveal that explainability does not merely function as a technical add-on but operates as a socio-technical enabler that reshapes how organizations perceive, evaluate, and rely on AI-driven insights. Furthermore, the study identifies persistent challenges, including explanation overload, contextual misinterpretation, and trade-offs between accuracy and interpretability. By articulating theoretical contributions, managerial implications, and future research directions, this article positions XAI as a foundational pillar for the next generation of trustworthy, human-centered decision support systems.