Bridging Prediction and Prescription: Explainable AI for Managerial Decision Analytics under Uncertainty

The increasing adoption of artificial intelligence in business environments has significantly enhanced predictive capabilities; however, a persistent gap remains between prediction and actionable decision-making. This study addresses this limitation by developing a conceptual framework that integrates predictive analytics, explainable artificial intelligence, and prescriptive decision-making within managerial contexts. The proposed framework is structured around three interconnected layers: predictive, explainability, and decision, where predictive models generate insights, explainability interprets these outputs, and the decision layer translates them into actionable strategies. Explainability is positioned as a central mechanism that bridges predictive outputs and decision logic, enhancing transparency, interpretability, and usability. The study further examines decision-making under uncertainty, emphasizing the role of explainability in improving decision quality, reducing ambiguity, and enabling adaptive decision processes. Theoretical propositions are developed to guide future empirical research, and conceptual insights demonstrate the framework’s value in strengthening decision effectiveness. Overall, the study contributes by offering an integrated perspective that connects prediction and prescription through explainability, with implications for improving managerial decision-making in complex and uncertain environments.