From Data to Strategy: The Role of Explainable AI in Executive Decision Confidence

Abstract As companies become more dependent on artificial intelligence (AI) to conduct predictive analytics, executive managers are often obliged to make highly strategic decisions relying on the opaque outputs of machine learning. A significant number of sophisticated AI solutions are black-box, which restricts transparency and could decrease managerial trust and confidence in decisions. This paper examines the use of Explainable Artificial Intelligence (XAI). to increase the confidence of strategic business decisions made by executives. Based on the IBM Telco Customer Churn Dataset (11.1.3+) (7,043 customers) of demographic, behavioral, financial and satisfaction data and the customer lifetime value, this study builds churn prediction models and incorporates the techniques of explainability to investigate its effect on executive decision-making. It involves a comparison of the predictive output of classical black-box predictors and the interpretable model descriptions that are explained by the importance of features in the model prediction and SHAP-based model explanations. A simulation framework of decision making based on experiment is used to determine the effect of transparency on perceived trust, strategic clarity, risk judgment, and decision confidence. The research places the concept of explainability in a strategic management context instead of in evaluating technical models since it aims at predicting the levels of revenue-critical churn. It is presumed that the results will show that explainable AI has a significant effect on enhancing executive trust and confidence in data-driven recommendations, resulting in more competent strategic retention initiatives and resource allocation decisions. Keywords: Explainable Artificial Intelligence (XAI), Confidence in Executive decision making. Customer Churn Prediction, Strategic Decision-Making and Trust in AI Systems Business Analytics