From Data to Strategy: The Role of Explainable AI in Executive Decision Confidence | IJORET – Volume 8- Issue 4 | IJORET-V8I4P2

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.

This study has investigated how Explainable Artificial Intelligence (XAI) can be used to improve the confidence of executives in decision making in the strategic environment of customer churn management. Using the data on Telco Customer Churn and the Support Vector machine (SVM) classification model, the study was able to show that the advanced machine learning methods can greatly enhance predictive accuracy rather than baseline methods. The technical reliability of the predictive model to detect churn risk is confirmed by the high performance based on the accuracy, ROC analysis, precision, recall, F1-score and confusion matrix analysis. Statistical performance is not the main contribution of this research. The results note that predictive strength is not enough to guarantee executive adoption and implementation of strategies. The variables that proved to be important drivers of churn were variables like type of contract, monthly payments and satisfaction of the customer, which offer valuable and interpretable data, which follows the managerial intuition and business sense. The support of explainability mechanisms by predictive outputs enables executives to have a more comprehensive understanding of the risk factors behind the data, and, as a result, the systems provide a stronger level of trust, less incertitude, and more confidence in data-driven recommendation. This study transparency makes predictive analytics more of a strategic decision-support system rather than a technical forecasting instrument.

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