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The obliging processing of them becomes AI/ML analytics, and U.S. businesses are increasingly relying on it to make decisions in a data-rich and competitive environment. While previous research has focused mostly on technical aspects of model performance, there is scant empirical evidence as to how ML analytics can be transformed into interpretable and actionable strategic insights for managers. This gap is addressed in this paper by presenting an AI-driven machine learning analytics framework that is directed towards improving strategic decision-making at the enterprise level. A quantitative empirical design applied through an enterprise workforce analytics data set containing 500 observations and prominent demographic, organizational structural, and behavioral measures. Turnover was chosen as the strategic dependent variable because it pertains to stability in workforce and long-term performance of an organization. Pre-model development data pre-processing and exploratory analysis were carried out. Two machine learning methods were used: logistic regression, chosen as an interpretable baseline model, and a non-linear ensemble model (Random Forest). The model was assessed for performance on the basis of accuracy, precision Recall F1 score, ROC–AUC, and confusion matrix analysis. Furthermore, explainable analytics tools such as feature relevance ranking and partial dependence plots were used to improve interpretability and managerial actionability. The findings demonstrate that in comparison to logistic regression, the Random Forest model performs significantly better with an ROC–AUC value of 0.94 against 0.86. Compensation, organizational tenure, and age joined up in the output as the most contributing factors for predicting attrition, while workload intensity also presented a relevant effect. The explainability analysis detected non-linear threshold effects, making it useful for strategic interpretation beyond prediction performance. Together, the results show that AI-based machine learning analytics can give robust, interpretable, and strategically useful insights. The concept proposal illustrates a promising explanation-based ML as a useful decision-support technology for improved business strategy making of U.S. companies.