This study investigates the impact of Generative Artificial Intelligence (Generative AI) on strategic decision-making and organizational performance in U.S. businesses using machine learning techniques. An empirical analysis is conducted using organizational data to evaluate Linear Regression, Decision Tree, Random Forest, and Gradient Boosting models for predicting performance outcomes based on AI adoption and strategic factors.Results indicate that Generative AI adoption significantly enhances organizational performance through improvements in strategic decision effectiveness, innovation, productivity, and operational efficiency. Among the evaluated models, Gradient Boosting Regression demonstrates the highest performance (R² = 0.94, lowest error rates), followed by Random Forest (R² = 0.91), while Linear Regression and Decision Tree show comparatively lower accuracy. Feature importance analysis identifies AI Adoption Level, Strategic Decision Effectiveness, and AI Investment as the most influential predictors of organizational performance. Overall, the findings confirm that ensemble machine learning models provide robust predictive capability for assessing the strategic value of Generative AI in organizations.

Generative AI, Strategic Decision-Making, Organizational Performance, Machine Learning, Gradient Boosting, Predictive Analytics

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