Leveraging Generative AI for Strategic Decision-Making: Enhancing Managerial Effectiveness in Dynamic Business Environment

AI has taken its root as an enabler of change in many industries and is reshaping the corporate world and strategies. This research explores the application of machine learning (ML) regression models to enhance strategic decision-making in organisational management, focusing on the World Management Survey Manufacturing dataset. The dataset, which includes 11,702 records and 27 independent variables from 2004 to 2014, covers critical management areas such as operational efficiency, performance assessment, goal-setting, and human capital management. The study involves data preprocessing, including handling missing values, one-hot encoding, and standard scaling, followed by data augmentation using a Generative Adversarial Network (GAN). Four ML regression models—Random Forest (RF), Gradient Boosting (GB), Extra Tree (ET), and XGBoost—are implemented to predict management efficacy outcomes. Model performance is evaluated using three metrics: RMSE, MSE, and MAE. Results indicate that the GB model outperforms others, achieving the lowest RMSE (1.66), MSE (2.76), and MAE (1.58), suggesting its potential for improving strategic decision-making. This study highlights the efficacy of ML models in forecasting management performance and supports the integration of generative AI in organisational leadership for enhanced decision-making in dynamic business environments.