The strategic implications of Artificial Intelligence-Generated Content (AIGC) in corporate management remain underexplored, particularly from a data-driven, predictive perspective. While AIGC technologies are increasingly adopted to enhance productivity, engagement, and decision-making, there is limited empirical evidence quantifying their impact on corporate value realisation. Addressing this research gap, the present study develops a simulation-based framework to model AIGC adoption and its effects on key organisational performance indicators using supervised machine learning. Synthetic corporate data were generated to reflect varying levels of AIGC integration, and multiple machine learning models, including Random Forest, XGBoost, Support Vector Regression, and Logistic Regression, were applied for both prediction and classification. The Random Forest Regressor achieved the best performance with an RMSE of 5.06 and an MAE of 4.11, while Logistic Regression achieved 72% accuracy and 0.79 recall in classifying firms by high or low value realisation. These results demonstrate that even in the absence of real-world data, simulation-driven ML analysis can provide meaningful strategic insights. The study contributes a novel, replicable approach for organisations and researchers to assess AIGC’s strategic value using synthetic data and predictive analytics. It is recommended that corporate decision-makers leverage interpretable ML models to guide digital transformation initiatives. However, findings are limited by the use of simulated data and should be validated against real corporate environments in future research.