Future Leader Prediction: AI-Driven Data Mining Approaches

This paper presents a new data-driven approach to the identification of leadership potential, overcoming limitations of traditional methods that are often subjective and biased. Given that effective leadership is at the core of any organizational innovation and development, we propose a machine learning framework for objective leadership assessment. We applied data mining techniques to systematically analyzing employee psychological and behavioral indicator-level data to train and evaluate predictive models. The Random Forest algorithm showed the best performance among the evaluated models, which, combined with PCA for dimensionality reduction and SMOTE for data balancing, resulted in high accuracy in leadership potential prediction. This AI-driven approach has thus given a substantial transformation to conventional processes by reducing human bias and uncovering hidden talent within an organization. It can reshape conventional notions of leadership and establish a culture of continuous improvement toward sustainable organizational success by offering a predictive data-driven framework for leadership identification.