PREDICTIVE MODELING: AI AND MACHINE LEARNING FOR NEXT-GENERATION LEADERSHIP ASSESSMENT

Abstract

The rapid digital transformation of modern organizations has intensified the need for data-driven approaches to identify and develop next-generation leaders. Traditional leadership assessment methods—often subjective, resource-intensive, and slow to scale—struggle to capture the complexity of human behavior in dynamic organizational environments. This research proposes an advanced predictive modeling framework that integrates deep learning, transformer-based natural language processing, and hybrid ensemble techniques to evaluate leadership potential from behavioral, psychometric, and communication-based data. The methodology leverages multimodal feature extraction, BERT-powered semantic understanding, and gradient-boosted decision mechanisms to generate highly accurate and explainable leadership competency scores. Experimental evaluations conducted across multiple leadership dimensions—including communication clarity, emotional intelligence, strategic reasoning, and group influence—demonstrate significant improvements in predictive performance. The hybrid BERT + XGBoost model achieved the highest accuracy (93.4%), outperforming traditional machine learning and standalone deep learning baselines. SHAP analysis further validated the transparency of the model by identifying key predictive behavioral indicators. The results showcase the potential of AI-driven leadership analytics to strengthen talent forecasting, enable unbiased evaluation, and support strategic human capital decisions for future-ready organizations.