AN AI AND NLP FRAMEWORK FOR EXTRACTING LEADERSHIP COMPETENCIES AND MAPPING PERSONALIZED TRAINING PATHS: A STRATEGIC APPROACH FOR HUMAN RESOURCE DEVELOPMENT

Abstract

The growing demands of Artificial Intelligence (AI) by organizations could enforce a strategic change in the activities of Human Resources (HR). Conventional practices in leadership development do not always align with data-driven guidelines that incorporate job requirements and training directions. This work examines the application of AI, combined with Natural Language Processing (NLP), to unstructured job descriptions to identify essential capabilities and associate them with the best training options for becoming a leader. A framework is proposed in this work that automatically analyses unwritten job descriptions of top-level positions and defines key competencies with AI-based text processing methods. The structure then correlates the competencies with tailor-made training programs by referring to a recommendation system. A graph-based structure is modified to represent and interrelate the competency clusters. At the same time, a multi-criteria decision-making model is applied to evaluate training options based on four criteria: cost, duration, relevance, and impact. Using datasets from related divisions, the system achieved high accuracy in competency extraction, confirming all three proposed assumptions. Results demonstrate a 28% improvement in matching relevance, indicating that it is 28% efficient on matching relevance, 19% efficient on cost efficiency, and 24% better on its planning when compared to the manual methods. Using a weighted scoring mechanism to evaluate training alternatives (e.g., Leadership Workshop scored 4.4/5, Online Financial Course 4.1/5, and Community Outreach 3.5/5), training options were quantitatively scored and ranked according to their relevance, cost, duration, and impact. In addition, the optimized overall strategy of training was the best overall training path strategy that emphasized Strategic Planning & Research, Compliance and Stakeholder Management, and Financial and Operational Management, which provided a measurable benefit over the long-term capability to establish a sense of impact, reduction of risks, and stability. The scalable solution that the proposed AI-powered framework helps to implement is an evidence-based solution that can help develop leadership more efficiently, align talents with organizational requirements, and help recruiters and recruitment leaders to adjust their talent policies to the digital era. 

 JEL Classification Codes:  J24, C61, D80, C69.