Large language models for competence-based HRM: A case study in the aerospace industry

Competence-based human resource management (HRM) emphasises the identification, development, and utilization of employee competence to boost the organizational performance, particularly in high-tech sectors that demand continuous competence advancement. Advanced artificial intelligence (AI)-based solutions, such as large language models (LLMs), are transforming competence-based HRM by streamlining job position selection, predicting emerging competencies, and designing targeted training plans, thereby enhancing knowledge sharing and transfer. However, there is a significant gap in the literature regarding comprehensive LLM-based solutions that automate the association of competence with professional roles and the semantic enrichment of corporate competence taxonomies. In this study, we present two innovative solutions: the automated semantic taxonomy enrichment methodology (ASTEM) and the role-competence embedding-based (RCE) framework. In particular, we demonstrated the effectiveness of LLMs in bridging the informational gaps by generating coherent competence descriptions and creating accurate role-competence associations through a qualitative case study involving a big company operating in the aerospace, defence, and security industry. The proposed solutions aim to reduce manual effort, improve the precision of role-competence matches, and support data-driven decision-making. This enables companies to efficiently identify the suitable candidates, develop focused training programs, and maintain a competitive edge by rapidly adapting to changes in the market and technology.