conducting an artifıcial intelligence supported study on human resources competency assessment

Abstract

This study comprehensively examines the innovative approaches and effects of AI supported qualification assessment systems implemented in large-scale organizations. Traditional qualification assessments are often conducted by managers or a limited group of peers, which restricts the evaluation process and fails to capture the full potential and individual competencies of all employees within an organization. The proposed AI-based system aims to derive results by analyzing a comprehensive dataset that includes individuals related to employees in technical and, if applicable, social club members, with managers also participating in the hierarchical evaluation. This method is especially crucial in large-scale organizations, where the high number of employees and the limited capacity of Human Resources departments to perform individualized analyses pose significant challenges. With AI support, employee performance, competency levels, and career development needs can be identified more rapidly and accurately, contributing critically to the achievement of organizational strategic goals. Additionally, compared to conventional methods, this system offers advantages in data analysis and interpretation processes, thereby enhancing overall efficiency and reducing costs. The findings of this study indicate that AI-supported qualification assessment models serve as a more inclusive, accurate, and effective management tool for organizations. Furthermore, the study’s results are expected to pioneer innovative approaches in internal performance management and contribute to the development of new strategies that can be integrated into organizational transformation processes, thereby informing future research.