Comparative Analysis of Traditional Employee Selection Methods and Selection Methods Supported by Artificial Intelligence

This bachelor’s thesis focused on a comparative analysis of traditional employee selection methods and methods supported by artificial intelligence. The aim of the thesis was to evaluate the effectiveness of AI-supported methods in comparison with traditional methods, particularly in terms of time requirements, cost requirements, and the quality of candidate selection. The theoretical part defined the process of employee recruitment and selection, traditional selection methods, and AI-supported tools. The practical part was based on qualitative research conducted through semi-structured interviews with six HR professionals working in Czech organizations. The results showed that AI-supported methods were used mainly as a supporting tool in the initial and administratively demanding stages of the selection process. Their main benefit lay in reducing time requirements and administrative workload, while their impact on costs and the quality of selection depended on the specific organizational context. Traditional methods remained important especially for assessing candidates’ motivation, personality traits, communication skills, and cultural fit with the organization. The main risks included algorithmic bias, low transparency of decision-making, limited understanding of the candidate’s individual context, and personal data protection. Based on the results, recommendations were formulated for the responsible use of AI in employee selection.