LEADERSHIP APPROACHES IN MANAGING AI-DRIVEN PROCESS AUTOMATION FOR SMALL AND MEDIUM-SIZED ENTERPRISES

Abstract

The analysis conducted in this paper aims to enhance the understanding of leadership roles in managing human resource automation changes in small and medium-sized enterprises (SMEs). A prerequisite for this analysis was the development of descriptive statistical parameters to organize collected data based on common sample characteristics, particularly in the context of automation and its impact on human resource management. Various statistical learning methods were applied throughout the paper to analyze and interpret the variables required for this level of research. The analysis followed successful data collection through surveys conducted within SMEs, along with data cleaning and validation. The data were gathered to explore the changes brought about by automation, specifically the impact these changes have on leadership roles in human resource management. Special attention was given to analyzing employee skills and knowledge, as well as how leadership can facilitate the transition to automated systems. The process involved identifying and analyzing variables based on survey responses, with the goal of confirming the research hypotheses. Finally, statistical tests were employed to determine data patterns and assess the effects of leadership on the success of automation initiatives in SMEs. This research provides valuable insights into how effective leadership can smooth the transition to automated processes in human resource management within the SME sector.