Optimizing Human Resources for Efficiency and Sustainability through Business Process Modelling with Large Language Models

In today's business situations, effective use of human resources is critical to organisational performance and long-term growth. Employees frequently squander important time on monotonous jobs that take up their time. This problem negatively affects not only business efficiency but also labour market satisfaction and economic growth, contrary to the goals of Sustainable Development Goals (SDGs) 8 (Decent work and economic growth) and 9 (Industry, innovation and infrastructure). The aim of the research was to see how large language models (LLM) can help to optimise human resources by automating less skill-intensive, time-consuming tasks. For the analysis, a case study was conducted using the methodology of business process modelling (BPM) to compare the efficiency of a project management task ('reporting') with and without the use of ChatGPT technology. The model was used to analyse quantitative data such as process duration, labour costs, overhead costs and overhead volume. The research shows that LLM can significantly reduce the time workers spend on routine tasks, allowing them to focus on higher-value jobs that match their skills. In the case where ChatGPT was used by the participants to prepare the report, the whole process took 455.5 h less. The time savings contributed to a reduction in wage costs and overheads, which in total represents a saving of € 8,046.30. Based on the results, it is believed that LLMs have the potential to increase efficiency and sustainability.