In recent years, progress in AI and NLP technology has significantly increased. Researchers are exploring various applications of this technology to boost process efficiency. This study examines how encoder-based transformer models can be integrated into sociological maturity models. The process is still largely manual, making it prone to errors and time-consuming. In this scientific paper, we describe how four transformer models were used to assign interview passages to the corresponding categories of a maturity model. It can be observed that two out of four models achieve excellent results and that the leading model correctly assigns 22 out of 23 inputs to a specific class. In this way, transformer models offer an effective method to improve the efficiency of processes in maturity models without compromising the quality of classification. The continuation of the research involves adding additional categories and training the corresponding models to ultimately determine the maturity level using a transformer model.