This research explores the integration of Generative Artificial Intelligence (GenAI) to automate the generation and refinement of BPMN 2.0 models directly from textual descriptions.

By leveraging natural language processing and generative transformers, the developed AI-driven tool simplifies the process modeling workflow, enhances precision, and reduces the dependency on BPM-specific expertise. The iterative refinement capabilities ensure adaptability to evolving business needs. Through design science research, this study evaluates the tool’s effectiveness in improving process accuracy, usability, and syntactic correctness, paving the way for more dynamic and efficient BPM practices.

This innovation aims to democratize BPMN modeling, making it accessible to non-experts while addressing critical challenges in process management.