The Second International Workshop on Generative AI for Process Mining
Held in conjunction with ICPM 2025
Generative AI has emerged as a powerful tool across various domains, and early prototypes in process mining have demonstrated its feasibility for tasks ranging from process analysis to automation. GenAI4PM 2025 will explore the next steps in this exciting field, focusing on advanced, systematic studies that:
The workshop is targeted toward:
Generative AI (GenAI) is a powerful tool for a multitude of activities, and prototypes implementing GenAI in process analyses have been recently proposed by industry and academia. However, while basic use cases are being covered, more advanced and systematic studies need to be discussed. Moreover, while prototypes have been implemented, their effectiveness in terms of improving a company’s KPIs was marginally discussed.
The GenAI4PM 2025 workshop aims to provide a premier platform for advancing the integration of generative AI within process mining. By fostering collaboration between academia and industry, we plan to stimulate innovative research and practical applications that address both current challenges and future opportunities in the field. We look forward to contributing to the rich combination of topics at ICPM 2025 and engaging a broad, interdisciplinary audience.
We invite submissions that explore diverse aspects of GenAI in process mining, including but not limited to:
We invite authors to submit the following types of papers (all should follow the Springer LNBIP format, see FAQ for details):
Full-length papers accepted (minimum 5 for a full-day workshop) will be published in the Springer LNBIP proceedings, with an acceptance rate capped at 50% as per Springer’s requirements. Additional papers or activities may be included pending negotiation and space availability.
These dates are designed to allow sufficient time for authors to plan travel and finalize papers, including reformatting from IEEE to LNBIP for Springer proceedings.
We propose a full-day workshop (tentative) scheduled on October 20, 2025, the day prior to the main ICPM 2025 conference, ensuring ample time for both presentations and in-depth interactive sessions. The full-day duration is contingent on the acceptance of more than 5 full-length papers eligible for Springer proceedings; otherwise, it will be allocated half a day (two sessions).
GenAI4PM 2024 received 19 submissions, of which 6 full papers and 3 short papers were accepted and presented at the workshop. For 2025, we aim to build on this success by encouraging high-quality submissions across all categories.
Our promotion strategy includes:
Mohammadreza Fani Sani is an accomplished Applied and Data Scientist at Microsoft, with a strong academic background in Process and Data Science from RWTH Aachen University. His doctoral research was conducted within the Process and Data Science (PADS) group, where he focused on preprocessing data to enhance the performance of process mining applications. Currently, his work at Microsoft involves productizing Large Language Models for Copilot AI and Process Mining. With his blend of academic expertise and industry experience, Mohammadreza is making strides in the integration of large language models in the field of process mining.
Cristina Cabanillas is a professor at the University of Seville, where she conducts research in the field of business process management and process mining. With a strong academic foundation, she has explored resource management and process optimization, contributing to both theoretical advancements and practical applications. Cristina has been actively involved in investigating how generative AI, particularly large language models, can enhance process mining techniques. Her work focuses on leveraging these models to improve process model discovery and analysis, enabling more intuitive and automated interpretations of complex process data. Through her publications and collaborations, Cristina is advancing the integration of generative AI into process mining, bridging the gap between cutting-edge AI technologies and real-world process improvement.
Humam Kourani is a research associate at Fraunhofer FIT and a member of the PADS group at RWTH Aachen University. His research centers on the intersection of process mining and data science, with a particular emphasis on harnessing generative AI to innovate process modeling. Humam is a key contributor to ProMoAI, a pioneering tool that utilizes large language models to automatically generate and refine process models from textual descriptions. His work demonstrates how generative AI can lower technical barriers, making process mining more accessible and efficient. Through his publications and practical implementations, Humam is driving forward the integration of generative AI and process mining, offering new possibilities for automation and insight generation in business processes.
Alessandro Berti is a Ph.D. student at RWTH Aachen University, affiliated with the Process and Data Science (PADS) group. His doctoral thesis focuses on object-centric process mining. He plays a role as the main developer of pm4py, a leading Python library for process mining. Alessandro has made significant contributions to the integration of large language models within the pm4py framework. His work includes both development and research, with some publications that bridge the gap between large language models and the field of process mining.
The following members have accepted our invitation to join the Program Committee (list to be updated):