How Can Generative AI Empower Domain Experts in Creating Process Models?

Nataliia Klievtsova (), Juergen Mangler (), Timotheus Kampik (), Janik-Vasily Benzin () and Stefanie Rinderle-Ma ()
Additional contact information
Nataliia Klievtsova: Technical University of Munich, TUM School of Computation, Information and Technology
Juergen Mangler: Technical University of Munich, TUM School of Computation, Information and Technology
Timotheus Kampik: SAP Signavio
Janik-Vasily Benzin: Technical University of Munich, TUM School of Computation, Information and Technology
Stefanie Rinderle-Ma: Technical University of Munich, TUM School of Computation, Information and Technology

A chapter in Digital Innovation and Organizational Transformation, 2026, pp 57-72 from Springer

Abstract: Abstract Considering the human factor in information systems is a key to future digitalization efforts, as stated in the Industry5.0 research and innovation actions of the EU. Especially in the design phase of a process-oriented information system, the human factor includes the empowerment of domain experts in process model creation lowering the entry hurdle for process modeling, and increasing modeling speed. In this work, we investigate how generative AI methods can support domain experts in creating process models in interaction with a chatbot based on textual process descriptions. We explore the amount of necessary information required as input to create process models with immediate visual representation using markdown-inspired languages and extend existing evaluation methods for assessing generated models, focusing on their completeness and correctness. Overall, an evaluation method has to consider the complex relationships between model completeness, correctness, textual process description, textual representation, and prompt engineering to support the domain expert.

Keywords: Digital transformation; Business process engineering and management; Process modeling; Generative AI (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-032-08483-5_5

Ordering information: This item can be ordered from
http://www.springer.com/9783032084835

DOI: 10.1007/978-3-032-08483-5_5

Access Statistics for this chapter

More chapters in Lecture Notes in Information Systems and Organization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().