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Process Improvement Copilot : Bridging the Gap Between Process Inefficiencies and Process Improvement Ideas

Title data

Smalei, Uladzimir ; Kecht, Christoph ; Kratsch, Wolfgang ; Röglinger, Maximilian:
Process Improvement Copilot : Bridging the Gap Between Process Inefficiencies and Process Improvement Ideas.
In: Process Science. Vol. 3 (2026) . - 1.
ISSN 2948-2178
DOI: https://doi.org/10.1007/s44311-025-00028-2

Official URL: Volltext

Project information

Project financing: Next Best Process

Abstract in another language

Business process improvement (BPI) is a crucial value-adding stage of business process management, as it introduces process changes to eliminate flaws and enhance performance. However, the inherent demands of BPI on domain knowledge, process expertise, time, and creativity in conjunction with a scarcity of adequate computational support, hinder organizations from fully leveraging BPI. Recognizing this gap, recent research calls for all types of contributions to process improvement and innovation systems (PIISs), from design knowledge to software artifacts. Leveraging the latest developments in generative artificial intelligence, increased availability of process execution data, and extensive collections of BPI knowledge, we propose a new technical approach to supporting the generation of process improvement ideas in BPI initiatives. To this end, we develop the Process Improvement Copilot – a retrieval-augmented generation (RAG)-enhanced PIIS that generates context-specific process improvement ideas and provides related justification, thereby facilitating their further evaluation and implementation. This research contributes a novel technical approach to automated BPI by exploring a RAG-based use case, designing a corresponding system architecture, developing a software prototype to demonstrate its technical feasibility, and evaluating the Process Improvement Copilot’s usefulness in a naturalistic workshop setting.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Business process management; Business process improvement; Process mining; Generative artificial intelligence; Retrieval-augmented generation
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration XVII - Information Systems and Value-Based Business Process Management
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration XVII - Information Systems and Value-Based Business Process Management > Chair Business Administration XVII - Information Systems and Value-Based Business Process Management - Univ.-Prof. Dr. Maximilian Röglinger
Research Institutions
Research Institutions > Affiliated Institutes
Research Institutions > Affiliated Institutes > Branch Business and Information Systems Engineering of Fraunhofer FIT
Research Institutions > Affiliated Institutes > FIM Research Center for Information Management
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Date Deposited: 23 Feb 2026 08:50
Last Modified: 23 Feb 2026 08:50
URI: https://eref.uni-bayreuth.de/id/eprint/96330