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2025-04-01
Special Issue on AI-Enhanced Business Process Management Submission Date: 2025-07-01 Artificial Intelligence (AI) is rapidly evolving, offering advanced techniques and applications across a wide range of domains. In recent years, there has been a significant increase in interest from both industry and academia in applying AI to Business Process Management (BPM), which combines insights from operations management, computer science, and data science. AI is set to revolutionize BPM by simplifying human interactions, enhancing task execution, and enabling the full automation of processes traditionally performed manually. The development of AI techniques is driving the emergence of AI-augmented BPM systems (ABPMS) that are autonomous, adaptive, intelligent, and self-optimizing.
The impact of AI on BPM is multifaceted. On one hand, AI can dramatically simplify human interactions with business processes by providing intelligent recommendations, automating routine tasks, and facilitating decision-making through advanced analytics. On the other hand, AI can support task execution by augmenting human capabilities, offering insights from vast amounts of data, and learning from historical performance to optimize future operations. Furthermore, AI enables the full automation of processes that have traditionally required manual intervention, thereby increasing efficiency, reducing errors, and lowering operational costs.
ABPMS represent a new generation of information systems designed to be more autonomous, adaptive, and intelligent. These systems continuously monitor and analyze business processes, adapting in real-time to changing conditions and optimizing performance based on predefined goals and experiential learning. The integration of AI into BPM allows for the creation of systems that are not only self-optimizing but also capable of evolving over time, making them more resilient and effective in achieving business objectives.
Guest editors:
Assoc. Prof. Valeria Fionda (Executive Guest Editor)
University of Calabria, Arcavacata di Rende, Italy
Email: valeria.fionda@unical.it
Dr. Antonio Ielo
University of Calabria, Arcavacata di Rende, Italy
Email: antonio.ielo@unical.it
Assist. Prof. Arik Senderovich
York University, Toronto, Ontario, Canada
Email: sariks@yorku.ca
Assist. Prof. Emilio Sulis
University of Turin, Turin, Italy
Email: emilio.sulis@unito.it
Special issue information:
This special issue aims to explore the foundational, conceptual, and technical challenges of integrating AI with BPM. We invite contributions from researchers, practitioners, and students that advance the synergy between AI and BPM. Topics of interest include, but arenot limited to:
Machine Learning and Deep Learning to support workflow management and process automation
Neuro-symbolic Approaches, integrating symbolic reasoning with neural networks for BPM
Business Process Monitoring, predictions and recommendations
Natural language processing and process modeling
AI-based techniques for new business models
Personalized recommendations to improve business processes
AI-based techniques for process mining
AI-assisted process design
AI-based techniques to manage process exceptions
Automated-planning for business processes
Business Process rule mining
Knowledge representation, management and reasoning on process specifications
Decision support systems for business processes
Robotic Process Automation (RPA)
Trustworthy AI, explainability, transparency in the field of BPM
New AI-enhanced BPM models
Social, Economic, and Business impacts of AI-enhanced BPM
Generative AI for BPM
Value alignment in AI-driven process management
Manuscript submission information:
Tentative Schedule:
Submission Open Date: November 1, 2024
Submission Deadline: July 1, 2025
Editorial Acceptance Deadline: December 31, 2025
Submission Guidelines:
All manuscripts should be submitted electronically through Editorial Manager ? at https://www.editorialmanager.com/infosys/default.aspx. When submitting papers, please select the Article Type as "VSI: AI-Enhanced BPM".
Authors should prepare their manuscripts according to the "Guide for Authors" of the Information Systems outlined at the journal website: https://www.sciencedirect.com/journal/information-systems/publish/guide-for-authors.
All papers will be peer-reviewed following a regular reviewing procedure.
For any further information, the authors may contact the Guest Editors.
Keywords:
Business Process Management; Process Mining; Artificial Intelligence
