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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.

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 are not 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

Practical information

• Journal: Information Systems (Elsevier)
• Submission deadline: 1 July 2025
• Submission portal: https://www.editorialmanager.com/infosys/default.aspx. When submitting papers, please select "VSI: AI-Enhanced BPM" as the article type.
• Submission guidelines: https://www.sciencedirect.com/special-issue/314209/ai-enhanced-business-process-management

Editors

• Valeria Fionda
• Antonio Ielo
• Arik Senderovich
• Emilio Sulis