Process Management (PM) is an evolving discipline integrating computer science, operations research, and data science (e.g., process mining). The rise of AI and Generative AI (GenAI) is driving a new wave of AI-augmented BPM systems (ABPMS), making process management more autonomous, adaptive, intelligent, and optimized. These AI-powered, trustworthy, and process-aware systems continuously analyze and act on data within constraints to enhance processes. Advances in domain expertise integration and Artificial Expert Intelligence (AEI) within Agentic AI infrastructures present both challenges and opportunities. This workshop unites researchers, practitioners, and students interested in AI-PM synergy to collaborate and present original work.

Call for Papers
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Submission Details
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Submissions must be written in English, prepared using the new CEUR-ART 1-column style, formatted in PDF, and submitted through EasyChair. An Overleaf template for LaTeX users is available here. Alternatively, you can download an offline version with the style files for both LaTeX and MS-Word.

There are two submission formats:
• Regular papers (max 12 pages, incl. references): Must be citable and unpublished.
• Short papers (2-4 pages, incl. references): Can present new ideas, systems, preliminary results, or overviews of accepted/submitted papers (with venue and status clearly stated).
Accepted papers must be presented at the conference, with at least one author attending in person. Multiple submissions to other ECAI workshops are prohibited. Papers undergo single-blind review by 2-3 reviewers, with author names visible.

Topics of interest for submission:
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• multi-perspective process models (including data, time, and resources)
• declarative processes
• causal process intelligence
• explainable and trustworthy AI-augmented processes
• conversational systems, natural language processing, and human-machine interaction for processes
• KR for processes: reasoning about actions and processes, planning, and synthesis
• AI techniques for process discovery, conformance checking, prescriptive and predictive monitoring
• AI techniques for clustering and classification of process execution traces
• PM tasks implemented with Generative AI
• generative AI for processes
• machine learning for event recognition on semi-structured and unstructured data
• association rule mining, specification mining, and decision mining from process execution traces
• declarative-based multi-perspective representation of process traces
• novel metrics for the measurement of process conformance
• uncertainty in AI for processes
• multiagent systems, strategic reasoning, game theory, and mechanism design for multi-party processes
• multi-objective optimization, decision-making, and continuous improvement
• value alignment in process management
• digital twin generation and operation for AI-augmented processes