Call for Papers

Process Automation technologies are rapidly transforming how organizations execute and manage business processes. Ranging from lightweight, low-code/no-code tools to advanced systems combining human expertise with Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and Large Language Models (LLMs) solutions, the spectrum of process automation offers both significant opportunities and complex challenges. This workshop aims to provide a holistic perspective on process automation, addressing both the managerial and engineering aspects from a Business Process Management (BPM) perspective. 

We invite you to submit original papers to be considered for inclusion in the workshop. After a short opening, the workshop will continue with paper presentations and discussions and close with an interactive session. Here, participants will collaboratively develop a manifesto on challenges and best practices regarding the management and implementation of both lightweight and advanced process automation solutions.

Topics of Interest

We invite submissions exploring visionary, empirical and practical perspectives on managing and implementing both lightweight and advanced process automation solutions. Contributions may address, but are not limited to, the following topics:

• Frameworks for integrating lightweight and advanced process automation technologies into BPM.

• Strategies to balance agility, compliance, and governance in process automation initiatives.

• Stakeholder perspectives from IT, citizen developers, and business units on process automation technologies.

• Approaches for addressing security, compliance, and privacy challenges specific to lightweight and advanced process automation technologies.

• Case studies exploring real-world implementations of process automation technologies as well as theoretical works on process automation technologies, including:

  1. Applications of Generative AI and LLMs applied to lightweight and advanced process automation technologies.

  2. Applications of ML and neuro-symbolic techniques in lightweight and advanced process automation tools.

  3. Applications of conversational AI integrated into process automation workflows.

• Role of contextual factors (e.g., organizational size, industry type) in shaping the success of process automation approaches.

• Human-in-the-loop approaches for lightweight and advanced process automation technologies.

• Proactive synthesis of software robots in RPA.

• ML, including Deep Learning, to support the discovery of routines to be automated.

• Semi-supervised or unsupervised segmentation of user interaction logs in RPA.

• Learning executable (structured or unstructured) routines using NLP techniques.