Dipartimento di Ingegneria informatica, automatica e gestionale

AI-Augmented Business Process Management Systems (ABPMSs) are innovative information systems with increased flexibility, autonomy, and conversational capability. These systems can be boosted by Large Language Models (LLMs), renowned for their ability to handle natural language processing tasks. Nevertheless, no significant empirical validations exist about their usefulness in process-driven decision support. In this study, we propose a business process-oriented LLM framework, for enacting actionable conversations with workers involved in a business process, leveraging Retrieval-Augmented Generation (RAG) to enrich process-specific knowledge. The methodology has been assessed to evaluate its capacity to produce precise responses to inquiries posed by users within a public administration context. The preliminary study shows the framework’s ability to identify specific activities and sequence flows within the targeted process model, thereby providing valuable insights into its potential for improving ABPMSs.