AI-Enhanced Business Process Automation: A Case Study in the Insurance Domain Using Object-Centric Process Mining

Recent advancements in Artificial Intelligence (AI), particularly Large Language Models (LLMs), have enhanced organizations' ability to reengineer business processes by automating knowledge-intensive tasks. This automation drives digital transformation, often through gradual transitions that improve process efficiency and effectiveness. To fully assess the impact of such automation, a data-driven analysis approach is needed - one that examines how traditional and AI-enhanced process variants coexist during this transition. Object-Centric Process Mining (OCPM) has emerged as a valuable method that enables such analysis, yet real-world case studies are still needed to demonstrate its applicability. This paper presents a case study from the insurance sector, where an LLM was deployed in production to automate the identification of claim parts, a task previously performed manually and identified as a bottleneck for scalability. To evaluate this transformation, we apply OCPM to assess the impact of AI-driven automation on process scalability. Our findings indicate that while LLMs significantly enhance operational capacity, they also introduce new process dynamics that require further refinement. This study also demonstrates the practical application of OCPM in a real-world setting, highlighting its advantages and limitations.

OCPM$^2$: Extending the Process Mining Methodology for Object-Centric Event Data Extraction

Object-Centric Process Mining (OCPM) enables business process analysis from multiple perspectives. For example, an educational path can be examined from the viewpoints of students, teachers, and groups. This analysis depends on Object-Centric Event Data (OCED), which captures relationships between events and object types, representing different perspectives. Unlike traditional process mining techniques, extracting OCED minimizes the need for repeated log extractions when shifting the analytical focus. However, recording these complex relationships increases the complexity of the log extraction process. To address this challenge, this paper proposes a method for extracting OCED based on PM\inst{2}, a well-established process mining framework. Our approach introduces a structured framework that guides data analysts and engineers in extracting OCED for process analysis. We validate this framework by applying it in a real-world educational setting, demonstrating its effectiveness in extracting an Object-Centric Event Log (OCEL), which serves as the standard format for recording OCED, from a learning management system and an administrative grading system.