Academic Perspective: How Object-Centric Process Mining Helps to Unleash Predictive and Generative AI

Process mining has emerged as a pivotal discipline that bridges the gap between process science and data science, evolving significantly since its inception in the late 1990s. The discipline of process mining has been instrumental in addressing fundamental questions about actual vs. assumed processes, identifying bottlenecks and deviations, and predicting performance and conformance problems. Despite advancements in process discovery, conformance checking, and data-driven simulation, (1) data extraction remains challenging, (2) traditional case-driven approaches fail to identify problems involving multiple organizational units and processes, and (3) organizations fail to reap the benefits of the rapid developments in Artificial Intelligence (AI). The introduction of Object-Centric Process Mining (OCPM) and the integration with predictive and generative AI represent a revolutionary shift in process management. OCPM allows for a more nuanced analysis of processes without the constraints of a single-case notion, enabling a deeper understanding of the interactions between different object types within processes. This evolution towards a more faithful view of operational processes is further enhanced by the capabilities of predictive and generative AI, offering new opportunities for diagnosing and addressing operational problems. Next to an integration of OCPM and Predictive and Generative AI, we advocate a domain-specific approach to process mining. Leveraging standardized reference models powered by OCPM helps to accelerate the adoption of process mining.