  Business processes are commonly represented by modelling languages, such as
Event-driven Process Chain (EPC), Yet Another Workflow Language (YAWL), and the
most popular standard notation for modelling business processes, the Business
Process Model and Notation (BPMN). Most recently, chatbots, programs that allow
users to interact with a machine using natural language, have been increasingly
used for business process execution support. A recent category of chatbots
worth mentioning is generative-based chatbots, powered by Large Language Models
(LLMs) such as OpenAI's Generative Pre-Trained Transformer (GPT) model and
Google's Pathways Language Model (PaLM), which are trained on billions of
parameters and support conversational intelligence. However, it is not clear
whether generative-based chatbots are able to understand and meet the
requirements of constructs such as those provided by BPMN for process execution
support. This paper presents a case study to compare the performance of
prominent generative models, GPT and PaLM, in the context of process execution
support. The research sheds light into the challenging problem of using
conversational approaches supported by generative chatbots as a means to
understand process-aware modelling notations and support users to execute their
tasks.
