The business process management discipline is a vastly studied field with significant contributions to creating value for organizations, being structured around various activities. Among these activities, creating and modifying business processes are particularly difficult and resource-intensive tasks within organizations. Recently, a new artificial intelligence architecture model was introduced, generative pre-trained transformers, changing the way machines can process and understand digital content. The integration of generative artificial intelligence into virtually any field of study is occurring at a very fast pace, but applying it to the optimization of business processes is still ongoing research. We have identified a particular area of improvements. A lot of work has been done on the automation of activities of a process but not on the process itself. In this paper, we conducted a business use case, consisting of dynamically meta-optimizing a credit application process, based on performance indicators (e.g. profit), using a software prototype system. Several implications derive from the execution of this business use case. (1) A significant decrease in the time a process manager needs to spend on designing and redesigning the business process; (2) An increase in the speed of adoption of business processes, even for small and medium-size enterprises; (3) Integrating such a system into the organization provides an element of agility, making it ready to environmental changes and able to adapt. (4) Ultimately, organizations that successfully adopt this technology, could achieve autonomous adaptation to the environment, leading the way for the ultimate digital enterprise.