These opportunities require a significant shift in the way the BPMS operates and interacts with its operators(both human and digital agents). While traditional BPMSs encode pre-defined flows and rules, an ABPMS is able to reason about the current state of the process(or across several processes) to determine a course of action that improves the performance of the process. To fully exploit this capability, the ABPMS needs a degree of autonomy. Naturally, this autonomy needs to be framed by operational assumptions, goals, and environmental constraints. Also, ABPMSs need to engage conversationally with human agents, they need to explain their actions, and they need to recommend adaptations or improvements in the way the process is performed. This manifesto outlined a number of research challenges that need to be overcome to realize systems that exhibit these characteristics.

In this direction, a number of techniques from the field of AI have been applied to BPMSs with the aim of increasing the degree of automated process adaptation (Marrella, 2018, 2019). In (Gajewski et al., 2005; Ferreira and Ferreira, 2006; Marrella and Lespérance, 2013, 2017), if a task failure occurs at run-time and leads to a process goal violation, a new complete process definition that complies with the goal is generated relying on a partial-order AI planner. As a side effect, this often significantly modifies the assignment of tasks to process participants. The work (Bucchiarone et al., 2011) proposes a goal-driven approach to adapt processes to run-time context changes. Process and context changes that prevent goal achievement are specified at design-time and recovery strategies are built at run-time through an adaptation mechanism based on service composition via AI planning.