What Automated Planning can do for Business Process Management

TL;DR

This paper explores how automated planning techniques from AI can enhance automation and adaptability in Business Process Management, especially in dynamic, data-rich environments, through practical applications across BPM lifecycle stages.

Contribution

It introduces the application of automated planning methods to BPM, demonstrating their potential to improve automation and flexibility in managing evolving business processes.

Findings

Automated planning can significantly improve BPM automation.

Planning techniques support dynamic and unpredictable process environments.

Successful applications demonstrate benefits across BPM lifecycle stages.

Abstract

Business Process Management (BPM) is a central element of today organizations. Despite over the years its main focus has been the support of processes in highly controlled domains, nowadays many domains of interest to the BPM community are characterized by ever-changing requirements, unpredictable environments and increasing amounts of data that influence the execution of process instances. Under such dynamic conditions, BPM systems must increase their level of automation to provide the reactivity and flexibility necessary for process management. On the other hand, the Artificial Intelligence (AI) community has concentrated its efforts on investigating dynamic domains that involve active control of computational entities and physical devices (e.g., robots, software agents, etc.). In this context, Automated Planning, which is one of the oldest areas in AI, is conceived as a model-based approach to synthesize autonomous behaviours in automated way from a model. In this paper, we discuss how automated planning techniques can be leveraged to enable new levels of automation and support for business processing, and we show some concrete examples of their successful application to the different stages of the BPM life cycle.