Business Process Management (BPM) is a central element of today’s organizations. Over the years, its main focus has been the support of business processes (BPs) in highly controlled domains. However—in the current era of Big Data and Internet-of-Things—several real-world domains are becoming cyber-physical (e.g., consider the shift from traditional manufacturing to Industry 4.0), characterized by ever-changing requirements, unpredictable environments and increasing amounts of data and events that influence the enactment of BPs. In such unconstrained settings, BPM professionals lack the needed knowledge to model all possible BP variants/contingencies at the outset. Consequently, 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). In this context, automated planning, which is one of the oldest areas in AI, is conceived as a model-based approach to synthesize autonomous behaviors 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 solving concrete problems in the BPM field that were previously tackled with hard-coded solutions. To this aim, we first propose a methodology that shows how a researcher/practitioner should approach the task of encoding a concrete problem as an appropriate planning problem. Then, we discuss the required steps to integrate the planning technology in BPM environments. Finally, we show some concrete examples of the successful application of planning techniques to the different stages of the BPM life cycle.

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