  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.
