In recent years, business processes that involve AI-empowered automation are gaining importance and market share. These business processes combine characteristics of classical Business Process Management, goal-driven chatbots, conversational recommendation systems and Robotic Process Automation. In the new context, prescriptive process monitoring demands innovative approaches. Unfortunately, data logs of these new processes are still unavailable in the public domain. In this paper, we describe the main challenges in the new domain and present a synthesized data that is based on an actual use case of an Intelligent Process Automation with chatbot orchestration. Using this dataset, we illustrate several alternative approaches to prescription including crowd-wisdom and corrective actions based on prediction of process outcome.