The use of chatbots has spread, generating great interest in the industry for the possibility of automating tasks within the execution of their processes. The implementation of chatbots, however simple, is a complex endeavor that involves many low-level details, which makes it a time-consuming and error-prone task. In this paper we aim at facilitating the development of decision-support chatbots that guide users or help knowledge workers to make decisions based on interactions between different process participants, aiming at decreasing the workload of human workers, for example, in healthcare to identify the first symptoms of a disease. Our work concerns a methodology to systematically build decision-support chatbots, semi-automatically, from existing DMN models. Chatbots are designed to leverage natural language understanding platforms, such as Dialogflow or LUIS. We implemented Dialogflow chatbot prototypes based on our methodology and performed a pilot test that revealed insights into the usability and appeal of the chatbots developed.