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
