  Intelligent Process Automation (IPA) is an emerging technology with a primary
goal to assist the knowledge worker by taking care of repetitive, routine and
low-cognitive tasks. Conversational agents that can interact with users in a
natural language are potential application for IPA systems. Such intelligent
agents can assist the user by answering specific questions and executing
routine tasks that are ordinarily performed in a natural language (i.e.,
customer support). In this work, we tackle a challenge of implementing an IPA
conversational assistant in a real-world industrial setting with a lack of
structured training data. Our proposed system brings two significant benefits:
First, it reduces repetitive and time-consuming activities and, therefore,
allows workers to focus on more intelligent processes. Second, by interacting
with users, it augments the resources with structured and to some extent
labeled training data. We showcase the usage of the latter by re-implementing
several components of our system with Transfer Learning (TL) methods.
