  In this short paper, we present early insights from a Decision Support System
for Customer Support Agents (CSAs) serving customers of a leading accounting
software. The system is under development and is designed to provide
suggestions to CSAs to make them more productive. A unique aspect of the
solution is the use of bandit algorithms to create a tractable
human-in-the-loop system that can learn from CSAs in an online fashion. In
addition to discussing the ML aspects, we also bring out important insights we
gleaned from early feedback from CSAs. These insights motivate our future work
and also might be of wider interest to ML practitioners.
