Towards designing an AI-based conversational agent for on-the-job training of customer support novices

Due to the high drop-out rates in IT support desks, efficient onboarding of novices becomes a relevant and recurring challenge. Especially in the case of IT support, solving technical issues and service requests while the conversation with the customer is still ongoing imposes high demands on novice support agents. As artificial intelligence (AI) can already classify service requests and help find solutions, AIbased augmentation holds great potential for improving the onboarding phase and reducing time-to-performance. For this reason, we propose an AI-based conversational (co-)agent during the onboarding phase of customer support novices to reduce the time spent on service tasks and enable on-the-job training. Following action design research, we aim to develop an instantiation of an AI-based co-agent to reduce the job demand for the service center agent novices and augment problem-solving capabilities by considering cognitive load. The co-agent will be implemented with one development partner and evaluated with two different case partner organizations. In this research-in-progress project, we developed a low-fidelity prototype and derived a tentative architecture that allows for a generalized development of such conversational agents in customer service organizations.