Mahei Li

Customer support service employees are facing an increased workload, while artificial intelligence (AI) appears to possess the potential to change the way we work. With the advent of modern types of generative AI, new opportunities to augment frontline service employees have emerged. However, little is known about how to integrate generative AI in customer support service organizations and purposefully change service employee work routines. Following multi-method qualitative research, we performed a literature review, conducted workshops, and interviewed IT support agents, managers, and AI experts. Thereby, we examine AI augmentation for frontline service employees in the context of IT support to carve out where and how GenAI can be leveraged to develop more efficient and higher-quality customer support. Our resulting framework reveals that especially adapting solutions and retaining knowledge is subject to a high degree of AI augmentation.

With the rapidly increasing number and complexity of service demands, service providers need to become even more flexible and faster. To accommodate this, the service system needs to dynamically reconfigure its required resourced based on the context of both service providers and the customer. The resulting chosen service system configuration is called a dynamic solution. Our research question is therefore as follow: How can we implement the characteristic of dynamic solutions based on the service system model (SSM)? We demonstrate our model using a real-life citizen-based produce delivery service. We contribute by being able to quantify service system configurations and compare different configurations dynamically. Keywords: Service operations, operationalizing