In an increasingly competitive business environment, firms must continuously adopt and use new technologies to enhance their operations and maintain their market positions. One of the most critical areas for technological advancement is customer support service, where high-quality interactions directly impact customer satisfaction and brand perception. Traditionally, businesses have relied on human expertise and skills to provide personalized, efficient and effective support. However, recent advancements in artificial intelligence (AI), particularly generative AI (GenAI), have introduced new possibilities for automating and enhancing customer interactions.

GenAI, exemplified by OpenAI’s ChatGPT, has rapidly gained traction due to its ability to generate human-like responses and adapt dynamically to customer inquiries. This technology has been increasingly integrated into customer support functions, promising improved efficiency by reduced response times, and enhanced customer experiences. Nevertheless, despite its potential, there remains a gap in empirical research on the practical implementation of GenAI in customer support service and its measurable impacts on productivity and customer satisfaction.

This study explores the use of GenAI within the customer support operations of Polestar, a premium automotive brand. The study is focused on the AI-driven solution CAiR, which assists customer support advisors by generating response suggestions for web form inquiries. The main purpose of this bachelor thesis in information systems is to examine the impact of GenAI response suggestions on the productivity of customer support centers from the perspective of individual customer support advisors. Additionally, the sub-purpose is to assess how the use of GenAI responses during the support inquiry resolution process influence customer satisfaction at a macro level. By addressing both internal efficiency (productivity) and external effectiveness (customer satisfaction), the study highlights the dual function of GenAI in enhancing operational performance while delivering customer value.

In this study a mixed-methods case study approach is employed, combining semi-structured interviews of six customer support advisors and different quantitative analyses of GenAI responses usage, productivity metrics, and customer satisfaction data. The relationship between GenAI response use, productivity, and satisfaction is examined through survey analysis and regression models.

The following main conclusions are drawn: the degree of GenAI responses usage in customer support is primarily driven by higher degree of ease of use, higher-quality outputs, and higher perceived helpfulness. Additionally, the use of GenAI responses significantly reduces the average handling time of web form inquiries by providing structured and ready-to-use replies, minimizing the need for manual drafting. However, the broader impact of GenAI on productivity in customer support and customer satisfaction is influenced by workload levels and the level of AI augmentation. Higher workloads and limited AI augmentation applied only to initial inquiries restrict improvements in SLA compliance and customer satisfaction.