Large Language Models (LLMs) hold the promise to significantly enhance conversational AI in customer service. There is however limited understanding of end-users’ expectations and anticipated use of LLM-powered conversational AI (CAI), which is crucial since expectation is directly linked to experience. This paper addresses this gap by exploring potential end-users’ perceptions and expectations of LLM-powered CAI in customer service, drawing on expectancy-disconfirmation theory. The findings show that users have modest expectations for LLM-powered CAI in customer service, shaped by their past experiences with existing CAI and their experiences and knowledge of LLM features. The findings underline the importance of expectations management and ensuring high-quality use cases where LLM-powered CAI for customer service is implemented.