Drawing on Task–Technology Fit (TTF) theory, this study examines how different levels of Artificial Intelligence (AI) reshape service tasks and employee experiences among vulnerable and minority employees in the hospitality industry. A three‐phase mixed‐method design was employed. First, a systematic literature review (SLR) identified and classified AI applications across four hotel functions: front office, housekeeping, food and beverage production and service operations. Second, survey data collected from vulnerable and minority hotel employees were analysed using structural equation modelling (SEM) to examine the effects of Mechanical, Analytical, Intuitive and Empathetic AI on the nature of tasks and employee experience. Third, fuzzy‐set qualitative comparative analysis (fsQCA) was used to identify configurational pathways leading to positive outcomes. The findings reveal that Mechanical, Analytical and Intuitive AI significantly transform task characteristics and enhance employee experience, whereas Empathetic AI does not exert a significant effect on task structure. Furthermore, fsQCA results demonstrate that multiple combinations of AI capabilities can generate favourable employee outcomes, highlighting the importance of task–technology alignment. By TTF theory to the context of vulnerable and minority employees, this study contributes to the AI and hospitality literature and offers practical insights for designing inclusive, human‐centred AI‐enabled service operations.