Wang, Pola QYan, LiweiSantoso, Carolin2025-07-282025-07-282025-07-04Current Issues in Tourism, ISSN: 1368-3500 (Print); 1747-7603 (Online), Informa UK Limited, ahead-of-print, 1-14. doi: 10.1080/13683500.2025.25289811368-35001747-7603http://hdl.handle.net/10292/19610As artificial intelligence (AI) becomes increasingly integrated into hospitality and tourism operations, it is essential to understand how employees from different generational cohorts engage with AI technologies in the workplace. This conceptual study introduces a generationally responsive framework to examine human AI engagement across three key service phases: pre-arrival, mid-arrival, and post-arrival. It distinguishes between two overarching modes of engagement: interaction, which includes coexistence, cooperation, and collaboration, and collaboration itself, which involves complementarity and augmentation models. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT), the framework applies four key dimensions: performance expectancy, effort expectancy, social influence, and facilitating conditions to explain how generational characteristics influence AI perceptions and behaviours. A unique contribution of this study is the identification of an emerging autonomous decision support model, especially relevant for Generation Z, in which AI makes and implements service decisions independently with minimal human involvement. These generational patterns vary across service tasks and reflect broader differences in digital fluency, workplace expectations, and trust in technology. The study concludes that effective AI integration in hospitality requires alignment with the values, preferences, and interaction styles of a multigeneration workforce.© 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.35 Commerce, Management, Tourism and Services3504 Commercial ServicesMachine Learning and Artificial Intelligence3 Good Health and Well Being1503 Business and Management1505 Marketing1506 TourismSport, Leisure & Tourism3506 Marketing3508 Tourism4406 Human geographyArtificial intelligencegenerational differenceshospitality workforcehuman–AI interactionservice processtechnology adoptionJournal ArticleOpenAccess10.1080/13683500.2025.2528981