<![CDATA[The rapid integration of artificial intelligence (AI) into workplace systems has transformed how employees perform, interact, and adapt within organizations, making human-AI adaptation an increasingly important issue in organizational studies. Although prior research has widely examined technology acceptance, most studies rely on quantitative approaches and provide limited insight into the lived experiences, emotional responses, and adaptation processes of employees, particularly in emerging-market regions outside major metropolitan areas. This study addresses this gap by exploring how employees in the manufacturing and banking sectors of South Sulawesi, Indonesia, experience and adapt to AI-enabled work environments. Using a qualitative phenomenological approach, this study integrates the Technology Acceptance Model (TAM) and Organizational Adaptation Theory to examine employees’ cognitive, emotional, and behavioural responses toward AI adoption. Data were collected through semi-structured in-depth interviews with 22 purposively selected informants from the manufacturing and banking industries. The data were analysed using thematic coding to identify recurring patterns of adaptation experiences. The findings reveal five major themes: differential perceptions of AI usefulness and usability, sector-specific human-AI interaction patterns, psychoemotional responses, organizational support, and professional identity reconfiguration. Manufacturing employees experienced AI as a physical and operational disruptor that intensified job-displacement anxiety, while banking employees viewed AI as an analytical enhancement tool that generated competitive professional pressure. Across both sectors, organizational support—including leadership communication, practical training, and psychological safety—emerged as the most significant factor influencing successful adaptation. This study concludes that AI adaptation extends beyond cognitive technology acceptance and involves emotional, social, and identity-related processes shaped by organizational context. The findings highlight the limitations of TAM when used independently and emphasize the importance of integrating organizational adaptation perspectives to better understand human-AI interaction in contemporary workplaces]]>
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