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Competition among companies has dramatically increased in the modern world. As artificial intelligence (AI) continues to transform digital marketing, increasing sales and revenues, AI-powered recommendation systems have become essential, offering enhanced efficiency and personalisation services to influence online shopping intentions and behaviours. Firms collect consumers’ information to design personalised offerings and enhance their online shopping experience. Despite their growing prevalence, limited research has explored the extent to which consumers are influenced by these systems. This study is guided by an extended version of the Technology Acceptance Model using perceived personalisation, which has emerged as a key factor in recent studies on the use of AI in shopping behaviours, to understand how these perceptions influence purchase intentions and actual behaviours. This qualitative study used 17 semi-structured interviews with frequent online shoppers and employed a thematic analysis to identify key patterns and themes in online shopping experiences. The findings reveal several insights, first this study contribute to the growing body of the literature on AI in digital marketing by offering a consumer-centred perspective on trust as a mediator in the relationship between perceived usefulness, perceived ease of use, personalisation, referenced comparison, and behavioural intentions. Second, this study discuss practical implications in relation to the design of AI systems that not only optimise personalisation but also foster authentic consumer trust and engagement using AI recommendations in decision-making processes. Finaly, the study discussed several implications for marketers and system designers, highlighting strategies to enhance user trust and optimise the effectiveness of AI-driven personalisation and referenced comparisons in online shopping settings.