AI personalisation has emerged as a powerful tool to boost consumer satisfaction in digital experiences. Satisfaction is a multifaceted concept, encompassing the fulfilment of needs and wants and resulting in a positive emotional and cognitive state. This study describes how AI personalisation can strengthen overall satisfaction by aligning AI recommendations along with both observable needs and deeper emotional and social desires. To investigate this, a qualitative research approach was employed, utilising semi-structured interviews to gather empirical data. This method ensured comprehensive coverage of the topic while allowing for individual variation. Participants were selected through purposive sampling, focusing on those with prior experience of AI. Interviews continued until theoretical saturation was reached. Data analysis was inspired by grounded theory, using a systematic coding process. The findings reveal that consumer satisfaction with AI recommendations is influenced by added value, contextual relevance, and timing. Additionally, factors such as speed, efficiency, quality responses, accessibility, adaptive empathy, communication style and humour play crucial roles. These components demonstrate how AI personalisation can strengthen consumer satisfaction by tailoring recommendations and interactions to individual tastes and preferences.