AI-Augmented Customer Experience Systems: A Holistic Approach to Personalisation and Recommendations

AI has been a revolutionary aspect of customer experience system transformation, as it has made it possible to personalise, offer smart suggestions, and engage intelligently on varying digital platforms. Firms are becoming more aware of the fact that customer satisfaction and loyalty require the provision of smooth and customised interactions that are accurate, scalable, and just. In the current paper, the issue of disjointed personalisation strategies is presented by recommending a unified AI-enhanced model that incorporates collaborative filtering, content-based modelling, and hybrid recommendation techniques. Taking the MovieLens 100K dataset as a reference, the study creates feature engineering pipelines, which are behavioural, content, and contextual signals, and the next step is hybrid recommendation delivery and feedback monitoring. The use case is centred around the e-commerce and online service setting, where personalisation has a direct effect on the level of loyalty and conversion rate. The mathematical evidence proves that hybrid models are better than individual methods. The average rating prediction of collaborative filtering was 3.72, content-based filtering was 3.81, and the hybrid fusion model was 4.05, which is an improvement of 7-9%. The metrics of evaluation also indicated that the hybrid model minimised the RMSE to 0.92 in comparison to 1.05 regarding collaborative filtering and 1.01 regarding content-based filtering. In the practical context of recommendation scenarios, Precision@10 increased by 8, which confirms the usefulness of hybrid personalisation. Genre preferences visualisation revealed that Drama and Comedy were rated above 3.9 on average, whereas Action was rated at 3.4, which can be important evidence of the need to personalise genres. These results confirm the capacity of the proposed framework to provide quantifiable customer satisfaction and system performance.