The blazing adoption of the concept of artificial intelligence has led to the dawn of the algorithmic manager role whereby the use of AI-based decision-making systems has grown to become a managerial process within consumer experience management. This study examines the effect of algorithmic decision-making on the consumer interactions through improvement of customization, responsiveness and customer satisfaction. Based on a set of 12,000 simulated records of consumer interaction, four AI algorithms were applied and tested Decision Tree, Random Forest, Support Vector Machine and Reinforcement Learning (Q-learning), which were compared with a classical rule-based system. The empirical evidence shows that algorithmic managers perform much better in comparison with the conventional styles in various performance indices. The Random Forest model was the most accurate in the level of consumer satisfaction prediction with the highest result of 91.6 compared to the rule-based system with the highest result of 19.1 and lowering the average responding time by 45.8. The effectiveness of structured and margin-based decision models was confirmed by getting support vector machines and decision trees with an accuracy of 88.4 and 84.3 respectively. Reinforcement learning was very adaptive with 0.76 to 0.89 improvement of long-term performance, which was 17.1 percent higher in cumulative reward. The effectiveness scores of personalization in rule-based systems were 58 and in algorithms-based management were 86. The results of these studies prove that AI-based decision systems may be effective managerial agents used in consumer experience control provided they are developed considering the performances, fairness, and flexibility. The paper offers scientific data and confirms the idea of responsible use of algorithmic managers in the consumer-driven online world.