The article examines the features of integrating artificial intelligence algorithms (Explainable AI, XAI) into CRM systems aimed at enhancing customer experience (Customer Experience, CX). Based on an analysis of recent publications, the study explores the principles of personalization as well as approaches to the explainability of machine learning algorithms, including chatbots and recommendation systems. It demonstrates that transparency and interpretability of model outputs positively influence customer trust and loyalty while simultaneously improving the efficiency of internal business processes. The article analyzes the implementation experience of XAI in the banking sector, insurance call centers, and online retail, which has led to improvements in retention, conversion, and satisfaction metrics. The information presented in the article is intended for researchers and professionals in the field of artificial intelligence focused on developing interpretable machine learning algorithms, as well as for analysts seeking to optimize CRM systems to enhance customer experience management. In addition, the material is useful for professionals in corporate governance and marketing who aim to integrate advanced Explainable AI methods into personalization strategies and decision-making processes, ensuring the transparency and adaptability of services under dynamic market conditions.

artificial intelligence, Explainable AI (XAI), Customer Experience (CX), personalization, CRM systems

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