Personalization of customer experience in CRM systems through artificial intelligence technologies

Abstract

The article substantiates the role of artificial intelligence (AI) technologies as a key tool for modern personalization of customer experience within CRM systems. The growing volume of data processed by businesses and increasing consumer expectations for individualized interaction necessitate intelligent approaches to customer relationship management. The study systematizes three main areas of AI-driven personalization: customer experience adaptation, analytics and prediction of future customer behavior, and automated support tailored to the user’s context and emotional state. The functional capabilities of such tools as generative content, recommendation systems, predictive analytics, intelligent chatbots, and emotionally responsive AI are described in detail, revealing new opportunities for enhancing customer loyalty, satisfaction, and retention. Special attention is paid to the core personalization functions implemented in modern AI-powered CRM systems. At the same time, the article addresses a range of ethical challenges, including the opacity of algorithmic decisions, potential model bias, risks of data privacy violations, and the diminishing role of human decision-making. It is demonstrated that the use of a four-stage integration model enables risk structuring and ensures efficiency at each stage. The model emphasizes the importance of cultivating a customer-oriented culture, involving interdisciplinary teams, defining key performance indicators, and testing solutions in pilot environments. This model demonstrates the importance of considering the implementation of AI personalization not only as a technical process, but as an element of strategic change management. Thus, AI-based personalization is viewed not only as a technological challenge but also as a crucial element of strategic change management, whose successful implementation requires adherence to the principles of responsible artificial intelligence. The results obtained can be used for further research in the areas of personalization in conditions of limited data, ethical assessment of AI solutions, and personalization in a B2B context.