The challenge of enhancing IT service delivery lies mainly in incorporating real-time user feedback to adapt solutions. Research investigates how AI sentiment analysis helps IT service management by supplying data-driven information for enhancement. The system uses modern natural language processing (NLP) models especially Bidirectional Encoder Representations from Transformers (BERT) to extract and categorize user sentiment from feedback obtained from multiple sources that include service tickets and customer surveys. Research findings demonstrate that negative customer sentiments create service delays which resulted in predictive systems that handle cases more efficiently and reorder service tasks according to importance. When teams employed sentiment-based methods they cut ticket resolution duration down by 35% and user satisfaction strengthened by 22%. The study provides scholars with a flexible system that combines AI-based sentiment evaluation with IT service management processes. The system shows its ability to adapt through automated responses which interact with changing expectational needs and emerging feedback patterns. Any implementation of AI requires focused attention on ethical elements such as how users' privacy will be maintained and the processes by which consent is secured. Sentiment analysis presents a valuable tool which helps providers maintain user need anticipation abilities alongside their capability to prevent bottlenecks and regulate performance statistics. Researchers should study how the integration of sentiment data with behavioral information might create service personalization models of higher quality. The paper provides applicable guidance to IT managers and policymakers which features sentiment analysis as an essential element that drives adaptable user-oriented service enhancement approaches.

Sentiment Analysis, Artificial Intelligence, IT Service Management

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