Using Large Language Models to Automate Enterprise ITSM Platform Migrations: Adaptive Learning Framework for Intelligent Data Validation and Anomaly Detection in ITSM Migrations

Enterprise IT Service Management (ITSM) platform migrations present formidable challenges characterized by data quality inconsistencies, prolonged manual reconciliation cycles, and substantial post-migration testing overhead. Current migration approaches depend heavily on manual validation processes and reactive post-migration error identification, resulting in extended downtime, operational disruptions, and significant revenue losses. To automate the data validation process and enable the real-time anomaly detection process, this study introduces an adaptive framework that makes use of Large Language Models (LLMs). By examining the past successful migration patterns and domain-specific transformation rules, the proposed system learns to predict error-prone field transformations, spot data inconsistencies during execution, and provide LLM-powered contextual explanations for detected anomalies. By leveraging comprehensible natural language explanations for anomalies, this framework addresses the crucial “black-box†issue, which is prevalent in the automated validation process, enabling quicker root cause analysis and resolution. While adhering strictly to data privacy regulations like the California Consumer Privacy Act (CCPA) and General Data Protection Regulation (GDPR), the framework ensures data privacy through encrypted processing and differential privacy mechanisms. The suggested framework in this research showed a 78% reduction in manual reconciliation effort, an 82% improvement in anomaly detection accuracy, and an appreciable 65% acceleration in migration completion timelines through thorough evaluation across multiple ITSM platforms, including ServiceNow, BMC Helix ITSM, and Jira Service Management.