A NOVEL ARCHITECTURE FOR AUTONOMOUS IT SERVICE MANAGEMENT: INTEGRATING GENERATIVE AI WITH WORKFLOW AUTOMATION PLATFORMS

Abstract

The current IT service management (ITSM) frameworks are under pressure to deliver quicker responses to incidents, lower the costs of operation, and get accustomed to more complicated hybrid infrastructure settings. The paper presents an innovative architecture that would allow the seamless integration of Generative AI (GenAI) functions and enterprise workflow automation platforms to make the IT services management fully autonomous. The presented system uses the large language models (LLM) to classify tickets based on their smartness, analyze their root causes, and remediate them using natural language, and organize automated processes via ServiceNow, Ansible, and Apache Airflow. The architecture can be used to do context-aware decision-making without human oversight by adding retrieval-augmented generation (RAG) and real-time telemetry ingestion to handle a large category of recurring incidents. Simulated enterprise settings have shown experimental evaluation results of mean time to resolution (MTTR) reduced by 67 percent and escalation rates reduced by 54 percent, and massive cost savings over traditional rule-based automation strategies. The architecture also uses explainability modules and human-in-the-loop override mechanisms to provide governance, compliance and auditability. The work provides a vendor-neutral blueprint of next-generation autonomous ITSM, including its theoretical background and a practical implementation roadmap which enterprises can adopt. The architecture is further extensible to Change Management, Problem Management, and Service Catalog Management, providing a comprehensive GenAI-driven framework across the full ServiceNow ITSM module suite.