Paper Title
An AI-Augmented and Optimization-Driven NLP Framework for Proactive IT Service Management Operations

Abstract
Conventional IT Service Management (ITSM) operations are predominantly reactive, resulting in delayed incident handling, inefficient resource utilization, and inconsistent service quality during high-priority service disruptions. This paper proposes an AI-augmented and optimization-driven framework for proactive ITSM operations, developed and evaluated through Python-based simulation and experimental analysis. The framework integrates Statistical Process Control (SPC) for early anomaly detection, hybrid SPC–LLM–based pre-incident classification, parallel Natural Language Processing (NLP) pipelines for intrusion pattern analysis and service degradation detection, Large Language Model (LLM)–assisted incident formalization, and Retrieval-Augmented Generation (RAG) for context-aware resolution guidance. Optimization techniques are incorporated to support capacity planning and resource allocation under simulated operational constraints. The proposed framework provides a scalable and methodologically rigorous blueprint for proactive, AI-driven ITSM, suitable for research-oriented and pre-deployment evaluation scenarios. From the perspective of service operations management, the framework helps to improve capacity planning through SAC-OOBO-based resource allocation, data-driven decision support made possible by integrated LLM-RAG intelligence, and process control through SPC-driven monitoring. Keywords - Proactive it Service Management; Artificial Intelligence–Driven ITSM; Statistical Process Control; Large Language Models; Retrieval-Augmented Generation; Resource Optimization.