Enhancing IT incident management with natural language processing and predictive analytics

Faheem, Muhammad and Awais, Muhammad and Iqbal, Aqib and Zia, Hasnain (2025) Enhancing IT incident management with natural language processing and predictive analytics. International Journal of Science and Research Archive, 15 (3). pp. 224-237. ISSN 2582-8185

High - quality IT incident management is critical in minimizing system downtime and maintaining business continuity. Traditional methods are normally bogged down by the volume of incident data and delayed response times. This paper expounds on the combination of Natural Language Processing (NLP) and Predictive Analytics to transform IT incident management systems into intelligent, forward-looking solutions. NLP techniques are used to automatically sort, classify, and extract actionable data from unstructured incident reports and support tickets, reducing manual effort by a considerable percentage. Meanwhile, Predictive Analytics applies historical incident information to forecast possible failures and recognize anomalies prior to them turning into major problems. When these technologies work in tandem with one another, the existing framework speeds up incident resolution, root cause identification, and resource assignment. Experimental results and case studies reflect enhanced precision in the categorization of incidents, decreased mean time to resolution (MTTR), and enhanced operational effectiveness. This research exemplifies the groundbreaking capability of AI-driven techniques in changing incident management processes in the context of modern IT infrastructures.