BERT (Bidirectional Encoder Representations from Transformers) is a masked language model often used for natural language processing (NLP) applications such as text classification, named entity recognition, and sentiment analysis. Integrating BERT with AIOps (Artificial Intelligence for IT Operations) helps improve the analysis and processing of IT-related data. IT ticket log analysis and classification is one possible application of BERT for AIOps. This poster paper proposes a framework using state-of-art algorithms to classify and predict the severity, Assignment group and Ticket resolution for IT incident prediction. We argue that the proposed framework would accelerate the handling IT incidents with improved accuracy. The experimentation was performed on the IT Service Management (ITSM) dataset containing 500,000 real-time incident descriptions with their encoded labels (Dataset 1) from a reputable IT firm. Our results indicates that transformers models assist IT teams to promptly identify and resolving issues, optimising system performance, and proactively preventing incidents or major outages. Our research aids IT operations teams to concentrate their efforts and resources more effectively by streamlining incident management processes. Besides BERT, we have compared state-of-the-art transformers models such as ERNIE and RoBERTa. The results demonstrate a significant improvement in reducing Mean time to resolution for IT incident outages.