Empowering AIOps: Leveraging Large Language Models for IT Operations Management

TL;DR

This paper explores how Large Language Models can be integrated into AIOps to improve IT operations by processing unstructured data and addressing existing challenges in automation and decision-making.

Contribution

It proposes combining traditional predictive models with LLMs to enhance AIOps capabilities and tackle challenges like data quality and complexity.

Findings

LLMs effectively analyze unstructured IT data.

Integration improves automation and decision support.

Addresses data and skill gaps in IT operations.

Abstract

The integration of Artificial Intelligence (AI) into IT Operations Management (ITOM), commonly referred to as AIOps, offers substantial potential for automating workflows, enhancing efficiency, and supporting informed decision-making. However, implementing AI within IT operations is not without its challenges, including issues related to data quality, the complexity of IT environments, and skill gaps within teams. The advent of Large Language Models (LLMs) presents an opportunity to address some of these challenges, particularly through their advanced natural language understanding capabilities. These features enable organizations to process and analyze vast amounts of unstructured data, such as system logs, incident reports, and technical documentation. This ability aligns with the motivation behind our research, where we aim to integrate traditional predictive machine learning models with generative AI technologies like LLMs. By combining these approaches, we propose innovative methods to tackle persistent challenges in AIOps and enhance the capabilities of IT operations management.