Empowering AIOps: Leveraging Large Language Models for IT Operations Management
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909464504827904 |
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| author | Vitui, Arthur Chen, Tse-Hsun |
| author_facet | Vitui, Arthur Chen, Tse-Hsun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_12461 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Empowering AIOps: Leveraging Large Language Models for IT Operations Management Vitui, Arthur Chen, Tse-Hsun Software Engineering 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. |
| title | Empowering AIOps: Leveraging Large Language Models for IT Operations Management |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2501.12461 |