Beyond Reactive IT: Quantifying the Transformative Impact of AIOps on Service Management
DOI:
https://doi.org/10.56472/ICCSAIML25-132Keywords:
AIOps, Anomaly Detection, Change Management, Incident Management, ITSM, Machine Learning, MTTD, MTTR, RCAAbstract
This study takes a mixed-methods approach to explore the impact of AIOps on IT Service Management process. Modern IT operations face unprecedented challenges in managing increasingly complex, distributed systems. This paper examines how Artificial Intelligence for IT Operations (AIOps) transforms traditional IT Service Management (ITSM) from reactive firefighting to proactive, intelligence-driven operations. This comprehensive framework demonstrates how organizations can implement AIOps across the Incident and Change management lifecycle to achieve quantifiable improvements in key performance metrics. Additionally, AIOps enables contextual enrichment of incidents, automated remediation workflows, and data-driven change risk assessment. This paper presents a maturity model for AIOps adoption, identifying critical success factors and implementation strategies that enable organizations to realize maximum ROI. Through analysis of implementation case studies across diverse industries, we document significant enhancements in operational efficiency, including average reductions of 73% in detection time and 62% in resolution time. Our findings illustrate how AIOps systematically addresses the fundamental challenges of modern IT environments by creating resilient operations that can anticipate and prevent disruptions before they impact users
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References
[1] Y. Liu, C. Pei, L. Xu, B. Chen, M. Sun, Z. Zhang, Y. Sun, S. Zhang and K. Wang, "Opseval: A comprehensive it operations benchmark suite for large language models," [Online]. Available: https://arxiv.org/abs/2310.07637.
[2] S. V. Joshi, S. Serebryakov, D. Nanjundaiah and T. Hegde, "AIOps and Sustainability: Transforming Data Centers for a Greener Future," in SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, 2024.
[3] M. Singh, "Enhancing Site Reliability Engineering Through AIOps: A Framework for Next-Generation IT Operations," Asian Journal of Research in Computer Science, vol. 18, pp. 272-284, 2025.
[4] P. P. Teggi, H. N. and B. Malakreddy, "AIOPs based Predictive Alerting for System Stability in IT Environment," in 2022 International Conference on Innovative Trends in Information Technology (ICITIIT), 2022.
[5] A. Sekar, "AIOps: Transforming Management of Large-Scales," European Journal of Computer Science and Information Technology, pp. 1-17, 2025.
[6] R. Mahindru, H. Kumar and S. Bansal, "Log Anomaly to Resolution: AI Based Proactive Incident Remediation," in 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE), 2021.
[7] K. Bhukar, H. Kumar, R. Mahindru, R. Arora, S. Nagar, P. Aggarwal and A. Paradkar, "Dynamic Alert Suppression Policy for Noise Reduction in AIOps," in Association for Computing Machinery, 2024.
[8] K. Ratra, G. Sharma and D. K. Seth, "Unlocking the Power of AI for Shift-Left Testing – A Game Changer in Automation," International Journal of Computer Trends and Technology, vol. 72, no. 12, pp. 25-37, 2024.
[9] W. Yu, J. Qian, R. Xu, C. Jin, H. Fang and X. Shi, "Improving Substation Network Security with DevSecOps and AIOps," in 2024 IEEE 10th Conference on Big Data Security on Cloud (BigDataSecurity, 2024.
[10] A. Garg and S. I. Abbas, "AIOps in DevOps: Leveraging Artificial Intelligence for Operations and Monitoring," in 2024 3rd International Conference on Sentiment Analysis and Deep Learning (ICSADL), 2014.
