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From ITIL to AIOps in Public Sector: A Systematic Literature Review
Systems Analyst, Information Resources Division, Texas Commission on Environmental Quality, Austin, Texas, 78753, United States
*whom correspondence should be addressed. E-mail: catherine.ganduri@tceq.texas.gov
Received: 4 May 2026, Revised: 1 June 2026, Accepted: 2 June 2026, Published Online: 22 June 2026
(This article belongs to the Section Interdisciplinary Applications – Computer Science (IAC))
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Ganduri, C. (2026). From ITIL to AIOps in Public Sector: A Systematic Literature Review. Journal of Engineering Research and Sciences, 5(6), 56–66. https://doi.org/10.55708/js0506005
Ganduri, C. (2026). From ITIL to AIOps in Public Sector: A Systematic Literature Review. Journal of Engineering Research and Sciences, 5(6), 56–66. https://doi.org/10.55708/js0506005
Chicago/Turabian Style
Catherine Ganduri. "From ITIL to AIOps in Public Sector: A Systematic Literature Review." Journal of Engineering Research and Sciences 5, no. 6 (June 2026): 56–66. https://doi.org/10.55708/js0506005
Catherine Ganduri. "From ITIL to AIOps in Public Sector: A Systematic Literature Review." Journal of Engineering Research and Sciences 5, no. 6 (June 2026): 56–66. https://doi.org/10.55708/js0506005
IEEE Style
C. Ganduri, "From ITIL to AIOps in Public Sector: A Systematic Literature Review," Journal of Engineering Research and Sciences, vol. 5, no. 6, pp. 56–66, Jun. 2026, doi: 10.55708/js0506005.
C. Ganduri, "From ITIL to AIOps in Public Sector: A Systematic Literature Review," Journal of Engineering Research and Sciences, vol. 5, no. 6, pp. 56–66, Jun. 2026, doi: 10.55708/js0506005.
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Abstract
Public-sector agencies rely on complex and highly regulated digital systems to deliver essential services. ITIL-based change and release management supports operational control, but many agencies still depend on manual approvals, fragmented operational data, and reactive monitoring. Artificial Intelligence for IT Operations (AIOps) and Machine Learning Operations (MLOps) can improve anomaly detection, failure prediction, release validation, and data-driven governance. However, the literature gives limited attention to how these technologies can be integrated into public-sector change and release workflows. A structured search of IEEE Xplore, ACM Digital Library, ScienceDirect, and Google Scholar identified 120 records. After duplicate removal, title/abstract screening, full-text assessment, and backward searching, 39 sources were retained for synthesis, including 25 primary studies and 14 complementary methodological, standards, and governance sources. The findings are organized into four themes: AI-driven monitoring and incident management, public-sector governance constraints, implementation challenges in legacy environments, and limited adoption of AI in change and release management. The review identifies gaps in explainability, empirical validation, AI-ready change records, and framework development, and proposes future directions for AI-supported operational governance in public-sector IT.
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