Self-Learning AI Systems in IT Operations: Transforming enterprises through autonomous intelligence

This article examines the revolutionary impact of self-learning artificial intelligence (AI) systems on IT operations, highlighting their pivotal role in transforming traditional enterprises into autonomous organizations. By leveraging machine learning (ML), deep learning, and AIOps frameworks, these intelligent systems can detect, diagnose, and resolve operational issues with minimal human intervention. The article explores the architecture of self-learning AI systems, their applications in IT operations, challenges in implementation, and measurable business outcomes. Evidence suggests organizations implementing mature self-learning AIOps solutions have achieved substantial improvements in operational metrics, including fewer critical outages, faster incident resolution, and significant cost reductions.

Authors

Publication Details

Journal
World Journal of Advanced Research and Reviews
Published
2025-05-29
DOI
https://doi.org/10.30574/wjarr.2025.26.2.2026
Primary Topic
Scheduling and Optimization Algorithms
Type
article
Field-Weighted Citation Impact
0.00
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article

Self-Learning AI Systems in IT Operations: Transforming enterprises through autonomous intelligence

Raghavan Gopalakrishnan
World Journal of Advanced Research and Reviews
Scheduling and Optimization Algorithms
article

Self-Learning AI Systems in IT Operations: Transforming enterprises through autonomous intelligence

Raghavan Gopalakrishnan
article en

Abstract

This article examines the revolutionary impact of self-learning artificial intelligence (AI) systems on IT operations, highlighting their pivotal role in transforming traditional enterprises into autonomous organizations. By leveraging machine learning (ML), deep learning, and AIOps frameworks, these intelligent systems can detect, diagnose, and resolve operational issues with minimal human intervention. The article explores the architecture of self-learning AI systems, their applications in IT operations, challenges in implementation, and measurable business outcomes. Evidence suggests organizations implementing mature self-learning AIOps solutions have achieved substantial improvements in operational metrics, including fewer critical outages, faster incident resolution, and significant cost reductions.

World Journal of Advanced Research and ReviewsVol. 26(2)
Openalex Percentile: Top 84%
Scheduling and Optimization Algorithms
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