AIOps and DevOps: Catalysts of Digital Transformation in the Age of Automated Operations

Sekhar Chittala

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024, vol. 10, issue 6, 155-166

Abstract: This article examines the pivotal roles of Artificial Intelligence for IT Operations (AIOps) and Development Operations (DevOps) as key drivers of digital transformation in contemporary organizations. As businesses increasingly rely on complex, distributed IT systems, the integration of AIOps and DevOps emerges as a critical strategy for maintaining operational efficiency, scalability, and innovation. Through a comprehensive analysis of current literature and industry case studies, we explore how these technologies are reshaping the landscape of IT operations, accelerating software delivery cycles, and enabling more resilient and adaptive infrastructures. The article delves into the evolving nature of release engineering, where automation and machine learning are becoming central to ensuring agile and robust IT systems. Furthermore, we critically assess the ethical implications of automated decision-making in operations, including issues of transparency, bias, and job displacement. Our findings suggest that while AIOps and DevOps offer significant benefits in terms of operational optimization and cost reduction, their successful implementation requires careful consideration of organizational culture, skill development, and ethical guidelines. This article contributes to the growing body of knowledge on digital transformation strategies and provides insights for practitioners navigating the complex intersection of artificial intelligence, automation, and IT operations.

Keywords: Artificial Intelligence for IT Operations; DevOps; AIOps; IT Automation; Release Engineering (search for similar items in EconPapers)
Date: 2024
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT24106163
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v10:y2024:i6:id:403

DOI: 10.32628/CSEIT24106163

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