IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE INTO THE INCIDENT MANAGEMENT PROCESS
DOI:
https://doi.org/10.32689/maup.it.2024.4.9Keywords:
AIOps, incident management, Artificial Intelligence, ITSM, process implementationAbstract
The rapid advancement of technology necessitates the transformation of IT Service Management (ITSM) practices. This article examines the integration of Artificial Intelligence for IT Operations (AIOps) and intelligent automation within ITSM, focusing on their impact on incident management, operational efficiency, and overall service quality. Based on a review of recent literature and case studies, the article aims to provide insights into the benefits, challenges, and future directions of these technologies in improving IT operations. The findings highlight the significant potential of AIOps and intelligent automation to enhance IT service management efficiency; however, their implementation requires careful planning and consideration of specific factors. The proposed implementation methodology can be widely utilized by organizations to further advance AIOps adoption. The goal of the study is to develop a methodology for integrating AI-driven automations into an organization’s technical and business processes. The tasks required to achieve this include researching existing approaches to the classification and application of AI automations in the field, analyzing current systems and implementation experiences, and describing the methodology for integrating AI automations. The methodology includes a review of literature and an analysis of existing use cases for automation in processes. Using system analysis tools, a methodology for integrating such automations into business processes has been developed. The scientific novelty lies in the adaptation of cutting-edge approaches in incident management to the current processes of technology-driven organizations. Conclusions. This study proposes a step-by-step methodology for implementing AI-based automation in business processes through the deployment of AIOps infrastructure. Applying the developed methodology and conducting further case studies to identify potential organizational process nuances represent promising directions for future research.
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