Methodological Aspects of Implementing Artificial Intelligence in the Processes of Monitoring and Maintenance of Network Systems

Abstract

This paper presents a comprehensive analysis of the methodological aspects of implementing artificial intelligence in network monitoring and maintenance processes. As modern networks evolve in scale and complexity, traditional monitoring techniques often fall short in ensuring optimal performance and reliability. The study reviews state-of-the-art AI approaches—including supervised, unsupervised, and deep learning methods—for anomaly detection, predictive maintenance, and automated fault response. It draws upon recent scholarly research and authoritative industry reports to evaluate the effectiveness of these methodologies. Key challenges such as data quality, model performance, and seamless integration into existing operational workflows are critically examined. The paper further discusses best practices and emerging trends, including intent-based networking, generative AI applications, and the use of digital twins for simulation and prediction. Through practical case studies and comparative analyses, the research demonstrates how AI-driven systems can significantly reduce downtime, lower operational costs, and transform traditional network operations into proactive, self-healing systems. The findings provide actionable recommendations for organizations aiming to enhance their network operations through AI, paving the way for future advancements in autonomous network management.