AI-powered network automation: Emerging trends and applications

This article provides a comprehensive analysis of artificial intelligence applications in network automation, examining how machine learning techniques are revolutionizing traditional network management approaches. Through systematic examination of supervised, unsupervised, and reinforcement learning methodologies, the article demonstrates the transformative impact of AI on routing optimization, anomaly detection, and adaptive network control systems. The comparative article reveals significant performance advantages of AI-driven methods over traditional approaches, including faster fault detection, improved resource utilization, and reduced operational complexity. The article explores how these technologies enable sophisticated cloud infrastructure optimization through predictive analytics, real-time scalability, and intelligent resource allocation, while simultaneously reducing environmental impact through energy consumption optimization. The article further examines AI's contribution to network security, highlighting advances in neural network-based threat detection and adaptive intrusion prevention systems that significantly reduce response times while minimizing false positives. By addressing interdisciplinary research approaches and future challenges—including ethical considerations, explainability, scalability, and integration with emerging technologies—this work provides a forward-looking perspective on the evolving landscape of intelligent network automation and its implications for network engineering professionals.