Harnessing AI for Dynamic Real-Time Network Optimization

Abstract

Artificial intelligence is revolutionizing network management by enabling dynamic real-time optimization to address the unprecedented demands faced by modern digital infrastructure. As global traffic volumes surge and latency-sensitive applications proliferate, traditional reactive frameworks to network management prove increasingly inadequate. This article explores the transformative potential of AI-driven systems that continuously analyze telemetry data and make preemptive adjustments to maintain optimal network performance. The technical foundations of these systems include comprehensive data collection frameworks, sophisticated AI algorithms for traffic analysis, and robust decision-making frameworks that operate within strict time constraints. A systematic implementation framework outlines the infrastructure requirements, phased deployment method, and operational integration considerations essential for successful adoption. Despite promising results, organizations face technical hurdles related to data quality and computational requirements, alongside organizational barriers including skills gaps and resistance to automation. Case studies across cloud providers, telecommunications carriers, and financial institutions demonstrate substantial improvements in latency, throughput, and fault recovery times, validating the business value of these implementations.