The Role of AI in Network Optimization and Self-Healing Networks

State-of-the-art communication networks are becoming increasingly distributed systems spanning the radio access network, transport network, data center, public cloud, edge, and billions of endpoints. The sheer scale of these networks and the rate at which they are evolving makes manual operations and static, threshold-based management a non-starter. AI and ML offer a way to take vast quantities of telemetry data and turn it into predictions, diagnoses, optimization decisions, and automated remediation. The focus of this article is on the application of AI to network optimization and the development of self-healing networks. We look at the application of AI to traffic engineering, capacity forecasting, anomaly detection, root cause analysis, and closed-loop automation. We relate these capabilities to a number of existing and emerging industry frameworks (3GPP SON, ETSI ZSM, ITU autonomous network architecture, O-RAN AI/ML workflows [1]-[5]). We also propose a practical architecture in which telemetry, network topology, policy guardrails, orchestration, and post-change validation form a continuous feedback loop. The key point is that AI will only create operational value if it is integrated with automation and policy rather than being an isolated analytics layer. When properly governed, AI has the potential to take network operations from a reactive model of incident management to a predictive, adaptive, and increasingly autonomous service assurance model.