The application of AI and machine learning to enterprise network operations is not a new concept. AIOps has made great strides over the past several years to improve resiliency through deeper assurance and by providing higher levels of security. However, the complexity of today’s networks — resulting from continued disaggregation and cloudification — is taxing the application of traditional AIOps tools. Ethernet’s continued evolution and modern network scale-up, -out, and -across architectures hold great promise in addressing these challenges, as do emerging agentic AI frameworks to facilitate deeper automation.
Despite these advancements, more is needed to support the performance and automation requirements of campus, branch, and datacenter network deployments given the increasing demands of modern applications and workloads. As AI adoption accelerates, networking teams will require tools that are intelligent — contextually aware of business processes, capable of dynamic policy control, and able to self-heal. This capability holds great promise for rooting out issues that impact end-user experiences and were previously undetectable.
Most importantly, a different architectural approach is needed to facilitate AI inferencing and non-human agent deployment at scale. There will also be an ongoing need to anticipate future operational capacity as AI usage matures — something that has traditionally been difficult to predict. Consequently, the future of network operations is not just about reacting faster but also about anticipating user demands and improving the mean time-to-resolution of network faults.
Moor Insights & Strategy (MI&S) believes that IBM is uniquely positioned to deliver what enterprises require from feature, function, and scalability perspectives with its IBM Network Intelligence solution. It enables dynamic automation that evolves from event-driven to AI-driven discovery, analysis, and remediation at massive scale, while offering a ground-up, full-stack intelligent architecture that supports multi-vendor infrastructure — a hallmark of enterprise IT and OT environments. Powered by an innovative, network-specific AI approach and an agentic architecture, IBM Network Intelligence understands intent, takes proactive action, and evolves over time to optimize network operations.
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TABLE OF CONTENTS
Summary
Moving from Intent-Based to Autonomous Networks
Unpacking IBM Network Intelligence
Call to Action
COMPANIES CITED
IBM
SevOne Network Performance Management
