What is AI networking?

It is an important component in modern computer networking, enabling interconnected compute resources to communicate seamlessly, automating routine network management tasks, and facilitating optimized AI model training and inference. AI-driven strategies can help development teams overcome the limitations of traditional networking practices, which are often insufficient for the scale, complexity and sophistication of today’s IT environments.

Traditional networks rely on manual processes, static configurations and scheduled maintenance, which isn’t a problem for small networks with simple device interactions. But modern networks aren’t simple or small. They span diverse, dynamic global environments and hybrid cloud infrastructures with thousands of interconnected devices and dependencies. The average multicloud environment spans 12 different services and platforms.

Augmenting existing network infrastructure with AI and ML tools can help enterprises streamline network management practices, improve network intelligence and expand automation capabilities. AI networking solutions enable:

• Intelligent traffic management
• Self-driving network automation
• Real-time anomaly and threat detection
• Detailed root cause analysis (RCA)
• Continuous, data-driven network optimization
• Predictive analytics and adaptive learning

In some instances, AI-driven networks can even create self-healing mechanisms and workflows.

AI networking is integral to large-scale AI model deployment and for building highly autonomous, data-driven enterprise networks. It shifts the paradigm from static, human-managed networking to dynamic, self-driving IT infrastructures capable of supporting the immense demands of modern technologies (5G, Internet of Things (IoT), edge computing, AI workloads and cloud-native services).

The result is smarter, faster, more resilient enterprise networks that help deliver frictionless experiences to end users.

AI networking is driven by telemetry collection. Every networking and compute element (including routers, switches and application programming interface (API) endpoints) across the network feeds massive real-time data streams (performance metrics, traffic flows and anomaly signals) into centralized or distributed data lakes.

Cloud-native AI and ML models continuously analyze the data, correlating events, learning what constitutes normal and abnormal behavior, and generating data-driven insights. They use unsupervised learning (for anomaly detection), supervised learning (for predictive analytics) and reinforcement learning to dynamically optimize network processes and interactions. Insights from the AI tools are then translated into automated responses.

When AI-driven network monitoring tools detect congestion or faults, they trigger remediation workflows to reroute traffic, balance workloads, update network policies or isolate security threats, reducing the need for manual intervention from IT personnel.

AI networking is designed to scale horizontally. As network demand and device ecosystems grow, the AI systems on the network automatically add more compute nodes, switches and links. AI networks also use multi-path connections and rapid failover mechanisms to create redundancy and help ensure high network availability.

AI networks rely on a set of key components to function. They include:

AI networking represents the convergence of AI-assisted automation and intelligent, responsive infrastructure. It helps enterprises build dynamic, secure, hyper-scalable networking environments. AI networks provide:

ML systems build dynamic models of what “normal” network behavior is over time, accounting for daily, weekly and seasonal patterns. This approach prevents benign fluctuations from triggering alerts and allows the system to focus on real anomalies that deviate significantly from network baselines.

AI systems integrate multiple data sources and use sophisticated algorithms (including unsupervised learning) to correlate subtle indicators of network performance issues that rule-based systems might overlook. AI tools can, for example, detect coordinated multi-vector attacks and low-and-slow malicious traffic that progresses gradually.

AI networks use ML models to continuously monitor network traffic, device logs and data patterns and analyze large volumes of data in real time. These capabilities help AI tools detect security vulnerabilities, unusual behaviors (spiky traffic flow, for instance), unauthorized access attempts and early signs of cyberattacks.

Unlike traditional static threshold-based anomaly detection methods, AI models use contextual and historical data to implement adaptive baselines, making detection more accurate and reducing false alarms that can distract IT teams.

AI tools provide features such as advanced analytics, natural language querying and data visualization to help network operators investigate incidents faster and more effectively. These features democratize access to complex network data, putting more resources toward data processing and analysis. They also help AI networks support collaborative issue resolution and accelerate root cause analysis.

When they detect an anomaly, AI networks trigger automated workflows to fix the problem immediately. They can, for example, reroute traffic around congested areas, block suspicious IP addresses and provision extra network capacity.

AI tools not only detect current anomalies, but they can help forecast future failures or congestion points by analyzing trends and signals in telemetry data. Forecasting features empower network engineers and administrators to take a proactive approach to network management, preventing downtime and outages before they happen.

AI networking differs fundamentally from traditional network architectures. It leverages real-time data, ML and automation to dynamically improve and secure computing networks.

Traditional networks typically rely on manually configured static rules, pre-set thresholds and reactive management practices. Traditional networking also uses a hierarchical architecture, which creates layers of networking devices for efficient data forwarding. Distributed control creates a predictable, stable network environment, but it also limits scalability (adding capacity often requires new hardware investments).

With the conventional model, each network device performs its own control and data plane functions independently. Network operators manage data traffic by manually configuring routing tables, switching rules and security policies on a device-by-device basis. Monitoring is limited to basic metrics, alerts are often triggered by fixed conditions (after a network issue arises), and troubleshooting tends to be isolated to individual devices, all of which slows down incident response network adaptation.

By contrast, AI networks span hybrid cloud and mulitcloud environments, frequently incorporating on-premises data centers, multiple cloud environments and edge servers. They continuously collect telemetry data from across the network, and they use AI algorithms to analyze real-time datasets, make sense of complex traffic flows and interpret user behavior.

AI networks can also support better optimization tools and boost network scalability. Instead of relying on static configurations, AI-powered networks dynamically adjust bandwidth allocation and routing based on live usage patterns, automatically scaling resources to meet demand spikes.

Furthermore, AI-driven networks provide more reliable, comprehensive security. Traditional networks generally use signature-based security models, which detect and prevent known threats by identifying unique patterns—or “signatures”—associated with malware or malicious activity. AI networking augments (or replaces) signature-based security models with AI-based threat detection that uses comprehensive behavior analysis to identify sophisticated attacks and address cyberthreats before they compromise network security.

Several key trends are shaping how AI networks are built, managed and secured.

Ethernet is becoming increasingly popular as a network fabric for AI workloads. It provides a versatile, cost-effective, low-latency networking solution, with speeds already reaching 400G and 800G (and 1.6T Ethernet on the horizon).

Ethernet-based AI networks have massive bandwidth that can handle the immense data throughput necessary for AI model training, real-time inference and large-scale AI data processing. And Ethernet’s simpler deployment processes and ability to facilitate lossless communication between on-premises and cloud AI resources make it a great option for connecting diverse, distributed AI infrastructures.

With advancements in generative AI (gen AI), AI network operations are becoming smarter and more automated. Gen AI helps network engineers network design by simulating and generating ideal network topologies and device settings.

Gen AI tools can create predictive models for AI networking and capacity planning. They use large historical and real-time datasets to build models that anticipate future network loads. These models enable network operators to forecast upcoming demand spikes and proactively adjust their infrastructure to prevent bottlenecks or service disruptions.

Gen AI-based networking tools also enable load balancing across multiple radio access technologies (such as wifi, Bluetooth, 4G LTE and 5G) and help reduce data interference in dense network environments.

Agentic AI is enabling enterprises to build more autonomous, adaptive AI networks. Agentic AI is “an AI system that can accomplish a specific goal with limited supervision.” AI agents use large language models (LLMs),  natural language processing (NLP) and ML to design workflows, perform tasks and execute processes on behalf of users and other systems.

Unlike traditional, static systems, agentic AI networks use decentralized architectures where AI agents move across systems and endpoints, exchanging data rapidly to support lightning-fast decision making. Agents can perceive their environment and independently take actions to optimize network connectivity, enhance security protocols and improve the user experience.

For instance, they can dynamically adjust network parameters (such as resource allocation and data routing) as conditions change. And if an agent detects suspicious network activity, it can isolate the compromised devices and implement countermeasures in real time to thwart a cyberattack.

As AI in networking advances, there is a considerable focus on building AI-ready infrastructure—switches, GPUs and high-bandwidth, low-latency fabrics optimized specifically for AI workloads.

AI network infrastructure as a service (NIaaS) is one such development. AI NIaaS simplifies network management and decreases deployment times from months to minutes by virtualizing and orchestrating AI network infrastructure on demand. It’s a cloud-based model that gives enterprises access to a full suite of networking and security functions—including virtual routers, firewalls, load balancers and AI management components—without requiring them to deploy or maintain physical hardware.

AI NIaaS service providers offer cloud-like, flexible consumption models (such as pay-as-you-go or subscription-based pricing), where network resources are provisioned according to the computing needs of specific AI projects.

Hyperscale networking with consolidated AI clusters is another AI networking trend. AI cluster consolidation is the process of organizing and consolidating AI computing resources into multiple AI “islands” to create streamlined data fabrics. It reduces the number of underutilized servers and nodes in a network by concentrating workloads into fewer, more powerful clusters.

And hyperscale environments (extremely large-scale computing environments designed to handle outsized workloads) provide the capacity, cooling and data storage required to support cluster consolidation at enterprise network scale. Together, cluster consolidation and hyperscale networking simplify AI model training and deployment for faster, more efficient AI networks.

According to the IBM Institute for Business Value (IBM IBV), “AI-enabled workflows—many driven by agentic AI—are poised to expand from 3% in 2024 to 25% by 2026,” representing an eightfold increase in AI deployments. Adopting an AI-based networking approach offers enterprises numerous benefits, including: