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Organizations need to plan for two related but different changes: using AI to improve network operations, and building networks that can carry AI workloads. A durable strategy addresses both—matching capacity, placement and controls to actual workloads while keeping the architecture adaptable as needs change.
What is AI for networking?
AI for networking means applying AI to the work of operating a network. Potential uses include improving visibility, identifying anomalies, predicting problems, supporting security response and automating remediation. The aim is to make operations more responsive and less dependent on manual troubleshooting.
Automation needs boundaries. In a June 2026 announcement, Nokia described a network-operations platform with AI agents for tasks such as event triage, anomaly reasoning and recommending actions. Nokia said humans would retain approval over critical control points, while policy-bounded closed-loop automation could handle lower-risk scenarios. That is Nokia’s approach, not a universal safeguard or proof that every automated network is safe by default. Nokia’s announcement also described the agents as built with Google Cloud technologies.
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Networking for AI means designing the infrastructure that moves data between AI workloads, compute, storage, applications and users. That can involve traffic inside a cluster, between facilities, or between cloud environments and an organization’s sites. The appropriate design depends on where workloads and data reside; it is not simply a matter of buying a faster switch.
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HPE’s vendor-authored overview of AI-factory architecture describes low-latency fabric within clusters and longer-distance connections between facilities. It names NVIDIA Spectrum-X switches and ConnectX-8 SuperNICs for AI fabric, and HPE Juniper PTX routers for long-distance transport, with Juniper MX platforms for multicloud routing and customer on-ramps. These are examples of specialized enterprise equipment in HPE’s architecture, not a neutral comparison or a recommendation for every organization. HPE’s AI-factory architecture article explains its design.
How does AI change network requirements?
AI workloads can change the volume and pattern of network traffic. Depending on the workload, data may move among distributed compute, data stores and users, making throughput, latency, reliability and visibility important design considerations. The cited material does not set universal bandwidth or latency targets; requirements have to be established for the workload and deployment.
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Network operations are also part of the equation. More traffic and more distributed components can make visibility and diagnosis consequential, while operational AI may help teams spot and investigate issues. These are complementary needs: operational intelligence does not supply missing transport capacity, and additional capacity does not by itself provide useful monitoring or safe automation.
What network infrastructure does AI need?
Start with the workload rather than a product list. Identify where data is generated and stored, where training or inference will run, and which users or systems need access. Then map the flows among those points and determine which requirements are strict: latency, throughput, reliability, security, continuity or data location.
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- Within a site or cluster: Determine whether the workload needs a dedicated, high-throughput fabric and what performance targets the application actually requires.
- Between sites: Account for distance, available connectivity, continuity needs and the volume of data exchanged.
- Across cloud and on-premises environments: Plan routing and access around the chosen workload locations rather than assuming one environment will host everything.
- For operations: Establish what telemetry and visibility teams need, which actions can be automated, and which decisions require human approval.
HPE reported that up to 96% of enterprises planned to double or quadruple cloud-connection bandwidth in its December 2025 AI-factory article. That is a vendor-reported figure; the page does not detail the underlying study or methodology, so it should not be treated as an independently established forecast for all enterprises.
How should organizations prepare networks for AI workloads?
- Inventory likely workloads. Distinguish training, inference and other AI-related traffic where relevant, and identify the systems, data and users each workload connects.
- Set placement constraints. Evaluate performance and cost alongside security, business continuity, digital sovereignty and latency. Decide which data and processing can be placed in each environment.
- Map traffic and operational gaps. Trace expected data flows, then assess whether existing capacity, routing and monitoring can support them. Separate a capacity problem from a visibility or troubleshooting problem.
- Choose automation boundaries. Define which actions are safe to automate, what policies constrain them, how exceptions are escalated and where a person must approve a change.
- Build for the present, preserve options. Meet known business requirements without hard-coding the assumption that all future workloads will run in one place. Favor an architecture that can adapt as use cases and placement decisions evolve.
- Validate against real workloads. Test the planned paths, operational processes and recovery behavior under conditions representative of the intended deployment before relying on them for critical services.
What do the published examples establish?
The examples illustrate different parts of the problem; they are not comparative proof that one approach or vendor is best.
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- Mercy Health: Andrew Fox’s September 2026 iTnews feature describes the organization modernizing connectivity across more than 40 sites using a cloud-managed network. This is a customer example attributed to that feature, not an independent evaluation of outcomes.
- AI-factory architecture: HPE’s article describes a design spanning fabric within AI clusters and transport between facilities, along with named HPE and NVIDIA platforms. It presents HPE’s architecture claims, not a head-to-head assessment.
- Telecommunications operations: Nokia’s June 2026 announcement describes six agents for Nokia Assurance Center: router, event triage, KPI selector, anomaly reasoner, action reasoner and dashboard agents. The announcement says the offering uses Google Cloud’s Agent Development Kit and Gemini Enterprise Agent Platform. It is a telecom operations example, not direct guidance for an enterprise LAN.
Nokia said its agents could reduce network problem-solving time by 50% to 80%. That is a vendor claim in the announcement, not an independently validated result there. Nokia also said the SaaS platform would launch in September 2026; the announcement does not establish whether that launch occurred or confirm current availability.
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Fox’s iTnews feature attributed two additional figures to Australian public bodies: an increase from 1% in 2022–23 to 12% in 2024–25 attributed to the Australian Bureau of Statistics, and 17.9 million publicly internet-visible devices observed over two months, including roughly 212,000 edge devices, attributed to the Australian Signals Directorate’s Australian Cyber Security Centre. The underlying publications were not identified in the available material. Treat these as figures attributed by the feature, not as independently checked statistics or direct measures of AI-network demand.
Quick Recap
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