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 useful strategy considers both—alongside where workloads and data will run, and the security, continuity, sovereignty and latency requirements that come with those choices.
What is AI for networking?
AI for networking means applying AI to the work of operating a network. The aim is to make operations more visible, predictive and responsive: identify anomalies, help explain problems, anticipate issues and automate appropriate responses.
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Automation is not automatically safe just because it is AI-assisted. Nokia’s June 22, 2026 announcement about its Assurance Center describes a model in which people retain approval over critical control points, while policy-bounded closed-loop automation can handle lower-risk scenarios. That is Nokia’s approach, not a universal rule for every network.
Nokia described six Gemini-based agents for its platform: router, event triage, KPI selector, anomaly reasoner, action reasoner and dashboard agents. The company said the platform uses Google Cloud’s Agent Development Kit and Gemini Enterprise Agent Platform. Nokia also reported that the agents could reduce network problem-solving time by 50% to 80%; that is a vendor claim, not an independently validated result established by the announcement.
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What is networking for AI?
Networking for AI means designing infrastructure to move the data AI workloads need among compute, storage, applications and users. Those resources may be distributed across facilities and cloud environments rather than located in one place. The network therefore has to support the workload’s data flows, not merely connect end users to ordinary business applications.
In a December 1, 2025 architecture article, Hewlett Packard Enterprise described an AI-factory design with low-latency fabric inside clusters and longer-distance interconnection between facilities. Its examples included NVIDIA Spectrum-X Ethernet switches and ConnectX-8 SuperNICs for AI fabric, HPE Juniper PTX routers for long-distance transport, and Juniper MX platforms for multicloud routing and customer on-ramps. These are vendor-described design choices for specialized infrastructure, not evidence that the same equipment suits every organization.
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How does AI change network requirements?
AI traffic can change both the volume and pattern of data moving through a network. That makes throughput, latency, reliability and visibility important design considerations. The cited feature does not set universal numeric thresholds for any of them; requirements depend on the workload and where its data, compute and users are located.
Visibility matters to both sides of the strategy. Operations teams need enough information to detect and diagnose network problems, while teams supporting AI workloads need to understand whether the network is meeting the workload’s transport needs. Better operational intelligence does not, by itself, create the capacity or topology a workload requires.
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What network infrastructure does AI need?
There is no single topology implied by the available examples. Start with the traffic path and operational problem rather than a product list. The distinction between operations and workload transport helps clarify what an investment is meant to solve:
| Planning question | AI for networking | Networking for AI |
|---|---|---|
| Primary purpose | Improve monitoring, diagnosis, prediction or response. | Carry AI workload data among compute, storage, applications and users. |
| What to examine | Operational visibility, anomaly handling, response policies and human oversight. | Traffic volume and paths, latency, throughput, reliability and workload placement. |
| Typical planning decision | Which operational tasks can be assisted or automated, and which require approval? | Where should the workload run, and what connections must support its data flows? |
| Evidence in the cited examples | Nokia’s announced network-assurance agents and governance approach. | HPE’s vendor-described cluster fabric and inter-facility design. |
An organization may need one direction before the other, or both. A useful first distinction is whether its immediate constraint is operational visibility and remediation, the capacity and architecture to move AI data, or a combination. The cited examples illustrate approaches; they do not provide a neutral head-to-head product evaluation.
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How should organizations choose where AI workloads run?
Performance and cost are not the only placement criteria. Workload location can also affect security, business continuity, digital sovereignty and latency. Those constraints may differ by dataset, application and user group, so a decision to run everything in one environment should not be assumed in advance.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteMap the workload’s data sources, processing location and consumers, then identify which constraints apply to each part of that path. A distributed workload may need local processing for some functions and connections to remote resources for others; the cited sources do not prescribe one placement pattern for all organizations.
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How can organizations prepare their networks for AI workloads?
- Separate the operational goal from the transport goal. Record whether the current need is better monitoring and remediation, more suitable infrastructure for AI data movement, or both.
- Map workload paths and constraints. Identify where data, inference, compute and users are expected to be, then assess latency, throughput, reliability, security, continuity and sovereignty needs for those paths.
- Decide what can be automated safely. Define policies and approval boundaries for network changes. Nokia’s announced use of human approval at critical controls is one example of a governance model, not a requirement that every organization adopt its particular platform.
- Plan for change rather than one fixed destination. Andrew Fox, HPE Networking’s general manager for Australia and New Zealand, argued in an iTnews feature published September 28, 2026, for infrastructure that meets present business requirements while leaving room for future environments and use cases. Treat that as vendor advice, and test it against your organization’s own workload and business plans.
What the reported figures do—and do not—show
Fox’s iTnews feature attributed two figures to Australian public bodies: that the Australian Bureau of Statistics reported AI use rising from 1% in 2022–23 to 12% in 2024–25, and that the Australian Signals Directorate’s Australian Cyber Security Centre counted 17.9 million publicly internet-visible devices over a two-month period, including roughly 212,000 edge devices. The underlying ABS and ASD/ACSC publications were not identified in the available material, so these figures should be treated as attributions made by the feature rather than independently verified statistics here.
HPE’s December 2025 article said up to 96% of enterprises were planning to double or quadruple cloud connection bandwidth. The cited page does not detail the study or its method; this is HPE’s reported figure, not a general forecast established for all enterprises.
Nokia’s June 2026 announcement said its SaaS platform would launch in September 2026. Because the announcement is prospective and does not establish whether that launch occurred, it does not confirm current availability as of October 2026.
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