AI for network admins

Starting with Nvidia

It seems logical that any investigation of AI and networking – or indeed, AI and anything – should start with Nvidia, a company that has played a pivotal role in developing the AI tech ecosystem, and is set to do so further.

Speaking in 2024 at a tech conference about how AI has established itself as an intrinsic part of business, Nvidia founder and CEO Jensen Huang observed that the era of generative AI (GenAI) is here and that enterprises must engage with “the single most consequential technology in history”. He told the audience that what was happening was the greatest fundamental computing platform transformation in 60 years, encompassing general-purpose computing to accelerated computing. 

“We’re sitting on a mountain of data. All of us. We’ve been collecting it in our businesses for a long time. But until now, we haven’t had the ability to refine that, then discover insight and codify it automatically into our company’s natural experience, our digital intelligence. Every company is going to be an intelligence manufacturer. Every company is built on domain-specific intelligence. For the very first time, we can now digitise that intelligence and turn it into our AI – the corporate AI,” he said.

“AI is a lifecycle that lives forever. What we are looking to do is turn our corporate intelligence into digital intelligence. Once we do that, we connect our data and our AI flywheel so that we collect more data, harvest more insight and create better intelligence. This allows us to provide better services or to be more productive, run faster, be more efficient and do things at a larger scale.” 

Concluding his keynote, Huang stressed that enterprises must now engage with the “single most consequential technology in history” to translate and condense a company’s intelligence into digital intelligence.

This is precisely what Swisscom is aiming to achieve. The company is Switzerland’s largest telecoms provider with more than six million mobile customers and 10,000 mobile antenna sites that have to be managed effectively. When its network engineers make changes to the infrastructure, they face a common challenge: how to update systems that serve millions of customers without disrupting the service.

The solution was partnering with Outshift to develop practical applications of AI agents in network operations to “redefine” customer experiences. That is, using Outshift’s Internet of Agents to deliver meaningful results for the telco, while also meeting customer needs through AI innovation.

But these advantages are not the preserve of large enterprises such as telcos. Indeed, from a networking perspective, AI can enable small- and medium-sized businesses to gain access to enterprise-level technology that can allow them to focus on growth and eliminate the costs and infrastructure challenges that arise when managing complex IT infrastructures. 

Engineering networks for AI

From a broader perspective, Swisscom and Outshift have also shown that making AI work effectively requires something new: an infrastructure that lets businesses communicate and work together securely. And this is where the two sides of AI and networking come into play.

At the event where Nvidia’s Huang outlined his vision, David Hughes, chief product officer of HPE Aruba Networking, said there were pressing issues about the use of AI in enterprise networks, in particular around harnessing the benefits that GenAI can offer. Regarding “AI for networking” and “networking for AI”, Hughes suggested there are subtle but fundamental differences between the two. 

“AI for networking is where we spend time from an engineering and data science point of view. It’s really about [questioning] how we use AI technology to turn IT admins into super-admins so that they can handle their escalating workloads independent of GenAI, which is kind of a load on top of everything else, such as escalating cyber threats and concerns about privacy. The business is asking IT to do new things, deploy new apps all the time, but they’re [asking this of] the same number of people,” he observed. 

What we are starting to see, and expect more of, is AI computing increasingly taking place at the edge to eliminate the distance between the prompt and the process

“Networking for AI is about building out, first and foremost, the kind of switching infrastructure that’s needed to interconnect GPU [graphics processing unit] clusters. And then a little bit beyond that, thinking about the impact of collecting telemetry on a network and the changes in the way people might want to build out their network.” 

And impact there is. A lot of firms currently investigating AI within their businesses find themselves asking how to manage the mass adoption of AI in relation to networking and data flows, such as the kind of bandwidth and capacity required to facilitate AI-generated output such as text, image and video content.

This, says Bastien Aerni, vice-president of strategy and technology adoption at global networking and security-as-a-service firm GTT, is causing companies to rethink the speed and scale of their networking needs. 

“To achieve the return on investment of AI initiatives, they have to be able to secure and process large amounts of data quickly, and to this end, their network architecture must be configured to support this kind of workload. Utilising a platform embedded in a Tier 1 IP [internet protocol] backbone here ensures low latency, high bandwidth and direct internet access globally,” he remarks.  

“What we are starting to see, and expect more of, is AI computing increasingly taking place at the edge to eliminate the distance between the prompt and the process. Leveraging software-defined wide area network [SD-WAN] services built in the right platform to efficiently route AI data traffic can reduce latency and security risk, and provide more control over data.” 

Managing network overload

At the end of 2023, BT revealed that its networks had come under huge strain after the simultaneous online broadcast of six Premier League football matches and downloads of popular games, with the update of Call of Duty Modern Warfare particularly cited. AI promises to add to this headache. 

Speaking at Mobile World Congress 2025, BT Business chief technology officer (CTO) Colin Bannon said that in the new, reshaped world of work, a robust and reliable network is a fundamental prerequisite for AI to work, and that it requires effort to stay relevant to meet ongoing challenges faced by the customers BT serves, mainly international business, governments and multinationals. The bottom line is that network performance to support the AI-enabled world is crucial in a world where “slow is the new down”. 

Bannon added that Global Fabric, BT’s network-as-a-service product, was constructed before AI “blew up” and that BT was thinking of how to deal with a hyper-distributed set of workloads on a network and to be able to make it fully programmable.

Looking at the challenges ahead and how the new network will resolve them, he said: “[AI] just makes distributed and more complex workflows even bigger, which makes the need for a fabric-type network even more important. You need a network that can [handle data] burst, and that is programmable, and that you can [control] bandwidth on demand as well. All of this programmability [is something businesses] have never had before. I would argue that the network is the computer, and the network is a prerequisite for AI to work.” 

The result would be constructing enterprise networks that can cope with the massive strain placed on utilisation from AI, especially in terms of what is needed for training models. Bannon said there were three key network challenges and conditions to deal with AI: training requirements, inference requirements and general requirements.  

He stated that the dynamic nature of AI workloads means networks need to be scalable and agile, with visibility tools that offer real-time monitoring, issue detection and troubleshooting. As regards specific training requirements, dealing with AI necessitates the movement of large datasets across the network, thus demanding high-bandwidth networks.

He also described “elephant” flows of data – that is, continuous transmission over time and training over days. He warned that network inconsistencies could affect the accuracy and training time of AI models, and that tail latency could impact job completion time significantly. This means robust congestion management is needed to detect potential congestion and redistribute network traffic. 

But AI training models generally spell network trouble. And now the conversation is turning from the use of generic large language models (see Preparing networks for Industry 5.0 box) to application/industry-dedicated small language models.

Focus on smaller models

NTT Data has created and deployed a small language model called Tsuzumi, described as an ultra-lightweight model designed to reduce learning and inference costs. According to NTT’s UK and Ireland CTO, Tom Winstanley, the reason for developing this model has principally been to support edge use cases.

“[That is] literally deployment at the edge of the network to avoid flooding of the network, also addressing privacy concerns, also addressing sustainability concerns around some of these very large language models being very specific in creating domain context,” he says.  

“Examples of that can be used in video analytics, media analytics, and in capturing conversations in real time, but locally, and not deploying it out to flood the network. That said, the flip side of this was there was immense power sitting in some of these central hyper-scale models and capacities, and you also therefore need to find out more [about] what’s the right network background, and what’s the right balance of your network infrastructure. For example, if you want to do real-time media streaming from a [sports stadium] and do all of the edits on-site, or remotely so not to have to deploy [facilities] to every single location, then you need a different backbone, too.” 

Winstanley notes that his company is part of a wider group that in media use cases could offer hyper-directional sound systems supported by AI. “This is looking like a really interesting area of technology that is relevant for supporter experience in a stadium – dampening, sound targeting. And then we’re back to the connection to the edge of the AI story. And that’s exciting for us. That is the frontier.” 

But coming back from the frontier of technology to bread-and-butter business operations, even if the IT and comms community is confident that it can address any technological issues that arise regarding AI and networking, businesses themselves may not be so sure. 

Roadblocks to AI plans

Research published by managed network-as-a-service provider Expereo in April 2025 revealed that despite 88% of UK business leaders regarding AI as becoming important to fulfilling business priorities in the next 12 months, there are a number of major roadblocks to AI plans by UK businesses. These include from employees and unreasonable demands, as well as poor existing infrastructure.  

Worryingly, among the key findings of Expereo’s Enterprise horizons 2025 study was the general feeling from a lot of UK technology leaders that expectations within their organisation of what AI can do are growing faster than their ability to meet them. While 47% of UK organisations noted that their network/connectivity infrastructure was not ready to support new technology initiatives, such as AI, in general, a further 49% reported that their network performance was preventing or limiting their ability to support large data and AI projects. 

Assessing the key trends revealed in the study, Expereo CEO Ben Elms says that as global businesses embrace AI to transform employee and customer experience, setting realistic goals and aligning expectations will be critical to ensuring that AI delivers long-term value, rather than being viewed as a quick fix.

“While the potential of AI is immense, its successful integration requires careful planning. Technology leaders must recognise the need for robust networks and connectivity infrastructure to support AI at scale, while also ensuring consistent performance across these networks,” he says. 

Summing up the state of the industry, Elms states that business is currently at a pivotal moment where strategic investments in technology and IT infrastructure are necessary to meet both current and future demands. In short, reflecting Düsener’s point about Swisscom’s aim to reduce the impact of service changes, reduce the risk of downtime and costs, and improve customer services.

Just switching on any AI system and believing that any answer is “out there” just won’t do. Your network could very well tell you otherwise.