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29 Oct 2025

by Dean Bubley

Impact of AI on Networks: Policy considerations

Introduction

Policymakers need to give more careful consideration to the impact of AI on telecoms networks. There are three critical areas here:

The impact of AI on design and operations of networks themselves

How AI changes users’ network traffic patterns

How governments can adjust regulation and incentives for AI-optimal networks

AI for network operation and efficiency

There is already a plethora of information, from many sources, about AI applications for running networks, whether those are for fixed or mobile operators, or for enterprises or new classes of providers. But there are also some new angles to understand and explore.

AI can improve network reliability, by spotting anomalies, false configurations or warning-signs of equipment failure. It can improve the modelling and forecasting of future network needs, assist in network planning for optimal coverage and performance, or automatically adjust capacity in advance of demand peaks.

But policymakers also need to think about the secondary impacts of this type of AI. Greater efficiency and optimisation might mean lower capex levels are needed, in order to meet realistic goals and targets. Efficiency improvements might yield more capacity from each Hz of spectrum, or better ways to share that capacity between multiple users or operators. That may in turn mean lower requirements for new spectrum. Efficiency gains (supply) may even outstrip demand growth, which could result in over-capacity

In other words, in the AI-led network era policymakers should look more at outputs of network innovation, rather than simply tracking metrics for inputs such as absolute levels of investment, site numbers or spectrum.. Capital and resources can then be re-directed to better uses, rather than used to chase arbitrary KPIs.

AI impact on network traffic

In terms of AI’s impact on network traffic, it is critical to realise that at the moment, almost all incremental AI-generated data is transmitted inside and between datacentres. There is almost no noticeable extra traffic on last mile “access” networks such as consumer fixed and mobile broadband.

Almost all data used for training AI models is already “in the cloud” – and most of the day-to-day work of AI involves analysing and processing data on intra-cloud links. When a user queries a chatbot, their local network sends and receives only the tiniest tip of the iceberg.

The latest trend - Agentic AI - will magnify that imbalance still further. It will mean software “agents” talk to each other mostly inside the cloud to create an action or find information. The user will get a message that their flight is booked, or that the cause of the manufacturing glitch has been resolved and rectified behind the scenes.

To be fair, there are some plausible views that uplink data on access networks will increase for both AI training and inferencing, as we send voice, IoT data or video for processing. But only a tiny amount will need to be “realtime” – the rest can be sent when the network is quiet and has enough capacity.

We should certainly keep alert to new AI applications that might (suddenly) create heavier loads on access networks, such as augmented reality with low-latency cloud-based analysis of video and sensor data. But policymakers should also watch the opposite trends, such as more use of on-device AI which processes data locally, or AI-based compression techniques, which could potentially result in decreased load.

In the meantime, we should remain skeptical of any claims of an AI-led upswing in either mobile or fixed data traffic. It’s mostly wishful thinking at this stage.

AI-optimised networks

The shift to “intra-cloud” AI networking leads to a third important trend: interconnection. For AI to work well, there will need to be a dense mesh of connections between the different clouds, and different AI models – as well as their data sources, and eventually output to users.

That puts an extra focus on interconnectedness and specialised locations and systems for network peering, IP and optical exchanges and “meeting points” for data, and assorted systems for network short-cuts and bypasses. This area is often overlooked by regulators and governments. Worse, some in the telecom industry see this domain as suitable for new classes of gatekeepers and bottlenecks.

Some of the proposals for the EU’s Digital Networks Act have suggested regulation of IP Interconnect, even though almost everyone recognises that part of the market works well today – including BEREC. The UK’s policymakers should vigorously push back on any similar suggestions for regulation, and indeed, should instead incentivise more interconnection for cloud and AI services. It would be useful to have a full analysis of the UK interconnect landscape, and how its evolution could enhance AI innovation and security / resilience.

Conclusion

AI will have multiple effects on the way operators and businesses build, run and use networks. It changes the underlying architecture of what data goes where, when and why. It will certainly require additional investment in specific places – but may mean less is needed in others. Policymakers should be aware that the rising AI tide will not lift all network boats to the same degree.

Tales Gaspar

Programme Manager, UK SPF and Satellite, techUK

Usman Ikhlaq

Programme Manager - Artificial Intelligence, techUK

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