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Telco AI is Alphafly

AI advancements are changing communications networks and there's a lot of opportunity ahead for telecom operators and suppliers.

Gabriel Brown

December 29, 2023

3 Min Read
Time to lace up and get to work.(Source: Andrea Raffin/Alamy Stock Photo)

Like 99.9% of the telecom industry, I don't know enough about artificial intelligence. By the same token (geddit?), that won't stop me from thinking about how the technology can deliver better network services. 

The networking and AI match is a good one. AI is advancing rapidly (DNNs, GNNs, LLMs, etc.), and telecom networks produce vast quantities of telemetry data and generate complexity, pushing the limits of classic networking expertise. If, as Jensen Huang from Nvidia says, AI fundamentally changes computing, telecom must adopt this technology.

In the medium term, two primary opportunities exist: AI in customer and business operations (chatbots, yield management, etc.) and AI in network operations (optimization, assurance, etc.). In a Goldilocks scenario, the two link customer service to an improved network experience. 

In each case, AI/ML is applied to existing architecture or workflow to generate a small gain on the previous process – let's say, for argument, a 10% gain. On their own, these gains are helpful, if not game-changing. Like in professional sports, however, marginal gains add up to a competitive advantage. 

The race is forever changed

In this sense, we can liken AI for telco to the Alphafly running shoe that enabled athletes to knock serious amounts of time out of long-distance records. A marathon is still a marathon, but the race has changed forever. 

Once everyone gets the same shoes, the field levels out again. The corollary in telco is that every operator will have to work with AI simply to stay competitive. In this analysis, there's plenty of work to keep operators and their vendors busy – most of the gains, operators will bank in efficiency improvements. Still, hopefully, customers should get better service as well. 

The French have an appropriate saying for this outcome: "Plus ça change, plus c'est la même chose."[Author’s note: too high brow for Light Reading?]

But this will only be the start of a journey. The longer-term opportunity for AI in the service provider sector is much greater.

Telco opportunities

Channeling my inner Jensen Huang, I see three areas of high promise, as follows:

  • The role of networks in hosting and enabling AI workloads – think edge training, federated learning and on-device inference. More broadly, there's no way AI, in general, achieves its potential without incredible networks.

  • Redesigned service models – rather than tweak an existing process here or there, service providers can use AI across the entire customer journey. Think, for example, of a telco enterprise business applying AI from customer engagement to network implementation and back again to reporting and monetization.

  • End-to-end AI-native networks – think protocols, architectures and AI-designed network components (analog devices, chipsets, algorithms, etc.). From the radio interface to the core, AI has myriad opportunities to be deep in the heart of future networks.

And let's give the last word to Nvidia's Huang. Creating an AI company, he says, "is way harder than you think."

More (heavy) reading

At Heavy Reading, Omdia, Light Reading and Telcoms.com, we're working hard on AI for service providers. Get in touch for details ([email protected], @gabeuk, LinkedIn). 

Webinars are just one example. AI topics are getting the biggest, most engaged audiences right now. 

Here are a few examples of our work from the last quarter of 2023:

And high-level data from Omdia's Q4 2023 GenAI operator survey is now out from behind the paywall: CSPs moving ahead with GenAI for cost reduction and efficiency gains by Roz Roseboro.

About the Author(s)

Gabriel Brown

Principal Analyst, Heavy Reading

Gabriel leads mobile network research for Heavy Reading. His coverage includes system architecture, RAN, core, and service-layer platforms. Key research topics include 5G, open RAN, mobile core, and the application of cloud technologies to wireless networking.

Gabriel has more than 20 years’ experience as a mobile network analyst. Prior to joining Heavy Reading, he was chief analyst for Light Reading’s Insider research service; before that, he was editor of IP Wireline and Wireless Week at London's Euromoney Institutional Investor.

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