Will AI spur revenue growth for the telcos?

• A global AI survey sponsored by Ciena highlights industry optimism 
• The telcos have unique networking assets that can serve users of AI.
• Much is still to play out and telcos have a history of missed opportunities.

The leading communications service providers have been on a decade-long journey to transform their networks and grow their revenues.

Jürgen Hatheier.

To the list of technologies the operators have been embracing can now be added artificial intelligence (AI). 

AI is a powerful tool for improving their business efficiency. The technology is also a revenue opportunity and service providers are studying how AI traffic will impact their networks. 

"This is the single biggest question that everyone in this industry is struggling with," says Jürgen Hatheier. "How can the service providers exploit the technology to grow revenues?"

However, some question whether AI will be an telecom opportunity.

"The current hype around AI has very little to do with telcos and is focused on hyperscalers and specifically the intra-data centre traffic driven by AI model training," says Sterling Perrin, senior principal analyst at HeavyReading. "There is a lot of speculation that, ultimately, this traffic will spread beyond the data centre to data centre interconnect (DCI) applications. But there are too many unknowns right now." 

AI survey 

Hatheier is chief technology officer, international at Ciena. He oversees 30 staff, spanning Dublin to New Zealand, that work with the operators to understand their mid- to long-term goals.

Ciena recently undertook a global survey (see note 1, bottom) about AI, similar to one it conducted two years ago that looked at the Metaverse. 

Conducting such surveys complements Ciena's direct research with the service providers. However, there is only so much time a telco's chief strategy officer (CSO) or chief technology officer (CTO) can spend with a vendor discussing strategy, vision, and industry trends.

"The survey helps confirm what we are hearing from a smaller set," says Hatheier. 

Surveys also uncover industry and regional nuances. Hatheier cites how sometimes it is the tier-two communications service providers are the trailblazers. 

Lastly, telcos have their own pace. "It takes time to implement new services and change the underlying network architecture," says Hatheier. "So it is good to plan."

Findings 

The sectors expected to generate the most AI traffic are financial services (46 per cent of those surveyed), media and entertainment (43 per cent), and manufacturing (38 per cent). Hatheier says these industries have already been using the technology for a while, so AI is not new to them. 

Sterling Perrin

For financial services, an everyday use of AI is for security, detecting fraudulent transactions and monitoring video streams to detect anomalous behavior at a site. The amount of traffic AI applications generate can vary greatly. This is common, says Hatheier; it is the use that matters here, not the industry.

"I would not break it down by the industries to say, okay, this industry is going to create more traffic than another," says Hatheier. "For financial services, if it is transaction data, it's a few lines of text, but if it is video for branch security, the data volumes are far more significant." 

AI is also set to change the media and entertainment sector, challenging the way content is consumed. Video streaming uses content delivery networks (CDNs) to store the most popular video content close to users. But AI promises to enable more personalised video, tailored for the end-user. Such content will make the traffic more dynamic.

Another example of personalised content is for marketing and advertising. Such personalisation tends to achieve better results, says Hatheier.

AI is also being applied in the manufacturing sector. Examples include automating supply-chain operations, predictive maintenance, and quality assurance.

Car manufacturers check a vehicle for any blemishes at the end of a production line. This usually takes several staff and lasts 10-15 minutes. Now with AI, the inspection can be completed as the cars passes by. "This is a potent application that could run on infrastructure within the manufacturing site but use a service provider's compute assets and connectivity," says Hatheier.

The example shows how AI produces productivity gains. However, AI also promises unique abilities that staff cannot match.

The 'Confident' category is 'Very confident' and 'Somewhat confident' combined. Source: Ciena.

Traffic trends 

If the history of telecoms is anything to go by, applications that drove traffic in the network rarely lead to revenue growth for the service providers. Hatheier cites streaming video, gaming, and augmented reality as examples.

However, the operators have assets at network edge and the metro that can benefit AI usage. They also have central offices that can act is distributed data centres for the metro and network edge.

Hatheier says users have an advantage if they consume AI applications across a fibre-based broadband network. But certain countries, such as Saudi Arabia and India, mainly use wireless for connectivity.  

“AI applications will need to adapt to what is available, and if people want to consume low-latency applications, there is 5G slicing,” says Hatheier. “At the end of the day, there is no way around fibre.”  

Optical networking

Government policy regarding AI and regulations to ensure data does not cross borders also play a part.

"It's an important decision criterion, as we saw in the survey response," says Hatheier. "So private AI and local computing will be an important decision factor." 

Another critical decision influencing where data centres are built is power. "We see all the gold rush in the Nordics right now with their renewable power and cool climates," says Hatheier. "You don't need to cool your servers as much, and it requires a lot of connectivity."

However, as well as these region-specific data centre builds, there will also be builds in metropolitan areas using smaller distributed data centres.

"Let's say there are 20 sizable edge or metro compute centres for AI, and you would need three or four to run a big training job," says Hatheier. "You will not create a permanent end-to-end connection between them because sometimes there will not be four that need to work together, but five, seven, and 11."

Such a metro network would require reconfigurable optical add-drop multiplexer (ROADM) technology to connect wavelengths between those clusters based on demand to keep sites busy, to avoid expensive AI clusters being idle. 

These are opportunities for the CSPs. And while much is still to happen, such discussions are taking place between systems vendors and the telcos. 

For Heavy Reading's Perrin, the more telling opportunity is the telcos' own use of AI rather than the networking opportunity.

"As a vertical industry, telecom is not typically a leading-edge adopter of any new technology due to many factors, including culture, size, legacy infrastructure and processes, and government regulations," he says. "I don't believe AI will be any different."

Hatheier points to the survey's finding of general optimism that sees AI as an opportunity rather than a challenge or business risk.

"We have seen very little differences between countries," says Hatheier. "That may have to do with the fact that emerging countries get as much attention of data centre investment than more developed ones."

Notes:

1: The survey had a sample size of 1517 full-time employees in IT or the telecoms industry