The Transformative Impact of AI in Telecommunications
Phil Kippen is currently Head of Industry, Telecommunications at leading technology company Snowflake. Kippen has over twenty years of experience working with more than two-hundred wireless & wireline Telecommunications Services Providers globally, advising on technology evolution strategies, driving customer & industry thought leadership, transforming network & cloud services delivery architectures and designing new innovative enterprise and subscriber services.
In his current role, Kippen is responsible for Telecommunications industry strategy, go-to- market (GTM) and business development. Prior to Snowflake, he spent seven years at VMware helping launch and build VMware’s Telecom Service Provider Business, where he was responsible for Telecom industry evangelization, solution architecture development, customer innovation, business development, customer engineering and internal enablement. Prior to VMware, Kippen was a Distinguished Engineer at Cisco.
He sat down with us to discuss how AI is impacting telecommunications.
How is global digitisation impacting the telecommunications industry?
The telecommunications industry is at a critical turning point. As it continues its rapid development beyond traditional services it is also becoming the foundational layer for various sectors, from transport to health, being relied upon daily by billions of consumers across the world. According to PwC, global data consumption over telecom networks will nearly triple, from 3.4 million petabytes in 2022, to 9.7 million petabytes in 2027. This data offers rich insights which, when harnessed correctly, will usher in a new era of modernisation, transforming how the sector operates and enabling telecom companies to offer a best-in-class service to their consumers and wider society beyond this.
Central to this evolution is technology including artificial Intelligence (AI) and automation, which are already being widely used throughout the sector. Automation is about making something happen, while AI is about intelligently deciding what it is you want to make happen. Working together, they will become the key foundations of the industry’s digital transformation journey, improving several areas from customer experience and field operation response times to helping deliver on sustainability targets.
How can AI boost and support the telecoms industry?
One of the key benefits of AI and automation in telecoms will be boosting customer experience (CX). Research from IDC found that improving CX in the sector was a top transformative initiative last year for more than half of telecom decision-makers, despite the industry continuing to appear at the bottom of CX rankings. Looking to the year ahead, generative AI tools and Large Language Models (LLMs) will increasingly have a role to play in helping the industry reach this target.
LLMs offer a faster, more effective way to access data. If a consumer has a query and needs to get in contact with their telecoms provider, LLMs enable them to simply and easily find the information that may be available but is difficult to find, which can often lead to a negative customer experience. LLMs can also bring those capabilities to service reps, better enabling them to assist customers with the speed and accuracy they expect. For example, when a customer needs to find information on installing a router in their home, LLMs can guide them to the information they need, or a customer service agent can rapidly find the same information and support them. Representatives on different continents can now deal with issues, with instant translation breaking down the language barrier. This ease of use is empowering telecom employees across the board.
How does this link into data?
For business users, until now, dealing with data has been forbiddingly complex. But, thanks to the generative AI boom, non-technical users are empowered to access and understand data that they might otherwise struggle to comprehend. The ability to ask questions using natural language gives business users access to the full power of data sharing. For network engineers, for example, who design networks but don’t engage with data directly, LLMs will offer the potential to find and extract the information they need, let’s say on local weather patterns, without having to deal with complicated and messy data.
This allows the benefits of big data to spread through a company, rather than being siloed to a department of trained and well-informed data scientists. As a result, businesses can become data-driven but in an accessible way, whether sat at a desk or in the field.
It’s well-known how useful AI technology is for pulling out the data needed for the big, strategic decisions around network engineering, but it can also be hugely helpful for in-the-moment field responses. If a tree falls down and damages a mobile service tower, for example, it can be difficult for field operatives who don’t have access to all of the data relating to the incident to make decisions without sending crews out to the site.
The data-sifting abilities of LLMs can come into play here, making it easier to rapidly access all the data and make the big decisions. LLMs enable teams to access data from geospatial and location-based services, which, combined with satellite imaging, means operatives can see the full picture of what has occurred. As a result, they can send out the right person at the right time, achieving the peak efficiency that only AI can deliver.
How can AI positively impact sustainability efforts?
When it comes to efficiency, this is becoming ever more important when considering a telecom’s sustainability targets. Companies in the sector face growing pressure from consumers, investors, and regulators to reduce their carbon footprint and achieve net-zero emissions. At the same time, telecom organisations are facing rising demand for their services, sparked by initiatives such as remote work, digitisation and cloud-based solutions. Energy-efficient technologies such as autonomous networks will be a critical player in the global decarbonisation effort.
So what is an autonomous network? In the same way that an autonomous car might save fuel by intelligently staying at an optimal speed without unexpected acceleration or deceleration, autonomous networks automatically find the optimal configuration for the network, reducing waste. AI and machine learning (ML) can automate network management tasks, which leads to significant cost savings, faster response to network issues, improved experience for customers, and importantly reduced energy consumption. In the future, truly autonomous networks will handle their own energy consumption as well as their operations, heralding a new era of high performance and sustainability.
What is the future of AI in telecommunications?
The telecoms industry is more important to the global economy than it has ever been, and it stands at an important crossroads when it comes to technology. Intelligent use of automation and AI holds the potential to create happier customers, more sustainable operations and to drive innovative services.
Telco leaders must recognise the benefits of data discoverability, move beyond legacy technology and leverage AI and automation to build a more intelligent future. Given how integral telecoms is to wider industries and society, it’s not a transition to take lightly.
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