Progress in telco cloud: How do we measure agility?

As we move into the Coordination Age, telcos are under increasing pressure to innovate and find new sources of revenue growth. AI is one of a host of new technologies which can help them to achieve this.

At STL Partners we speak of the Coordination Age, the third age of telecoms. The first age of telecoms was the Communication Age and was about connecting people, first through telegraph and then through telephony. The Information Age was the second age of telecoms and began with the inception of the Internet. The Coordination Age will be defined by an increase in the number of devices that are connected as IoT becomes widespread, this will lead to a massive increase in data volumes and a need for greater resource efficiency.

Figure 1: The Coordination Age

Source: STL Partners

The Coordination Age coincides with a time of stagnating telco revenue growth. Telcos need to find new channels of revenue growth and move to a more decentralised B2B2X business model. 5G will be a key technology for helping telcos to re-invigorate revenue growth, but there will also be a key role for other technologies. Artificial intelligence should be at the centre of telco strategy in the Coordination Age.

Artificial intelligence (AI) is the ability of a computer or a robot controlled by a computer to do tasks that would usually be carried out by humans as they need human intelligence or decision making.

AI can be applied at all stages of a telco’s operations. It will allow telcos to better utilise the vast wealth of data that is available to them. As other technology is adopted (5G standalone, Open RAN, IoT, and the move to edge computing and greater automation) the role of AI will only grow, enabling telcos to manage and optimise operations, as well as to plan for future deployments more easily. It should enable greater automation which is integral to the Coordination Age.

There are many use cases for AI in telecoms and in this article we run through some of those that we believe will be key for telcos in the Coordination Age.

Make network innovation the engine of growth

Our Network Innovation service supports telcos and vendors in the creation and adoption of innovative technologies and services

Book a demo

AI use cases for telco networks

STL Partners has previously written several reports looking at AI in telecoms, including this report looking at AI use cases in the network. We split the use cases into 3 main categories:

1. Fault detection, prediction, and resolution: speeding up the process of identifying and resolving network faults, including predictive maintenance. Having a functioning network is vital when seeking to move to a B2B2X model.
2. Network optimisation: routing traffic and balancing workloads across telco infrastructure to deliver a cost-effective service at the highest quality possible. While this could be a manual process, automation allows network engineers to allocate time to other important areas.
3. Network planning and upgrades: this will be particularly important with the rollout of 5G. Making sure that networks are deployed in an optimised and efficient way is a key aspect of the Coordination Age.

There are a number of sub-use cases within each of these 3 overarching categories.

Within fault detection, prediction, and resolution, root cause analysis is an important use case. There is a huge bank of data on historical network faults and machine learning (ML) models and AI can learn to predict future network faults. Even for newer sources of network faults, AI can work to ensure processes predict faults beyond what current models or network engineers could. AI can also help with fixing the actual problems. Using this huge bank of data AI models can calculate different solutions by speed, cost, or impact on customer experience. Again, this will save network engineers time and resources.

Use cases within network optimisation are likely to target either cost or quality of service. Regarding cost, AI may be used to maximise the use of existing network assets or perhaps to prioritise and plan firmware updates for times that will be least disruptive for customers. For quality of service, AI may optimise the distribution of traffic between cell sites, maybe due to service disruptions, and then revert to the default configuration after the event or peak is over.

Within network planning there are a multitude of potential use cases for AI. For example, AI can map rural populations with real network coverage to identify underserved areas, Telefónica have already deployed it in this way (see figure below). This may be used for 4G networks in less developed countries, but it has its uses for 5G as well. Factories or other commercial sites outside of urban areas could be mapped to ensure that 5G coverage includes these areas that are likely to take advantage of IoT and other applications. It is important for telcos to enable applications such as these as we move into the Coordination Age.

Figure 2: Telefónica have used AI for network planning and maintenance

Source: STL Partners

Other AI use cases

There are use cases that go beyond telco networks. We now run through a couple of the most prominent examples.

AI in customer service

Virtual Assist for Customer Support is one of the most popular uses of AI across industries, not just for telecoms. There has never been a greater demand for telecommunication services, not just due to the Covid-19 pandemic but also by virtue of the fact that more and more of the world’s population is gaining access to technology and connectivity. This will only increase as 5G causes more and more devices to be connected, hence the Coordination Age is defined by increasing data volumes.

This increase in data volumes will inevitably lead to an increase in demand for customer service within telecommunications. As highlighted earlier in this article, AI will be able to minimise the faults in the network, but it can help in other ways. By equipping customer support lines, whether online chats or over the phone, with AI you will not only improve customer satisfaction by cutting wait times down, but telcos can also save costs by using fewer human operators. AI could either support a chatbot and hence resolve queries automatedly, or it can support customer service employees with information such as next best action to solve a customer’s query or routing customers to the most appropriate agent. There is increasing appetite for virtual assistants, indeed 74% of organisations view conversational intelligent virtual assistants as an important enabler of successful customer engagement.

AI in marketing

AI is also increasingly being put to use in marketing. Telcos should seek to take advantage of these use cases as well, Google research found that 90% of marketers believed that personalisation significantly contributes to business profitability. The massive amount of data that telcos have available on customers puts them in a good position to personalise their marketing for individual customers. The use cases may include:

• Personalised ads and messaging: if you can automate the process of targeting the right customers with the right content when they need it, this should bring increased sales.
• Customer segmentation: AI can help telcos to obtain more granular segmentation for groups of customers.
• Predictive analytics: this should help with retaining customers or pushing them onto more profitable solutions for the telco.

Data monetisation

Another area where there are a lot of use cases is in telco data monetisation. This involves telcos selling their data or insights from their data to third parties. These third parties will often be enterprise customers, and previous STL Partners research looked at 200+ use cases spread across twelve different verticals within this space.

Robotic Process Automation

An automation use case that can run along AI use cases is Robotic Process Automation (RPA). This involves the automation of rules-based business processes, configuring software to capture and interpret applications for processing a transaction, manipulating data, triggering responses, and communicating with other digital systems. Essentially it means getting software to emulate the actions of a human interacting with digital systems to carry out business processes. This should enable telcos to reduce costs and increase efficiency.

Why do telcos need AI?

These use cases and many more that have not been mentioned in this article will be essential for telcos in the Coordination Age. There is a need for innovation to encourage revenue growth and ensure that enterprise customers do not move to the hyperscalers and other big tech players. Enterprises need automated, programmable networks that are highly flexible and adaptable to a wide range of customer requirements. If telcos do not sustain momentum in implementing AI and automation in their networks and services, then others including tech players will find ways around that, as they did in 4G by running services independently from networks.

These groups of competition are already using AI on a wide scale, but even if telcos do not feel under pressure from external groups, they should be wary of within their own market. Telcos who buy into new technologies including AI will gain an advantage and may pull ahead of competitors. Indeed, according to this report by Anodot, CSPs that are serious AI adopters with proactive strategies report current profit margins that are 5 – 7 percentage points higher than the industry average.

AI will be one of a number of new technologies that telcos will need to use as we enter the Coordination Age. In a previous report, STL Partners estimates that telcos can save up to 7% of annual revenues through the adoption of AI, automation, and analytics. Telcos cannot afford to miss out on these benefits.

Author

Matt Bamforth

Senior Consultant

Matt is a Senior Consultant at STL and has experience in consulting projects across a wide range of topics. These span areas such as 5G, private networks, telco cloud, and edge computing. Matt has previous experience in strategy consulting, as well as in the Fintech sector. He holds a BSc in Economics from University College London.