The Connectivity Backbone: Telecoms in the AI era

Since the pandemic, there has been a shift in the telecommunications industry. Connectivity in the home came under scrutiny, as more people began to work from home, and a boom in unhappy customers brought the fragility of networks into the light. This was the start of digital transformation accelerating across industries, as businesses began to refocus on improving the customer experience. After years of telcos attempting to diversify into the world of media and entertainment, their minds have been refocused. Telcos are now looking to fulfil the promise of their original manifesto: providing robust connectivity to it’s customers. To do this, telcos are now investing more money and time into providing better services.

Much of the investment is being directed towards artificial intelligence (AI). Like many other industries, telcos have woken up to the power of the technology that not only can help with improving services but also with more human-centric roles to make day-to-day tasks easier too.

With 6G coming down the line and consumer demand for seamless services greater than ever, providing high-performing connectivity is key. If AI is to be successful at helping achieve this mission, telcos need to ensure they have access to large volumes of high quality data.

“Why is my broadband so slow?”

Telecoms networks are highly complex. They consist of multiple generations of technologies, and many different interconnected systems – which makes predicting and preventing network failures and downtime extremely challenging. Networks also have physical elements to contend with – such as the weather and volume of traffic on the network. AI can show enormous value in tackling issues in both of these areas. In fact, an Accenture analysis estimates that AI has the potential to reduce network downtime by up to 50% for telecom companies.

This is because AI systems can do things that humans simply can’t, and in the blink of an eye. Take outages and network faults. An AI can identify patterns of weather that can be layered with machine learning (ML) algorithms trained on past incidents. By analysing previous instances of adverse weather, and their likelihood to occur again, these two technologies can recommend preventative measures engineers can take to avoid an outage. An example might be predicting the severity of high wind, with AI able to make a call on whether telco towers need more robust defences to avoid being taken offline.

AI and ML takes a lot of the guesswork and grunt work out of engineers’ hands, enabling them to address problems before they become a major issue. When a major issue does occur, AI-powered decisions also reduce the mean time to repair drastically.

Proactivity in predicting surges of traffic and advising customers can also be aided by AI. Systems can be trained to autonomously manage and optimise network workloads, enabling telcos to make informed decisions about what technologies should be used at times of high demand. There are a range of technologies available to manage demand – from 2G-5G in wireless networks, and copper to fibre in wired networks. Most telcos will have these capabilities in use – all of which are useful for different solutions and enable telcos to be flexible. But only if they’re smart about how to use them to provide network stability.

For example, the pandemic put a huge strain on networks. Parents were working at home, whilst their children were streaming TV shows and playing games online. This stretched fibre networks and slowed speeds dramatically. But with AI powering decisions and identifying bottlenecks, telcos were able to come up with solutions to solve this problem and advise customers on how to get the best service. These remediations were often counter-intuitive, such as asking parents to stream TV on wireless networks instead of fibre, but they made a huge difference in keeping the network stable and customers happy.

AI is only as strong as its data

As AI continues to mature, other use cases will emerge. But organisations need to understand that AI is only as good as the data it learns from before letting it loose on the network. Models trained on data from only a subset of an organisation’s data may miss crucial insights, or provide “hallucinated” responses.

With the global AI market for telcos projected to grow from $1.2 billion in 2021 to almost $40 billion by 2030, AI solutions are clearly the future of the industry. So, it’s essential that the technology is unbiased, fair, secure, and well-rounded, which relies on clean and accurate data.

So, organisations must build AI use cases from robust groundwork, giving it access to a complete set of data. This will require a modern data architecture built around a unified data platform that enables AI to draw insights from data across the enterprise – from cloud environments to on-premise data centres. Strict governance must also be enforced always and everywhere, ensuring that compliance is met.

Honing connectivity with AI

Having refocused on core offerings, AI will play a crucial role as telcos look to deliver a better service. With 6G networks on the horizon, the landscape will only become more complex. It’s vital that telcos prepare now and unify their data so services are unhindered.
Without quality data powering AI models, there will be a risk of the AI failing and misunderstanding context – or even providing inaccurate recommendations that will tarnish brand reputation. To fully unleash AI’s potential, the industry must prioritise curating diverse, unbiased data, coupled with thoughtful data practices to protect that data. AI has the potential to revolutionise the industry, but only if it’s built on solid foundations.

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Anthony leads the communication, media, and entertainment industry business for Cloudera, a global leader in hybrid data cloud solutions. He is responsible for developing and executing the industry strategy, driving thought leadership, leading sales, and a globally matrixed team across marketing, product management, and sales enablement. He is also an IBM Industry Academy alumnus.

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