How AI could drive growth in European telecoms

Recent artificial intelligence (AI) developments have jumpstarted change in many sectors of the economy. The telecom sector is no exception, where much of the initial impact has been seen in customer service. However, AI is now increasingly being used for network efficiency, predictive maintenance, and smarter capital expenditure (capex) strategies. This transition is very welcome as telecom operators face increasing pressure to improve margins and reduce operational costs. The dual role of AI in enhancing operational efficiency while unlocking cost reductions makes it an essential tool for European telecom operators.

Customer service is perhaps the most well-known use case of generative AI in recent years. This is no different for telecoms; European telecom operators are already making significant strides forward with implementing AI for customer service. Generative AI for customer service enables not only cost savings but also the personalisation of services, as AI systems process large volumes of customer data. This allows operators to offer hyper-personalised services. By analysing user behaviour, AI can suggest tailored plans, add-ons, and solutions that meet individual needs. Swisscom, for instance, uses AI analytics to predict customer preferences and deliver tailored offerings (e.g., for their mobile subscription plans). In addition, offering personalised service also enables cross- and upselling by telecoms. This is where AI’s predictive analytics come in handy.

Capital expenditure remains a significant challenge for telecom operators, especially as the creditworthiness of telecoms has decreased over recent decades. In 2024, capex by European telecoms was roughly €50 billion. As 5G and fibre rollouts continue, capex expenditures will remain significant over the coming years and AI could aid telecoms with optimising these costs.

Firstly, we believe that AI will help with predictive maintenance. Traditionally, operators rely on periodic maintenance schedules, which can lead to either overspending or unexpected downtime. AI-driven predictive maintenance solves this issue by analysing real-time data from network equipment. Deutsche Telekom, for example, uses AI to predict hardware failures before they occur. By leveraging machine learning algorithms, operators can replace equipment only when necessary, significantly reducing maintenance costs and downtime.

Secondly, AI can aid telecoms with more efficient planning of network expansions. Through machine learning models traffic patterns, user behaviour, and infrastructure performance can be analysed to identify areas that require investment. This, in turn, ensures smarter capex allocation.

AI’s dual ability to reduce costs and enhance revenue opportunities makes it an important opportunity for telecom operators. European operators with a clear AI strategy – like Deutsche Telekom and Swisscom – are well-positioned to lead this transformation. As AI adoption scales up, telecom companies could improve their bottom lines and create innovative services that redefine customer experiences. In a rapidly evolving digital landscape, AI is more than just a tool for cost reduction – it could be one of the foundations for smarter networks and sustainable growth.