For decades, telecom operators focused their capital primarily on moving data: building towers, laying fibre, and acquiring spectrum formed the backbone of their business. That focus is now shifting. In many markets, operators are allocating more funds to AI infrastructure as demand for computing power grows and regulations tighten around where data can be stored and processed.
Traditional telecom networks were designed to transport traffic efficiently, not to host continuous, intensive AI workloads close to where data is generated. Routing data back and forth to distant cloud regions introduces cost, latency, and regulatory risk. For many emerging applications, that model is no longer adequate.
Industry research shows this transition is becoming widespread. According to Omdia’s report, Telcos’ Strategic Investments in AI Infrastructure, operators around the world are ramping up capital expenditure on AI infrastructure to address rising compute needs and sovereignty requirements that often favour local providers and domestic solutions. These pressures are causing telcos to reconsider where and how they invest and what role they want to play in national and regional digital ecosystems.
Practically, this means increased spending on data centres, cloud platforms, specialised accelerators, and software capable of supporting AI training and inference. Some operators are expanding existing facilities; others are building new data centres or upgrading edge sites so data can be processed closer to users, factories, or public services.
Sovereignty rules push compute closer to home
National and regional initiatives are reinforcing the trend toward localised compute. Programmes such as the European Union’s planned AI Gigafactories aim to establish large-scale compute capacity within Europe. These initiatives create opportunities for telecom operators to lead or co-lead major infrastructure projects rather than cede them entirely to global cloud providers. For governments focused on control, resilience, and data location, local operators often appear to be safer partners.
Data sovereignty remains a central driver. Regulators are tightening rules in sectors like healthcare, finance, defence, and public administration. Businesses that operate in these areas increasingly demand assurances that sensitive data remains within national borders.
Telecom operators, already subject to local regulation and oversight, are well positioned to provide that assurance when connectivity and compute are integrated under one roof.
Early revenue signals, uneven paths forward
Signs of monetisation are emerging, even if the market is still at an early stage. Omdia’s analysis highlights initial revenue signals for AI infrastructure. For example, in South Korea, data centre services represented about 4% of SK Telecom’s revenue in the third quarter of 2025, with the operator targeting KRW 1 trillion in related revenue by 2030.
In the Middle East, Ooredoo anticipates digital infrastructure will contribute 12% of group revenue by 2030, up from 3% in 2025. While these figures remain modest compared with core connectivity today, they indicate a shifting revenue mix.
Operators are supporting the transition with multi-year capital commitments. Omdia finds telcos across Asia, Europe, Canada, and the Middle East investing heavily in cloud platforms, data centres, GPU-as-a-service offerings, and AI-enabled radio access networks. For many groups, this involves long-term planning rather than one-off projects.
There is no single blueprint. Approaches vary in structure, risk appetite, and ambition. Some operators have created dedicated subsidiaries to house digital and AI infrastructure, such as STC’s Centre3 or Iliad’s Scaleway. Others pursue joint ventures, as Singtel has done. For companies like SK Telecom, SoftBank, and Ooredoo, AI infrastructure is part of a broader strategic realignment rather than a peripheral business.
In some cases, the shift reflects a rebalancing of capital allocation. As returns from traditional connectivity face pressure, a smaller share of CAPEX flows into legacy network expansion, with more directed toward compute and digital platforms. That reallocation carries risk: AI infrastructure is capital-intensive, demand forecasts can change quickly, and running these platforms requires new technical and operational skills many operators are still building.
Rather than chasing every possible use case, many telcos are concentrating on areas where AI and network assets naturally intersect. Common focus areas include private networks for industrial sites, video analytics, traffic optimisation, energy management, and fraud detection. In these scenarios, the value of keeping local data, ensuring low latency, and delivering immediate AI-driven outcomes is easier to demonstrate to customers.
As Julia Schindler, Principal Analyst, Strategy, at Omdia, observes: “AI infrastructure has become a bet for telcos. The rapid growth of AI traffic, in combination with national sovereignty initiatives, creates a unique opportunity that more telcos will want to capture in the future.”
Whether that bet pays off will depend on execution. Underused AI capacity could strain finances, while platforms that support clear demand and steady workloads could move operators beyond competing solely on price. What is clear is that the old model—where networks simply carried data to distant clouds—is showing its limits.
As AI becomes integrated into everyday business systems, telecom operators are adjusting investment plans to keep compute and intelligence closer to the network and closer to home.
(Photo by David Arrowsmith)