AI-RAN automation spending to hit USD $1.1bn by 2030

SNS Telecom & IT forecasts annual spending on AI-RAN automation and optimisation will reach USD $1.1 billion by 2030, according to its latest market research on radio access network automation.

The estimate covers software and services that use artificial intelligence and machine learning to automate and optimise mobile radio networks. It indicates operators will spend more as they expand service management and orchestration platforms, deploy non-real-time RAN intelligent controllers, and adopt software upgrades that apply AI directly within RAN platforms.

Radio access networks account for a large share of mobile infrastructure costs and energy use, making them a key focus for operators seeking greater autonomy in network operations. The report places RAN automation within a broader shift towards autonomous networks, as mobile groups aim for highly autonomous and eventually fully autonomous operations.

SNS forecasts annual spending on AI-RAN automation and optimisation will grow at a compound annual growth rate of 9% over the next four years. The broader RAN automation market, which includes traditional self-organising network tools, proprietary automation systems, RF planning tools, and automated test and measurement products, is expected to grow by about 4% over the same period.

Operator uptake

The research points to a market shift from older self-organising network technology to more open, application-based architectures built around RAN intelligent controller and service management and orchestration platforms. In this model, software applications known as rApps, running on the non-real-time RIC layer, are emerging as a preferred route to network-wide automation across multi-vendor environments.

Adoption is increasing among operators including AT&T, Verizon, Telus, Vodafone, Deutsche Telekom, Swisscom, and Telstra. The report adds that several operators, including Rakuten Mobile, China Mobile, STC, and TDC NET, have already reached a limited level of highly autonomous operation for specific use cases in live RAN environments.

In some cases, those deployments have produced measurable results. According to the research, benefits range from energy savings of 20% without harming customer experience to reductions of as much as 60% in cell outages during major events such as the Hajj pilgrimage.

Real-time tools

The market outlook also highlights debate over the technologies likely to drive the next phase of automation. While uptake of xApps and the near-real-time RIC has been slowed by resistance from distributed unit and centralised unit vendors, interest is growing in the proposed E3 interface, which would allow dApps to connect directly with Open RAN baseband nodes.

This approach is intended to support closed-loop control beyond the 10 millisecond latency limits associated with xApps. Potential applications include lower-layer performance optimisation, geolocation, integrated sensing, spectrum sharing, and interference cancellation in 5G and later 6G networks.

Some suppliers are taking a different route by offering AI-based real-time optimisation as software upgrades to existing RAN platforms under subscription licensing models. The report describes this segment, often referred to as AI-for-RAN, as the most commercially mature part of the broader AI-RAN theme.

Beyond public mobile networks, the study says RAN automation is also gaining ground in other parts of the cellular sector. It cites uses including non-terrestrial network resource orchestration, satellite and terrestrial interworking, private 5G network management, adaptive power control in offshore deployments, integrated sensing and communications for counter-drone operations, and emission control for tactical 5G networks.

6G outlook

The report links part of the long-term opportunity to the arrival of 6G networks, which it says are being designed as AI-native systems from the outset. That approach is expected to expand the role of software-led innovation and move operators closer to fully autonomous networks.

It also points to a larger role for RAN digital twins, which can test AI-enabled air interface features and train optimisation algorithms in virtual environments before they are introduced into live networks. That could help operators validate changes before applying them to commercial infrastructure.

The findings are drawn from SNS Telecom & IT's report on RAN automation in the AI-RAN, which examines market drivers, barriers, use cases, technology trends, standardisation, and regional forecasts across the sector.