Operators worldwide will spend $1.1 billion annually on AI-RAN automation by 2030, according to a new forecast from research house SNS Telecom & IT. The figure anchors a market that today remains small relative to overall RAN investment, but which analysts expect to expand as operators confront rising network complexity and flat revenue.

The forecast covers spending on artificial intelligence applied to radio access network operations, a category that spans automated network planning, self-optimising base stations, predictive maintenance and AI-assisted traffic management. SNS Telecom & IT, a specialist telecom research firm, positions the segment as one of several AI-driven growth areas within operator capex and opex budgets.

The report arrives as mobile operators look for tools to manage networks that are steadily harder to run. 4G and 5G deployments have multiplied the number of cells, bands and parameters an operator must tune. AI-RAN products promise to shift that work from engineers to software.

What does this change for operators?

For network teams, the projection signals where vendor roadmaps are heading. Ericsson, Nokia, Huawei and Samsung have all made AI-driven RAN automation central to their 5G and 5G-Advanced pitches, and analyst forecasts of this kind tend to shape operator procurement timelines. A $1.1 billion market by 2030 implies meaningful but not transformative budgets. It is a fraction of the roughly $200 billion operators spend each year on mobile networks overall.

The commercial argument rests on operating cost. Vendors claim AI-RAN systems can cut energy consumption at cell sites, reduce truck rolls through predictive fault detection and lift spectral efficiency without new spectrum. Those claims come from vendors with a clear interest in selling the software; independent, large-scale field data remains limited. Operators evaluating the technology should treat vendor performance figures as targets to verify in trials, not settled results.

The standards context matters here. 3GPP has been expanding automation hooks across its releases, including work on network data analytics and intent-driven management in Release 18 and beyond. That standards work gives AI-RAN vendors a common foundation and reduces the risk of lock-in to proprietary interfaces. It also sets a rough timeline: capabilities standardised now reach commercial RAN software over multi-year release cycles, consistent with a market still building toward the end of the decade.

A slow build, not a surge

The shape of the forecast matters as much as the headline number. Spending that reaches $1.1 billion only by 2030 suggests gradual adoption, not a rapid shift. Operators typically fold RAN automation into scheduled software upgrades and network refreshes rather than buying it as a standalone product. Growth in this market therefore tracks the broader rhythm of 5G-Advanced rollouts and legacy RAN replacement cycles.

There is also a competitive dimension. Hyperscalers and AI specialists are pushing into telecom network operations, competing with incumbent RAN vendors for the same automation budgets. Which side captures the $1.1 billion depends on whose tools prove easier to run across multi-vendor networks, a question several operator groups have made central to their open RAN work.

For now, the number to watch is 2030. SNS Telecom & IT's forecast gives operators and vendors a shared benchmark against which to measure whether AI in the RAN moves from pilot projects to standard practice over the next six years.

James Calloway, Telecom Gazette