AI-RAN Automation Spending Is a $1.1B Opportunity—But the Real Story Is the Fight Over Where Intelligence Lives

The Headline Number

A new report from SNS Telecom & IT projects that annual spending on AI-RAN automation and optimization software and services will reach $1.1 billion by the end of 2030, growing at a 9% CAGR over the next four years. That figure sits inside a wider RAN automation market—traditional SON and proprietary network-level automation, AI-for-RAN upgrades, Open RAN SMO/RIC and d/x/rApps, RF planning tools, and automated test and measurement—that is expected to grow at roughly 4% annually over the same period.

The gap between those two growth rates is the story. The overall RAN automation market is a mature, slow-growth bucket dominated by incumbent tooling. The AI-RAN slice is where the incremental dollars are going, and where the architectural arguments that will define the next five years of Open RAN are being fought.

For operators and vendors tracking this space, the report is less a market forecast than a map of a contested battlefield. The question is not whether AI will be embedded in the RAN. It already is. The question is where that intelligence sits, who controls it, and whether the Open RAN ecosystem can capture the value or watch it get absorbed into proprietary software subscriptions.

Two Paths to the Same Destination

SNS describes two distinct investment vectors driving the $1.1 billion figure. The first is centrally coordinated automation at the network level—SMO and Non-RT RIC deployments running AI/ML-enabled rApps. This is the path most closely associated with the Open RAN architecture as originally conceived: standardized interfaces, multi-vendor interoperability, and a control loop operating on timescales of a second or more.

The second is AI-for-RAN software upgrades embedded directly in RAN nodes—real-time intelligence that replaces static, rule-based algorithms with learned behavior. This is the path where incumbent vendors have a structural advantage, because they already own the baseband and can ship intelligence as a subscription license rather than as a standalone application.

The report notes that reluctance among DU/CU vendors has held back adoption of xApps and the Near-RT RIC. That is a polite way of describing a years-long standoff. Vendors with large installed bases have little incentive to open their baseband to third-party applications that could commoditize their differentiation. Operators, meanwhile, have been unwilling to pay a premium for openness that delivers uncertain performance gains.

The E3 Interface: A Way Around the Logjam

Into that standoff comes the E3 interface, which SNS flags as a source of growing interest. E3 provides a standardized mechanism for dApps to interface directly with Open RAN baseband nodes, enabling closed-loop control beyond the 10-millisecond latency limits that constrain xApps in the Near-RT RIC.

The use cases SNS lists are not marginal. Lower-layer performance optimization, geolocation, integrated sensing, spectrum sharing, and interference cancellation in 5G and future 6G deployments all sit in the sub-10ms domain where the Near-RT RIC cannot reach. If E3 works as intended, it opens a class of applications that were previously the exclusive province of vendor-internal algorithms.

That is a significant architectural development, and it explains why the Open RAN blog has already covered the E3 breakthrough in depth. What the SNS report adds is a market-sizing lens: E3 is not just a technical curiosity, it is the mechanism by which the dApp economy could become a real revenue pool rather than a slide in a conference deck.

The Proprietary Alternative

SNS is careful to note that E3 is not the only path being pursued. Some vendors are offering proprietary AI-enabled, real-time optimization capabilities as software upgrades to existing RAN platforms, sold through subscription-based licensing models.

This is the crux of the competitive dynamic. An operator can buy a subscription that delivers measurable performance, energy efficiency, and operational autonomy improvements today, with no integration risk and no multi-vendor testing burden. Or the operator can invest in an open dApp ecosystem that promises more choice and potentially lower long-run costs, but requires standards to mature, interfaces to stabilize, and a supply of third-party applications that may or may not materialize at scale.

For a CFO looking at a 2027 budget, the proprietary subscription is the easier decision. For a CTO thinking about 2030 vendor leverage, it is the more dangerous one. The $1.1 billion market SNS describes will be split between these two models, and the split matters enormously for the Open RAN ecosystem.

Why the Growth Rate Is Only 9%

A 9% CAGR is healthy but not explosive. It is worth asking why AI-RAN automation is not growing faster, given the volume of AI-RAN announcements crossing the wire every week.

The answer is that automation spending is gated by deployment reality, not by enthusiasm. SMO and Non-RT RIC rollouts require operational integration work that does not scale linearly with marketing spend. rApps must be validated against specific network configurations. dApps depend on E3 interfaces that are still being standardized and implemented. And the embedded AI-for-RAN upgrades are, by definition, tied to the installed base of RAN nodes that can support them.

The 4% growth rate for the wider RAN automation market tells a similar story. Traditional SON and proprietary automation tools are not disappearing—they are being maintained and selectively upgraded. The transition to AI-native operations is incremental, not a rip-and-replace event. Operators are layering intelligence onto existing infrastructure rather than rebuilding from scratch.

That is a more sober picture than the AI-RAN hype cycle would suggest, and it is consistent with what we have seen in field trials and commercial launches over the past year. The technology works. The economics are harder.

The rApp and dApp Economy: Where Value Accrues

If the $1.1 billion figure is going to grow beyond 2030, the rApp and dApp economy has to become real. That means a functioning market with multiple suppliers, standardized packaging, and clear attribution of performance gains.

Today, that market is nascent. rApps exist, but they are often bundled with SMO platforms rather than sold as independent products. dApps are even earlier, with the E3 interface only recently proposed and implementation experience limited. The report’s framing—that operators are “increasingly pursuing real-time dApps and embedded AI-for-RAN software upgrades”—suggests demand is building, but supply is still catching up.

The vendors that win in this market will be the ones that can demonstrate measurable outcomes: spectral efficiency gains, energy savings, reduced operational headcount, faster fault resolution. The Open RAN blog has covered several field trials that point in this direction, including NVIDIA AI Aerial’s reported 3x spectral efficiency improvement in SoftBank trials and Nokia’s expanding AI-RAN field trial program across eight operators. Those data points matter because they shift the conversation from architecture to economics.

The Energy Efficiency Angle

One of the most commercially compelling applications of AI-RAN automation is energy efficiency. RAN energy consumption is a significant and growing operational cost for operators, and AI-driven sleep modes, load balancing, and traffic steering can deliver measurable savings without degrading user experience.

SNS lists energy efficiency as one of the three primary motivations for AI-RAN investment, alongside performance and operational autonomy. That triad is important because it maps directly to operator KPIs. Performance improvements justify capex. Energy savings justify opex reduction. Operational autonomy justifies headcount redeployment. Together, they form a business case that does not depend on speculative new revenue streams.

This is where AI-RAN automation differs from some of the more exotic AI-RAN narratives around generative AI at the edge or network-as-a-service monetization. Energy efficiency and performance optimization are unglamorous but bankable. They are also the applications most likely to drive the $1.1 billion in spending SNS projects.

The Vendor Landscape

The report does not name specific vendors, but the competitive dynamics are visible from the broader news flow. Nokia has launched a commercial AI-RAN platform and is running field trials with eight operators. Ericsson has launched AI-in-RAN subscription offerings and landed a sole global role in SK Telecom’s AI-RAN pilot. Samsung is developing AI-native network technology with LG Uplus, Verizon, and SoftBank, and participating in NVIDIA-led AI-RAN research with all three Korean carriers. NVIDIA is positioning its GPU platforms as the compute substrate for AI-RAN, with AI Aerial as the software layer.

On the operator side, SK Telecom has been the most aggressive, with a national AI-RAN test network and now a Network X innovation award for AI-RAN-based autonomous vehicle movement. The company’s AVM technology uses lidar sensors and an AI-RAN base station to build a real-time digital twin, calculate collision-free routes, and issue driving commands to vehicles—including ordinary vehicles retrofitted with a module and software. SK Telecom plans to demonstrate the technology at a domestic automobile plant in 2027 through a state-funded project, then expand to ports and airports.

That use case is notable because it goes beyond traditional telecom services into what SK Telecom calls “physical AI.” It is a reminder that AI-RAN is not just about making existing networks more efficient. It is about creating new service categories that justify the investment.

What Operators Should Watch

For operators evaluating AI-RAN automation investments, the SNS report suggests several practical considerations.

First, the SMO and Non-RT RIC layer is the near-term opportunity. rApps that deliver measurable energy and performance gains are available today, and the integration work is manageable. Operators that have not yet deployed SMO should be asking why.

Second, the E3 and dApp layer is the medium-term bet. The interface is promising but not yet mature. Operators should be tracking standards progress and vendor implementation roadmaps, and should be cautious about committing to proprietary alternatives that foreclose the open path.

Third, the embedded AI-for-RAN subscription model is the competitive threat. Every proprietary AI upgrade an operator buys is a dollar that does not flow into the open ecosystem. Operators that care about long-term vendor leverage should be explicit about how much of their automation budget goes to open interfaces versus proprietary upgrades.

Fourth, the economics have to be proven, not assumed. The 9% CAGR reflects a market that is growing steadily but not explosively. Operators should demand measurable outcomes from AI-RAN investments and be prepared to walk away from tools that do not deliver.

The Bottom Line

The $1.1 billion AI-RAN automation market by 2030 is a credible forecast, and it represents a real opportunity for the Open RAN ecosystem. But the forecast also contains a warning. The wider RAN automation market is growing at only 4%, and much of the AI-RAN growth could be captured by proprietary software subscriptions rather than open interfaces.

The E3 interface is the most important technical development to watch, because it determines whether the dApp economy becomes real. The SMO and Non-RT RIC layer is the most important commercial development to watch, because it determines whether operators build the operational muscle for AI-native networks. And the energy efficiency and performance optimization use cases are the most important business cases to watch, because they determine whether AI-RAN spending survives contact with the CFO.

The architecture is converging. The intelligence is coming. The only question is who captures the value.

Sources

• AI-RAN Automation & Optimization a $1.1 Billion Opportunity
• SK Telecom Wins Global Innovation Award for Driverless Vehicle Transport - Seoul Economic Daily
• SK Telecom Wins ‘Network X’ Innovation Award for AI-RAN Driverless Vehicle Transport Technology — BigGo Finance
• Samsung showcases AI-driven 6G in Silicon Valley, expands Korea–US carrier ties - CHOSUNBIZ
• Samsung Puts AI Inside Base Stations, Expands Global 6G Alliances - Seoul Economic Daily
• MTN Opens 2026 Technology Hackathon to African Innovators and Employees - TechAfrica News
• The week in AI-native telco (3-9 Oct 2026), The AI-Native Telco | TelecomTV