Stefan Pongratz, Author at Dell'Oro Group

Open RAN has made significant progress since the O-RAN Alliance was formed in 2018 to “reshape the RAN industry and ecosystem towards more intelligent, open, virtualized, and interoperable networks”. But the results have been mixed so far.

Cumulative Open RAN revenues are approaching $10 billion, the Open Fronthaul (OFH) interface has become an increasingly important requirement, and most leading operators now regard O-RAN, Cloud RAN, and AI-driven RAN as core pillars of their next-generation RAN roadmaps. However, multi-vendor RAN adoption and expectations remain limited. According to the latest 1H25 RAN report, market concentration—as measured by the Herfindahl-Hirschman Index (HHI)—is rising across several regions. The RAN market is now classified as “highly concentrated” (HHI > 2500) in five of the six tracked regions. This suggests that the supplier diversity element of the Open RAN vision is fading.

Market traction has also proven uneven. Open RAN initially scaled rapidly, fueled by large-scale deployments in Japan and the U.S. However, after this sharp rise, total Open RAN revenues declined by roughly 40% within two years, as activity outside early adopters failed to offset the slowdown in the U.S. and Japan. While some moderation was expected, the pace of deceleration was sharper than anticipated, in part because of weaker 5G investments overall.

Open RAN is beginning to show signs of stabilization. Preliminary data indicate that Open RAN revenues grew year-over-year (Y/Y) in 2Q25 and were nearly flat Y/Y in the first half, supported by easier comparisons, stronger capex tied to existing Open RAN deployments, and increased activity among early majority adopters.

Our long-term view has remained largely consistent since we began tracking the market in 2019. Long-term Open RAN growth prospects remain favorable. Although near-term headwinds and business case challenges persist, the broader trajectory continues to point toward greater openness, virtualization, intelligence, and automation across the RAN. We remain optimistic about the outlook for Open RAN and Cloud RAN in the latter part of the forecast period, though the attractiveness of the multi-vendor RAN model continues to be limited.

Additional highlights from the 2Q25 RAN report and August 2025 Open RAN 5-year forecast include:

The top 3 Virtualized RAN suppliers based on worldwide revenue (2Q25, 4-Quarter Trailing) are Samsung, Rakuten Symphony, and 1Finity (formerly Fujitsu).
Virtualized RAN revenue is expected to grow in 2025.
Multi-vendor RAN is projected to reach $2 billion to $3 billion by 2029.

For more information about our RAN and Open RAN coverage, please visit https://www.delloro.com/advanced-research-report/openran/

We recently attended the Telco AI Forum hosted by RCR Wireless. It was an excellent event, touching on many AI Telco topics. Some of the key takeaways related to AI RAN include:

Near-term focus on AI-for-RAN
Monetization is part of the long-term vision
Transition will be gradual
AI from day 1 with 6G

AI in the RAN has been around for decades. But the focus is shifting. Operators now focus on AI-for-RAN, meaning opex, performance, and efficiency improvements. In addition to the role AI can play in the automation journey and with energy savings, operators are increasingly looking at how to squeeze more out of the existing spectrum by using AI for channel estimation, MIMO, CA, and beamforming, among other things. Michael Irizarry, CTO of US Cellular, mentioned they are seeing 10% to 15% of potential efficiency gains in the network.

Monetization is part of the long-term vision. Not surprisingly and consistent with what we discussed in a recent AI RAN blog post, “AI RAN Should We be Excited“, there is strong consensus that AI RAN can improve the user experience, enhance performance, spur efficiency gains, reduce power consumption, and play a critical role in the broader automation journey. However, there is greater skepticism about AI’s ability to turn the RAN sites into profit engines. While many still buy into this concept of improving the overall site utilization by using the workloads for both RAN and AI, the key change over the past year is simply the realization that the GPU business case needs to be justified in the “RAN only” scenario. AI-and-RAN or multi-tenant RAN can improve the ROI, but it should not be a requirement to justify the investment.

The transition from RAN to AI RAN will be an evolution steered by the business case. Rob Hughes, head of wireless marketing at Fujitsu, envisions an approach similar to that of 5G SA. In some cases, the operators start small and go after specific pockets before expanding more broadly. Michael Irizarry with US Cellular believes rip & replace is unlikely. “The competitive nature of this Industry puts a lot of pressure on the margins, and the ROI will guide the investment cycle”. As the natural cycle ends, operators can assess their best options. Guy Turgeon, Senior Principal Industry Specialist at Red Hat, envisions that the operators that have already started the journey by moving to a cloud native architecture leveraging COTS HW might be in a better position from a readiness and skillset perspective. At the same time, there is no question that the asymmetric starting point between appliance-based RAN and COTS HW will impact the transition, and custom HW will likely comprise a significant share of the 5G AI-for-RAN market. The AI-RAN Alliance discussed the hybrid CPU/GPU roadmap.

AI RAN will be a reality from the start with 6G. While 6G does not change the existing site grid dynamics and the splits between C-RAN and D-RAN, the expectation is that AI will be a focus from the start with 6G. Since the base case scenario is that the “anchor” band for 6G will be in the upper 6 GHz+ range and the business case is hinging on carriers’ ability to leverage the existing macro grid, the expectation is that AI is needed for RAN optimization and in the MAC layer for scheduling, beam management, and MIMO optimization. Ofir Zemer, Vice President, Product Management at Qualcomm and responsible for its RAN Automation suite, believes a powerful agentic layer is essential for Level 3+ automation to manage the increased complexity in the networks (According to the TM Forum, the average operator is currently at Autonomous Networks Level 2, Level 3+ is expected by the time 6G comes around).

For more info, please visit the RCR Wireless Telco AI Forum site to watch the webinar on-demand.

The use of intelligence in the RAN is not new—both 4G and 5G deployments rely heavily on automation and intelligence to replace manual tasks, automate the RAN, manage increasing complexity, enhance performance, and control costs. What is new, however, is the rapid proliferation of AI and generative AI, along with a shifting mindset toward leveraging AI in cellular networks. More importantly, the scope of AI’s role in the RAN is expanding, with operators now looking beyond efficiency gains and performance improvements, cautiously exploring whether AI could also unlock new revenue streams. In this blog, we will review the scope and progress.

AI RAN Vision

Considering the opportunities with AI RAN, its evolving scope, the proliferation of groups working on AI RAN, the challenges of measuring its gains, and the absence of unified frameworks in 3GPP, it’s not surprising that marketing departments have some flexibility in how they interpret and present the concept of AI RAN.

Still, some common ground exists even with multiple industry bodies (3GPP, AI-RAN Alliance, ETSI, NGMN, O-RAN Alliance, TIP, TM Forum, etc) and key ecosystem participants working to identify the most promising AI RAN opportunities. At a high level, AI RAN is more about efficiency gains than new revenue streams. There is strong consensus that AI RAN can improve the user experience, enhance performance, reduce power consumption, and play a critical role in the broader automation journey. Unsurprisingly, however, there is greater skepticism about AI’s ability to reverse the flat revenue trajectory that has defined operators throughout the 4G and 5G cycles.

The 3GPP AI/ML activities and roadmap are mostly aligned with the broader efficiency aspects of the AI RAN vision, primarily focused on automation, management data analytics (MDA), SON/MDT, and over-the-air (OTA) related work (CSI, beam management, mobility, and positioning).

The O-RAN Alliance builds on its existing thinking and aims to leverage AI/ML to create a fully intelligent, open, and interoperable RAN architecture that enhances network efficiency, performance, and automation. This includes embedding AI/ML capabilities directly into the O-RAN architecture, particularly within the RIC/SMO, and using AI/ML for a variety of network management and control tasks.

Current AI/ML activities align well with the AI-RAN Alliance’s vision to elevate the RAN’s potential with more automation, improved efficiencies, and new monetization opportunities. The AI-RAN Alliance envisions three key development areas: 1) AI and RAN – improving asset utilization by using a common shared infrastructure for both RAN and AI workloads, 2) AI on RAN – enabling AI applications on the RAN, 3) AI for RAN – optimizing and enhancing RAN performance. Or from an operator standpoint, AI offers the potential to boost revenue or reduce capex and opex.

TIP is actively integrating AI/ML into its Open RAN vision, focusing on automating and optimizing the RAN using AI/ML-powered rApps to manage and orchestrate various aspects of the network, including deployment, optimization, and healing.

While operators generally don’t consider AI the end destination, they believe more openness, virtualization, and intelligence will play essential roles in the broader RAN automation journey.

What is AI RAN

AI RAN integrates AI and machine learning across various aspects of the RAN domain. For the broader AI RAN vision, the boundaries between infrastructure and services are not clearly defined, and interpretations vary. The underlying infrastructure (location, hardware, software, interface support, tenancy) varies depending on multiple factors, such as the latency and capacity requirements for a particular use case, the value-add of AI, the state of existing hardware, power budget, and cost.

AI-RAN, aka the AI-RAN Alliance version of AI RAN, is a subset of the broader AI RAN opportunity, reflecting AI RAN implementations utilizing accelerated computing and fully software-defined/AI-native principles. AI-RAN enables the deployment of RAN and AI workloads on a shared, distributed, and accelerated cloud infrastructure. It capitalizes on the demand for AI inferencing and converts the RAN infrastructure from a single-purpose to multi-purpose cloud infrastructure (NVIDIA AI-RAN Paper, March 2025).

While the ideal reference solution is AI-native/Cloud-native, AI RAN can be offered until that vision is achieved. The majority of the AI RAN deployments to date are implemented using existing hardware.

Why integrate AI and RAN

With power and capex budget requirements rising on the RAN priority list, one of the fundamental questions now is where AI can add value to the RAN without breaking the power budget or growing capex. It is a valid question. After all, RAN cell sites have been around for 40+ years, and the operators have had some time to fine-tune the algorithms to improve performance and optimize resources. AI can make sense in the RAN, but given preliminary efficiency gains, it will not be helpful everywhere.

Topline growth expectations are muted at this juncture. However, operators are optimistic that integrating AI and RAN will produce a number of benefits:

Reduce opex/capex
Improve performance and experience
Boost network quality
Lower energy consumption

AI can help introduce efficiencies that help to lower ongoing costs to deploy and manage the RAN network. According to Ericsson, Intelligent RAN automation can help reduce operator opex by 60%. AI will play an important role here, accelerating the automation transition, simplifying complexity and curbing opex growth. Most of the greenfield networks are clearly moving toward new architectures that are more automation-conducive. Rakuten Mobile operates 350 K+ cells with an operational headcount of around 250 people, and the operator claims an 80% reduction in deployment time through automation. China Mobile reported a 30% reduction in MTTR using Huawei’s AI-based O&M. Nokia has seen up to 80% efficiency gain in live networks utilizing machine learning in RAN operations.

The RAN automation journey will likely take longer with the existing networks. The average brownfield operator today falls somewhere between L2 (partial autonomous network) and L3 (conditional autonomous network), with some way to go before reaching L4 (high autonomous network) and L5 (full autonomous network). Even so, China Mobile recently reported it remains on track to activate its L4 autonomous networking on a broader scale in 2025. Vodafone is exploring how AI can help to automate multi-vendor RAN deployments, while Telefonica is implementing AI-powered optimization and automation in its RAN network. According to the TM Forum, 61% of the telcos are targeting L3 autonomy over the next five years.

AI can help improve the RAN performance by optimizing various RAN functions, such as channel estimation, resource allocation, and beamforming, though the upside will vary. Recent activity shows that the operators can realize gains in the order of 10 to 30% when using AI-powered features, often with existing hardware. For example, Bell Canada, using Ericsson’s AI-native link adaptation, increased spectral efficiency by up to 10 percent, improving capacity and reliability of connections, and up to 20 percent higher downlink throughput.

Initial findings from Smartfren’s (Indonesia) commercial deployment of ZTE’s AI-based computing resulted in a 15% improvement in user experience. There could be more upside as well. DeepSig, demonstrated at MWC Barcelona, its AI-native air interface, OmniPHY, running on the NVIDIA AI Aerial platform, could achieve up to 70% throughput gains in some scenarios.

With the RAN accounting for around 70% of the energy consumption at the cell site and comprising around 1% to 2% of global electricity consumption (ITU), the intensification of climate change, taken together with the current power site trajectory, forms the basis for the increased focus on energy efficiency and CO2 reduction. Preliminary findings suggest that AI-powered RAN can play a pivotal role in curbing emissions, cutting energy consumption by 15% to 30%. As an example, Vodafone UK and Ericsson recently showed on trial site across London that the daily 5G radio power consumption can be reduced by up to a third using AI-powered solutions. Verizon shared field data indicating a 15% cost savings with Samsung’s AI-powered energy savings manager (AI-ESM), Similarly, Zain estimates that the AI-powered energy-saving feature provided by Huawei can reduce power consumption by about 20%, while Tele2 believes that smarter AI-based mobile networks can reduce energy consumption in the long term by as much as 30% to 40%, while simultaneously optimizing capacity.

AI RAN Outlook

Operators are not revising their topline growth or mobile data traffic projections upward as a result of AI growing in and around the RAN. Disappointing 4G/5G returns and the failure to reverse the flattish carrier revenue trajectory is helping to explain the increased focus on what can be controlled — AI RAN is currently all about improving the performance/efficiency and reducing opex.

Since the typical gains demonstrated so far are in the 10% to 30% range for specific features, the AI RAN business case will hinge crucially on the cost and power envelope—the risk appetite for growing capex/opex is limited.

The AI-RAN business case using new hardware is difficult to justify for single-purpose tenancy. However, if the operators can use the resources for both RAN and non-RAN workloads and/or the accelerated computing cost comes down (NVIDIA recently announced ARC-Compact, an AI-RAN solution designed for D-RAN), the TAM could expand. For now, the AI service provider vision, where carriers sell unused capacity at scale, remains somewhat far-fetched, and as a result, multi-purpose tenancy is expected to account for a small share of the broader AI RAN market over the near term.

In short, improving something already done by 10% to 30% is not overly exciting. However, suppose AI embedded in the radio signal processing can realize more significant gains or help unlock new revenue opportunities by improving site utilization and providing telcos with an opportunity to sell unused RAN capacity. In that case, there are reasons to be excited. But since the latter is a lower-likelihood play, the base case expectation is that AI RAN will produce tangible value-add, and the excitement level is moderate — or as the Swedes would say, it is lagom.

Conditions improved in the second half, but overall, it was a challenging year for the telecom suppliers. Preliminary findings suggest that worldwide telecom equipment revenues across the six telecom programs tracked at Dell’Oro Group—Broadband Access, Microwave & Optical Transport, Mobile Core Network (MCN), Radio Access Network (RAN), and SP Router & Switch—declined 11% year-over-year (YoY) in 2024, recording the steepest annual decline in more than 20 years (decline was >20% in 2002), propelling total equipment revenue to fall by 14% over the past two years. This remarkable output deceleration was broad-based across the telecom segments and driven by multiple factors, including excess inventory, challenging macro environment, and difficult 5G comparisons.

In 4Q24, stabilization was driven by growth in North America and EMEA, which nearly offset constrained demand in Asia Pacific (including China).

The full-year decline was uneven across the six telecom programs. Optical Transport, SP Routers, and RAN saw double-digit contractions, collectively shrinking by 14% in 2024. Microwave Transport and MCN experienced a more moderate combined decline in the low single digits, while Broadband Access revenues were fairly stable.

Similarly, regional developments were mixed in 2024. While the slowdown was felt across the five regions — North America, EMEA, Asia Pacific, China, and CALA — the deceleration was more pronounced in the broader Asia Pacific region, reflecting challenging conditions in China and Asia Pacific outside of China.

Supplier rankings were mostly unchanged globally, while revenue shares shifted slightly as both Huawei and Ericsson positions improved. Overall market concentration was stable with the 8 suppliers comprising around ~80% of the worldwide market in 2024.

Rankings changed outside of China. Initial estimates suggest Huawei passed Nokia to become the #1 supplier, followed by Nokia and Ericsson. Huawei’s revenue share outside of China was up 2 to 3 percentage points in 2024, relative to 2021, while Ericsson is down roughly two percentage points over the same period/region.

Market conditions are expected to stabilize in 2025 on an aggregated basis, though it will still be a challenging year. The analyst team is collectively forecasting global telecom equipment revenues across the six programs to stay flat.

It was an intense week in Barcelona. After 50+ meetings during and before the event, below are some initial key takeaways.

RAN outlook remains tepid
Open RAN marketing is morphing
Vendor concentration will likely increase
Near-term AI RAN driven by cost efficiencies and performance improvements

Somber RAN Outlook

The RAN forecast remains unchanged, but downside risks persist. One of our primary objectives was to assess whether the 0% CAGR RAN forecast issued in January 2025 still holds. Our preliminary analysis indicates that our long-standing message remains valid—regional imbalances will continue to impact the RAN market in the near term, while the underlying fundamentals shaping the long-term trajectory will continue to exert pressure on the market.

Since RAN spending is constrained by capex, and capex is tied to operators’ revenue growth, the entire wireless industry is urgently seeking new revenue streams to break the cycle of increasing data consumption without corresponding increases in revenue. While we encountered numerous discussions and demos centered on charging premiums for guaranteed or enhanced performance, service providers recognize the difference between monetizing “fun” content and business-critical applications. For example, Uber may be willing to pay extra at airports to ensure timely pings for its users. However, expectations remain low for consumers to pay extra for faster video uploads from congested areas or an improved gaming experience. While AI may drive the development of new applications and content, it is unlikely to fundamentally change consumers’ willingness to pay for “fun” content.

With limited justification for revising carrier topline growth expectations, the focus remains on mobile data traffic growth and performance differentiation. Video accounts for approximately three-fourths of total mobile traffic but still represents a small fraction of the total time users spend streaming on cellular networks. As mobile data traffic growth slows, the industry is increasingly looking for a new device that could shift user behavior and, ultimately, increase video consumption. While a future dominated by smart glasses—where data is continuously recorded and uploaded—would present significant network challenges, we have to spread out the probabilities of any new device for the masses gaining traction.

The general sentiment from the event is clear: the slowdown in data traffic growth, combined with ongoing struggles to monetize consumer connectivity, remains a significant challenge. In this post-peak 5G rollout environment, even flat RAN projections are seen as an optimistic.

Open RAN Losing Marketing Steam

Open RAN is happening (>67% of Ericsson’s 2025 deliveries will be Open RAN prepared), but its marketing power is fading. Incumbent RAN suppliers prefer the Cloud RAN term, while the smaller suppliers are starting to look past Open RAN. Whether this is because the commonly used HHI (Herfindahl Hirshman Index) market concentration gauge was similar in 2024 as in 2018 when the O-RAN Alliance was formed, the original Open RAN multi-vendor vision is morphing, the entire RAN equipment market is down around $9 B, RAN outlook is flat, or the smaller suppliers are tired of waiting for larger Tier1 multi-vendor projects, the outcome is the same – the meaning of Open RAN is changing and marketing departments are aware (multiple suppliers are now looking to shift the message/focus).

In our 5-year forecasts, we track and show Open RAN, vRAN, Cloud-RAN, and multi-vendor RAN. However, our 10-year outlook consolidates the tracking/terms and only shows Cloud RAN.

Vendor Concentration Expected to Increase

Open RAN and Cloud RAN are unlikely to alter the long-term RAN concentration trajectory. After improving between 2020 and 2022—partly due to Open RAN adoption and market share shifts among the top five suppliers—the RAN HHI index rebounded in 2024. While we do not forecast HHI, historical trends suggest that market concentration is on the rise.

Although history is not always the best predictor of future outcomes, several factors indicate that a highly concentrated RAN market by 2030 is a strong possibility. These include recent RAN market developments, the scale required to sustain a competitive RAN portfolio, the ratio of greenfield to brownfield deployments (including FWA, enterprise 5G, and MBB), the challenges faced by smaller suppliers, and ongoing discussions about potential M&A activity.

AI RAN — Performance and Efficiency Gains in the Driver Seat

The use of intelligence in the RAN is not new—both 4G and 5G deployments rely heavily on automation and intelligence to replace manual tasks, manage increasing complexity, enhance performance, and control costs. What is new, however, is the rapid proliferation of AI in both consumer and enterprise domains, along with a shifting mindset toward leveraging AI in cellular networks. More importantly, the scope of AI’s role is expanding, with operators now looking beyond efficiency gains and performance improvements, cautiously exploring whether AI could also unlock new revenue streams.

Given the growing interest in AI RAN, it is no surprise that definitions and interpretations of AI vary across the industry. As the ecosystem gains a deeper understanding of AI’s value in RAN, definitions and expectations will likely continue to evolve.

Currently, the industry’s broader perspective aligns with the AI RAN vision outlined by the AI-RAN Alliance. At a high level, AI is expected to add value in three key areas: asset utilization, application growth, and RAN efficiency improvements. From an operator’s standpoint, AI offers the potential to either boost revenue or reduce capex and opex.

One of the observations in Barcelona was that near-term AI activity is primarily focused on cost savings and efficiency rather than topline growth. For example, China Mobile reported a 30% reduction in MTTR using AI-based O&M, Verizon shared field data indicating a 15% cost savings with Samsung’s AI-powered energy savings manager (AI-ESM), and an Ericsson AI-RAN demo at MWC demonstrated a 20% increase in throughput using AI to optimize performance in non-ideal radio conditions. Similarly, T-Mobile is evaluating how its collaboration with Nokia on AI-RAN can enhance network performance and efficiency.

With revenue growth stagnating, operators are exploring new revenue streams, showing interest in NVIDIA’s latest edge computing initiatives. However, they are also keenly aware of power, energy, and cost constraints at the cell site. The macro-RAN market, valued at approximately $30 billion, supports 1 to 2 million base stations annually, leaving little flexibility in DU pricing. For vRAN to compete with purpose-built RAN, server and acceleration costs must decrease rather than increase. While a GPU-driven RAN-only business model currently has limited viability, the potential for multi-purpose RAN supporting both RAN and non-RAN workloads presents a larger TAM.

That said, our overall impression is that the AI service provider (AI SP) vision—where carriers sell unused AI capacity at scale—remains somewhat farfetched for now. However, as costs and energy consumption decrease, the concept could have more potential in the future.

In short, it was another eventful show in Barcelona with a reasonable balance between hype and reality, perhaps because the RAN market is down nearly $9 B and the outlook remains tepid. Still, the event was also a reminder that there is a lot of innovation and activity underneath the flat topline trajectory. We did not cover all the MWC topics in this blog, but we will likely share more updates in the future.

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