Summary

Automation of the RAN (Radio Access Network) – the most expensive, technically complex and power-intensive part of cellular infrastructure – is a key aspect of mobile operators' AN (Autonomous Network) journeys towards L4 (Highly Autonomous) and eventually L5 (Fully Autonomous) operations. In conjunction with AI (Artificial Intelligence) and ML (Machine Learning), RAN automation has the potential to significantly transform mobile network economics by reducing the OpEx (Operating Expenditure)-to-revenue ratio, minimizing energy consumption, lowering CO2 (Carbon Dioxide) emissions, deferring avoidable CapEx (Capital Expenditure), optimizing performance, improving user experience and enabling new services. 

Rakuten Mobile, China Mobile, STC (Saudi Telecom Company), TDC NET and several other operators have already achieved a limited level of L4 autonomy for specific use cases in their live RAN operations. The proven, quantifiable benefits of this level of intent-driven, closed-loop RAN automation range from 20% energy savings without compromising customer experience to a reduction of up to 60% in cell outages during major events such as the annual Hajj pilgrimage. Beyond terrestrial public mobile networks, RAN automation is also gaining traction in other segments of the cellular industry, supporting applications ranging from NTN (Non-Terrestrial Network) resource orchestration and terrestrial-satellite interworking to agentic AI-enabled private 5G network management, adaptive power control in offshore cellular deployments, ISAC (Integrated Sensing & Communications) for counter-drone operations and emission control for tactical 5G networks.

The RAN automation market traces its origins to the early 2010s when SON (Self-Organizing Network) technology – in the form of both embedded D-SON (Distributed SON) and C-SON (Centralized SON) implementations – was introduced to reduce cellular network complexity through self-configuration, self-optimization and self-healing. The pursuit of higher levels of autonomy, coupled with the multi-vendor interoperability, scalability and latency-related limitations of legacy SON solutions, is accelerating the transition to Open RAN automation with standards-based components that enable greater levels of RAN programmability and automation. These include the Near-RT (Real-Time) and Non-RT RICs (RAN Intelligent Controllers), SMO (Service Management & Orchestration) framework, rApps (RAN Applications), xApps (Extended Applications) and dApps (Distributed Applications). 

In particular, centrally coordinated rApps hosted on the SMO and Non-RT RIC layer have emerged as the preferred approach to multi-vendor RAN automation on a network-wide basis, especially as hybrid and Open RAN architectures become increasingly common in new RAN swap and rollout contracts. Additionally, SMO platforms are being positioned as a common foundation for cross-domain network autonomy that links observability, intelligence, intent management and closed-loop automation. From Swedish wireless giant Ericsson to Spanish private 5G orchestration specialist Neutroon, multiple SMO providers have already incorporated core network interfaces for end-to-end management and automation.  

SMO/Non-RT RIC adoption is beginning to accelerate among brownfield operators – including but not limited to AT&T, Verizon, Telus, Vodafone, DT (Deutsche Telekom), Swisscom, STC, NTT DoCoMo, Chunghwa Telecom and Telstra – to enable network-wide automation across thousands of cell sites through RAN vendor-supplied, third-party and internally developed rApps. This follows greenfield operator Rakuten Mobile’s nationwide RIC deployment, along with in-house and third-party rApp integration, across its Open RAN-compliant footprint of 350,000 cells supplied by 10 distinct RU (Radio Unit) and DU/CU (Distributed & Centralized Unit) vendors. 

While reluctance among DU/CU vendors has held back adoption of xApps and the Near-RT RIC, there is growing interest in the recently proposed E3 (Edge‑Enhanced Exposure) interface, which provides a standardized mechanism for dApps to directly interface with Open RAN baseband nodes for closed-loop control beyond the 10-millisecond latency limits of xApps, including lower-layer performance optimization, geolocation, integrated sensing, spectrum sharing and interference cancellation in 5G and future 6G deployments. RAN vendors and application developers – such as Nokia, NVIDIA, Radisys, IS-Wireless, Cohere Technologies, DeepSig, ISCO International, Unikie, Booz Allen Hamilton and ODC (ORAN Development Company) – have already committed to support the E3 interface. An alternative to dApps being pursued by some vendors is to offer proprietary AI-enabled, real-time optimization capabilities as software upgrades to existing RAN platforms through subscription-based licensing models. For example, Ericsson has recently launched a software subscription offering for service providers that embeds AI/ML models directly into RAN basebands and radios to improve performance and efficiency through microsecond-level inference for use cases such as scheduling, link adaptation, beamforming and multi-layer coordination. 

Commonly referred to as AI-for-RAN, the deeper integration of AI, ML and their derivatives for RAN automation and optimization is the most commercially mature facet of the broader AI-RAN movement, which also encompasses AI-and-RAN – the co-hosting of RAN and external AI workloads on shared, GPU-accelerated infrastructure; and AI-on-RAN – the enablement of edge AI services for mobile subscribers. The launch of the first 6G networks – which are being standardized as AI-native systems from the outset – around 2030 is expected to significantly increase the scope for hardware-agnostic, software-led innovation and take the industry closer to fully autonomous network operations. RAN digital twins are also expected to play an increasingly important role, allowing operators to validate AI-enabled air interface features and train optimization algorithms in high-fidelity virtual environments before introducing them into live 6G networks.

Largely driven by growing investments in both centrally coordinated SMO/RIC-enabled automation at the network level and AI-for-RAN software upgrades for embedded real-time intelligence and the replacement of static rule-based algorithms in RAN nodes, annual spending on AI-RAN automation and optimization software and services is expected to grow at a CAGR of 9% over the next four years, eventually reaching $1.1 billion by the end of 2030. Total spending on the wider RAN automation market – which includes traditional SON/proprietary network-level automation, AI-for-RAN upgrades, Open RAN SMO/RIC and d/x/rApps, RF planning tools and automated test/measurement solutions – is expected to grow at a CAGR of approximately 4% during the same period. 

The “RAN Automation, SON, RIC, SMO, rApps, xApps & dApps in the AI-RAN Era: 2026 – 2040 – Opportunities, Challenges, Strategies & Forecasts” report presents an in-depth assessment of the RAN automation market, including the value chain, market drivers, barriers to uptake, enabling technologies, functional areas, use cases, key trends, future roadmap, standardization, case studies, ecosystem player profiles and strategies. The report also provides global and regional market size forecasts for RAN and end-to-end mobile network automation from 2026 to 2040. The forecasts cover three network domains, nine functional areas, three access technology generations, 12 use case categories, three network types, two supplier categories and five regional markets.

The report comes with an associated Excel datasheet suite covering quantitative data from all numeric forecasts presented in the report.

Key Findings

The report has the following key findings:

•     Largely driven by growing investments in both centrally coordinated SMO/RIC-enabled automation at the network level and AI-for-RAN software upgrades for embedded real-time intelligence and the replacement of static rule-based algorithms in RAN nodes, annual spending on AI-RAN automation and optimization software and services is expected to grow at a CAGR of 9% over the next four years, eventually reaching $1.1 billion by the end of 2030. 
•     Total spending on the wider RAN automation market – which includes traditional SON/proprietary network-level automation, AI-for-RAN upgrades, Open RAN SMO/RIC and d/x/rApps, RF planning tools and automated test/measurement solutions – is expected to grow at a CAGR of approximately 4% during the same period. 
•     Mobile operators are making tangible progress towards L4 RAN operations as they work towards the longer-term goal of L5 networks. Rakuten Mobile, China Mobile, STC, TDC NET and several other operators have already achieved a limited level of L4 autonomy for specific use cases in their live networks. The proven, quantifiable benefits of this level of intent-driven, closed-loop RAN automation range from 20% energy savings without compromising customer experience to a reduction of up to 60% in cell outages during major events such as the annual Hajj pilgrimage. 
•     The pursuit of higher levels of autonomy, coupled with the multi-vendor interoperability, scalability and latency-related limitations of legacy SON solutions, is accelerating the transition to Open RAN automation with standards-based components that enable greater levels of RAN programmability and automation.
•     In particular, centrally coordinated rApps hosted on the SMO and Non-RT RIC layer have emerged as the preferred approach to multi-vendor RAN automation on a network-wide basis, especially as hybrid and Open RAN architectures become increasingly common in new RAN swap and rollout contracts. Additionally, SMO platforms are being positioned as a common foundation for cross-domain network autonomy that links observability, intelligence, intent management and closed-loop automation. From Swedish wireless giant Ericsson to Spanish private 5G orchestration specialist Neutroon, multiple SMO providers have already incorporated core network interfaces for end-to-end management and automation.  
•     SMO/Non-RT RIC adoption is beginning to accelerate among brownfield operators – including but not limited to AT&T, Verizon, Telus, Vodafone, DT, Swisscom, STC, NTT DoCoMo, Chunghwa Telecom and Telstra – to enable network-wide automation across thousands of cell sites through RAN vendor-supplied, third-party and internally developed rApps. This follows greenfield operator Rakuten Mobile’s nationwide RIC deployment, along with in-house and third-party rApp integration, across its Open RAN-compliant footprint of 350,000 cells supplied by 10 distinct RU and DU/CU vendors.
•     While reluctance among DU/CU vendors has held back adoption of xApps and the Near-RT RIC, there is growing interest in the recently proposed E3 interface, which provides a standardized mechanism for dApps to directly interface with Open RAN baseband nodes for closed-loop control beyond the 10-millisecond latency limits of xApps, including lower-layer performance optimization, geolocation, integrated sensing, spectrum sharing and interference cancellation in 5G and future 6G deployments. RAN vendors and application developers – such as Nokia, NVIDIA, Radisys, IS-Wireless, Cohere Technologies, DeepSig, ISCO International, Unikie, Booz Allen Hamilton and ODC – have already committed to support the E3 interface.
•     An alternative to dApps being pursued by some vendors is to offer proprietary AI-enabled, real-time optimization capabilities as software upgrades to existing RAN platforms through subscription-based licensing models. For example, Ericsson has recently launched a software subscription offering for service providers that embeds AI/ML models directly into RAN basebands and radios to improve performance and efficiency through microsecond-level inference for use cases such as scheduling, link adaptation, beamforming and multi-layer coordination.
•     The deeper integration of AI, ML and their derivatives for RAN automation and optimization – commonly referred to as AI-for-RAN – is the most commercially mature facet of the broader AI-RAN movement. The launch of the first 6G networks – which are being standardized as AI-native systems from the outset – around 2030 is expected to significantly increase the scope for hardware-agnostic, software-led innovation and take the industry closer to fully autonomous network operations. RAN digital twins are also expected to play an increasingly important role, allowing operators to validate AI-enabled air interface features and train optimization algorithms in high-fidelity virtual environments before introducing them into live 6G networks.
•     Beyond terrestrial public mobile networks, there is growing interest in RAN automation for other segments, including the use of an SMO/RIC governance layer for dynamic resource orchestration in NTNs and interworking with terrestrial RAN infrastructure, from mobility and handover management to on-demand satellite coverage activation – particularly during disaster response scenarios.
•     In the private 5G segment, RAN automation applications are being pursued for diverse use cases, including agentic AI to simplify network deployment and administration, converged management of Wi-Fi and private 5G networks, dynamic spectrum allocation between incumbents and other user groups, adaptive power control in offshore cellular deployments, indoor positioning for physical AI and industrial automation in 5G-connected factories, ISAC for drone detection and emission control to limit the electromagnetic signature of tactical 5G infrastructure during military operations.
•     One recent example is the implementation of an NVIDIA-powered agentic AI solution for autonomous private 5G network optimization aboard vessels operated by Norwegian shipping company Color Line. In Japan, domestic integrator NTT East has recently concluded a series of tests with 26 vendors to evaluate RIC functionality for AI-enabled autonomous network control applications supporting transmit power optimization and interference mitigation in private 5G networks.
•     The Open RAN automation ecosystem is exhibiting signs of consolidation with Nokia’s acquisition of Juniper Networks' SMO/RIC assets from HPE (Hewlett Packard Enterprise) and VMware's exit from the market altogether. We anticipate seeing further M&A activity reminiscent of the SON boom in the previous decade. However, there’s no lack of diversity in the application segment with dozens of companies – including RAN vendors, third-party developers and mobile operators themselves – actively engaged in the development of rApps, xApps and, more recently, dApps.  
•     Nordic telecommunications infrastructure giants Ericsson and Nokia are presenting an unusually united front by joining each other’s SMO/rApp ecosystems, seemingly moving away from the SON era walled garden approach to clear the path for true cross-vendor automation.

Topics Covered

The report covers the following topics:

•     Introduction to RAN automation
•     Value chain and ecosystem structure
•     Market drivers and challenges
•     Functional areas of RAN automation
•     RAN automation technology and architecture, including D-SON, C-SON, H-SON, Near-RT/Non-RT RICs, SMO, d/x/rApps, AI-for-RAN software upgrades, RAN planning tools and test/measurement solutions
•     Review of over 85 RAN automation use cases, ranging from ANR, PCI and RACH optimization to automated cell shaping, advanced traffic steering, QoE-based resource allocation, energy savings, anomaly detection, dynamic RAN security, adaptive network slicing, ISAC, NTN orchestration, private 5G automation and RAN digital twins
•     Key trends in intelligent RAN implementations, including the SON-to-SMO/RIC transition, closed-loop automation, intent-driven management, embedded real-time intelligence, operational AI/ML, deep reinforcement learning, Gen AI, agentic AI, data analytics and application awareness
•     Cross-domain mobile network automation enablers and application scenarios across the RAN, core and xHaul transport segments of cellular infrastructure
•     Detailed case studies of 25 production-grade RAN automation deployments across multiple operator groups, national mobile operators and private network operators
•     Worldwide review of ongoing RAN automation projects across over 100 operators, covering traditional SON, Open RAN and AI/ML-driven automation approaches
•     Future roadmap of RAN automation
•     Standardization and collaborative initiatives
•     Profiles and strategies of more than 300 ecosystem players, including RAN vendors, companies active in the core and xHaul segments, RIC, SMO and OSS platform providers, d/x/rApp developers, AI/ML technology specialists, RAN planning software suppliers and test/measurement solution providers   
•     Strategic recommendations for RAN automation solution providers and mobile operators
•     Market analysis and forecasts from 2026 to 2040

Forecast Segmentation

Market forecasts are provided for each of the following submarkets and their subcategories:

•     Mobile Network Automation Submarkets
• •         RAN
  •         Mobile Core
  •         xHaul Transport
•     RAN Automation Functional Areas
  •         Traditional SON & Vendor-Specific RAN Automation
    •             SON & Proprietary Network-Level Automation Platforms
    •             AI-for-RAN Software Upgrades for Real-Time RAN Intelligence & Automation
  •         SMO/RIC & Open RAN Automation
    •             Non-RT RIC & SMO
    •             Near-RT RIC
    •             rApps
    •             xApps
    •             dApps
  •         RF Planning & Optimization Tools
  •         Test & Measurement Solutions
•     Access Technology Generation Submarkets
  •         LTE
  •         5G
  •         6G
•     Use Case Segments
  •         PCI, RACH & Configuration Automation
  •         Mobility, Handover & Load Management
  •         RAN Resource & Performance Optimization
  •         Energy Efficiency & Sustainability
  •         Spectrum Management & Multi-RAT Connectivity
  •         Network Healing & Protection
  •         Massive MIMO, Beamforming & Lower-Layer Optimization
  •         Network Slicing & Vertical Applications
  •         ISAC, Positioning & Tracking
  •         Network Planning & Evolution
  •         Automation & AI Enablement
  •         Multi-Domain Use Cases
•     Network Types
• •         Public Mobile Operator Networks
  •         NTN Infrastructure
  •         Private Cellular Networks
•     Supplier Categories
  •         RAN Vendors
  •         NTN Infrastructure
  •         Third-Party Suppliers

•     Regional Markets
  •         North America
  •         Asia Pacific
  •         Europe
  •         Middle East & Africa
  •         Latin & Central America

Release Date: October 2026
Number of Pages: 648
Number of Tables and Figures: 67