Ericsson’s AI-in-RAN Subscription Launch: 5 Things It Means for Open RAN Operators

Ericsson’s AI-in-RAN Subscription Launch: 5 Things It Means for Open RAN Operators

Introduction

On July 6, 2026, industry analyst Joe Madden highlighted Ericsson’s recent “AI-in-RAN” portfolio release in a Fierce Wireless opinion piece, framing it as a pivotal step in transforming RAN from a hardware-centric business into a standalone software market. Ericsson’s offering delivers telco-grade AI models directly into basebands and radios via an annual subscription model, with no additional hardware required. This aligns with the simultaneous expansion of its Ericsson Intelligent Automation Platform (EIAP) to encompass core network automation, creating a unified open platform for RAN and core.

These developments arrive amid growing momentum for AI-native networks. Mobile Experts forecasts AI-RAN software revenue growing at a 100% CAGR through 2032, with independent RAN software (including vRAN, AI-for-RAN, and SMO) reaching 20% of the total RAN market by 2031, up from 7% in 2025. For Open RAN stakeholders, this represents a concrete commercialization path for embedded intelligence that complements disaggregated architectures.

Below, we break down the five most significant implications.

1. Subscription Pricing Accelerates Software Monetization in Open RAN

Ericsson’s AI-in-RAN is positioned explicitly as a software upgrade purchased through annual subscriptions rather than one-time hardware bundles. This marks a departure from traditional RAN economics and signals operator willingness to embrace recurring revenue models for advanced capabilities.

Analyst commentary notes that operators are now accepting this shift, enabling AI-RAN software to scale independently. In an Open RAN context, this could encourage similar subscription offerings from other vendors or even open-source contributors, reducing upfront capex barriers for smaller operators or greenfield deployments. The model supports incremental feature rollouts, aligning with the phased introduction of AI models in live networks.

2. Real-Time AI Inference Moves Inside Existing Basebands and Radios

The portfolio embeds AI models that run in real time within the RAN itself, targeting performance, efficiency, and energy savings on current infrastructure. First features became available in Q2 2026, with further enhancements scheduled later in the year.

Specific capabilities include AI-native Scheduler for Link Adaptation, AI-powered Macro Positioning, AI-managed Beamforming, AI-powered Multi-layer Coordination, Performance Management Event Schema Files, and Augmented Observability for AI in RAN. Live trials on T-Mobile’s 5G Advanced network reportedly delivered approximately 10% gains in spectral efficiency and up to 15% higher downlink throughput.

For Open RAN deployments relying on virtualized or cloud-native elements, this on-device inference complements RIC-based rApps by handling sub-millisecond optimizations that centralized controllers cannot match, while still exposing data for higher-layer orchestration.

3. EIAP Expansion Creates Unified Open Automation Across RAN and Core

Ericsson’s Intelligent Automation Platform now extends its open, multi-vendor automation capabilities from RAN into the core network. This creates a single platform for autonomous networks, helping CSPs streamline operations, optimize costs, and improve resilience.

In Open RAN ecosystems, where multi-vendor interoperability and SMO frameworks are central, a unified automation layer reduces integration friction. Operators can apply consistent AI-driven policies across domains, accelerating the journey toward zero-touch networks. The move also positions EIAP as a potential foundation for broader O-RAN Alliance-aligned automation use cases.

4. Hardware-Agnostic Path Supports Both Purpose-Built and Cloud RAN

Ericsson emphasizes that AI-in-RAN works with its 5G Advanced portfolio across purpose-built and Cloud RAN platforms. This dual compatibility is critical for operators pursuing hybrid strategies—retaining high-performance radios while virtualizing baseband functions.

In the broader RAN hardware landscape, where debates rage between custom ASICs and merchant silicon (including GPUs), Ericsson’s software-only approach avoids mandating new silicon. It complements ongoing Open RAN efforts to disaggregate while delivering immediate AI benefits on deployed equipment, potentially easing migration timelines for operators balancing legacy and new builds.

5. Strong Growth Projections Validate Embedded AI as a Standalone Revenue Driver

Mobile Experts projects AI-RAN software revenue expanding at 100% CAGR through 2032, with independent software licensing becoming a meaningful slice of the overall RAN market. This forecast underscores that AI capabilities are evolving from optional enhancements into core, monetizable products.

For Open RAN vendors and operators, the implication is clear: intelligence features can now be productized separately from radios or servers. This could spur ecosystem competition in AI models, observability tools, and optimization applications, ultimately lowering costs and spurring innovation in multi-vendor environments.

Conclusion

Ericsson’s AI-in-RAN subscription and EIAP core expansion provide a practical blueprint for embedding intelligence in today’s networks while preparing for AI-native futures. As Open RAN continues its evolution, these software-centric moves highlight how disaggregation and AI can reinforce rather than compete with each other. Operators should evaluate subscription pilots and unified automation roadmaps now to capture early efficiency and performance gains.

The trajectory points toward networks that are not only open but inherently intelligent—capable of adapting in real time to the demands of 5G Advanced and the emerging physical AI era.

Sources

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