Open RAN’s AI Era Puts Interoperability and Testing Back in the Spotlight

There is a moment in every technology shift when the exciting part — the demos, the promises, the flashy new capabilities — starts to bump into the boring part: making sure it all actually works together. That moment has arrived for Open RAN and artificial intelligence.

At the RCR Wireless News Intelligent RAN Forum 2026, two people who spend their working lives in the unglamorous trenches of network integration and measurement made a case that deserves more attention than it is getting. Viavi Solutions marketing manager Owen O’Donnell and Battelle vice president of product development Mark Reudink talked through how open and intelligent radio access networks are evolving together — and why interoperability, integration, and testing are now the real bottlenecks.

Their message, in short: openness and network intelligence are a powerful combination, but they only deliver if the pieces genuinely fit together and can be proven to work. That is a lot less headline-friendly than “AI runs the network,” but it is arguably the more important story for anyone planning to spend money on this stuff.

What Open RAN and AI each bring to the table

To understand why this matters, it helps to separate the two ideas that are often lumped together under the “AI-RAN” banner.

Open RAN is an architectural approach. Instead of buying a radio access network as one tightly bundled product from a single vendor, operators split it into components — radios, baseband processing, management software — connected through standardized interfaces. The promise is that different companies’ parts can be mixed and matched, which should lower costs and widen the supplier field.

AI in the RAN is about putting intelligence into how that network runs: optimizing traffic, managing resources, saving energy, predicting when equipment will fail, and gradually automating decisions that humans make today.

O’Donnell framed the combination as close to ideal for operators. “I think, in a way, openness and network intelligence is nearly a perfect storm for the operators,” he said, “because the openness is bringing in new players, it’s increasing the playing field, it’s reducing the cost, while network intelligence is allowing operators to head towards the goal of fully autonomous networks.”

That is the optimistic version, and there is real substance behind it. More vendors means more competition. More intelligence means fewer manual interventions and, eventually, networks that tune and heal themselves. Both are things operators have wanted for years.

The catch: more moving parts, more ways to break

The trouble is that these two trends multiply each other’s complexity. Open RAN already introduces more interfaces, more software, and more integration work than a traditional single-vendor deployment. Layer AI applications on top — especially ones that influence real network decisions in real time — and the number of things that can go wrong grows quickly.

That is why testing keeps coming up. O’Donnell’s core point at the forum was that as AI applications begin to shape network behavior, validation becomes critical. Operators need to be able to benchmark competing solutions against each other and confirm that an AI-driven decision is actually an improvement, not a subtle regression that shows up three weeks later as dropped calls in one neighborhood.

This is a familiar pattern in telecom. Every generation of network technology has eventually run into the same wall: the technology works in a lab, but proving it works in a live, multi-vendor, real-world environment is a separate and harder problem. AI makes that harder still, because AI systems are not static. They learn, they adapt, and their behavior can change over time in ways that are difficult to predict from a spec sheet.

Integration timelines are shrinking — and that is the good news

Not everything in this story is a warning. Reudink offered a genuinely encouraging data point: integration timelines for active antenna units — the combined radio and antenna hardware used in modern networks — have fallen from months to weeks. That improvement comes as vendor software and testing platforms have matured.

That matters more than it might sound. Integration time is one of the hidden costs that has made operators skeptical of Open RAN. If plugging a new radio into a multi-vendor network takes months of engineering effort, the promised cost savings from vendor competition can evaporate. If it takes weeks, the math starts to look much better.

The improvement also suggests something important about how this market matures. Early Open RAN deployments were full of bespoke integration work because the standards were young and the tooling was immature. As testing platforms get better and vendors get more practice, the process becomes more routine. That is exactly the trajectory Open RAN needs if it is going to move from flagship trials to everyday procurement.

Why testing is the unglamorous hero here

It is worth pausing on why test and measurement companies like Viavi keep showing up in these conversations. They are not the ones building radios or writing AI algorithms, but they sit in a uniquely useful position: they see whether the whole system actually behaves as advertised.

In an AI-driven network, that role becomes more important, not less. If an AI application is adjusting spectrum allocation or managing handovers, someone has to verify that those adjustments are working across equipment from multiple vendors. That is not a job you can do with a clipboard and a stopwatch. It requires instrumented testing, benchmarking, and continuous validation.

There is also a competitive angle. Operators evaluating AI features from different suppliers need a way to compare them fairly. Without standardized testing, vendors can claim impressive-sounding improvements that are measured under favorable conditions and never replicated in a live network. Independent validation is what turns marketing claims into procurement decisions.

The bigger picture: AI-native Open RAN is a real market now

The forum conversation did not happen in a vacuum. It reflects a market that has moved from theoretical to financial. According to market research summarized by OpenPR, the AI-native Open RAN market was valued at about $3.20 billion in 2025 and is projected to reach roughly$24.52 billion by 2035, growing at a compound annual growth rate of about 22.63% between 2026 and 2035.

Within that, the United States market is expected to grow from about $1.12 billion in 2025 to roughly$7.29 billion by 2035, at a CAGR of about 20.66%. The U.S. accounted for around 91% of the North American market in 2025, according to the same research.

Those are the kind of numbers that get boards of directors interested. They also explain why so many companies — from traditional equipment vendors like Ericsson, Nokia, Samsung, and NEC to chipmakers like Intel, NVIDIA, Qualcomm, and AMD, to software players like Mavenir and Rakuten Symphony — are positioning themselves in this space.

The growth drivers cited in that research read like a checklist of what the forum panelists were talking about: AI-powered RAN automation improving traffic management and energy efficiency, cloud-native network functions enabling software-driven deployments, near-real-time RIC applications for AI-driven control, and edge AI pushing intelligence closer to where decisions need to be made.

The near-RT RIC: where a lot of this comes together

One piece of Open RAN architecture deserves a closer look because it sits at the center of the AI conversation: the near-real-time RAN Intelligent Controller, usually shortened to near-RT RIC.

Think of it as a control layer that sits above the radio network and runs small applications — often called xApps — that make decisions on a timescale of milliseconds to seconds. An xApp might adjust how spectrum is allocated, manage handovers between cells, or tune energy usage based on demand.

This is where AI and Open RAN genuinely intersect. The near-RT RIC gives AI algorithms a standardized place to live and a standardized way to influence the network. Without it, AI in the RAN would be a collection of vendor-specific features. With it, intelligence becomes something closer to an app ecosystem.

But the same architecture that enables this also creates the testing challenge. An xApp from one vendor, running on a RIC from another, controlling radios from a third, has to work as a system. That is precisely the kind of multi-vendor scenario that requires rigorous interoperability testing.

What operators should take away

For telecom executives and engineers watching this space, the forum discussion suggests a few practical conclusions.

First, do not treat AI and Open RAN as separate projects. They are converging, and the convergence is where both the opportunity and the risk live. An AI feature that works beautifully in a single-vendor network may behave differently in a disaggregated one.

Second, budget for testing and integration from the start. The fact that integration timelines are shrinking from months to weeks is good news, but it also means the remaining integration work is where the value is captured or lost. Operators who treat validation as an afterthought will pay for it later.

Third, be skeptical of benchmarks that cannot be independently verified. As AI features proliferate, the ability to compare them objectively becomes a competitive advantage. Operators with strong testing practices will make better procurement decisions than those that rely on vendor demonstrations.

Fourth, recognize that autonomy is a journey, not a switch. The goal of fully autonomous networks that O’Donnell described is real, but it depends on trust — and trust depends on evidence. Every step toward autonomy requires proving that the previous step works reliably across the whole system.

The road ahead

Open RAN’s AI era is not a story about whether the technology will work. The building blocks are in place, the market is growing fast, and the major vendors are all committed. The story is about whether the ecosystem can make multi-vendor, AI-driven networks reliable enough and predictable enough to deploy at scale.

That is a less dramatic narrative than the AI hype cycle would suggest. It is about interfaces, test cases, integration schedules, and benchmark methodologies. But it is also where the real work is happening, and where the difference between a successful Open RAN deployment and an expensive science experiment will be decided.

As O’Donnell put it, openness and intelligence together give operators a path toward autonomous networks while bringing in new players and reducing costs. The catch — and it is a significant one — is that this path runs straight through interoperability and testing. The industry has spent years talking about the destination. The forum conversation was a reminder that the road there is paved with validation.

Sources

• Open RAN’s AI era puts interoperability and testing in focus — RCR Wireless News
• AI-Native Open RAN Market to Expand Worldwide as AI, 5G, and Open Source — OpenPR
• 1Finity: sell idle AI-RAN compute to enterprises first — RCR Wireless News
• Global Telecom Capex Rises 5% in H1 2026 as Operators Shift Spending to Capacity, AI and Network Modernization — TelecomLead