Three radio access architectures that operators have been evaluating in parallel are now converging onto a single roadmap, according to an RCR Wireless report, with the lines between AI-RAN, Open RAN and Cloud RAN increasingly blurred in vendor pitches and operator test plans.

The shift matters because the telecom industry has spent several years running these as separate workstreams. Open RAN, the disaggregated architecture promoted by the O-RAN ALLIANCE and deployed commercially by operators including Rakuten Mobile, DISH Network and Vodafone, focused on multi-vendor interoperability. Cloud RAN, backed by Ericsson, Nokia and Huawei, emphasized virtualized baseband running on commercial servers. AI-RAN, a more recent framing with NVIDIA as a prominent advocate, positioned graphics processing units as a way to add intelligence to the radio access network.

Now the marketing decks are merging. Vendors increasingly describe a single architecture in which the same hardware platform runs virtualized baseband functions, supports multi-vendor disaggregation, and allocates spare compute to AI inference workloads.

What changed? Two things, mostly. First, the underlying hardware converged. The same x86 and Arm server platforms that host Cloud RAN can also run AI workloads, and accelerators built for AI training handle Layer 1 signal processing adequately. Second, operators grew tired of tripling their integration budgets to test three architectures in isolation. Joint labs, shared reference designs and common procurement frameworks started to look more attractive than yet another parallel pilot.

What does the convergence mean for operators?

The practical effect is that an operator evaluating a new RAN deployment is no longer choosing between Open RAN, Cloud RAN or AI-RAN as discrete options. It is choosing how much of each to deploy, and on what timeline. That is a different procurement conversation, and it puts pressure on vendors that built their businesses around a single architecture.

It also concentrates risk. If one converged stack fails to perform, the industry loses three alternatives at once. The skeptical reading: convergence may be driven less by technical inevitability than by vendor fatigue and operator budget pressure.

Is the standards work keeping up?

No, not yet. The O-RAN ALLIANCE has published specifications for disaggregated RAN interfaces, but AI workloads sit outside its scope. The 3GPP has discussed AI and machine learning for the RAN in recent releases, but the work focuses on specific use cases such as beam management and channel state feedback, not on a unified AI-RAN architecture. Industry groups have formed to fill the gap, but none has produced binding specifications that would let a buyer treat the converged stack as a commodity.

That gap is the story to watch. Until standards bodies produce interoperable interfaces for AI-native RAN functions, "converged" will mean whatever each vendor says it means — in other words, a marketing term more than a technical reality.

What to watch next

Two timelines will determine whether the convergence holds. First, the O-RAN ALLIANCE and 3GPP need to publish specifications that explicitly address AI workloads in the RAN — work that has not yet appeared on either group's published roadmaps. Second, operators need to disclose whether their converged deployments are actually carrying commercial traffic at the scale vendors claim.

Without those two pieces, the convergence story remains a forecast, not a result.