The telecom industry's lingering question about open RAN — whether disaggregated networks can match the performance of integrated vendor stacks — has shifted toward a new answer: artificial intelligence.

A recent analysis published by SDxCentral examines whether AI is the key to open RAN success, reframing a debate that has run since operators began splitting radio access network hardware from software. The piece lands at a moment when open RAN deployments have expanded but have not yet displaced traditional RAN architectures at scale across major markets.

The core argument is structural. Open RAN replaces a single vendor's tightly integrated stack with components from multiple suppliers, coordinated through open interfaces. That flexibility introduces complexity: more configuration choices, more parameter tuning, more potential points of failure. AI-driven automation, the reasoning goes, is what makes that complexity manageable — using machine learning to optimize radio performance across multivendor components in ways manual engineering cannot.

Why does AI matter for open RAN specifically?

The RAN is the most complex part of a mobile network to operate. In a traditional single-vendor deployment, the supplier tunes the system end to end. In an open RAN deployment, an operator must integrate radios from one vendor, software from another, and a RAN intelligent controller from a third — then make them perform as a coherent whole.

This is where the RAN intelligent controller, or RIC, enters the picture. The RIC is the open RAN architecture's platform for hosting third-party applications and automation logic, and AI is expected to be its principal workload. Proponents argue the RIC will let operators run control-loop applications that adjust network behavior in near real time, improving spectral efficiency and cutting operating costs.

Skeptics counter that the RIC ecosystem remains immature, with few applications proven in large commercial networks. The gap between demonstration projects and production-grade automation is still wide, and operators have been cautious about committing to large-scale open RAN rollouts without evidence of equal-or-better performance against incumbent vendors.

Commercial reality versus the AI narrative

The SDxCentral analysis fits a broader pattern: vendors and operators increasingly frame open RAN progress through an AI lens, partly because AI carries more investor and media attention than interface standardization work. Marketing claims about self-optimizing, AI-native networks abound at industry events.

The commercial record is more measured. Open RAN has gained real traction in specific markets — notably where regulators or geopolitics pushed operators toward vendor diversification — but large-scale greenfield 5G deployments built on open interfaces remain the exception rather than the rule. Established RAN vendors continue to hold the majority of global 5G radio deployments.

AI itself does not solve open RAN's other unresolved issues: the maturity of open fronthaul implementations, the economics of small vendors competing against scale players, and the integration effort operators must absorb. What AI potentially changes is the operational equation — if automation reduces the cost of running multivendor networks, the business case for openness strengthens.

What comes next?

The O-RAN Alliance continues to expand its specifications, and operators in Europe, Japan and India have committed to open RAN in future procurement rounds. Whether AI closes the performance and operations gap will become clearer as RIC-based applications move from trials into live networks over the coming rollout cycles.