Speaking during RCR Wireless News’ Telco AI Forum, Red Hat’s Shujaur Mufti said operators are initially focusing on AI for RAN because it delivers measurable operational benefits without requiring major upgrades to existing radio networks
In sum – what to know:
Operational gains – AI for RAN is leading adoption because it delivers immediate benefits—including lower operating costs, improved energy efficiency and better network performance—without requiring major RAN upgrades.
Shared infrastructure – Red Hat expects operators to gradually move toward common AI and RAN infrastructure, with proof-of-concepts accelerating through the remainder of the decade ahead of commercial 6G.
Economic value – AI-RAN deployments will expand only where they improve network quality and generate measurable business returns, making monetization and operational benefits the industry’s primary decision criteria.
AI-RAN will evolve through a phased transition spanning the rest of the decade, beginning with operational optimization before progressing toward shared AI and radio infrastructure and eventually enabling AI-native services across telecom networks, according to Shujaur Mufti, director of telco ecosystem solution architecture at Red Hat.
Speaking during RCR Wireless News’ Telco AI Forum, Mufti said operators are initially focusing on AI for RAN because it delivers measurable operational benefits without requiring major upgrades to existing radio networks. “I think AI for RAN is starting first because you can see, visualize the savings, for example, opex,” Mufti said.
He highlighted use cases including energy savings, network efficiency, spectral efficiency, and fault detection, noting that operators can deploy these capabilities on existing RAN infrastructure. “You don’t have to modernize your RAN network, and then you can get the benefits right there,” he said.
Mufti added that traditional self-organizing networks (SON) are evolving into AI-enhanced SON platforms, while service management and orchestration (SMO) systems for Open RAN are incorporating AI-powered xApps and rApps that can also manage conventional radio networks.
He described AI-RAN as a three-stage evolution. The first phase focuses on AI for RAN, followed by AI and RAN, where AI and radio workloads share common infrastructure. The final stage is AI on RAN, where the radio access network itself becomes a platform for AI-native applications and new revenue opportunities.
According to Mufti, AI for RAN will remain the industry’s primary focus through roughly 2027. Between 2027 and 2030, operators are expected to expand AI and RAN proof-of-concepts as 6G research matures and early standards emerge. He pointed to SoftBank and T-Mobile as operators already exploring this shared infrastructure model.
The final phase, expected after 2030 alongside commercial 6G deployments, would see AI becoming native across the entire mobile network.
Mufti also cautioned against assuming GPU acceleration will become universal across radio networks. Instead, operators are likely to begin with targeted deployments where the business case is strongest, particularly for AI inferencing at the network edge before introducing RAN workloads. “We should not think GPU everywhere in the RAN,” he said. “Maybe some selected sites as a starting point.”
Drawing on Red Hat’s work with SoftBank, Fujitsu and Nvidia, Mufti said early GPU-accelerated RAN deployments have already demonstrated technical advantages, including the ability to run Layer 1 and Layer 2 functions without requiring a real-time kernel.
He added that Red Hat has expanded its ecosystem collaborations around AI-RAN proof-of-concepts while extending its AI Grid initiative as a RAN-ready AI infrastructure platform at the edge.
While AI-RAN continues to gain momentum, Mufti said that widespread deployment will ultimately depend on demonstrating clear operational and financial value. “AI-RAN only makes sense if it has technology and economic benefits for the mobile operators,” he said.
He argued that operators will expand deployments only if AI-RAN improves network quality while creating new monetization opportunities. One possible approach is to begin with AI inferencing workloads at the edge, assess the revenue potential, and then determine how much GPU capacity should be allocated to radio functions.
Mufti concluded by encouraging operators to treat AI-RAN as part of a broader AI-native transformation rather than a standalone radio initiative.
Instead, operators should apply lessons learned from AI deployments across the core, OSS/BSS, and autonomous networks when designing future radio architectures. He argued that a common cloud-native platform and AI fabric spanning the data center, core, edge, and RAN will ultimately provide the operational consistency needed as telecom networks evolve toward AI-native infrastructure.
