Telecom operators have to deal with flatlining revenues and network trickiness as they redirect their attention from looking at AI as a new idea to deploy it as an operational tool in the Radio Access Network (RAN). Telcos need to deliver immediate, bottom-line returns via excessive energy savings, spectral gains, and automation, all through practical implementation of AI RAN.
Not theoretically, but through live network trials, telcos building an AI-RAN alliance will deliver operational return on investment (ROI).
According to Ericsson, up to 20% in download throughput and 10% gains in spectral efficiency.
In 2026, AI RAN will be among live mobile networks that cut costs and push performance, as vendors and mobile network operators (MNOs) push practical deployments that make AI’s value measurable across global mobile infrastructure.
Telecom leaders are redirecting their attention from ideas to implementation, and now AI RAN can deliver clear savings, starting at the radio layer where complexity and costs are highest.
AI in Telecom Markets
AI RAN targets smarter use of power and capacity at the radio edge and RAN itself is the largest cost center for most MNOs Managing RAN energy consumption has become a business priority rather than an environmental addition.
“10 to 15 percent power savings [have been] demonstrated in multiple trials,” says Fatih Nahr, Distinguished Chief Architect at the CTO Office at Red Hat.
Nahr also describes energy management as the “clearest win” for operators.
Industry momentum is also building around the AI-RAN alliance, which seeks to align vendors and MNOs on how AI should be deployed inside the RAN. According to analysts, this cooperation is helping speed up trials and reduce fragmentation as deployments scale.
Beyond power savings, vendors are showing gains in radio network optimization. Ericsson reports that AI-driven link adaptation in live trials improved throughput and spectrum use, allowing operators to deliver more capacity without adding new spectrum or sites.
AI RAN holds a major advantage, and it’s the role in radio access network automation, reducing manual configuration and repetitive operational tasks. Automated tuning and closed-loop control help networks respond faster to changes in demand and interference.
However, the worry lies in building separate systems just for AI. Instead, operators are being advised to integrate intelligence into existing cloud-native platforms, letting smoother open ran AI integration take over hardware and software levels.
Automation also improves resilience. AI-based monitoring supports protective maintenance RAN by spotting errors early, reducing outages and limiting the need for physical site visits. This lowers costs while improving service stability.
According to Ericsson Middle East and Africa Chief Technology Officer, Zoran Lazarević, these capabilities are central to 5G advanced AI.
Lazarević argues that intelligence embedment now lays out the groundwork for future networks, including 6G, for competitive advantage without forcing MNOs into risky services.
Still, Nahr cautions that physical factors like weather and radio interference mean humans will stay involved, even as AI RAN takes on more operational decisions.
ROI, Markets, and Long-Term Position
It all depends on AI RAN investment ROI. Juniper Research predicted that 2026 will be the year operators will spend around $21 billion on AI technologies but returns vary widely depending on how virtualised networks already are.
Sam Barker of Juniper Research notes that AI delivers strong gains where networks are software driven. In less virtualized regions, high RAN energy consumption limits what automation can control, pushing ROI further into the future.
As networks grow more complex, automation is pretty essential, and AI RAN is becoming inevitable. By cutting costs, boosting efficiency, and enabling intent-driven control, AI RAN is restructuring the radio access network automation into a programmable, revenue making asset rather than a fixed cost burden.
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