How AI-RAN delivers operational ROI for telcos
AI-RAN is moving from theory to measurable results. The gains are simple to track: lower energy use, higher spectral efficiency, and more capacity from the same assets.
Operators are under pressure. Revenues are flat while networks get more complex. That's why attention is shifting from "new tech" to operational impact. This shift aligns with O-RAN principles-openness and disaggregation-and industry sentiment backs it. A Heavy Reading survey shows 30% of operators expect AI-RAN and O-RAN to be very closely linked in five years, rising to 47% among those deploying O-RAN by 2025.
For operations leaders, the message is clear: prioritize immediate value, not architectural theory for its own sake.
The clearest business case: energy savings
Energy optimization is the fastest path to ROI. Radio sites are power-hungry, and small efficiency gains stack up quickly across national footprints.
Trials highlighted by Fatih Nahr report 10-15% power savings without impacting user experience. Scale that across hundreds or thousands of sites and the OPEX reduction is significant.
Ericsson reports similar quantitative wins in live networks. An AI native link adaptation feature tested with Bell Canada delivered up to 20% higher downlink throughput and a 10% boost in spectral efficiency versus baselines. More capacity from existing spectrum improves capital efficiency and buys time before costly expansions.
Integration over isolation
Standing up a separate AI platform is a trap. Run AI on the same cloud-native application platform you use for microservices and network functions. Don't fragment your stack.
Nahr's guidance is direct: avoid building "another platform yet for AI only." Enrich the current platform so workloads can move between on-prem and hybrid cloud without friction.
On hardware, concerns about GPU cost and power at the edge are fading. Kanika Atri points to solutions that meet site constraints from day one. NVIDIA's ArcPro platform, for example, fits under 300 watts in a small form factor while supporting 5G capability sets-showing accelerated computing can align with RAN deployment realities.
From runbooks to intents
AI-RAN enables intent-based operations: define the outcome, not the configuration. Think about a stadium event where you need high-throughput slices spinning up fast-intents allow the network to configure itself in minutes, not days.
Still, aim for automation, not full autonomy. Even hyperscalers haven't achieved fully autonomous operations. Radio networks have physical variables-weather, interference, site constraints-that need human oversight.
The human capital shift
Don't separate AI talent from network engineering. A "Centre of Excellence" parked on the side slows you down and misses context.
Build a "Community of Practice" embedded within RAN operations. Domain expertise is non-negotiable. Telco models aren't LLMs; they need fluency in RF propagation, interference patterns, and the "language" of wireless environments.
Practical next steps
- Leverage O-RAN principles: Even if you're not rolling out O-RAN end-to-end, align architecture with openness and disaggregation to ensure data portability and AI-friendly interfaces. See the O-RAN Alliance for reference models.
- Start with energy: Prioritize pilots for energy management, sleep modes, and cell switch-off. Savings of 10-15% are realistic and verifiable within weeks.
- Unify platforms: Keep AI workloads on your existing cloud-native platform alongside network functions. One platform, common pipelines, shared observability.
- Integrate teams: Embed data scientists with radio engineers. Tie models to physical realities and production runbooks, not lab benchmarks.
What this means for operations leaders
Set targets you can prove: energy per site, spectral efficiency, and capacity per MHz. Avoid parallel platforms and bespoke tools that add maintenance overhead. Plan for human-in-the-loop controls with clear guardrails and escalation paths.
Upskill your teams on AI for automation and operational metrics. If you need a structured path, explore role-based learning tracks and automation resources here: Courses by job and Automation resources.
Looking ahead
The groundwork you lay now-data pipelines, model governance, platform consolidation, and team integration-will decide who enters 6G with a programmable, high-performance RAN. Start where ROI is obvious, prove value, then scale.
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