Is AI-RAN Becoming the Telecom Industry's Next Major Platform Shift?

AI
1

For the past few years, AI in telecom has largely focused on operational efficiency—predicting outages, automating customer support, and improving network planning.

The conversation is now moving beyond optimization.

Recent announcements from major infrastructure vendors suggest that AI-RAN is becoming a strategic priority rather than a research initiative. Nokia recently introduced what it describes as the industry’s first commercial AI-native RAN platform, while companies across the Open RAN ecosystem are increasingly positioning radio networks as distributed AI computing platforms instead of simply connectivity infrastructure.

This raises an interesting question for the broader telecom ecosystem.

If radio networks become programmable AI platforms, does that also change what operators expect from their OSS/BSS platforms?

As networks become more autonomous, the operational layer must be able to react in real time. Billing, provisioning, policy control, charging, customer management, and partner integrations can no longer operate as isolated systems if AI is making decisions across the network.

At TelcoEdge Inc., this evolution reinforces the importance of cloud-native, API-first telecom platforms. Modern BSS/OSS platforms will increasingly need to support AI-driven automation, real-time operations, and open integration models rather than simply digitizing existing processes. These capabilities align with TelcoEdge’s Operator OS and API-first architecture.

AI-RAN is still in its early stages, but the direction of the industry appears clear.

The discussion is no longer whether AI will become part of telecom networks.

It’s becoming where AI creates the most business value—within the network itself, or within the operational platforms that manage it.

What does the Telecom Hall community think?

Will AI-RAN fundamentally reshape telecom operations over the next five years, or will it remain a technology adopted primarily by large Tier-1 operators?

2

The RAN-vs-OSS framing skips the layer where AI-RAN actually lives or dies: the real-time control
loop inside the RAN, not the operational platform around it.

AI-RAN’s value is sub-millisecond — beam management, scheduler decisions, interference prediction —
which is exactly why it runs in the DU/RIC (near-RT RIC, ~10ms; real-time even tighter), not in
OSS/BSS. That’s a hard latency boundary: billing, provisioning and charging operate in seconds to
minutes and can’t be in that loop by physics, no matter how API-first they are. So “does AI-RAN
change what we expect from OSS/BSS” is partly a category error — they’re different time domains.

Where it does reshape operations is the non-RT layer (rApps, SMO): AI-RAN generates far more
telemetry and makes more autonomous local decisions, so the operational platform’s job shifts from
controlling the network to governing what the RAN’s own AI is allowed to do — policy guardrails,
intent, and closed-loop assurance, not real-time reaction. The operator layer becomes the referee,
not the driver.

On your Tier-1 question: mostly Tier-1 first, and not because of AI maturity — because AI-RAN’s
economics assume GPU/accelerator capacity co-located at the RAN, which is a capex and power story.
For most operators, especially outside Tier-1 and in emerging markets, the near-term AI win stays in
the non-RT / core-ops layer (RCA, anomaly detection, capacity) where you get 80% of the value on
existing hardware. AI in the radio is a 5-year Tier-1 story; AI around the network is deployable
now.

3

As networks get smarter, the systems managing them (billing, provisioning) can’t stay static either. If AI is making real decisions inside the network, the operational side has to keep up in real time too, otherwise you end up with a smart RAN sitting on top of slow, siloed back-end systems.