Nokia and AWS pilot AI automation for real-time 5G network slicing

Telecom networks may soon begin adjusting themselves in real time, as operators test systems that allow AI agents to manage traffic and service quality. AI may soon be making operational decisions.

This week, Nokia and AWS presented a new network slicing system that uses AI agents to monitor network conditions and adjust resources automatically. The setup is being tested by telecom operators du in the United Arab Emirates and Orange in Europe and Africa, according to a joint announcement from Nokia.

Adaptive AI-driven networks

Network slicing lets operators create multiple virtual networks on the same physical infrastructure, each tuned for a different purpose. For example, a slice may be configured for emergency services or high-bandwidth consumer traffic. While slicing is part of the 5G standard, it has often required manual planning and fixed configurations, which limits how quickly networks can respond to changing demand.

The new system aims to close that gap by introducing AI agents that track network performance indicators like latency and congestion, and consider data like event schedules or weather conditions. Agents can then adjust network settings to keep services running to agreed performance levels, according to Nokia’s description of the pilot.

AWS said the solution combines Nokia’s slicing and automation tools with AI models delivered through Amazon Bedrock, its managed AI service platform. The companies describe the approach as “agentic AI”.

Autonomous connectivity

The interest in such systems reflects a long-standing challenge: 5G networks have delivered higher speeds and lower latency, but operators have struggled to turn those technical gains into new revenue streams. Research firm GSMA Intelligence notes many operators view network slicing as a potential source of enterprise income, though adoption has been slow due to operational complexity and uncertain demand.

If networks can adapt quickly to sudden demand, like a crowded stadium or emergency responders entering a disaster area, operators may be able to offer temporary connectivity or guaranteed service levels without manual setup.

Orange has said previously enterprise customers expect connectivity to behave more like cloud computing, where resources can scale on demand. Systems that allow automated control of network resources could help move telecom services closer to that model.

Cloud platforms and telecom network operations

The tests also highlight how cloud providers are getting involved in telecom operations. Over the past few years, some operators have moved parts of their core networks onto public cloud platforms or built cloud-based control systems. Industry analysts at Dell’Oro Group report that telecom cloud spending is rising as operators modernise networks and adopt software-driven infrastructure.

Adding AI-driven control loops on top of cloud platforms represents the next step, with AI systems monitoring conditions and applying adjustments quickly.

The technology remains in a testing phase. Nokia’s announcement described the work with Orange as demonstrations and pilots rollouts. Questions remain about how such systems can be deployed, how operators will supervise automated decisions, and how regulators will view AI control of critical communication infrastructure.

Telecom networks carry important traffic so reliability and accountability remain central concerns. Operators typically introduce automation gradually, keeping human oversight in place while validating system behaviour under real conditions.

The experiments suggest that AI is beginning to function as operational controller, adjusting physical and virtual resources in response to live events.

Enterprises that rely on private 5G networks for factories or large venues may gain access to connectivity that adjusts automatically. That could influence how businesses design applications that depend on stable, predictable network performance.

(Photo by M. Rennim)

See also: How e& is using HR to bring AI into enterprise operations

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