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AI is changing telecom in two directions: operators use it to run terrestrial networks more efficiently, while some AI applications create new demands on the networks that carry their data. That means more than faster downloads: depending on the application, uplink capacity, predictable latency, reliability and where computing happens can matter too.
How operators use AI inside telecom networks
AI can support several parts of a telecom system: the radio access network (RAN), which connects devices over radio; the core, which manages services and connections; transport networks, which carry traffic between network elements; and operations and business support systems. It does not have to mean one model controlling the entire network, or that every model runs at the edge. Ericsson CTO Erik Ekudden describes an AI-native network as one in which trustworthy AI capabilities are built into design, deployment, operation and maintenance. Ericsson is a network vendor, so its descriptions are best read as industry perspectives and use cases, not proof of the same results at every operator. Ericsson’s discussion of AI-native networks and its overview of AI for networks and networks for AI provide the company’s framing.
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Operational tasks AI can assist
- Spotting anomalies: detecting unusual network behavior that may signal a developing problem.
- Finding or predicting faults: helping operations teams identify where service issues may originate.
- Optimizing performance: adjusting network resources to meet changing traffic and service conditions.
- Managing energy use: using learning loops to improve how network resources are used while maintaining service.
- Supporting intent-driven operations: translating a desired outcome into network actions, subject to policies and oversight.
These are operator-facing capabilities described by Ericsson and represented in GSMA’s use-case library. GSMA lists examples involving mobility assurance, energy saving, latency management and AI-assisted customer support. Its library also reports individual cases: a mobility-assurance solution spanning 124,000 5G cells; a China Mobile model associated with a 24% reduction in network power consumption per server and RMB 202 million in annual electricity costs; and a Telkomsel/ZTE case reporting a 47% reduction in gaming latency. These are case-specific reported figures, not general forecasts or independently established results across operators. The GSMA AI Use Case Library provides the examples.
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Why some AI applications change network requirements
AI is also a workload that networks may need to carry. Interactive applications such as smart-glasses assistance, robots sending video and telemetry, or connected vehicles exchanging status and map updates can generate substantial upstream sensor traffic. Some also depend on low, predictable latency and reliable connections. These requirements vary by application; they are not characteristics of every AI service. Ericsson describes these patterns as potentially more uplink-heavy and session-rich than traditional mobile broadband. Ericsson’s AI overview gives illustrative examples, not universal technical specifications.
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A network planned mainly around bursty, download-heavy browsing and video may therefore need to adapt for particular interactive workloads. “Networks for AI” is not simply a request for higher peak download speeds: operators may also need to consider uplink performance, latency consistency, reliability, coverage and the placement of computing resources. How much each factor matters depends on the application and where its AI processing occurs.
What autonomy means—and why oversight matters
Automation can progress from recommendations to systems that take actions across network domains. That does not mean an AI system should be allowed to make every change without limits. In Ekudden’s description, specialized agents can coordinate work, monitor key performance indicators and roll back a change if performance regresses. He also describes policy constraints, approval tiers for high-impact actions, and digital twins for testing changes before execution. These are governance approaches described by Ericsson, not a guarantee that all operators use them.
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Safe operation also depends on shared definitions for network data, current visibility into network conditions, privacy and security controls, governance, and a record of decisions. For an operator evaluating an AI deployment, the important question is not only whether it can automate a task, but also which actions it may take, what triggers human approval, how a rollback works and how outcomes are monitored.
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Formal evaluation work is under way, but a work item is not the same as a finished standard or a widely deployed capability. ITU-T Study Group 13 lists TR.GenAI-Telecom, “Potential requirements and methodology for deploying and assessing Generative AI models in telecom networks,” as agreed on March 14, 2025, for the 2025–2028 study period. Its scope includes methods for assessing GenAI models used in network modelling, performance analysis and decision support.
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GSMA’s 2026 material describes strategic interest and deployment patterns using different measures:
| Measure | Reported figure | What it indicates |
|---|---|---|
| Operators identifying monetisation as a strategic priority | 45% | Strategic priority, as reported on the GSMA Mobile Economy 2026 page. |
| Telecom GenAI deployments applied to network operations | 16% | Application area of deployments, according to the GSMA announcement of Open Telco AI on March 2, 2026. |
The percentages should not be combined into a single adoption rate: one concerns operators’ strategic priorities, while the other concerns where GenAI deployments are applied. GSMA is an industry association, and the figures are presented as its reports; their underlying methodology is not detailed here.
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Where operators may seek business value
Three possible models follow from the combination of AI-enabled networks and new application demands. They are opportunities identified by Ericsson, not evidence of industry-wide realized revenue.
- Network exposure APIs: let developers or enterprises access selected network capabilities through software interfaces.
- Differentiated connectivity: offer connectivity tailored to applications that need stronger uplink, lower latency or greater reliability.
- Enterprise edge computing: process data closer to the user or device when speed, data sovereignty or compliance requirements make that useful.
GSMA’s monetisation-priority figure indicates strategic interest, not revenue already generated by these models. Whether an opportunity becomes a viable service depends on customer demand, the network capability offered and the operator’s ability to deliver it.
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