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Telecom companies use AI to detect network faults, correlate alarms, plan capacity, manage mobility between cells, monitor customer equipment and help engineers resolve support cases. The evidence supports nine named deployments or case summaries—not a definitive list of 11—and one separately announced project whose completion is not established here.
What counts as a real telecom AI deployment?
“AI in telecom” covers different technologies and levels of use. Predictive models can estimate faults from network data; other systems classify alarms, recommend actions or help automate network operations. Generative AI (GenAI) creates or summarizes content, while agentic AI systems are designed to carry out multi-step tasks. These are not interchangeable, and a use-case category or announcement alone does not prove a system is operating in production.
The examples below distinguish operator-reported results and commercial rollouts from case-study summaries. The figures are attributed to the organizations reporting them; they should not be read as independently audited or as typical results for other networks. When a source does not provide a measured outcome, none is inferred.
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Nine named deployments and case summaries
| Example | Operational job | Evidence described by the source | Reported result |
|---|---|---|---|
| Airtel and Ericsson | Multi-vendor network operations and alarm handling | TM Forum case study of an operator transformation | Airtel-reported: 69% of alarms automatically correlated and resolved; MTTR down 29%; network unavailability down 47%. |
| Airtel and Avanseus | Predictive maintenance | Airtel announcement of rollout after a successful trial and commercial deployment | No quantified outcome stated. |
| China Unicom and Huawei | Network planning, operations and fault prediction | TM Forum case study describing an AI-based platform | China Unicom-reported fault-prediction accuracy of 85%, using more than 1,000 KPIs. |
| SK Telecom TANGO | Mobile-network operations, RAN planning and load balancing | GSMA case-study summary available in search-result text | No quantified outcome established. |
| Huawei and Shandong Unicom | Wireless resource allocation for VIP customers | GSMA summary of a use case | GSMA reports a 98% success rate in maintaining seamless service for the described use case. |
| Qualcomm | Mobility management across 5G cells | GSMA summary of a use case | GSMA says the system supports mobility assurance across 124,000 5G cells; this is a scale figure, not a performance result. |
| Nokia | Customer-premises equipment monitoring | GSMA summary of an AI-driven use case | No quantified outcome stated. |
| Nokia | Technical-support assistance for engineers | GSMA summary of a use case | GSMA reports response times down 40%, assisted support cases down 31%, and 20% more tickets solved at Tier 1. |
| NTT DOCOMO | Network maintenance and fault response | Operator-announced commercial deployment in February 2026 | DOCOMO describes intended automation and reduced service impact; no independently measured customer or cost outcome is established. |
Airtel and Ericsson: automating multi-vendor operations
TM Forum describes Airtel’s work with Ericsson Operations Engine, a multi-vendor operations solution combining AI, automation and analytics. The case is notable for its operating scale: Airtel CTO Randeep Sekhon described a 2G/4G network carrying 112 petabytes of traffic per day, with 220,000 towers and typically 12 radios per base site. Those figures describe the environment in the case study, not Airtel’s current network scale. The reported improvements in alarms, repair time and unavailability are Airtel’s results as relayed by TM Forum; the case-study publication year is not established.
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Airtel and Avanseus: predicting maintenance needs
In an announcement dated December 10, 2021, Airtel said Avanseus predictive maintenance was rolling out across its operations after a successful trial and commercial deployment across transport, enterprise and core networks. The system uses network data and AI analytics to identify actionable insights and predict incidents. Airtel did not quantify a reduction in failures, alarms or costs in that announcement.
China Unicom and Huawei: planning and fault prediction
TM Forum describes a platform developed by China Unicom’s Intelligent Network Innovation Center with Huawei, based on Huawei AUTIN and other operations systems. It centralizes network data for planning and operations and includes self-healing and fault-prediction capabilities. China Unicom’s reported prediction accuracy and KPI count are specific to the described use case; the publication year is not established.
SK Telecom: TANGO operations support
A GSMA case-study summary describes TANGO, or Telco Advanced Next-Generation OSS, as a unified operations-support platform for SK Telecom’s mobile network. The available summary mentions real-time data analytics, optimized automation, 3D radio access network (RAN) planning and automatic load balancing. It does not establish a quantified outcome, so none should be inferred.
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Huawei and Shandong Unicom: allocating wireless resources
The GSMA describes AI-powered wireless intelligent agents that identify high-value users and optimize network-resource allocation for VIP customers. Its reported service-continuity result applies to this described use case, not to all users, networks or traffic.
Qualcomm: managing mobility across 5G cells
In the GSMA summary, a dynamic neural network supports cross-vendor mobility management. The cited 5G-cell count indicates the scale across which mobility assurance is described; it does not establish a measured improvement in dropped calls, handover performance or customer experience.
Nokia: monitoring customer equipment
The GSMA summary says Nokia’s AI-driven monitoring of customer-premises equipment (CPE)—equipment such as a modem or router at a customer location—can detect service issues in real time and help improve response efficiency. The summary supplies no outcome figure for this example.
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Nokia: assisting technical-support teams
A separate GSMA summary describes a Nokia assistant that helps engineers resolve technical issues. The reported support metrics belong to that summarized use case. The GSMA summary does not establish a measurement period in the material available here, so the percentages should not be treated as a universal or time-current benchmark.
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In February 2026, NTT DOCOMO announced commercial deployment of an agentic AI system for network maintenance, built with Amazon Bedrock AgentCore and data systems for agentic workloads. DOCOMO says the system is intended to automate fault response and reduce service impact. The announcement establishes the operator’s deployment claim and intended purpose, not independently measured savings or customer outcomes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What these deployments show—and what they do not
The most established pattern across the examples is operational: use network data to surface faults, prioritize work, support planning or automate parts of incident handling. Other examples focus on service assurance for particular users or cells, monitoring customer equipment, and helping support engineers. These tasks depend on connecting useful data to operational systems and processes; an AI model cannot act on information it cannot access, and recommendations still need a defined path into network operations.
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- Reported outcomes are context-specific. Operator and case-study figures depend on the network, scope and measurement method described by the reporting source. They are not guaranteed savings or performance improvements for another carrier.
- Automation does not always mean autonomous control. Some examples describe assistance, prediction or recommendations; others describe automated alarm handling or an agentic maintenance system. The available summaries do not establish the same degree of human oversight for every deployment.
- Integration is part of the work. Multi-vendor environments, legacy systems, telemetry quality and operational handoffs shape what a system can detect and what action it can safely take. The Airtel and China Unicom examples explicitly involve consolidating or using network data across operational contexts.
- GenAI use cases are not deployment evidence. ITU-T’s March 2025 technical report organizes potential GenAI applications across customer service, marketing and sales, network, IT and support functions, and discusses requirements, risks and assessment methods. That taxonomy does not demonstrate that every category is live in telecom operations.
A planned AI RAN project is not a completed deployment
Samsung announced projects with KT and SK Telecom intended to begin in October 2026. The plan is to deploy 5G standalone private networks in industrial environments and validate AI RAN capabilities. The available announcement establishes a planned demonstration, not that the project has started or that the capabilities have been validated. It is therefore not counted among the nine deployments and case summaries above.
Sources and attribution
The deployment descriptions and reported outcomes above are attributed to TM Forum case studies, Airtel’s December 2021 announcement, GSMA case-study or use-case summaries, and NTT DOCOMO’s February 2026 announcement. The Samsung project is described as announced. ITU-T’s March 2025 report is used for the GenAI taxonomy and risk context. Some GSMA and DOCOMO source pages were not fully accessible in the reviewed material, so their descriptions are limited to the information stated in those summaries or announcements. Publication years or measurement periods are not stated for several case-study figures.
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