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Where AI is already used in telecom operations
Airtel and Ericsson: correlating alarms and automating operations
A TM Forum case study describes Airtel’s work with Ericsson Operations Engine, a multi-vendor operations solution combining AI, automation and analytics. Airtel reported that it doubled network automation; 69% of alarms were automatically correlated and resolved without human intervention; mean time to repair fell by 29%; and network unavailability fell by 47%. These are operator-reported figures relayed by TM Forum, not independently audited results, and the case study’s measurement period is not established here.
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The example illustrates why integration matters: Airtel described operating a multivendor 2G/4G network across 220,000 towers. AI-driven operations have to work with the telemetry and processes of a complex live network, rather than a single isolated system.
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In a 10 December 2021 announcement, Airtel said Avanseus predictive-maintenance technology 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 resulting reductions in failures, alarms or costs in that announcement.
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Using AI to plan networks and predict faults
China Unicom and Huawei: planning, operations and fault prediction
TM Forum reports that China Unicom’s Intelligent Network Innovation Center worked with Huawei on an AI-based platform built around Huawei AUTIN and other operations systems. It centralizes network data to support planning and operations, including self-healing and fault prediction. China Unicom reported 85% fault-prediction accuracy using more than 1,000 KPIs. The accuracy figure is attributed to China Unicom through TM Forum; the source material does not establish its measurement period or independent verification.
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 says it uses real-time data analytics and optimized automation, including 3D radio-access-network planning and automatic load balancing. It provides no quantified result, so the case supports the described capabilities rather than a numerical performance claim.
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Managing radio resources and customer equipment
Huawei and Shandong Unicom: prioritizing wireless resources
A GSMA summary describes AI-powered wireless agents that identify high-value users and optimize network-resource allocation for VIP customers. The summary reports a 98% success rate in maintaining seamless service. That figure belongs to this specific use case as summarized by GSMA; it should not be read as a service-success rate for all subscribers or networks.
Qualcomm: mobility management across 5G cells
The GSMA summary also describes a dynamic neural network for cross-vendor mobility management, supporting mobility assurance across 124,000 5G cells. The figure indicates the reported scale of the use case, not a measured improvement in handover success or customer experience.
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Nokia: monitoring customer-premises equipment
GSMA describes Nokia’s AI-driven monitoring of customer-premises equipment (CPE)—the equipment at a customer location, such as a broadband gateway—as detecting service issues in real time and helping improve response efficiency. The summary gives no specific outcome figure for this example.
Assisting engineers and maintaining networks
Nokia: an AI assistant for technical support
According to the GSMA use-case summary, a Nokia assistant helps engineers resolve technical issues. GSMA reports 40% lower response times, 31% fewer assisted support cases and 20% more tickets solved at Tier 1. The summary does not establish the measurement period or comparison conditions for these figures, so they describe the reported case rather than a general expected benefit.
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NTT DOCOMO: agentic AI for maintenance
NTT DOCOMO announced commercial deployment of an agentic AI system for network maintenance in February 2026. The system, built with Amazon Bedrock AgentCore and data systems for agentic workloads, is intended to automate fault response and reduce service impact. This is an operator-announced commercial deployment; the announcement does not establish independently measured customer, service or cost outcomes.
Agentic AI is distinct from conventional prediction or an assistant that suggests next steps: it is designed to coordinate actions toward a task. In network maintenance, that makes the boundaries of permitted actions, escalation rules and human oversight important parts of the deployment—not optional additions to the model.
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What is announced, but not established as a live deployment
Samsung, KT and SK Telecom: planned AI RAN demonstrations
Samsung announced projects with KT and SK Telecom to deploy 5G standalone private networks in industrial environments and validate AI radio-access-network capabilities, with work planned to begin in October 2026. The announcement described a future start. As of 11 October 2026, the information available here does not confirm that the projects have begun, so they should not be counted as completed deployments.
An Airtel, Apollo Hospitals and AWS AI-guided colonoscopy over 5G was also identified as a trial, but the available source material does not substantiate its details or clinical outcomes. It is therefore not included as a verified telecom-network deployment.
How to interpret telecom AI claims
- Separate the evidence levels. A trial, a commercial rollout, an operator-announced deployment, a case-study description and a future plan are not interchangeable. The examples above include several kinds; check the wording of each claim before treating it as proof of production results.
- Keep reported figures attached to their source and scope. The performance numbers here come from operators as relayed by TM Forum or from GSMA summaries. They do not establish results that other operators should expect.
- Distinguish the technology. Predictive machine learning, rules-based automation, generative AI and agentic AI do different jobs. A broad list of possible AI uses does not show that every use is already deployed.
- Look for data and integration requirements. The operational examples depend on network telemetry and, in complex environments, integration across equipment, systems and existing workflows. A model’s output is useful only if operations teams can act on it reliably.
- Ask what remains under human control. Some systems help engineers prioritize or resolve issues; others automate alarm handling or are intended to take maintenance actions. Claims about automation should say what actions the system performs and when a person is involved.
ITU-T’s March 2025 technical report groups generative-AI use cases across customer service, marketing and sales, network, IT and support functions, and discusses requirements, risks and ways to assess deployment. That taxonomy helps explain where GenAI may fit; it is not evidence that every listed function is already in production.
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