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Telecom operators are building around open AI models because network operations, telecom standards, customer care and local-language services require capabilities that general-purpose models may not provide on their own. Shared development can help operators and industry partners build and evaluate those capabilities together, while deployment frameworks address how models work with operator data, systems and security requirements. The strategy is taking shape, but the available evidence does not show that open models have replaced proprietary ones or that they are in production across the industry.
Why are telecom operators using open AI models?
Telecom work is specialised. Diagnosing a fault across a radio access network, interpreting technical standards or helping a customer resolve a service issue calls for relevant data and task-specific performance—not just fluent general-purpose answers. GSMA’s Open Telco AI initiative describes its goal as advancing “telco-grade” models, datasets, benchmarks and agentic systems for these kinds of needs.
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Open collaboration offers a way to share parts of that development. Operators, vendors, AI developers and academic institutions can contribute models, data, computing resources or evaluation methods, rather than each operator having to create every capability independently. Common benchmarks can also make it easier to assess whether a model performs well on telecom tasks, instead of relying on general reasoning scores alone.
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What is Open Telco AI?
GSMA launched Open Telco AI on 2 March 2026 as an industry collaboration focused on telco-specific AI. GSMA’s announcement identifies open telco models, including a new model family from AT&T, compute contributions from AMD and TensorWave, datasets and an industry contribution portal. It also describes a Telco Capability Index intended to track progress on telecom-specific tasks.
The initiative matters because it brings shared assets and evaluation into the strategy, not just model development. A capability index can help focus attention on whether systems handle relevant telco work; it is not, by itself, evidence of deployment quality or a guarantee that a model will work in a particular operator’s network.
How could open models fit into network operations?
Network operations are a natural area to explore because operators manage complex, multi-vendor infrastructure and need to identify causes of service problems. Models may assist with troubleshooting or analysis, but connecting them reliably to operational data and systems is a separate engineering and governance challenge.
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| Example | What is happening | Evidence stage |
|---|---|---|
| AT&T OTEL models | TM Forum describes specialized telecom models being adapted into Model-as-a-Service and Open Digital Architecture (ODA) Canvas environments to support interoperability. | Adaptation and integration work; the source does not establish universal deployment. |
| Globe Telecom | The Philippine operator is exploring multi-vendor radio access network root-cause analysis using TM Forum solution packs to expose normalized APIs across vendors, and working with Open Telco AI on models and agents for analysis automation. | Exploration, not a completed production rollout. |
| TelecomGPT | A GSMA Foundry and Khalifa University collaboration announced a telco-first large language model and an Open Telco Knowledge Graph focused on 3GPP documentation. Its announcement names a chat interface hosted on LightOn and a knowledge graph hosted on Hugging Face. | Announced project; the announcement alone does not establish broad operator use. |
These examples show different parts of the problem: adapting a specialised model to a service framework, connecting analysis to multi-vendor network interfaces, and grounding answers in telecommunications standards. They should not be read as evidence that one model or approach has become an industry-wide operating standard.
Why models need deployment and governance frameworks
A model is only one component of an operator’s AI system. It needs governed access to useful data, integration with APIs and workflows, security controls for both models and agents, and a plan for monitoring and updating services. Operators may also need to choose where a workload runs, based on latency, data-handling requirements and available infrastructure.
At MWC 2026, TM Forum launched three AI-Native Blueprint projects: Model as a Service (MODaaS), Data Products Lifecycle Management and Agentic Interactions Security. MODaaS addresses sourcing and operating multiple models across cloud, edge and on-premises environments. The data-product and security projects address governed access to data and the risks of interactions involving AI agents.
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- AI NPU with XDNA 2 ARCHITECTURE - Powered by 16 “Zen 5” CPU cores, 50+ peak AI TOPS XDNA 2 NPU and a truly massive integrated GPU driven by 40 AMD RDNA 3.5 CUs, the Ryzen AI MAX+ 395 is a transformative upgrade and delivers a significant performance boost over the competition. The Ryzen AI Max+ 395 excels in consumer AI workloads like the llama.cpp-powered application: LM Studio. Shaping up to be the must-have app for client LLM workloads, LM Studio allows users to locally run the latest language model without any technical knowledge required and unleash their creativity and productivity.
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- EIGHT CHANNEL LPDDR5X - LPDDR5X is a new ground breaking memory small form factor installed on-board. With blazing speeds up to to 8000MT/s, it runs 1.5x faster than the DDR5 SODIMMs; 90% better performance over DDR5 SODIMMs in video conferencing and photo editing; 30% better performance in productivity apps; 12% better performance in digital content workloads.
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This work frames interoperability and governance as part of the strategy rather than afterthoughts. A model that performs well in isolation may still be difficult to operate if it cannot work with an operator’s data products, interfaces, deployment environment or security requirements. The blueprints are framework projects; their launch is not evidence that every operator has implemented them.
Is the goal to cut costs or create new revenue?
Current activity points more strongly to efficiency than to new external sales as the immediate focus. In GSMA’s 2026 Bridging the Language Gap report, GSMA Intelligence reported that customer-care automation accounted for 47% of tracked AI deployments globally in 2025, citing Telco AI: State of the Market, Q4 2025. The figure describes the share of tracked deployments, not the share of operators using open models or the share of all AI spending.
Revenue-generating applications are emerging, but they are not shown as the dominant present-day objective. GSMA Intelligence’s 2026 Charting AI Monetisation report put the share of operator AI deployments with an external revenue-generating focus at around 15–20%. Its reporting base was 700 telecom operators worldwide as of September 2025. Neither measure indicates how many operators use open models specifically.
Open models can serve needs beyond network operations
Telecom-specific AI is not limited to troubleshooting. GSMA’s 2026 report describes Swisscom as a strategic partner in the development of Apertus, a multilingual model with openly accessible development assets and permissive licensing. It also describes the African AI Language Models Initiative, a collaboration involving mobile operators, ecosystem actors, research institutions and civil society.
These examples point to another potential benefit: models and language resources adapted to local contexts. They do not establish that every operator needs to train its own model. Depending on the task, an operator might adapt an existing model, combine it with a knowledge base, use a shared model service or choose a proprietary model.
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What open-model claims should operators—and readers—treat cautiously?
The evidence supports an emerging strategy: a launched GSMA collaboration, named operator and model projects, and TM Forum frameworks in development. It does not establish universal production-scale adoption, a definitive open-model market share or a general performance advantage over proprietary systems.
- Check what “open” means. Confirm which assets are accessible, what the license permits and whether the operator can inspect or adapt the parts it needs.
- Look for task-specific evaluation. A useful comparison tests relevant network, standards or customer-service tasks—not only broad language or reasoning ability.
- Assess integration and controls. Consider compatibility with operator APIs and vendors, data governance, security, lifecycle management and deployment options.
- Distinguish project stage. A benchmark, announced collaboration, exploration, proof of concept, integration effort and production service are different levels of evidence.
For an operator, the practical question is not simply whether a model is open. It is whether its access terms, task performance and operating model fit the intended service better than the available alternatives.
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