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Telecom operators are building around open AI models because network operations, customer care and telecom standards create specialized problems that a general-purpose model may not handle well on its own. Shared development can help operators and technology partners build and evaluate models, datasets and tools for those jobs, while common frameworks address how to connect and govern them. This is an emerging strategy, not evidence that open models have replaced proprietary systems or reached universal production use.
Why are telecom operators using open AI models?
Telecom networks combine large volumes of operational data, equipment from multiple vendors, detailed technical standards and services that must remain dependable. AI may help with tasks such as troubleshooting a network fault, interpreting standards documentation, assisting customers or supporting network operations. These tasks call for more than general reasoning: the system needs relevant telecom knowledge, access to the right data and a way to work with operator systems.
Open-model initiatives offer a way for operators, AI developers, vendors and researchers to contribute models, datasets, compute and evaluation methods to a shared effort. In principle, that lets participants develop and test telecom-specific capabilities together instead of requiring each operator to build every component alone. The value is not simply that a model is open; it is whether it performs on the operator’s tasks and can be integrated and governed in its environment.
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Open is not a single licensing or access category. For any model or project, check which assets are available: model weights, training data, training methods, APIs, evaluation materials or some combination. A permissive license and publicly accessible development assets can provide room to inspect or adapt a model, but an initiative that shares only selected components offers a different degree of control. Operators still need to evaluate usage rights, data handling, security and support arrangements.
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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-grade AI. GSMA’s announcement describes work on open telco models, datasets, benchmarks and agentic systems. It identifies a new AT&T model family, compute contributions from AMD and TensorWave, and an industry contribution portal. The initiative’s Telco Capability Index is intended to track progress on telecom-specific tasks.
The strategy is broader than producing a model. Shared datasets and benchmarks can help participants assess whether a system is useful for telecom work, while contributions from different organizations can expand the capabilities and infrastructure available to the effort. The initiative’s existence signals coordinated development; it does not by itself establish that every operator is using its models in production.
How could open models help with network operations?
In network operations, an AI system might help an engineer investigate an alarm, identify a likely cause or navigate technical documentation. For multi-vendor networks, a practical system also has to work with information and interfaces that span different suppliers. This is why integration and interoperability matter alongside model quality: a capable model that cannot safely access relevant data or use operator tools may not help resolve an operational issue.
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Globe Telecom: an exploration, not a confirmed rollout
Globe Telecom in the Philippines is exploring multi-vendor radio access network root-cause analysis using TM Forum solution packs to expose normalized APIs across vendors. It is also working with Open Telco AI on models and agents for analysis automation. The reported status is exploration; it should not be described as a completed production deployment.
TelecomGPT and standards knowledge
A separate GSMA Foundry and Khalifa University collaboration announced TelecomGPT, a telco-first large language model, alongside an Open Telco Knowledge Graph focused on 3GPP documentation. The announcement names a chat interface hosted on LightOn and a knowledge graph hosted on Hugging Face. This example illustrates a standards-focused direction: organize telecom knowledge so it can support model-based question answering and related applications.
Why the model is only one part of the strategy
Operators need a way to source, serve, update and monitor models, connect them to governed data, and control how automated agents interact with systems. Deployment constraints also vary: some workloads may run in cloud environments, while others may require edge or on-premises infrastructure. A strategy that addresses only model selection leaves these operational questions unanswered.
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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 describes how operators can source and operate multiple models across cloud, edge and on-premises environments. The data-products work concerns governed data access and lifecycle management; the security project addresses agent interactions. These are framework projects, not proof that a single deployment pattern has already been adopted across the industry.
TM Forum’s collaboration also reflects a goal of reducing fragmentation by bringing model operations, data, security and deployment practices into a more coherent approach. In practical terms, operators evaluating a model should ask how it will connect to existing systems, what data it can use, how access is controlled, where it runs and who manages its lifecycle.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are operators trying to gain first: efficiency or new revenue?
Available industry figures point more strongly to operational efficiency than to external revenue as the current focus of AI activity. GSMA Intelligence’s 2025 reporting, as cited in GSMA’s 2026 Bridging the Language Gap report, puts customer-care automation at 47% of tracked AI deployments globally. Separately, GSMA Intelligence’s 2026 Charting AI Monetisation report says around 15–20% of operator AI deployments had an external revenue-generating focus. That report’s base was 700 telecom operators worldwide as of September 2025.
These measures describe AI deployment focus, not the share of operators using open models. They suggest why operators may prioritize use cases that improve existing services and operations, while also exploring ways to sell AI-enabled services. They do not show that revenue generation is the dominant objective today, or that open models are responsible for a particular share of either category.
Where else can open-model collaboration matter?
Telecom-specific AI is not limited to network troubleshooting. Language capability can shape whether customers and employees can use AI effectively, especially in multilingual markets. GSMA’s 2026 report describes Swisscom as a strategic partner in developing Apertus, a multilingual model with openly accessible development assets and permissive licensing. The same report describes the African AI Language Models Initiative as a collaboration involving mobile operators, ecosystem participants, research institutions and civil society.
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These examples show a different potential benefit of collaboration: helping build language resources and models for local needs, rather than focusing only on network operations. They do not establish that every model is equally open or suitable for every operator’s data, languages or deployment requirements.
How to judge an open-model strategy
For an operator, the useful comparison is not simply open versus closed. It is whether a particular model and its surrounding tools fit a real workload and can be used responsibly within the operator’s environment.
- Task fit: Evaluate performance on the intended telecom task, such as network troubleshooting, standards interpretation or customer assistance—not general-purpose reasoning alone.
- What is actually open: Establish whether weights, data, training methods, APIs and evaluation materials are available, and review the applicable license.
- Interoperability: Check compatibility with multi-vendor infrastructure, operator APIs and model-service frameworks.
- Deployment and governance: Determine whether the system can run in the required cloud, edge or on-premises setting, and how data access, security, lifecycle management and cost controls will work.
- Evidence of maturity: Distinguish a benchmark or announced initiative from a proof of concept, exploratory work, integration effort or production deployment.
How mature is the shift to open models?
The evidence shows an industry collaboration launched, operator examples at different stages, and framework projects aimed at making model use more interoperable and governable. It does not establish widespread production adoption or show that open models outperform closed models, cost less in every deployment or are safer by default. Operators should treat openness as one factor in a deployment decision, alongside task performance, rights, integration, security and operational requirements.
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