Telecom operators are increasingly turning to open AI models as they build artificial intelligence into network operations, customer service and other critical systems.
The shift is not simply about finding cheaper alternatives to proprietary AI. Operators want more control over how models work, where they run and how they can be adapted to their own networks and customer data.
According to NVIDIA’s State of AI in Telecommunications report, 89% of surveyed telecom companies said open-source models and software are important to their AI strategies. That puts open AI closer to the centre of telecom planning, rather than leaving it as an experimental technology on the sidelines.
Open AI Models Give Telecom Operators More Room to Customize
Telecom networks generate enormous amounts of specialised data, and general-purpose AI models do not necessarily understand the details of those environments out of the box. Open models give operators the ability to fine-tune systems for specific network, customer and operational workloads.
That flexibility can be useful for tasks ranging from network management to customer-care systems. Operators can adapt models to their own data and requirements instead of depending entirely on the capabilities and restrictions of a proprietary model provider.
The appeal is particularly strong for telecom companies that want AI systems designed around their own infrastructure rather than forcing existing operations to fit a generic AI platform.
SoftBank and Indosat Are Developing Telecom-Specific AI
The move toward open models is already showing up in individual operator strategies. SoftBank Corp. is developing its Large Telecom Model using NVIDIA’s Nemotron framework, with the goal of applying AI to network operations and management.
In Indonesia, Indosat Ooredoo Hutchison has taken another route with its Sahabat-AI family of models. The project focuses on local languages and cultural contexts, showing how open AI can be adapted for markets where a one-size-fits-all model may not be enough.
These examples point to one of the strongest arguments for open models in telecommunications: operators can build AI around the markets and infrastructure they actually serve.
AT&T Is Building AI Around Telecom-Specific Data
AT&T is also using an open-model approach to develop AI for telecommunications. The company’s OTel 2.0 model was built using 400 billion telecom-specific tokens, giving the system a training base tailored to the industry’s terminology and operating environment.
Andy Markus, AT&T’s chief data and AI officer, has highlighted open models as important to creating a flexible and governed AI strategy.
That focus on governance is significant. Telecom operators manage networks and customer systems that cannot simply be treated like ordinary software applications. AI needs to fit within business priorities, operational controls and privacy requirements.
NVIDIA Is Pushing Open Models Deeper Into Telecom
NVIDIA is positioning its Nemotron family of open models and the surrounding NeMo software ecosystem as tools for telecom companies developing their own AI systems.
The technology can be adapted for workloads such as network configuration, customer incident triage and autonomous network operations. NVIDIA has also introduced the Nemotron 3 Large Telco Model, which has been fine-tuned using telecommunications datasets for industry-specific applications.
For operators, the attraction is the ability to take an existing foundation and adapt it rather than starting an AI development programme entirely from scratch.
NVIDIA’s role also illustrates how the open-model ecosystem is developing beyond individual models. Hardware, training tools, model libraries and deployment infrastructure increasingly form part of the same AI stack.
Privacy and Governance Are Becoming Major Reasons to Choose Open Models
Telecom operators have another reason to prefer open AI: control over sensitive information.
Network data and customer information can be subject to privacy, security and regulatory requirements. Open models can give operators greater visibility into the technology and more options for deciding where models run and how they are modified.
That does not automatically make an open model secure or compliant. Those outcomes still depend on how an operator trains, deploys and governs the system. But having greater control over the model and its environment can make those requirements easier to address.
For telecom companies operating across different jurisdictions, that flexibility is becoming increasingly valuable.
Telecom AI Is Moving Toward a Hybrid Model Strategy
The growing interest in open AI does not mean proprietary models are disappearing from telecom. Operators can use different models for different jobs.
Closed models may remain attractive for certain high-performance or real-time applications, while open models can handle workloads where customization, privacy and control matter more.
This creates a hybrid approach in which operators do not have to commit their entire AI strategy to one model provider. They can select technology according to the workload, data requirements and deployment environment.
That flexibility may become one of the defining features of enterprise AI as telecom companies move from pilots into larger production deployments.
Open AI Could Help Operators Build More Localized Services
Telecom companies operate in markets with different languages, regulations and customer expectations. Open models give them an opportunity to adapt AI systems to those local conditions.
Indosat’s Sahabat-AI is a useful example because its development focuses on Indonesian languages and cultural contexts. The approach shows how telecom operators can use AI not only for internal automation but also to create services that reflect the markets they serve.
That localized capability could become increasingly important as governments and businesses place greater emphasis on data sovereignty and national AI infrastructure.
Telecom Operators Are Looking for More Than Lower AI Costs
Cost remains part of the argument for open models, but it is no longer the whole story.
Telecom operators want AI that they can customize, govern and deploy across the environments where their networks operate. They also want to reduce dependence on a small group of proprietary AI providers while retaining the ability to choose different technologies for different workloads.
The 89% figure from NVIDIA’s telecom survey captures how far that thinking has moved. Open models are increasingly being treated as part of the strategic architecture for telecom AI rather than simply another option on an AI procurement list.
Open AI Models Could Reshape the Telecom AI Stack
Telecom operators sit on a huge amount of infrastructure and operational data. Their challenge now is turning that advantage into useful AI systems without giving up control over critical networks and information.
Open models offer one route. They allow operators to adapt AI to specialised workloads, build around local requirements and maintain more control over deployment.
The next question is less about whether telcos will use open AI and more about how much of their AI stack they are willing to build themselves.
If SoftBank, Indosat, AT&T and others continue expanding these initiatives, open models could become a much more permanent part of the telecom industry’s AI architecture.
Sources
- Blockchain.News — Telecom Operators Embrace Open AI Models for Customization and Control
https://blockchain.news/news/telecom-operators-open-ai-strategy - NVIDIA — State of AI in Telecommunications
https://www.nvidia.com/en-us/lp/state-of-ai-in-telecommunications/ - NVIDIA — AI for Telecommunications
https://www.nvidia.com/en-us/industries/telecommunications/ - AT&T
https://www.att.com/ - SoftBank Corp.
https://www.softbank.jp/en/corp/ - Indosat Ooredoo Hutchison
https://ioh.co.id/
