Summary:
Research from GSMA Intelligence presented at Mobile World Congress 2026 shows that telecom operators are shifting their AI strategies from primarily reducing operational costs to generating new revenue streams. While most AI deployments in 2025 focused on internal automation—such as customer service chatbots and predictive maintenance—companies are now exploring higher-value services to counter slowing connectivity revenue. Operators are expanding into areas like AI platforms, GPU-as-a-service, IoT and cloud gaming, often through partnerships with hyperscalers and cloud providers. The report identifies three emerging monetisation models: AI connectivity providers, AI compute providers and AI solutions partners, reflecting a broader industry move up the digital value chain despite higher investment needs and increased competition.
Telecom companies are increasingly redirecting their artificial intelligence strategies from internal cost reduction toward creating new revenue streams, according to fresh research from GSMA Intelligence unveiled during Mobile World Congress 2026.
Although AI adoption throughout 2025 was mainly centred on low-risk automation projects, the sector is now moving toward higher-value AI-based services as operators attempt to counter slower growth in traditional connectivity income.
AI Implementations Centred on Efficiency in 2025
Most operator activity in AI has focused on automating internal processes through AI agent frameworks. Customer support represented close to half of all AI deployments in 2025, while applications within network management accounted for slightly less than 20 percent.
In a low-growth environment, reducing operational costs remains a key objective, particularly as telecom providers contend with rising data traffic and increasing energy use. Around 80 percent of AI initiatives were primarily designed to improve internal efficiency—such as predictive maintenance systems and AI-powered chatbots—according to the GSMA Intelligence report.
AI as a Driver of Revenue Growth
Telecom operators are now repositioning AI as a central element of their revenue expansion strategies. In addition to internal optimisation, they are pursuing external AI opportunities by collaborating with hyperscalers, cloud platforms and data centre operators to monetise AI capabilities.
This shift reflects a broader effort to move further up the digital value chain. Historically concentrated on infrastructure services, telecom companies are now expanding into higher-value areas including GPU-as-a-service offerings, AI platforms, Internet of Things solutions and cloud gaming. By expanding vertically, operators hope to secure a larger share of the emerging AI-driven revenue landscape.
However, entering these segments also places operators in closer competition with hyperscalers and enterprise IT providers, increasing both innovation demands and execution risks. While AI-enabled services offer stronger margin potential, they also require substantial investment in computing infrastructure, software ecosystems and specialised expertise.
Range of AI Monetisation Opportunities
AI monetisation opportunities extend across several layers—from core and private cloud environments to enterprise edge deployments and device-level processing. Each layer presents different trade-offs related to capital requirements, revenue potential, latency performance and regulatory factors such as data sovereignty and sovereign AI compliance.
These challenges are accelerating the adoption of partnership-based strategies, enabling telecom operators to shorten time-to-market and limit capital exposure while differentiating through localisation, system integration capabilities and AI functions embedded directly into network infrastructure.
Emerging AI Revenue Models for Telecom Providers
GSMA Intelligence highlights three main AI monetisation approaches taking shape across the telecom sector:
1. AI Connectivity Provider
In this model, operators utilise high-capacity networks, edge computing facilities and data centres to deliver secure, low-latency connectivity designed for AI workloads. Revenue opportunities include network slicing, connectivity-as-a-service and edge-based AI processing.
Examples include:
- Singtel’s Paragon platform, combining private 5G and edge computing with Nvidia’s AI technology stack for compute-intensive AI applications.
- Reliance Jio’s enterprise connectivity services supporting AI collaborations with partners such as Meta.
2. AI Compute Provider
Here, operators repurpose existing infrastructure to deliver GPU-powered computing resources, sovereign AI cloud environments and high-performance processing designed for enterprise AI workloads. Revenue streams typically include GPU-as-a-service and subscription-based AI infrastructure access.
Examples include:
- Deutsche Telekom’s Industrial AI Cloud initiative, supported by a €1 billion investment aimed at enabling industrial AI use cases.
- SK Telecom’s sovereign GPU-as-a-service platform Haein, providing enterprise-grade GPU capacity.
3. AI Solutions Partner
Under this approach, telecom operators work with hyperscalers and enterprise software providers to build complete AI solutions tailored to specific industries. Revenue sources include joint go-to-market programmes, managed AI services and system integration fees.
Examples include:
- KDDI partnering with service providers from Amazon Web Services to support adoption of generative AI technologies.
- China Telecom deploying its Xingchen large language model across more than 50 industry sectors.
- Reliance Jio introducing the JioBrain platform to help enterprises build and deploy customised large language models.
