?Rising AI demand: Enterprises adopting LLMs, real-time analytics, and edge computing are driving explosive growth in infrastructure needs, with U.S. data center capacity projected to double by 2030.Telco advantage: Telecom providers’ vast networks, edge presence, and data centers uniquely position them to deliver scalable AI-ready infrastructure and services.GPU-as-a-Service: Telcos can offer on-demand GPUaaS with orchestration, accelerators, and flexible pricing to support training, inference, and enterprise AI workloads.Strategic shifts: To lead in AI, telcos must evolve into platform enablers — modernizing cores, building AI ecosystems, monetizing edge assets, and embedding intelligent agents.
The rapid proliferation of artificial intelligence (AI) and machine learning (ML) across enterprises is creating an unprecedented demand for robust and scalable infrastructure. This surge presents a unique strategic opportunity for telecom providers, who are uniquely positioned to become central enablers of advanced AI services due to their distributed edge presence, extensive connectivity, and data center capabilities.
Fortunately, by reimagining their infrastructure as a foundation for AI acceleration, telcos can unlock significant strategic relevance within the evolving enterprise technology stack.
The Growing Demand for Enterprise AI Infrastructure
Enterprise demand for AI and ML infrastructure is experiencing rapid growth, driven by the widespread adoption of AI-driven workloads. Enterprises investing in applications such as large language models (LLMs) and real-time analytics are pushing existing infrastructure to scale rapidly in both centralized and edge environments.
This acceleration is evident in projections for global data center infrastructure spending, which is expected to double U.S. capacity from 17 GW in 2022 to 35 GW by 2030. This is largely propelled by generative AI workloads that demand significantly more power and cooling than traditional systems.
Furthermore, edge deployments are becoming increasingly vital as they extend AI computing closer to data sources, which is crucial for low-latency applications like real-time analytics, 5G, autonomous vehicles, and industrial IoT. This approach also helps reduce bandwidth and central cloud costs.
Ultimately, this dynamic fuels the demand for infrastructure that’s designed to support these accelerator-driven AI rollouts. Enterprises are also leveraging agentic AI frameworks to automate key steps in the AI pipeline, improving accuracy and reducing costs, with some showing up to 50% gains in operational efficiency.
Unique Advantages of Telecom Providers in Supporting Enterprise AI Workloads
Enterprises now face the necessity of dual-track investments in both hyperscale and edge infrastructure, requiring new facilities, upgraded power and cooling systems, sustainable energy sourcing, and intelligent orchestration across centralized and distributed nodes. This scenario presents a strategic opportunity for telcos to evolve beyond traditional infrastructure roles and position themselves as enablers of advanced AI services.
Telcos possess unique advantages in supporting enterprise AI workloads due to their expansive network footprints, existing data center infrastructure, and established cloud connectivity. These assets enable them to offer flexible, high-performance computing (HPC)-ready, and edge-integrated infrastructure.
However, simply providing infrastructure or connectivity is insufficient; enterprises increasingly require advanced platform engineering expertise to seamlessly orchestrate AI workloads across cloud, edge, and on-premises environments. Telcos can integrate generative AI accelerators and orchestration platforms to rapidly scale AI development, automate workload management, and improve data autonomy.
Real-world implementations demonstrate the potential for telco infrastructure to evolve into AI-ready platforms supporting both edge and centralized workloads.
GPU-as-a-Service: A Key Offering for Telcos
Telcos can offer GPU-as-a-service (GPUaaS) to meet the escalating demand for enterprise AI infrastructure. By leveraging their expansive network footprints and data center capabilities, telcos can provide on-demand, scalable GPU resources that are optimized for AI and machine learning workloads. GPUaaS allows enterprises to rapidly deploy and scale resource-intensive AI applications without significant upfront capital investments in high-performance hardware.
To differentiate their offerings, telcos can embed AI workload accelerators that include pre-integrated orchestration layers, inference runtime environments, and workload optimization blueprints, which reduce onboarding friction and improve performance benchmarking for enterprise clients. They can further enhance their GPUaaS by bundling managed services that provide workload orchestration, resource optimization, and seamless integration across cloud, edge, and private environments.
Additionally, offering specialized layers tailored to diverse enterprise needs — from high-end training to edge-based inference — complemented by flexible consumption-based pricing models can further attract clients. Through GPUaaS, telcos can transition from mere connectivity providers to strategic AI-enablement partners, aligning directly with enterprises’ accelerating demands for high-performance, flexible, and cost-effective AI infrastructure.
Agentic architecture can further enhance seamless GPU service orchestration by integrating with cloud-native ecosystems, reducing onboarding time, and enabling multi-tenant usage. These frameworks also support modular governance and observability, improving transparency and compliance across AI workloads.
Developing AI-Ready Edge Platforms
AI-ready enterprise platforms are comprehensive technology environments purpose-built to manage and scale sophisticated AI workloads. These platforms seamlessly integrate infrastructure orchestration, cloud-native capabilities, and advanced AI model lifecycle management, which enables rapid innovation and enterprise agility. Telcos can deploy these platforms on the edge to drive transformative business outcomes across critical domains.
For example, these platforms can:
Enhance customer experience through hyper-personalization and proactive insights;
Empower developers with intuitive self-service environments for faster AI integration;
Enable autonomous networks that self-optimize and adapt in real-time; and
Pioneer intelligent connectivity management through AI-driven resource allocation and predictive network capabilities.
These capabilities collectively position telcos not merely as infrastructure providers, but as strategic enablers of enterprise innovation that are accelerating business performance, operational excellence, and customer-centric differentiation.
Localized AI Inference Solutions for Enterprise Value
Localized AI inference at the edge presents substantial business value to enterprises by significantly reducing latency, improving operational efficiency, enhancing customer experiences, and supporting data sovereignty requirements. Unlike centralized AI deployments, edge-based inference processes data locally, ensuring real-time responsiveness — critical for use cases such as industrial IoT predictive maintenance, real-time retail analytics, intelligent video surveillance, and autonomous vehicles.
Telcos hold a distinctive advantage in delivering these solutions by leveraging their existing edge infrastructure (such as network towers, edge data centers, and distributed cloud nodes) and advanced connectivity, including 5G. This is where pre-built inference accelerators come in by offering containerized, lightweight runtimes optimized for edge GPUs and CPUs. Combined with telcos’ distributed infrastructure and 5G backbone, these accelerators enable real-time AI delivery with central governance and local autonomy.
By bundling infrastructure with these pre-engineered capabilities, telcos can deliver turnkey edge inference environments, empowering enterprises to deploy AI close to action without building everything from scratch. This approach translates directly into improved productivity, reduced operational costs, and differentiated real-time customer interactions while also addressing regulatory compliance around data privacy and residency.
Prebuilt accelerators, such as data profiling and insight agents, enable telcos to offer interactive or autonomous AI environments at the edge, improving latency, SLA performance, and compliance. These agent-based environments support real-time customer interactions and contextual insights, delivering measurable improvements in AI operations.
5 Strategic Shifts for Telcos in the Enterprise AI Ecosystem
To effectively reposition themselves in the enterprise AI ecosystem, telcos need to undertake several strategic shifts:
1. Move from Connectivity to Platform-Driven AI Enablement
2. Build an Ecosystem of Trusted AI Partners
Forming alliances and strategic ecosystem partners — spanning hyperscalers, AI specialists, and system integrators — allows telcos to co-develop scalable, reusable AI products for both internal operations and enterprise clients.
3. Modernize Digital Core & Data Infrastructure
Telcos must prioritize removing technical debt and modernizing legacy systems. Cloud-native architectures and unified data management are critical to unlocking AI scale and agility.
4. Develop AI-Native Operating Models
Establishing enterprise-wide AI governance that’s championed by the C-suite is essential to integrate data-as-product strategies and lifecycle intelligence, which accelerates adoption in both customer-facing channels and internal workflows.
5. Monetize Edge & Data Center Assets for AI Workloads
Telcos can capitalize on their owned fiber, edge data centers, and 5G infrastructure to host AI workloads closer to enterprises and hyperscalers. This includes collaborating with hyperscalers, ISVs, and AI specialists to co-create reusable components like intelligent agents, domain data models, and lifecycle management toolkits.
Beyond that, telcos should embrace agent-first design and embed intelligent agents across the customer lifecycle (from onboarding to support) to enable hyper-personalized and autonomous operations. Ecosystem collaboration and workforce upskilling on frameworks like LangGraph and modular AI architecture will be critical to accelerating this shift.
Together, these strategic shifts move telcos beyond traditional connectivity roles, positioning them as central AI-platform enablers that deliver differentiated, high-value offerings aligned directly to enterprise needs for agile, intelligent, and scalable infrastructure.
About the Author: Maha Hashmi is the Executive Managing Director of Customer Success & Chief Revenue Officer at Brillio , where she leads the Communications, Media, and Technology (CMT) vertical. A seasoned executive with deep experience across global IT services and consulting, Maha specializes in digital transformation, client success, and building high-performing teams. At Brillio, she is focused on delivering innovative, AI-powered solutions and driving strategic growth for clients in fast-evolving industries.
Edited by
Erik Linask
