5G Core Summit Blog

Summary

Telecom operators are transitioning from basic 5G connectivity to more sophisticated 5G-Advanced (5G-A) implementations foundational to the artificial intelligence (AI) era. The architecture of 5G-A is designed to help telcos deliver AI applications and embed intelligence into common communication services. Enabling these capabilities demands more efficient operational methodologies, driving networks toward autonomous operations. AI tools and technologies are being used to build these capabilities.

Telco cloud architecture is foundational to this transformation. The increasing use of AI is starting to influence telco cloud design decisions. Its architecture needs to evolve to support the AI training and inferencing needs from various service- and network-driven use cases. As this happens, operators must optimize resource allocations for containerized network functions and the additional compute demanded by AI.

The creation of an integrated infrastructure where GPU and CPU resources are managed within a single unified cloud environment enables better hardware resource utilization. In addition to augmenting telco cloud with the necessary AI compute capabilities, telcos also need to simplify AI/ML management through an enablement platform. Common platform capabilities and services can simplify telcos’ on-premises AI environments.

Differentiated service experience and agile operations with cloud and AI

After the initial 5G rollouts, which were based on 5G New Radio (NR), telcos are now deploying 5G core. According to Omdia research, at the end of 2Q25, 70 communications service providers (CSPs) had commercially launched 5G core. A key reason for deploying 5G core is its ability to monetize new use cases and increase the level of operational agility.

Many forward-looking CSPs are already planning and executing the next steps by introducing 5G-A. After migration to 5G-A, they will be able to launch more sophisticated services. Unlike the early unlimited-data 5G plans with basic speed tiers, 5G-A service packages offer guarantees for uplink and downlink speeds and latency. Delivering such quality of experience assurance and real-time experience insights requires the ability to not only access data across different network systems but to act on it in near-real time.

AI is an important technology for delivering this closed-loop monitoring and assurance of the service KPIs and related network resources. Intelligence gained through AI is also used by other 5G core elements (such as policy management) for decision-making. An example use case is enabling dynamic policy capabilities, with which CSPs can offer premium service upgrade packages to customers willing to pay a higher tariff for specific enhancements. However, delivering such services is highly complex and necessitates more efficient operational methodologies. Therefore, the strategic evolution is toward combining intent-driven networking with Level 4 network autonomy, where comprehensive AI tools and technologies are used to build systems that are self-adapting, self-optimizing, and self-healing.

Augmenting on-premises telco cloud with AI capabilities

To deliver a differentiated service experience that combines connectivity and AI-enabled intelligence, the underlying network infrastructure needs to evolve. For many telcos, the increasing use of AI will influence their telco cloud design decisions.

In an annual survey, Omdia asked telecom operators whether support for ML training and inferencing workloads is becoming a critical factor in their telco network cloud infrastructure choices. The results showed that more than 60% of CSPs believe that their telco cloud infrastructure should also be able to host AI training and inferencing.

As telcos evolve their cloud infrastructure, they must take a holistic view of their existing deployments, founded mainly in VM-based infrastructure (such as OpenStack), the need for more dynamic resource allocations for containerized workloads, and the additional compute demanded by AI.

When the AI inferencing hardware is being introduced, creating an integrated infrastructure, where GPU (primarily used for inferencing) and CPU (used for other general computing) resources are managed within a single unified cloud environment, can offer several benefits. Such heterogeneous cloud infrastructure should enable better hardware (compute, storage) management to support network functions and AI. It should also reduce the overheads for managing separate resource pools.

To allow efficient collaboration across diverse computing resources, telco cloud must overcome the limitations of traditional CPU-centric architecture. One of the ways the industry is tackling this challenge is by introducing a high-speed peer-to-peer interconnection bus to converge general-purpose and intelligent computing.

Simplify AI/ML management with a holistic approach

In addition to the unified management and scheduling of general-purpose and AI computing hardware, another key consideration is an AI enablement platform. Such a platform provides capabilities for AI model management, scheduling, and acceleration. It supports agentic AI application development and rapid deployment, further strengthening the telco cloud’s support for AI-driven service innovation.

Adding common platform capabilities (e.g., MLOps tools, access to large language models) and services (e.g., model training and fine-tuning, application integration) will help telcos improve their on-premises AI capabilities. Telcos’ internal teams of data scientists can bring in and integrate AI/ML models developed in-house or codevelop with partners. The AI enablement platform helps operators to develop (or leverage out-of-the-box) AI/ML solutions for network-centric use cases such as fault management, resource management, and traffic management. A critical success factor for such holistic telecom AI platforms is adopting open architectures founded on open source frameworks and standardized APIs (such as those defined by TM Forum) and protocols (e.g., MCP and A2A). These features ensure seamless interoperability with diverse external systems, enabling operators to integrate new technologies and scale innovation efficiently while avoiding vendor lock-in.

Appendix

Inderpreet Kaur, Senior Analyst, Telco Cloud and Network Automationaskananalyst@omdia.com

Citation Policy

Request external citation and usage of Omdia research and data via citations@omdia.com.

Omdia Consulting

Omdia is a market-leading data, research, and consulting business focused on helping digital service providers, technology companies, and enterprise decision makers thrive in the connected digital economy. Through our global base of analysts, we offer expert analysis and strategic insight across the IT, telecoms, and media industries.

We create business advantage for our customers by providing actionable insight to support business planning, product development, and go-to-market initiatives.

Our unique combination of authoritative data, market analysis, and vertical industry expertise is designed to empower decision-making, helping our clients profit from new technologies and capitalize on evolving business models.

Omdia is part of Informa TechTarget, a B2B information services business serving the technology, media, and telecoms sector. The Informa group is listed on the London Stock Exchange.

We hope that this analysis will help you make informed and imaginative business decisions. If you have further requirements, Omdia’s consulting team may be able to help your company identify future trends and opportunities.

Get in touch

www.omdia.com

askananalyst@omdia.com

Copyright notice and disclaimer

The Omdia research, data, and information referenced herein (the “Omdia Materials”) are the copyrighted property of TechTarget, Inc. and its subsidiaries or affiliates (together “Informa TechTarget”) or its third-party data providers and represent data, research, opinions, or viewpoints published by Informa TechTarget and are not representations of fact.

The Omdia Materials reflect information and opinions from the original publication date and not from the date of this document. The information and opinions expressed in the Omdia Materials are subject to change without notice, and Informa TechTarget does not have any duty or responsibility to update the Omdia Materials or this publication as a result.

Omdia Materials are delivered on an “as-is” and “as-available” basis. No representation or warranty, express or implied, is made as to the fairness, accuracy, completeness, or correctness of the information, opinions, and conclusions contained in Omdia Materials.

To the maximum extent permitted by law, Informa TechTarget and its affiliates, officers, directors, employees, agents, and third-party data providers disclaim any liability (including, without limitation, any liability arising from fault or negligence) as to the accuracy or completeness or use of the Omdia Materials. Informa TechTarget will not, under any circumstance whatsoever, be liable for any trading, investment, commercial, or other decisions based on or made in reliance of the Omdia Materials.