The report thoroughly examines the global AI in Cellular Networks market; assessing market trends, technological developments, and commercial opportunities which are shaping the market both in the present and the future. Alongside this analysis, the document includes a comprehensive analysis of the different areas of AI deployment, such as in RAN, datacentre management, and network slicing; with this analysis supporting stakeholders in evaluating how they can separate from their competition and become a market leader.
This innovative ecosystem report also includes a breakdown and evaluation of eight leading operators’ investments and deployments for network AI. These case studies allow players in the network AI market to better understand the direction of leaders in the market, in turn providing insight into key trends and a foundation to develop their own business and product or technology development strategies.
Please note: the online download version of this report is for a global site license.
- 1. Key Takeaways Strategic Recommendations
- 1.1 Key Takeaways
- 1.2 Key Strategic Recommendations
- 2. Market Landscape
- 2.1 Introduction
- Figure 2.1: Total Operator Investment in Network AI ($m), Split By 8 Key Regions, 2024-2029
- 2.1.1 Why Are Operators Seeking to Deploy AI in Their Networks
- 2.1.2 Using AI to Reduce Network TCO
- Figure 2.2: Total Number of 5G Connections (m), Split By 8 Key Regions, 2024-2029
- 2.1.3 Using AI to Meet Net Zero Goals
- Figure 2.3: Total Operator Energy Savings (TWh), Split By 8 Key Regions, 2024-2029
- Table 2.4: Examples of Areas Explored for AI Use for Energy Efficiency in 5G
- 2.1.4 Using AI to Improve and Expand Operator Services
- Figure 2.5: Total Operator Revenue ($m), Split By 8 Key Regions, 2024-2029
- 2.2 How Leading Operators Are Using AI in Their Networks Around the World
- 3. Key Technologies and Future Opportunities
- 3.1 Key Technologies for AI in Networks
- 3.1.1 Agentic AI
- i. TM Forum’s Autonomous Networks
- Figure 3.1: TM Forum’s Autonomous Network Levels
- 3.1.2 6G
- Figure 3.3: 3GPP Timeline and Ericsson Expectations for First Commercial System
- 3.1.3 LLMs
- Figure 3.4: Use Cases for LLMs in Operator Networks
- i. GSMA Open Telco LLM Benchmarks and Custom Operator LLMs
- Table 3.5: Accuracy Comparison Between GPT-3.5, GPT-4, and Active Professionals
- 3.2 Key Opportunities for AI Network Deployments
- 3.2.1 AI RAN
- Figure 3.6: Benefits Expected to be Provided by AI-RAN
- ii. AI Services and Multi-tenant RAN Infrastructure
- Table 3.7: NVIDIA and Softbank’s Achievements With AI-RAN as of February 2025
- Figure 3.8: Schematic of Multi-tenant AI RAN Reference Architecture
- Figure 3.9: GPT-4 3-Shot Accuracy on MMLU Languages
- Tables 3.10: Examples of Sovereign AI Initiatives, Investments and Policies
- 3.2.2 AI for Network Datacentre and Cloud Management
- Figure 3.11: Total Operator Expenditure on Cloud ($m), Split by 8 Key Regions, 2023-2028
- 3.2.3 AI for Network Security
- i. Operator Strategies for Using AI to Protect Their Networks
- Figure 3.12: Key Use Cases for AI Security in Cellular Networks
- ii. The Threat of AI to Operator Networks
- 3.2.4 AI for Network Maintenance
- 3.2.5 AI for Network Planning
- 3.2.6 AI for Network Slicing and Differentiated Connectivity
- Figure 3.13: Key Types of Network Slicing
Research Assistance
Learn how to effectively navigate the market research process to help guide your organization on the journey to success.
Download eBook