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Global AI in Cellular Networks Market 2025-2029: Full Research Suite

Publisher Juniper Research
Published Jul 04, 2025
Length 149 Pages
SKU # JNRH19997490

Description

Our AI in Cellular Networks research suite provides operators and AI in network vendors with analysis and actionable insights. It also includes data which enables stakeholders in the market, such as mobile network operators (MNOs) and network AI vendors, to make informed decisions on business strategy for their involvement with AI in networks. The research suite covers eight case studies into operators’ AI in cellular networks deployments, as well as a further case study for Indosat Ooredoo Hutchison’s AI-RAN strategy. These case studies include:

AT&T
China Mobile
Deutsche Telekom
Telefónica
SK Telecom
stc
Verizon
Vodafone
Each of these case studies breaks down how a leading operator is deploying and innovating with AI in their networks, with analysis from Juniper Research on the core strengths of their deployments and innovations, and evaluation of how these deployments position the operator in the future. This allows other operators and network AI vendors to understand how those at the forefront of the market are approaching network AI; supporting informed decision-making and strategy formulation.

The research suite also includes a breakdown of the key goals of operators’ AI in networks deployments, with analysis of how Juniper Research expects these goals to evolve in the future. This is coupled with strategic analysis of key concepts and technologies, including AI in Radio Access Network (RAN), the AI-RAN Alliance, the development of horizontal RAN stacks, sovereign AI, AI in network planning, AI in network maintenance, and AI in network slicing and differentiated connectivity.

It further provides recommendations and assessments on how operators can use AI to improve their network security, as well as protect their own AI deployments from fraudsters and malicious actors, and strategic analysis of how operators can maximise the impact of AI in their datacentres and cloud infrastructure. Through this, operators, network AI vendors, and other stakeholders can effectively evaluate and make informed business decisions regarding different areas of AI deployments.

As well as this, the report offers insight into technologies and standards including agentic AI, TeleManagement (TM) Forum’s Autonomous Networks, 6G, large language model (LLM), and the GSMA’s Open-Telco LLM Benchmarks. Accompanied by Juniper Research’s recommendations and analysis, each of these sections identifies future development opportunities and strategies, in addition to providing an understanding of key trends.

The market forecast suite includes several different options that can be purchased separately, including access to data mapping and a forecast document, a strategy and trends document detailing critical trends in the market, and strategic recommendations for monetising and innovating AI in cellular networks.

The research suite includes a Competitor Leaderboard, which can be purchased separately; containing analysis and market sizing for 16 leading network AI vendors, who each provide operators with software for AI in network deployments.

Collectively, the suite provides a critical tool for understanding the AI in cellular networks market allowing operators, AI in network vendors, and other stakeholders to optimise their future business and product development strategies for the market; providing a competitive advantage over their rivals.

Please note: the online download version of this report is for a global site license.

Table of Contents

149 Pages
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 ($m), Split by 8 Key Regions, 2023-2028
3.2.3 AI for Network Security
i. Operator Strategies for Using AI to Protect Their Networks
Figure 3.12: Key Use Cases for AI Security in Cellular Networks
ii. The Threat of AI to Operator Networks
3.2.4 AI for Network Maintenance
3.2.5 AI for Network Planning
3.2.6 AI for Network Slicing and Differentiated Connectivity
Figure 3.13: Key Types of Network Slicing
1. Competitor Leaderboard
1.1 Why Read This Report
AI Development Must Be Focused on Creating Dynamic Infrastructure and Operations
Table 1.1: Juniper Research Competitor Leaderboard Vendors and Product Portfolios
Figure 1.2: Juniper Research Competitor Leaderboard: Network AI Vendors
Source: Juniper ResearchTable 1.3: Juniper Research Competitor Leaderboard: Network AI Vendors
Table 1.4: Juniper Research Competitor Leaderboard Heatmap: Network AI Vendors (1 of 2)
Table 1.5: Juniper Research Competitor Leaderboard Heatmap: Network AI Vendors (2 of 2)
2. Vendor Profiles
2.1 Vendor Profiles
2.1.1 Blue Planet
i. Corporate Information
Figure 2.1: Blue Planet Revenue ($m), Financial Year 2023-2024
ii. Geographical Spread
iii. Key Clients & Strategic Partnerships
iv. High-level View of Offerings
Figure 2.2: Blue Planet 5G Network Planning and Deployment Solution
v. Juniper Research’s View: Key Strengths & Strategic Development Opportunities
2.1.2 Cisco
i. Corporate Information
ii. Geographical Spread
iii. Key Clients & Strategic Partnerships
iv. High-level View of Offerings
Figure 2.3: Cisco Crosswork Network Automation Tenets
v. Juniper Research’s View: Key Strengths & Strategic Development Opportunities
2.1.3 Ericsson
i. Corporate Information
Table 2.4 Ericsson‘s Financial Information ($m), 2021-2024
ii. Geographical Spread
iii. Key Clients & Strategic Partnerships
iv. High-level View of Offerings
Figure 2.5: Ericsson Intelligent Automation Platform (EIAP)
v. Juniper Research’s View: Key Strengths & Strategic Development Opportunities
2.1.4 Google Cloud
i. Corporate Information
ii. Geographical Spread
Figure 2.6: Google Cloud Platform Regions
iii. Key Clients & Strategic Partnerships
iv. High-level View of Offerings
v. Juniper Research’s View: Key Strengths & Strategic Development Opportunities
2.1.5 Huawei
i. Corporate Information
ii. Geographical Spread
iii. Key Clients & Strategic Partnerships
iv. High-level View of Offerings
v. Juniper Research’s View: Key Strengths & Strategic Development Opportunities
2.1.6 IBM
i. Corporate Information
Table 2.7: IBM’s Select Financial Information ($m), 2021-2023
ii. Geographical Spread
Figure 2.8: IBM Datacentre and Machine-readable Zones (MZRs) Location Map
iii. Key Clients & Strategic Partnerships
iv. High-level View of Offerings
Figure 2.9: IBM Cloud Paks for Network Automation
v. Juniper Research’s View: Key Strengths & Strategic Development Opportunities
2.1.7 Jio Platforms
i. Corporate Information
ii. Geographical Spread
iii. Key Clients & Strategic Partnerships
iv. High-level View of Offerings
v. Juniper Research’s View: Key Strengths & Strategic Development Opportunities
2.1.8 Juniper Networks
i. Corporate Information
ii. Geographical Spread
iii. Key Clients & Strategic Partnerships
iv. High-level View of Offerings
Figure 2.10: Juniper Networks’ O-RAN Offering
v. Juniper Research’s View: Key Strengths & Strategic Development Opportunities
2.1.9 Mavenir
i. Corporate Information
ii. Geographical Spread
iii. Key Clients & Strategic Partnerships
iv. High-level View of Offerings
Figure 2.11: Mavenir’s AI & Analytics Solutions
v. Juniper Research’s View: Key Strengths & Strategic Development Opportunities
2.1.10 Microsoft
i. Corporate Information
ii. Geographical Spread
iii. Key Clients & Strategic Partnerships
iv. High-level View of Offerings
Figure 2.12: Azure Operator Nexus
v. Juniper Research’s View: Key Strengths & Strategic Development Opportunities
2.1.11 Netcracker
i. Corporate Information
ii. Geographical Spread
iii. Key Clients & Strategic Partnerships
iv. High-level View of Offerings
Figure 2.13: Netcracker Network Automation Suite
Figure 2.14: E2E Service and Slice Automation
Figure 2.15: Network Domain Orchestration
v. Juniper Research’s View: Key Strengths & Strategic Development Opportunities
2.1.12 Nokia
i. Corporate Information
Table 2.16: Nokia’s Select Financial Information ($m), 2021-2024
ii. Geographical Spread
iii. Key Clients & Strategic Partnerships
iv. High-level View of Offerings
v. Juniper Research’s View: Key Strengths & Strategic Development Opportunities
2.1.13 NVIDIA
i. Corporate Information
ii. Geographical Spread
iii. Key Clients & Strategic Partnerships
iv. High-level View of Offerings
Figure 2.17: NVIDIA Aerial CUDA-accelerated RAN Stack Diagram Showing Full-Stack Virtualised RAN Acceleration
v. Juniper Research’s View: Key Strengths & Strategic Development Opportunities
2.1.14 Samsung
Table 2.18: Samsung’s Financial Information ($b), 2022-2023
ii. Geographical Spread
iii. Key Clients & Strategic Partnerships
iv. High-level View of Offerings
Figure 2.19: Samsung SMO
Figure 2.20: Samsung VISTA
v. Juniper Research’s View: Key Strengths & Strategic Development Opportunities
2.1.15 Subex
i. Corporate Information
ii. Geographical Spread
iii. Key Clients & Strategic Partnerships
iv. High-level View of Offerings
v. Juniper Research’s View: Key Strengths & Strategic Development Opportunities
2.1.16 ZTE
i. Corporate Information
ii. Geographical Spread
iii. Key Clients & Strategic Partnerships
iv. High-level View of Offerings
v. Juniper Research’s View: Key Strengths & Strategic Development Opportunities
2.2 Juniper Research Leaderboard Assessment Methodology
2.3 Limitations & Interpretations
Table 2.21: Juniper Research Competitor Leaderboard: Global AI in Cellular Networks Market
2.4 Related Research
1. Introduction and Methodology
1.1 Introduction: AI in Networks Market
Figure 1.1: Total Operator Investment in Digital Transformation ($m), 2024-2029
1.2 Forecast Methodology
Figure 1.2: AI in Networks Forecast Methodology
2. Market Summary and Future Market Outlook
2.1 Total Operator Revenue
Figure & Table 2.1: Total Operator Revenue ($m), Split By 8 Key Regions, 2024-2029
2.2 Total Operator Investment in Network AI
Figure & Table 2.2: Total Operator Investment in Network AI ($m), Split By 8 Key Regions, 2024-2029
2.3 Total Operator Investment in Network AI for AI for RAN
Figure & Table 2.3: Total Operator Investment in Network AI for AI for RAN ($m), Split By 8 Key Regions, 2024-2029
2.4 Total Operator Investment in Network AI for Network Orchestration and Management
Figure & Table 2.4: Total Operator Investment in Network AI for Network Orchestration and Management ($m), Split By 8 Key Regions, 2024-2029
2.5 Total Operator Investment in Network AI for Network Security
Figure & Table 2.5: Total Operator Investment in Network AI for Network Security ($m), Split By 8 Key Regions, 2024-2029
2.6 Total Operator Investment in Network AI for Operations and Maintenance
Figure & Table 2.6: Total Operator Investment in Network AI for O&M ($m), Split By 8 Key Regions, 2024-2029

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