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.
- 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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