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蜂窝网路中的人工智慧：2025-2029 年全球市场
市场调查报告书
商品编码
1698611

蜂窝网路中的人工智慧：2025-2029 年全球市场

Global AI in Cellular Networks Market: 2025-2029
出版日期: | 出版商: Juniper Research Ltd | 英文 | 商品交期: 最快1-2个工作天内

价格
简介目录

随着 "零接触" 成为焦点，预计未来四年电信公司 AI 投资将超过 860 亿美元

关键统计
2025 年电信业者在蜂巢网路的 AI 支出： 135亿美元
2029 年电信业者在蜂巢网路的 AI 支出： 229亿美元
电信公司正在投资数位转型： 1080亿美元
预测期间： 2025-2029

此研究套件为电信业者和网路 AI 供应商提供分析和可行的见解。它还包含数据，以帮助市场利益相关者（例如行动网路营运商 (MNO) 和网路 AI 供应商）就其网路中 AI 参与的业务策略做出明智的决策。该研究包括八个关于营运商在蜂窝网路中采用人工智慧的案例研究，以及一个关于 Indosat Ooredoo Hutchison 的 AI-RAN 策略的案例研究。这些案例研究包括：

  • 美国电话电报公司
  • 中国移动
  • 德国电信
  • 西班牙电信公司
  • SK电信
  • 标准
  • 威瑞森
  • 沃达丰

这些案例研究分析了领先的电信营运商如何在其网路中部署和创新人工智慧、其部署和创新的核心优势，并评估了这些部署如何为营运商的未来定位。这将使其他电信营运商和网路AI供应商瞭解市场领先的营运商如何处理网路AI，帮助他们做出更明智的决策并制定策略。

该研究套件还包括营运商部署网路 AI 的关键目标的细分，以及他们预期这些目标未来如何发展的分析。此外，它还对无线存取网路（RAN）中的人工智慧、AI-RAN联盟、水平RAN堆迭的开发、主权人工智慧、网路规划中的人工智慧、网路维护中的人工智慧、网路切片中的人工智慧和差异化连接等关键概念和技术进行了策略分析。

此外，它还就电信业者如何利用人工智慧来提高网路安全、如何保护其人工智慧部署免受诈欺者和恶意行为者的侵害提供了建议和评估，并就营运商如何最大限度地发挥人工智慧在其数据中心和云端基础设施中的影响提供了战略分析。这将使电信营运商、网路 AI 供应商和其他利害关係人能够有效地评估 AI 采用的不同领域并做出明智的商业决策。

该报告还提供了对其他技术和标准的见解，包括基于代理的人工智慧、TeleManagement™ Forum 的自主网路、6G、大型语言模型 (LLM) 和 GSMA 的 Open-Telco LLM 基准。每个部分都包含来自 Juniper Research 的建议和分析，以帮助瞭解主要趋势以及确定未来研发的机会和策略。

主要特点

  • 市场动态：深入瞭解蜂窝网路市场中 AI 的主要趋势和机遇，包括 AI-RAN 联盟对 AI-RAN 的开发、主权 AI 的作用、AI 在网路安全中的应用，以及运营商 AI 用例的演变；对八大运营商在其网络中使用人工智能的战略分析；以及运营商 AI 用例的演变；对八大运营商在其网络中使用人工智能的战略分析；以及运营商部署示例和投资。
  • 关键要点和策略建议：本报告深入分析了蜂窝网路市场人工智慧的关键发展机会和见解，并为希望增加收入并在产品供应中获得优势的营运商和人工智慧网路供应商提供了策略建议。
  • 基准产业预测：提供 SIM 卡总数、营运商收入、营运商数位转型总投资、营运商 AI 总投资、营运商网路 AI 总投资的数据。营运商在网路 AI 方面的总投资分为营运商在 RAN 方面的网路 AI 总投资、营运商在编排和管理方面的网路 AI 总投资、营运商在网路安全方面的网路 AI 总投资以及营运商在营运和维护 (O&M) 方面的网路 AI 总投资。
  • Juniper Research 的未来领导者指数：评估 16 家网路 AI 供应商的能力，并为每个供应商提供市场规模和详细分析。

范例视图

市场数据与预测


市场数据与预测

研究套件包括存取包含超过 7,900 个资料点的全套预测资料。该调查套件包括以下指标：

  • 承运商总收入
  • 电信业者数位转型投资总额
  • 电信公司在人工智慧方面的总投资
  • 网路人工智慧总投资
  • 营运商对 RAN 网路 AI 的总投资
  • 电信公司在网路 AI 编排与管理的总投资
  • 电信业者在网路安全方面对网路人工智慧的总投资
  • 营运商在网路维运人工智慧方面的总投资

Juniper Research Interactive Forecast (Excel) 包含以下功能：

  • 统计分析：能够搜寻资料期间所有地区和国家显示的特定指标。可以轻鬆修改图表并将其汇出到剪贴簿。
  • 国家资料工具：此工具可让您查看预测期间内所有地区和国家的指标。您可以使用搜寻栏缩小显示的指标范围。
  • 国家比较工具：您可以选择特定的国家进行比较。该工具具有汇出图表的功能。
  • 假设分析：透过三个互动式场景，使用者可以比较预测假设。

目录

市场趋势/策略

第1章 重点与策略建议

第2章 市场状况

  • 为什么电信业者希望在其网路中部署人工智慧
  • 利用人工智慧降低网路整体拥有成本
  • 利用人工智慧实现净零目标
  • 利用人工智慧改善和扩展营运商服务 世界领先的电信营运商如何在其网路中使用人工智慧

第3章 关键科技与未来机会

  • 网路人工智慧关键技术
    • 代理人工智慧
    • 6G
    • 法学硕士
  • 人工智慧网路应用的重大机遇
    • 艾然
    • 网路资料中心和云端管理的人工智慧
    • 网路安全人工智慧
    • 人工智慧用于网路维护
    • 人工智慧用于网路规划
    • 用于网路切片和差异化连接的人工智慧

竞技排行榜

第1章竞技排行榜

第2章 供应商简介

  • 供应商资料
    • Blue Planet
    • Cisco
    • Ericsson
    • Google Cloud
    • Huawei
    • IBM
    • Jio Platforms
    • Juniper Networks
    • Mavenir
    • Microsoft
    • Netcracker
    • Nokia
    • NVIDIA
    • Samsung
    • Subex
    • ZTE
  • Juniper Research 排行榜评估方法
  • 限制和解释
  • 相关研究

数据和预测

第1章 引言与研究方法

第2章市场总结及未来市场展望

  • 营运商总收入
  • 电信业者在网路人工智慧方面的总投资
  • 营运商对 RAN AI 的网路 AI 总投资
  • 电信业者在网路编排和管理方面对网路 AI 的总投资
  • 电信业者在网路安全方面对网路人工智慧的总投资
  • 电信业者在网路运维人工智慧方面的总投资
简介目录

'Operator AI Investment to Exceed $86bn Over the Next Four Years as 'Zero Touch' Becomes the Focus'

KEY STATISTICS
Total operator investment in AI in cellular networks in 2025:$13.5bn
Total operator investment in AI in cellular networks in 2029:$22.9bn
Total operator investment in digital transformation:$108bn
Forecast period:2025-2029

Overview

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
  • Telefonica
  • 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.

All report content is delivered in the English language.

Key Features

  • Market Dynamics: Insights into the key trends and opportunities within the AI in cellular networks market, including the development of AI-RAN by the AI-RAN Alliance, the role of sovereign AI, how AI is being used in network security, and how operators are progressing their AI use cases. It also includes strategic analysis of eight leading operators' use of AI in their networks, with a case study into each operator's deployments and investments.
  • Key Takeaways & Strategic Recommendations: In-depth analysis of key development opportunities and findings within the AI in cellular networks market, accompanied by strategic recommendations for operators and AI in network vendors seeking to grow their revenue or gain an advantage in their product offerings.
  • Benchmark Industry Forecasts: The suite provides four-year forecasts for the global AI in cellular networks market; providing data for the total number of SIMs, total operator revenue, total operator investment in digital transformation, total operator investment in AI, and total operator investment in network AI. Total operator investment in network AI is provided with splits for total operator investment in network AI for RAN, total operator investment in network AI for orchestration and management, total operator investment in network AI for network security, and total operator investment in network AI for operations and maintenance (O&M).
  • Juniper Research Future Leaders' Index: Key player capability and capacity assessment for 16 AI in networks vendors, with market sizing and detailed analysis for each vendor's offering.

SAMPLE VIEW

Market Data & Forecasts


The numbers tell you what's happening, but our written report details why, alongside the methodologies.

Market Data & Forecasts

The market-leading research suite for the AI in networks market includes access to the full set of forecast data, comprising more than 7,900 datapoints. Metrics in the research suite include:

  • Total Operator Revenue
  • Total Operator Investment in Digital Transformation
  • Total Operator Investment in AI
  • Total Operator Investment in Network AI
  • Total Operator Investment in Network AI for RAN
  • Total Operator Investment in Network AI for Orchestration and Management
  • Total Operator Investment in Network AI for Network Security
  • Total Operator Investment in Network AI for O&M

Juniper Research's Interactive Forecast Excel contains the following functionality:

  • Statistics Analysis: Users benefit from the ability to search for specific metrics, displayed for all regions and countries across the data period. Graphs are easily modified and can be exported to the clipboard.
  • Country Data Tool: This tool lets users look at metrics for all regions and countries in the forecast period. Users can refine the metrics displayed via a search bar.
  • Country Comparison Tool: Users can select and compare specific countries. The ability to export graphs is included in this tool.
  • What-if Analysis: Here, users can compare forecast metrics against their own assumptions, via three interactive scenarios.

Market Trends & Strategies Report

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.

Competitor Leaderboard Report

The Competitor Leaderboard included in this report provides detailed evaluation and market positioning for 16 network AI vendors. These key companies are positioned as established leaders, leading challengers, or disruptors and challengers, based on a capacity, capability, and product assessment. This includes analysis of their key advantages in the market, future development plans, and key partnerships.

The AI in Cellular Networks Competitor Leaderboard includes the following key vendors:

  • Blue Planet
  • Cisco
  • Ericsson
  • Google Cloud
  • Huawei
  • IBM
  • Jio Platforms
  • Juniper Networks
  • Mavenir
  • Microsoft
  • Netcracker
  • Nokia
  • NVIDIA
  • Samsung
  • Subex
  • ZTE

Table of Contents

Market Trends & Strategies

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

Competitor Leaderboard

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

Data & Forecasting

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