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市场调查报告书
商品编码
1750494

云端电信 AI 市场机会、成长动力、产业趋势分析及 2025 - 2034 年预测

Cloud Telecommunications AI Market Opportunity, Growth Drivers, Industry Trend Analysis, and Forecast 2025 - 2034
出版日期: | 出版商: Global Market Insights Inc. | 英文 170 Pages | 商品交期: 2-3个工作天内

价格
简介目录

2024年，全球云端电信人工智慧市场规模达48亿美元，预计到2034年将以21.7%的复合年增长率成长，达到327亿美元，这得益于电信业人工智慧与云端技术的融合。随着网路日益复杂，电信业者纷纷转向人工智慧云端解决方案，以更有效率地管理系统、提供无缝连接并优化客户体验。 5G的持续部署，加上智慧型装置的广泛应用，正在加速对智慧可扩展网路的需求。人工智慧云端平台用于实现关键网路功能的自动化、优化资源分配，并降低动态网路环境中的延迟。

云端电信人工智慧市场 - IMG1

随着营运商寻求能够适应不断变化的需求的灵活基础设施，全球对基于云端的电信人工智慧的投资不断增加。数位转型的推动力——以及近期全球疫情的衝击——凸显了对高弹性通讯系统的需求。人工智慧整合有助于管理资料密集型营运、预测维护需求并透过自动化降低营运成本。此外，对即时分析和机器学习模型的日益依赖，使得人工智慧成为转型电信基础设施以支援未来创​​新的基石。云端架构与人工智慧的协同作用重新定义了电信营运商设计、营运和扩展其服务的方式。

市场范围
起始年份 2024
预测年份 2025-2034
起始值 48亿美元
预测值 327亿美元
复合年增长率 21.7%

2024年，解决方案细分市场占据59%的市场份额，预计到2034年将达到170亿美元。由于对用于增强网路可靠性和简化内部工作流程的整合AI工具的需求不断增长，该细分市场正日益受到关注。 AI驱动的平台被部署用于自动检测故障、管理网路拥塞并即时优化频宽。随着资料量的增加，电信公司需要能够解读复杂流量模式并主动应对潜在中断的系统。

大型企业细分市场在2024年引领市场，创造了31亿美元的收入。企业高度依赖人工智慧技术来管理庞大的网路并支援庞大的用户群。这些企业需要先进的自动化和预测能力，以提供稳定的性能并最大限度地减少停机时间。人工智慧能够优化资源并更好地规划基础设施，尤其是在营运规模庞大且复杂性高的环境中。

2024年，美国云端电信人工智慧市场占最大份额，达23%。强劲的市场地位促使其对先进电信基础设施进行大规模投资，尤其是在人工智慧整合和5G部署方面。联邦机构大力支持数位转型和智慧网路发展，进一步巩固了美国的领先地位。公共和私营部门的合作加速了基于云端的人工智慧技术的采用，重点是提高网路效率、即时分析和服务自动化。

SAP SE、Salesforce、腾讯、IBM、微软、Nvidia、甲骨文、ATOS、Alphabet 和亚马逊网路服务等公司正在推动人工智慧在电信领域的应用。该领域的公司正在实施一系列策略性倡议。许多公司正在扩展其全球云端基础设施，并提供针对电信应用量身定制的人工智慧即服务 (AI-as-a-service)。与电信业者的合作有助于提供客製化解决方案，以应对特定的网路挑战。企业也透过收购、合资企业和内部创新来增强人工智慧能力，以强化其产品组合。此外，对边缘运算、即时分析和跨平台整合的投资有助于这些企业在快速发展的市场环境中保持领先地位。

目录

第一章：方法论与范围

第二章：执行摘要

第三章：行业洞察

  • 产业生态系统分析
    • 供应商格局
      • 核心AI组件提供者。
      • 软体和平台供应商
      • 应用程式提供者
      • 最终用户
    • 利润率分析。
  • 川普政府关税的影响
    • 对贸易的影响
      • 贸易量中断
      • 报復措施
    • 对产业的影响
      • 供给侧影响（原料）
        • 主要材料价格波动
        • 供应链重组。
        • 生产成本影响
      • 需求面影响（售价）
        • 价格传输至终端市场。
        • 市占率动态
        • 消费者反应模式
    • 策略产业反应
      • 供应链重组。
  • 定价和产品策略
  • 技术与创新格局
    • 当前的技术趋势
      • 人工智慧驱动的网路优化
      • 基于云端的统一通讯（UCaaS）
      • 5G边缘运算
      • 电信云端编排和自动化
    • 新兴技术
      • 电信网路的量子计算
      • 融合人工智慧的6G网络
      • 区块链驱动的网路安全
      • 智慧虚拟网路功能
    • 先进材料科学
  • 定价策略
  • 专利分析
  • 用例
  • 重要新闻和倡议
  • 监管格局
  • 对部队的影响
    • 成长动力
      • 5G网路扩展和人工智慧集成
      • 边缘运算的进步
      • 改进的人工智慧预测性维护
      • 云端原生网路功能的进展
    • 产业陷阱与挑战
      • 资料品质、治理与安全问题
      • 人工智慧开发和部署的复杂性和人才缺口
  • 成长潜力分析
  • 波特的分析
  • PESTEL分析

第四章：竞争格局

  • 介绍
  • 公司市占率分析
  • 竞争定位矩阵
  • 战略展望矩阵

第五章：市场估计与预测：按组件，2021 - 2034 年

  • 主要趋势
  • 解决方案
    • 网路最佳化
    • 网路安全
    • 客户分析
    • 虚拟助手
    • 诈欺侦测
    • 预测性维护
    • 其他的
  • 服务
    • 专业服务
    • 託管服务
    • 咨询与培训

第六章：市场估计与预测：依技术分类，2021 - 2034 年

  • 主要趋势
  • 机器学习
  • 自然语言处理（NLP）
  • 巨量资料
  • 深度学习
  • 其他的

第七章：市场估计与预测：依组织规模，2021 - 2034 年

  • 主要趋势
  • 中小企业（SME）
  • 大型企业

第八章：市场估计与预测：按部署，2021 - 2034 年

  • 主要趋势
  • 公共云端
  • 私有云端
  • 混合云端

第九章：市场估计与预测：依最终用途，2021 - 2034 年

  • 主要趋势
  • 电信业者<
    • 行动网路营运商 (MNO)
    • 固定电话运营商
    • 卫星营运商
  • 网际网路服务供应商 (ISP)
    • 宽频网际网路服务供应商
    • 无线 ISP
  • 託管服务提供者 (MSP)
    • 云端服务供应商
    • 网路管理供应商

第十章：市场估计与预测：按地区，2021 - 2034 年

  • 北美洲
    • 我们
    • 加拿大
  • 欧洲
    • 英国
    • 德国
    • 法国
    • 义大利
    • 西班牙
    • 比利时
    • 瑞典
    • 俄罗斯
  • 亚太地区
    • 中国
    • 印度
    • 日本
    • 澳洲
    • 新加坡
    • 韩国
    • 东南亚
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 阿根廷
  • MEA
    • 南非
    • 沙乌地阿拉伯
    • 阿联酋

第 11 章：公司简介

  • Alibaba Cloud
  • Altair Engineering
  • Amazon Web Services (AWS)
  • ATOS SE
  • Avaamo
  • Baidu
  • Alphabet Inc
  • Hewlett Packard Enterprise Development
  • Huawei Cloud Computing Technologies
  • IBM
  • Intel
  • Microsoft
  • NICE Ltd.
  • Nvidia
  • Oracle
  • Qualcomm Technologies
  • SalesForce
  • SAP SE
  • Snowflake
  • Tencent
简介目录
Product Code: 13894

The Global Cloud Telecommunications AI Market was valued at USD 4.8 billion in 2024 and is estimated to grow at a CAGR of 21.7% to reach USD 32.7 billion by 2034, fueled by the convergence of artificial intelligence and cloud technologies within the telecom industry. As networks become more complex, telecom providers shift to AI cloud solutions to manage systems more efficiently, deliver seamless connectivity, and optimize customer experiences. The ongoing rollout of 5G, coupled with the widespread use of smart devices, is accelerating demand for intelligent, scalable networks. AI-powered cloud platforms are used to automate critical network functions, improve resource allocation, and reduce latency across dynamic network environments.

Cloud Telecommunications AI Market - IMG1

Global investments in cloud-based AI for telecom have intensified as operators seek flexible infrastructure capable of adapting to shifting demands. The push for digital transformation-amplified by recent global disruptions-has highlighted the need for highly resilient communication systems. AI integration helps manage data-heavy operations, predict maintenance needs, and cut operational costs through automation. Additionally, the growing reliance on real-time analytics and machine learning models makes AI a cornerstone in transforming telecom infrastructure to support future innovations. The synergy between cloud architecture and AI redefines how telecommunications providers design, operate, and scale their services.

Market Scope
Start Year2024
Forecast Year2025-2034
Start Value$4.8 Billion
Forecast Value$32.7 Billion
CAGR21.7%

In 2024, the solutions segment represented a 59% share and is projected to generate USD 17 billion by 2034. This segment is gaining traction due to rising demand for integrated AI tools that enhance network reliability and streamline internal workflows. AI-powered platforms are deployed to automate fault detection, manage network congestion, and optimize bandwidth in real time. As data volumes increase, telecoms need systems capable of interpreting complex traffic patterns and proactively addressing potential disruptions.

The large enterprises segment led the market in 2024, generating USD 3.1 billion. Enterprises rely heavily on AI technologies to manage expansive networks and support large user bases. These businesses require advanced automation and predictive capabilities to deliver consistent performance while minimizing downtime. AI enables resource optimization and better infrastructure planning, especially in environments where operational scale is vast and complexity is high.

United States Cloud Telecommunications AI Market held the largest share in 2024, accounting for 23%. This strong market position results in large-scale investments in advanced telecommunications infrastructure, particularly in AI integration and 5G deployment. The country's leadership is further solidified by robust support from federal agencies promoting digital transformation and smart network development. Public and private sector collaboration has accelerated the adoption of cloud-based AI technologies, focusing on enhancing network efficiency, real-time analytics, and service automation.

Companies like SAP SE, Salesforce, Tencent, IBM, Microsoft, Nvidia, Oracle, ATOS, Alphabet, and Amazon Web Services are advancing AI adoption in telecom. Companies in this space are implementing a range of strategic initiatives. Many are expanding their global cloud infrastructure and offering AI-as-a-service tailored for telecom applications. Collaborations with telecom providers are helping deliver customized solutions that address specific network challenges. Firms are also enhancing AI capabilities through acquisitions, joint ventures, and internal innovation to strengthen their offerings. Additionally, investment in edge computing, real-time analytics, and cross-platform integration helps these players stay ahead in a rapidly evolving landscape.

Table of Contents

Chapter 1 Methodology & Scope

  • 1.1 Research design
    • 1.1.1 Research approach
    • 1.1.2 Data collection methods
  • 1.2 Base estimates & calculations
    • 1.2.1 Base year calculation
    • 1.2.2 Key trends for market estimation
  • 1.3 Forecast model.
  • 1.4 Primary research and validation
    • 1.4.1 Primary sources
    • 1.4.2 Data mining sources
  • 1.5 Market scope & definition

Chapter 2 Executive Summary

  • 2.1 Industry synopsis, 2021 - 2034

Chapter 3 Industry Insights

  • 3.1 Industry ecosystem analysis
    • 3.1.1 Supplier landscape
      • 3.1.1.1 Core AI component provider.
      • 3.1.1.2 Software & platform vendors
      • 3.1.1.3 Application provider
      • 3.1.1.4 End users
    • 3.1.2 Profit margin analysis.
  • 3.2 Impact of Trump administration tariffs
    • 3.2.1 Impact on trade
      • 3.2.1.1 Trade volume disruptions
      • 3.2.1.2 Retaliatory measures
    • 3.2.2 Impact on industry
      • 3.2.2.1 Supply-side impact (raw materials)
        • 3.2.2.1.1 Price volatility in key materials
        • 3.2.2.1.2 Supply chain restructuring.
        • 3.2.2.1.3 Production cost implications
      • 3.2.2.2 Demand-side impact (selling price)
        • 3.2.2.2.1 Price transmission to end markets.
        • 3.2.2.2.2 Market share dynamics
        • 3.2.2.2.3 Consumer response patterns
    • 3.2.3 Strategic industry responses
      • 3.2.3.1 Supply chain reconfiguration.
  • 3.3 Pricing and product strategies
  • 3.4 Technology & innovation landscape
    • 3.4.1 Current technological trends
      • 3.4.1.1 AI-powered network optimization
      • 3.4.1.2 Cloud-Based Unified Communications (UCaaS)
      • 3.4.1.3 5G-enabled edge computing
      • 3.4.1.4 Telecom cloud orchestration and automation
    • 3.4.2 Emerging Technologies
      • 3.4.2.1 Quantum computing for telecom networks
      • 3.4.2.2 6G networks with ai integration
      • 3.4.2.3 Blockchain-driven network security
      • 3.4.2.4 Intelligent virtual network functions
    • 3.4.3 Advanced material sciences
  • 3.5 Pricing strategies
  • 3.6 Patent analysis
  • 3.7 Use cases
  • 3.8 Key news & initiatives
  • 3.9 Regulatory landscape
  • 3.10 Impact on forces
    • 3.10.1 Growth drivers
      • 3.10.1.1 5G network expansion and ai integration
      • 3.10.1.2 Advancements in edge computing
      • 3.10.1.3 Improved AI-powered predictive maintenance
      • 3.10.1.4 Progress in cloud-native network functions
    • 3.10.2 Industry pitfalls & challenges
      • 3.10.2.1 Data quality, governance, and security concerns
      • 3.10.2.2 Complexity and talent gap in AI development and deployment
  • 3.11 Growth potential analysis
  • 3.12 Porter's analysis
  • 3.13 PESTEL analysis

Chapter 4 Competitive Landscape, 2024

  • 4.1 Introduction
  • 4.2 Company market share analysis
  • 4.3 Competitive positioning matrix
  • 4.4 Strategic outlook matrix

Chapter 5 Market Estimates & Forecast, By Component, 2021 - 2034 ($Bn)

  • 5.1 Key trends
  • 5.2 Solution
    • 5.2.1 Network optimization
    • 5.2.2 Network security
    • 5.2.3 Customer analytics
    • 5.2.4 Virtual assistants
    • 5.2.5 Fraud detection
    • 5.2.6 Predictive maintenance
    • 5.2.7 Others
  • 5.3 Services
    • 5.3.1 Professional services
    • 5.3.2 Managed services
    • 5.3.3 Consulting & training

Chapter 6 Market Estimates & Forecast, By Technology, 2021 - 2034 ($Bn)

  • 6.1 Key trends
  • 6.2 Machine learning
  • 6.3 Natural Language Processing (NLP)
  • 6.4 Big data
  • 6.5 Deep learning
  • 6.6 Others

Chapter 7 Market Estimates & Forecast, By Organization Size, 2021 - 2034 ($Bn)

  • 7.1 Key trends
  • 7.2 Small & Medium-sized Enterprises (SME)
  • 7.3 Large Enterprises

Chapter 8 Market Estimates & Forecast, By Deployment, 2021 - 2034 ($Bn)

  • 8.1 Key trends
  • 8.2 Public cloud
  • 8.3 Private cloud
  • 8.4 Hybrid cloud

Chapter 9 Market Estimates & Forecast, By End Use, 2021 - 2034 ($Bn)

  • 9.1 Key trends
  • 9.2 Telecom Operators<
    • 9.2.1 Mobile Network Operators (MNOs)
    • 9.2.2 Fixed line operators
    • 9.2.3 Satellite operators
  • 9.3 Internet Service Providers (ISPs)
    • 9.3.1 Broadband ISPs
    • 9.3.2 Wireless ISPs
  • 9.4 Managed Service Providers (MSPs)
    • 9.4.1 Cloud service providers
    • 9.4.2 Network management providers

Chapter 10 Market Estimates & Forecast, By Region, 2021 - 2034 ($Bn)

  • 10.1 North America
    • 10.1.1 U.S.
    • 10.1.2 Canada
  • 10.2 Europe
    • 10.2.1 UK
    • 10.2.2 Germany
    • 10.2.3 France
    • 10.2.4 Italy
    • 10.2.5 Spain
    • 10.2.6 Belgium
    • 10.2.7 Sweden
    • 10.2.8 Russia
  • 10.3 Asia Pacific
    • 10.3.1 China
    • 10.3.2 India
    • 10.3.3 Japan
    • 10.3.4 Australia
    • 10.3.5 Singapore
    • 10.3.6 South Korea
    • 10.3.7 Southeast Asia
  • 10.4 Latin America
    • 10.4.1 Brazil
    • 10.4.2 Mexico
    • 10.4.3 Argentina
  • 10.5 MEA
    • 10.5.1 South Africa
    • 10.5.2 Saudi Arabia
    • 10.5.3 UAE

Chapter 11 Company Profiles

  • 11.1 Alibaba Cloud
  • 11.2 Altair Engineering
  • 11.3 Amazon Web Services (AWS)
  • 11.4 ATOS SE
  • 11.5 Avaamo
  • 11.6 Baidu
  • 11.7 Alphabet Inc
  • 11.8 Hewlett Packard Enterprise Development
  • 11.9 Huawei Cloud Computing Technologies
  • 11.10 IBM
  • 11.11 Intel
  • 11.12 Microsoft
  • 11.13 NICE Ltd.
  • 11.14 Nvidia
  • 11.15 Oracle
  • 11.16 Qualcomm Technologies
  • 11.17 SalesForce
  • 11.18 SAP SE
  • 11.19 Snowflake
  • 11.20 Tencent