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市场调查报告书
商品编码
1698339

人工智慧在电信市场的机会、成长动力、产业趋势分析以及 2025 - 2034 年预测

AI in Telecommunication Market Opportunity, Growth Drivers, Industry Trend Analysis, and Forecast 2025 - 2034
出版日期: | 出版商: Global Market Insights Inc. | 英文 165 Pages | 商品交期: 2-3个工作天内

价格
简介目录

2024 年全球电信人工智慧市场价值为 27 亿美元，预计 2025 年至 2034 年期间的复合年增长率将达到 32.6%。人工智慧驱动的解决方案正在彻底改变电信业的网路营运、客户服务和基础设施管理。人工智慧与电信网路和 5G 技术的融合增强了自动化、即时分析和异常检测能力。借助人工智慧，频谱控制得到改善，确保优化频宽管理并减少高流量区域的延迟。人工智慧驱动的诈欺侦测系统可以降低网路安全风险，帮助电信供应商保护其网路和财务。人工智慧也在改变客户互动方式，因为聊天机器人和数位助理简化了回应并减少了人工干预的需要。人工智慧驱动的自然语言处理 (NLP) 工具可以自动解决问题，提高客户满意度，同时降低营运成本。

电信市场中的人工智慧 - IMG1

市场根据组件细分为解决方案和服务。解决方案部分的价值在 2024 年为 17 亿美元，预计到 2034 年将超过 263 亿美元。人工智慧广泛应用于业务流程自动化、诈欺侦测和网路效能监控，从而提高整个电信营运的效率和安全性。

市场范围
起始年份 2024
预测年份 2025-2034
起始值 27亿美元
预测值 451亿美元
复合年增长率 32.6%

电信业者依靠人工智慧驱动的诈欺侦测工具来对抗安全威胁。机器学习演算法自动侦测并防止诈欺活动，确保遵守监管标准，同时最大限度地减少财务损失。人工智慧驱动的自动化加强了服务回应，提高了网路的安全性和可靠性。

借助人工智慧聊天机器人和虚拟助手，电信业的客户服务得到了显着改善。透过使用 NLP 实现服务自动化，电信公司可以更快地处理查询并解决问题，而无需人工干预。这种自动化不仅降低了成本，还增强了品牌忠诚度。人工智慧还可以透过学习过去的互动来个性化回应并提高服务质量，在简化客户互动方面发挥至关重要的作用。

根据部署模式，市场进一步细分为基于云端的解决方案和内部部署的解决方案。基于云端的人工智慧在 2024 年占据主导地位，占有 65% 的份额，预计在整个预测期内将大幅成长。云端运算的可扩展性、灵活性和成本效益使电信供应商无需大量基础设施投资即可整合人工智慧，从而加速整个产业的采用。

对于处理敏感客户资料的电信公司来说，私人 AI 实施仍然是首选，可确保更好地控制安全性、合规性和资料隐私。许多电信公司正在部署私有人工智慧模型，以遵守行业特定的法规并提高网路效率。

人工智慧即服务 (AIaaS) 在电信营运商中越来越受欢迎，它无需内部开发团队即可提供数据驱动的洞察。 2024 年 AIaaS 市值为 97 亿美元，预计到 2032 年复合年增长率将超过 33%。这种模式使小型电信公司能够以较低的成本利用人工智慧驱动的商业智慧解决方案。

人工智慧驱动的边缘运算透过在网路边缘实现即时资料处理来优化网路效能。这项创新减少了延迟，改善了频宽管理，并确保即使在高峰流量负载下也能无缝运行，从而增强了行动和宽频服务。

电信市场的人工智慧按应用细分，包括机器学习、NLP 和深度学习。机器学习领域价值到 2024 年将超过 10 亿美元，因其在预测性维护、网路优化和诈欺检测中的作用而占据主导地位。基于人工智慧的机器学习模型可协助电信公司减少网路故障，进而提高服务可靠性。

NLP 正在改变客户服务自动化，它允许 AI 系统根据先前的互动来分析和回应用户查询，从而提高保留率。 2023 年 NLP 市场价值为 55 亿美元，预计 2024 年至 2032 年将成长 25% 以上。

深度学习越来越多地用于语音识别和自动呼叫路由，从而实现更有效率的客户服务营运。人工智慧驱动的语音转文本解决方案提高了残疾人的可访问性，同时增强了电信系统内的内容索引和检索。

人工智慧自动化还透过监控系统效能、预测故障和分配资源来最大限度地提高效率，从而改善电信网路管理。预计到 2028 年，智慧型手机用户数量将达到 77 亿，凸显了对人工智慧电信解决方案日益增长的需求。

北美在电信市场的人工智慧领域处于领先地位，到 2024 年将占据 35% 以上的份额。美国仍处于领先地位，其主要

目录

第一章：方法论与范围

  • 研究设计
    • 研究方法
    • 资料收集方法
  • 基础估算与计算
    • 基准年计算
    • 市场估计的主要趋势
  • 预测模型
  • 初步研究与验证
    • 主要来源
    • 资料探勘来源
  • 市场定义

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
    • 平台提供者
    • 技术整合商
    • 经销商
    • 最终用途
  • 利润率分析
  • 技术与创新格局
  • 重要新闻和倡议
  • 已使用案例
    • 使用案例 1
      • 好处
      • 投资报酬率
    • 使用案例 2
      • 好处
      • 投资报酬率
  • 案例研究
    • 案例研究 1
      • 消费者姓名
      • 挑战
      • 解决方案
      • 影响
    • 案例研究 1
      • 消费者姓名
      • 挑战
      • 解决方案
      • 影响
  • 价格趋势分析
  • 专利分析
  • 监管格局
  • 衝击力
    • 成长动力
      • 网路自动化和客户服务领域人工智慧应用日益增多
      • 人工智慧与5G技术的融合
      • 电信业对预测分析的需求不断增加
      • 扩展人工智慧驱动的网路安全解决方案
    • 产业陷阱与挑战
      • 资料隐私和安全问题
      • 初期投资成本高
  • 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
  • 服务

第六章：市场估计与预测：依部署模型，2021 - 2034 年

  • 主要趋势
  • 本地
  • 基于云端

第七章：市场估计与预测：依技术分类，2021 - 2034 年

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

第八章：市场估计与预测：按应用，2021 - 2034 年

  • 主要趋势
  • 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

第九章：市场估计与预测：按地区，2021 - 2034 年

  • 主要趋势
  • 北美洲
    • 我们
    • 加拿大
  • 欧洲
    • 英国
    • 德国
    • 法国
    • 西班牙
    • 义大利
    • 俄罗斯
    • 北欧人
  • 亚太地区
    • 中国
    • 印度
    • 日本
    • 韩国
    • 澳新银行
    • 东南亚
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 阿根廷
  • 中东及非洲
    • 阿联酋
    • 南非
    • 沙乌地阿拉伯

第十章：公司简介

  • Accenture
  • Amazon Web Services (AWS)
  • Amdocs
  • AT&T
  • Baidu
  • Cisco Systems
  • Ericsson
  • Google
  • Hewlett Packard Enterprise (HPE)
  • IBM
  • Intel
  • Microsoft
  • NEC
  • Nokia
  • NVIDIA
  • Oracle
  • Qualcomm
  • Salesforce
  • Samsung Electronics
  • ZTE
简介目录
Product Code: 13184

The Global AI In Telecommunication Market, valued at USD 2.7 billion in 2024, is on track to expand at a CAGR of 32.6% from 2025 to 2034. AI-driven solutions are revolutionizing network operations, customer service, and infrastructure management within the telecom industry. The integration of AI with telecom networks and 5G technology enhances automation, real-time analysis, and anomaly detection. With AI, spectrum control improves, ensuring optimized bandwidth management and reduced latency in high-traffic areas. AI-powered fraud detection systems mitigate cybersecurity risks, helping telecom providers safeguard their networks and finances. AI is also transforming customer interactions, as chatbots and digital assistants streamline responses and reduce the need for human intervention. AI-driven natural language processing (NLP) tools allow for automated issue resolution, enhancing customer satisfaction while cutting operational costs.

AI in Telecommunication Market - IMG1

The market is segmented based on components into solutions and services. The solutions segment, valued at USD 1.7 billion in 2024, is projected to surpass USD 26.3 billion by 2034. AI is widely applied in business process automation, fraud detection, and network performance monitoring, increasing efficiency and security across telecom operations.

Market Scope
Start Year2024
Forecast Year2025-2034
Start Value$2.7 Billion
Forecast Value$45.1 Billion
CAGR32.6%

Telecom providers rely on AI-powered fraud detection tools to combat security threats. Machine learning algorithms automatically detect and prevent fraudulent activities, ensuring compliance with regulatory standards while minimizing financial losses. AI-driven automation strengthens service response, improving network security and reliability.

Customer service within the telecom sector has seen significant improvements with AI-powered chatbots and virtual assistants. By automating services using NLP, telecom companies handle inquiries faster and resolve issues without human intervention. This automation not only reduces costs but also strengthens brand loyalty. AI also plays a crucial role in streamlining customer interactions by learning from past engagements to personalize responses and improve service quality.

The market is further segmented by deployment models into cloud-based and on-premises solutions. Cloud-based AI dominated with a 65% share in 2024 and is expected to grow substantially throughout the forecast period. The scalability, flexibility, and cost efficiency of cloud computing enable telecom providers to integrate AI without heavy infrastructure investments, accelerating adoption across the industry.

Private AI implementations remain a preferred choice for telecom companies handling sensitive customer data, ensuring better control over security, compliance, and data privacy. Many telecom firms are deploying private AI models to adhere to industry-specific regulations and enhance network efficiency.

AI-as-a-Service (AIaaS) is gaining traction among telecom operators, providing access to data-driven insights without the need for in-house development teams. The AIaaS market, valued at USD 9.7 billion in 2024, is expected to grow at a CAGR of over 33% through 2032. This model allows smaller telecom firms to leverage AI-driven business intelligence solutions at reduced costs.

AI-driven edge computing is optimizing network performance by enabling real-time data processing at the network edge. This innovation reduces latency, improves bandwidth management, and ensures seamless operations even during peak traffic loads, enhancing mobile and broadband services.

The AI in telecommunication market is segmented by applications, including machine learning, NLP, and deep learning. The machine learning segment, valued at over USD 1 billion in 2024, dominates due to its role in predictive maintenance, network optimization, and fraud detection. AI-based machine learning models help telecom companies improve service reliability by reducing network failures.

NLP is transforming customer service automation by allowing AI systems to analyze and respond to user queries based on previous interactions, leading to higher retention rates. The NLP market, valued at USD 5.5 billion in 2023, is expected to grow over 25% from 2024 to 2032.

Deep learning is increasingly used for speech recognition and automated call routing, enabling more efficient customer service operations. AI-driven speech-to-text solutions improve accessibility for people with disabilities while enhancing content indexing and retrieval within telecom systems.

AI automation is also improving administrative telecom network management by monitoring system performance, predicting failures, and allocating resources to maximize efficiency. The rise in smartphone users, projected to reach 7.7 billion by 2028, highlights the growing need for AI-powered telecom solutions.

North America leads the AI in telecommunication market, holding over 35% of the share in 2024. The US remains at the forefront, with major t

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 and calculations
    • 1.2.1 Base year calculation
    • 1.2.2 Key trends for market estimates
  • 1.3 Forecast model
  • 1.4 Primary research & validation
    • 1.4.1 Primary sources
    • 1.4.2 Data mining sources
  • 1.5 Market definitions

Chapter 2 Executive Summary

  • 2.1 Industry 360° synopsis, 2021 - 2034

Chapter 3 Industry Insights

  • 3.1 Industry ecosystem analysis
  • 3.2 Supplier landscape
    • 3.2.1 Platform provider
    • 3.2.2 Technology integrators
    • 3.2.3 Distributors
    • 3.2.4 End Use
  • 3.3 Profit margin analysis
  • 3.4 Technology & innovation landscape
  • 3.5 Key news & initiatives
  • 3.6 Used cases
    • 3.6.1 Used case 1
      • 3.6.1.1 Benefits
      • 3.6.1.2 ROI
    • 3.6.2 Used case 2
      • 3.6.2.1 Benefits
      • 3.6.2.2 ROI
  • 3.7 Case study
    • 3.7.1 Case study 1
      • 3.7.1.1 Consumer name
      • 3.7.1.2 Challenge
      • 3.7.1.3 Solution
      • 3.7.1.4 Impact
    • 3.7.2 Case study 1
      • 3.7.2.1 Consumer name
      • 3.7.2.2 Challenge
      • 3.7.2.3 Solution
      • 3.7.2.4 Impact
  • 3.8 Pricing trend analysis
  • 3.9 Patent analysis
  • 3.10 Regulatory landscape
  • 3.11 Impact forces
    • 3.11.1 Growth drivers
      • 3.11.1.1 Rising AI adoption for network automation and customer service
      • 3.11.1.2 Integration of AI with 5G technology
      • 3.11.1.3 Increasing demand for predictive analytics in telecom
      • 3.11.1.4 Expansion of AI-driven cybersecurity solutions
    • 3.11.2 Industry pitfalls & challenges
      • 3.11.2.1 Data privacy and security concerns
      • 3.11.2.2 High initial investment costs
  • 3.12 Growth potential analysis
  • 3.13 Porter’s analysis
  • 3.14 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
  • 5.3 Services

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

  • 6.1 Key trends
  • 6.2 On-premises
  • 6.3 Cloud-based

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

  • 7.1 Key trends
  • 7.2 Machine Learning (ML)
  • 7.3 Natural Language Processing (NLP)
  • 7.4 Deep Learning
  • 7.5 Others

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

  • 8.1 Key trends
  • 8.2 Network optimization
  • 8.3 Network security
  • 8.4 Customer analytics
  • 8.5 Virtual assistants
  • 8.6 Fraud detection
  • 8.7 Predictive maintenance
  • 8.8 Others

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

  • 9.1 Key trends
  • 9.2 North America
    • 9.2.1 U.S.
    • 9.2.2 Canada
  • 9.3 Europe
    • 9.3.1 UK
    • 9.3.2 Germany
    • 9.3.3 France
    • 9.3.4 Spain
    • 9.3.5 Italy
    • 9.3.6 Russia
    • 9.3.7 Nordics
  • 9.4 Asia Pacific
    • 9.4.1 China
    • 9.4.2 India
    • 9.4.3 Japan
    • 9.4.4 South Korea
    • 9.4.5 ANZ
    • 9.4.6 Southeast Asia
  • 9.5 Latin America
    • 9.5.1 Brazil
    • 9.5.2 Mexico
    • 9.5.3 Argentina
  • 9.6 MEA
    • 9.6.1 UAE
    • 9.6.2 South Africa
    • 9.6.3 Saudi Arabia

Chapter 10 Company Profiles

  • 10.1 Accenture
  • 10.2 Amazon Web Services (AWS)
  • 10.3 Amdocs
  • 10.4 AT&T
  • 10.5 Baidu
  • 10.6 Cisco Systems
  • 10.7 Ericsson
  • 10.8 Google
  • 10.9 Hewlett Packard Enterprise (HPE)
  • 10.10 IBM
  • 10.11 Intel
  • 10.12 Microsoft
  • 10.13 NEC
  • 10.14 Nokia
  • 10.15 NVIDIA
  • 10.16 Oracle
  • 10.17 Qualcomm
  • 10.18 Salesforce
  • 10.19 Samsung Electronics
  • 10.20 ZTE