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市场调查报告书
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1871866

全球人工智慧通讯网路市场：预测至 2032 年—按产品、部署方式、技术、应用、最终用户和地区进行分析

AI-Powered Telecom Networks Market Forecasts to 2032 - Global Analysis By Offering (Solution and Services), Deployment Mode, Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的一项研究，预计到 2025 年，全球人工智慧通讯网路市场规模将达到 11.2 亿美元，到 2032 年将达到 81.5 亿美元，预测期内复合年增长率将达到 32.8%。

人工智慧驱动的通讯系统正在将传统的网路管理转变为更智慧、自动化和自适应的环境。借助机器学习演算法和即时分析，营运商可以预测效能瓶颈、识别异常情况、优化频宽分配并最大限度地减少服务中断。人工智慧透过更快的回应速度、智慧故障排除和客製化服务选项来提升客户满意度。随着 5G、物联网设备和不断增长的资料流量对传统系统造成压力，自动化可确保稳定的速度、低延迟和强大的安全性。这些智慧网路可降低营运成本、提高能源效率，并实现网路切片和自主服务监控等进阶功能。

根据英伟达发布的《2024 年通讯业人工智慧现况报告》（基于对 400 多名通讯专业人士的全球调查），95% 的通讯公司正在使用或计划在其营运中采用人工智慧。

数据流量不断增长和5G网路的扩展

网路使用量的不断增长和5G的部署是推动人工智慧驱动型通讯网路发展的关键因素。智慧型手机、云端应用和物联网设备产生大量数据，使得传统的网路管理效率低。人工智慧工具能够自动管理频宽、预测网路拥塞并优化网路路由，从而维持低延迟和稳定的效能。工业自动化、互联移动和智慧基础设施等5G赋能的创新需要高度反应和智慧化的网路。通讯业者可以利用人工智慧来最大限度地减少服务中断、确保无缝效能并提升客户服务水准。随着数据消费量逐年增长，基于人工智慧的自动化对于高效处理流量和支援下一代数位服务至关重要。

高昂的实施和整合成本

建构人工智慧驱动的通讯网路需要对软体授权、智慧硬体、云端伺服器和专业人员进行大量投资。维修或升级现有网路系统会进一步增加成本。小规模的通讯业者面临预算限制，难以大规模部署人工智慧。员工还需要接受培训才能操作自动化工具和分析平台，这会产生额外的成本。向人工智慧驱动环境的转型需要先进的IT基础设施、资料安全系统和持续的系统维护。这些高昂的初始成本和营运成本阻碍了许多营运商，尤其是在发展中市场的营运商采用人工智慧解决方案，从而减缓了整个产业的成长。

对自主网路运作的需求日益增长

电信业者正朝着智慧网路转型，这些网路能够自主管理，最大限度地减少人工干预。人工智慧 (AI) 可实现自动故障排除、频宽调整、预测性维护和即时系统监控，从而降低营运风险并加快问题解决速度。自主网路也有助于增强安全性、减少服务中断并提供稳定的服务品质。随着数位流量的成长，通讯公司正在寻求减少人力投入和控製成本的解决方案。云端基础平台、虚拟化核心网路和边缘基础设施进一步推动了对智慧自动化的需求。鑑于这些优势，采用人工智慧驱动的自主网路营运为全球技术开发商和通讯服务供应商带来了巨大的市场机会。

供应商依赖和专有技术

许多人工智慧通讯平台依赖专有工具、专利软体和客製化硬体。这可能导致通讯业者在升级、支援和安全补丁方面被锁定在特定供应商的生态系统中。这降低了营运商选择技术合作伙伴的灵活性，并增加了长期成本。迁移到新供应商会因资料相容性和整合问题而变得复杂。此外，专有系统会在整合多家供应商解决方案的网路中造成互通性差距。如果供应商改变政策、提高价格或停止产品支持，通讯业者将面临服务风险和财务压力。因此，过度依赖少数技术供应商会对市场稳定构成严重威胁。

新冠疫情的影响：

随着全球数位依赖程度的加深，新冠疫情为人工智慧驱动的通讯网路提供了强劲的推动力。远距办公、视讯会议、远端医疗和串流媒体服务导致网路负载激增，亟需更高程度的自动化。人工智慧透过优化流量、预测故障以及在高峰时段提升服务质量，为营运商提供了支援。由于现场人员有限，远距离诊断和智慧监控对于关键基础设施的运作至关重要。疫情凸显了对无需人工干预即可扩展的自主、弹性网路系统的迫切需求。儘管经济放缓导致部分计划延期，但总体而言，疫情带来了积极影响，鼓励对基于人工智慧的通讯创新进行长期投资。

在预测期内，云端基础市场将占据最大的市场份额。

由于其高扩充性、灵活的整合以及降低营运商的基础设施负担，预计在预测期内，云端基础市场将占据最大的市场份额。云端系统使通讯业者能够快速部署人工智慧功能、自动化网路功能并分析即时流量，而无需大规模的实体部署。它们还支援集中监控、远端故障排除以及随着需求增长而无缝扩展容量。云端环境也支援虚拟化网路功能、边缘连接和持续软体更新，从而提高服务效率。随着对 5G、物联网和数位服务的依赖性日益增强，基于云端的人工智慧平台具有成本节约、快速创新和强大效能等优势，使其成为通讯业应用最广泛的部署方式。

机器学习领域在预测期内将实现最高的复合年增长率。

预计在预测期内，机器学习领域将以最高速度成长，因为它能够使网路从数据中学习并做出无需人工干预的智慧决策。通讯业者正在利用机器学习工具进行拥塞预测、故障检测和即时性能优化。随着物联网设备、5G 服务和数位应用产生的资料量不断增长，机器学习能够为流量管理、网路安全和服务客製化提供精准的洞察。它还能促进网路各层的自动化，降低营运复杂性并提高可靠性。其多功能性和处理大型动态资料集的能力正在加速其应用，使机器学习成为成长最快的领域。

占比最大的地区：

由于北美拥有成熟的基础设施、快速的5G部署以及通讯业者对人工智慧技术的早期整合，预计该地区将在预测期内占据最大的市场份额。该地区完善的研究生态系统、对下一代网路的巨额投资以及有利的政策，正在推动人工智慧在营运、服务个人化和网路弹性方面的应用。该地区的领先营运商正在网路堆迭中全面采用自动化、巨量资料分析和机器学习技术，领先全球竞争对手。随着数据量的指数级增长和网路复杂性的不断提高，北美先进的能力和充分的准备使其在利用基于人工智慧的通讯解决方案方面拥有显着优势，并有望占据最大的区域市场份额。

年复合成长率最高的地区：

预计亚太地区在预测期内将实现最高的复合年增长率，这主要得益于5G部署的不断扩展和庞大的行动用户群。中国、印度、日本和韩国等国家正迅速将人工智慧融入通讯运营，以实现自动化、智慧流量管理和智慧客户支援。为了应对激增的数据需求和物联网连接，区域通讯业者正在利用云端平台、分析技术和基于人工智慧的编配升级其网路。政府推动数位转型、提供价格合理的优质服务和建设先进基础设施的措施也进一步推动了这一进程。在日益激烈的竞争和对下一代网路的大量投资的推动下，亚太地区有望在该领域实现最高的成长率。

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订阅本报告的用户可从以下免费自订选项中选择一项：

  • 公司简介
    • 对最多三家其他公司进行全面分析
    • 对主要企业进行SWOT分析（最多3家公司）
  • 区域分类
    • 根据客户兴趣对主要国家进行市场估算、预测和复合年增长率分析（註：基于可行性检查）
  • 竞争基准化分析
    • 基于产品系列、地域覆盖和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 引言

  • 概述
  • 相关利益者
  • 分析范围
  • 分析方法
  • 分析材料

第三章 市场趋势分析

  • 介绍
  • 司机
  • 抑制因素
  • 市场机会
  • 威胁
  • 技术分析
  • 应用分析
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的感染疾病

第四章 波特五力分析

  • 供应商的议价能力
  • 买方议价能力
  • 替代产品的威胁
  • 新参与企业的威胁
  • 公司间的竞争

第五章 全球人工智慧通讯网路市场（按产品/服务划分）

  • 介绍
  • 解决方案
  • 服务
    • 託管服务
    • 专业服务

第六章 全球人工智慧通讯网路市场（依部署方式划分）

  • 介绍
  • 云端基础的
  • 本地部署

7. 全球人工智慧通讯网路市场（按技术划分）

  • 介绍
  • 机器学习
  • 自然语言处理（NLP）
  • 深度学习
  • 巨量资料分析

第八章 全球人工智慧通讯网路市场（按应用划分）

  • 介绍
  • 自主网路优化
  • 预测性故障检测与修復
  • 人工智慧驱动的客户体验平台
  • 通讯诈骗侦测系统
  • 虚拟代理聊天机器人介面
  • AIOps（人工智慧驱动的IT运维）
  • 智慧CRM和宣传活动自动化
  • 基于人工智慧的无线存取网效能分析

第九章 全球人工智慧通讯网路市场（按最终用户划分）

  • 介绍
  • 通讯业者
  • 公司

第十章：全球人工智慧通讯网路市场（按地区划分）

  • 介绍
  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 亚太其他地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美洲国家
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十一章：主要趋势

  • 合约、商业伙伴关係和合资企业
  • 企业合併（M&A）
  • 新产品发布
  • 业务拓展
  • 其他关键策略

第十二章：公司简介

  • Vodafone
  • Bharti Airtel
  • Reliance Jio
  • Huawei Technologies
  • IBM
  • Microsoft
  • Intel
  • Cisco Systems
  • Google Cloud
  • Nokia
  • NVIDIA
  • Ericsson
  • Juniper Networks
  • Sand Technologies
  • XenonStack
Product Code: SMRC32236

According to Stratistics MRC, the Global AI-Powered Telecom Networks Market is accounted for $1.12 billion in 2025 and is expected to reach $8.15 billion by 2032 growing at a CAGR of 32.8% during the forecast period. Telecom systems enhanced by artificial intelligence transform conventional network management into smarter, automated, and adaptive environments. Using machine learning algorithms and real-time analytics, operators can foresee performance bottlenecks, identify irregularities, optimize bandwidth allocation, and minimize outages. AI improves customer satisfaction through faster response, intelligent troubleshooting, and tailored service options. As 5G, IoT devices, and rising data traffic strain traditional systems, automation ensures consistent speed, lower latency, and strong security. These intelligent networks reduce operational expenses, boost energy efficiency, and enable advanced capabilities such as network slicing and autonomous service monitoring.

According to NVIDIA's 2024 State of AI in Telecommunications Report, based on a global survey of over 400 telecom professionals 95% of telecom companies are either using or planning to use AI in their operations.

Market Dynamics:

Driver:

Rising data traffic & 5G expansion

Increasing internet usage and 5G rollout are key reasons behind the growth of AI-driven telecom networks. As smartphones, cloud applications, and IoT devices generate heavy data loads, conventional network management becomes inefficient. AI tools automatically manage bandwidth, forecast congestion, and optimize network paths to maintain low latency and steady performance. 5G-supported innovations like industry automation, connected mobility, and smart infrastructure need highly responsive and intelligent networks. By using AI, telecom operators minimize disruptions, ensure seamless performance, and improve customer service. With data consumption expanding every year, AI-based automation is becoming critical to handle traffic efficiently and support next-generation digital services.

Restraint:

High implementation & integration costs

Setting up AI-enabled telecom networks involves heavy financial commitments for software licenses, intelligent hardware, cloud servers, and expert personnel. Older network systems must be modified or replaced, which raises the cost further. Smaller telecom companies find it difficult to invest in large-scale AI rollouts due to budget limitations. Employees also need training to operate automation tools and analytics platforms, adding additional expenses. Migrating to AI-driven environments requires advanced IT infrastructure, data security systems, and continuous system maintenance. These high upfront and operational costs discourage many operators from adopting AI solutions quickly, particularly in developing markets, slowing overall industry growth.

Opportunity:

Rising demand for autonomous network operations

Telecom companies are moving toward smart networks that manage themselves with minimal human intervention. AI enables automatic troubleshooting, bandwidth adjustment, predictive maintenance, and real-time system monitoring. This lowers operational risks and speeds up problem resolution. Autonomous networks also improve security, reduce outages, and deliver consistent service quality. As digital traffic grows, telecom firms look for solutions that reduce manual effort and control expenses. Cloud-based platforms, virtualized cores, and edge infrastructure strengthen the need for intelligent automation. Because of these advantages, adoption of AI-driven autonomous network operations presents a major market opportunity for technology developers and telecom service providers worldwide.

Threat:

Vendor dependency and proprietary technologies

Many AI telecom platforms rely on exclusive tools, patented software, and custom-built hardware. Operators may become locked into one vendor's ecosystem for upgrades, support, and security patches. This reduces flexibility in choosing technology partners and increases long-term costs. Migrating to new vendors becomes complicated because of data compatibility and integration problems. Proprietary systems also create interoperability gaps when networks combine solutions from multiple providers. If a vendor changes policies, raises prices, or ends product support, telecom operators face service risks and financial pressure. Therefore, heavy reliance on a limited number of technology suppliers represents a serious threat to market stability.

Covid-19 Impact:

COVID-19 created strong momentum for AI-enabled telecom networks as digital dependence expanded worldwide. Remote working, video conferencing, telemedicine, and streaming services generated heavy network loads, requiring smarter automation. AI supported operators by optimizing traffic flow, predicting faults, and improving quality of service during peak demand. With restrictions on field workforce, remote diagnostics and intelligent monitoring became essential to run critical infrastructure. The pandemic emphasized the need for autonomous and resilient network systems capable of scaling without manual intervention. Although some projects were postponed due to economic slowdown, the overall outcome was positive, driving long-term investments in AI-based telecom innovation.

The cloud-based segment is expected to be the largest during the forecast period

The cloud-based segment is expected to account for the largest market share during the forecast period because it offers high scalability, flexible integration, and reduced infrastructure burden for operators. Cloud systems allow telecom companies to launch AI features quickly, automate network functions, and analyze live traffic without extensive physical installations. They enable centralized monitoring, remote troubleshooting, and seamless expansion of capacity as demand rises. Cloud environments also support virtualized network functions, edge connectivity, and continuous software updates, improving service efficiency. With increasing reliance on 5G, IoT, and digital services, cloud-driven AI platforms provide cost savings, faster innovation, and stronger performance, making them the most widely adopted deployment approach in the telecom industry.

The machine learning segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the machine learning segment is predicted to witness the highest growth rate because it enables networks to learn from data and make intelligent decisions without manual input. Telecom companies rely on machine learning tools to predict congestion, detect faults, and optimize performance in real time. With rising data volumes from IoT devices, 5G services, and digital applications, machine learning provides accurate insights for traffic management, cyber security, and service customization. It enhances automation across network layers, reducing operational complexity and improving reliability. Its versatility and ability to handle large, dynamic datasets drive strong adoption, making machine learning the segment with the highest growth rate.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share owing to its mature infrastructure, rapid 5G deployment, and early integration of AI technologies by telecom firms. The region's well-established research ecosystem, significant investment in next-gen networks, and favorable policies encourage adoption of AI for operations, service personalization, and network resilience. Leading operators there implement automation, big-data analytics, and machine-learning across their network stacks ahead of global peers. As data volumes escalate and network complexity grows, North America's advanced capabilities and readiness give it a substantial advantage in leveraging AI-based telecom solutions and driving the largest regional market share.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, driven by expanding 5G deployments and massive mobile user populations. Nations such as China, India, Japan, and South Korea are rapidly embedding AI into telecom operations for automation, intelligent traffic handling, and smart customer support. Regional telecom providers are upgrading networks with cloud platforms, analytics, and AI-based orchestration to manage soaring data demand and IoT connectivity. Government initiatives promoting digital transformation, affordable services, and advanced infrastructure further boost progress. With rising competition and heavy investment in next-generation networks, APAC is positioned to achieve the highest growth rate in this sector.

Key players in the market

Some of the key players in AI-Powered Telecom Networks Market include Vodafone, Bharti Airtel, Reliance Jio, Huawei Technologies, IBM, Microsoft, Intel, Cisco Systems, Google Cloud, Nokia, NVIDIA, Ericsson, Juniper Networks, Sand Technologies and XenonStack.

Key Developments:

In November 2025, Microsoft Corp. has signed an approximately $9.7 billion deal to purchase AI cloud capacity from IREN Ltd., becoming the Australian company's largest customer. The five-year agreement will provide Microsoft access to Nvidia Corp. accelerator systems in Texas built using the GB300 architecture for AI workloads and include a 20% prepayment.

In March 2025, Huawei has announced the signing of a cooperation agreement with Telecom Egypt - WE. The agreement aims to equip Telecom Egypt's network with advanced technological solutions in preparation for the launch of 5G services in Egypt, ensuring high-quality broadband for users.

In March 2025, Bharti Airtel said it has signed an agreement with Elon Musk's SpaceX to bring high-speed satellite internet service Starlink to India. In an exchange filing on the BSE, Bharti Airtel said Starlink would sell its services in India and explore opportunities to collaborate with Airtel's existing telecom infrastructure.

Offerings Covered:

  • Solution
  • Services

Deployment Modes Covered:

  • Cloud-Based
  • On-Premises

Technologies Covered:

  • Machine Learning
  • Natural Language Processing (NLP)
  • Deep Learning
  • Big Data Analytics

Applications Covered:

  • Autonomous Network Optimization
  • Predictive Fault Detection & Maintenance
  • AI-Powered Customer Experience Platforms
  • Telecom Fraud Detection Systems
  • Virtual Agent & Chatbot Interfaces
  • AI-Driven IT Operations (AIOps)
  • Intelligent CRM & Campaign Automation
  • AI-Augmented RAN Performance Analytics

End Users Covered:

  • Telecom Operators
  • Enterprises

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Technology Analysis
  • 3.7 Application Analysis
  • 3.8 End User Analysis
  • 3.9 Emerging Markets
  • 3.10 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global AI-Powered Telecom Networks Market, By Offering

  • 5.1 Introduction
  • 5.2 Solution
  • 5.3 Services
    • 5.3.1 Managed Services
    • 5.3.2 Professional Services

6 Global AI-Powered Telecom Networks Market, By Deployment Mode

  • 6.1 Introduction
  • 6.2 Cloud-Based
  • 6.3 On-Premises

7 Global AI-Powered Telecom Networks Market, By Technology

  • 7.1 Introduction
  • 7.2 Machine Learning
  • 7.3 Natural Language Processing (NLP)
  • 7.4 Deep Learning
  • 7.5 Big Data Analytics

8 Global AI-Powered Telecom Networks Market, By Application

  • 8.1 Introduction
  • 8.2 Autonomous Network Optimization
  • 8.3 Predictive Fault Detection & Maintenance
  • 8.4 AI-Powered Customer Experience Platforms
  • 8.5 Telecom Fraud Detection Systems
  • 8.6 Virtual Agent & Chatbot Interfaces
  • 8.7 AI-Driven IT Operations (AIOps)
  • 8.8 Intelligent CRM & Campaign Automation
  • 8.9 AI-Augmented RAN Performance Analytics

9 Global AI-Powered Telecom Networks Market, By End User

  • 9.1 Introduction
  • 9.2 Telecom Operators
  • 9.3 Enterprises

10 Global AI-Powered Telecom Networks Market, By Geography

  • 10.1 Introduction
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
    • 10.2.3 Mexico
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 Italy
    • 10.3.4 France
    • 10.3.5 Spain
    • 10.3.6 Rest of Europe
  • 10.4 Asia Pacific
    • 10.4.1 Japan
    • 10.4.2 China
    • 10.4.3 India
    • 10.4.4 Australia
    • 10.4.5 New Zealand
    • 10.4.6 South Korea
    • 10.4.7 Rest of Asia Pacific
  • 10.5 South America
    • 10.5.1 Argentina
    • 10.5.2 Brazil
    • 10.5.3 Chile
    • 10.5.4 Rest of South America
  • 10.6 Middle East & Africa
    • 10.6.1 Saudi Arabia
    • 10.6.2 UAE
    • 10.6.3 Qatar
    • 10.6.4 South Africa
    • 10.6.5 Rest of Middle East & Africa

11 Key Developments

  • 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 11.2 Acquisitions & Mergers
  • 11.3 New Product Launch
  • 11.4 Expansions
  • 11.5 Other Key Strategies

12 Company Profiling

  • 12.1 Vodafone
  • 12.2 Bharti Airtel
  • 12.3 Reliance Jio
  • 12.4 Huawei Technologies
  • 12.5 IBM
  • 12.6 Microsoft
  • 12.7 Intel
  • 12.8 Cisco Systems
  • 12.9 Google Cloud
  • 12.10 Nokia
  • 12.11 NVIDIA
  • 12.12 Ericsson
  • 12.13 Juniper Networks
  • 12.14 Sand Technologies
  • 12.15 XenonStack

List of Tables

  • Table 1 Global AI-Powered Telecom Networks Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI-Powered Telecom Networks Market Outlook, By Offering (2024-2032) ($MN)
  • Table 3 Global AI-Powered Telecom Networks Market Outlook, By Solution (2024-2032) ($MN)
  • Table 4 Global AI-Powered Telecom Networks Market Outlook, By Services (2024-2032) ($MN)
  • Table 5 Global AI-Powered Telecom Networks Market Outlook, By Managed Services (2024-2032) ($MN)
  • Table 6 Global AI-Powered Telecom Networks Market Outlook, By Professional Services (2024-2032) ($MN)
  • Table 7 Global AI-Powered Telecom Networks Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 8 Global AI-Powered Telecom Networks Market Outlook, By Cloud-Based (2024-2032) ($MN)
  • Table 9 Global AI-Powered Telecom Networks Market Outlook, By On-Premises (2024-2032) ($MN)
  • Table 10 Global AI-Powered Telecom Networks Market Outlook, By Technology (2024-2032) ($MN)
  • Table 11 Global AI-Powered Telecom Networks Market Outlook, By Machine Learning (2024-2032) ($MN)
  • Table 12 Global AI-Powered Telecom Networks Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
  • Table 13 Global AI-Powered Telecom Networks Market Outlook, By Deep Learning (2024-2032) ($MN)
  • Table 14 Global AI-Powered Telecom Networks Market Outlook, By Big Data Analytics (2024-2032) ($MN)
  • Table 15 Global AI-Powered Telecom Networks Market Outlook, By Application (2024-2032) ($MN)
  • Table 16 Global AI-Powered Telecom Networks Market Outlook, By Autonomous Network Optimization (2024-2032) ($MN)
  • Table 17 Global AI-Powered Telecom Networks Market Outlook, By Predictive Fault Detection & Maintenance (2024-2032) ($MN)
  • Table 18 Global AI-Powered Telecom Networks Market Outlook, By AI-Powered Customer Experience Platforms (2024-2032) ($MN)
  • Table 19 Global AI-Powered Telecom Networks Market Outlook, By Telecom Fraud Detection Systems (2024-2032) ($MN)
  • Table 20 Global AI-Powered Telecom Networks Market Outlook, By Virtual Agent & Chatbot Interfaces (2024-2032) ($MN)
  • Table 21 Global AI-Powered Telecom Networks Market Outlook, By AI-Driven IT Operations (AIOps) (2024-2032) ($MN)
  • Table 22 Global AI-Powered Telecom Networks Market Outlook, By Intelligent CRM & Campaign Automation (2024-2032) ($MN)
  • Table 23 Global AI-Powered Telecom Networks Market Outlook, By AI-Augmented RAN Performance Analytics (2024-2032) ($MN)
  • Table 24 Global AI-Powered Telecom Networks Market Outlook, By End User (2024-2032) ($MN)
  • Table 25 Global AI-Powered Telecom Networks Market Outlook, By Telecom Operators (2024-2032) ($MN)
  • Table 26 Global AI-Powered Telecom Networks Market Outlook, By Enterprises (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.