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市場調查報告書
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1871866

全球人工智慧通訊網路市場：預測至 2032 年—按產品、部署方式、技術、應用、最終用戶和地區進行分析

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個工作天內

價格

根據 Stratistics MRC 的一項研究，預計到 2025 年，全球人工智慧通訊網路市場規模將達到 11.2 億美元，到 2032 年將達到 81.5 億美元，預測期內複合年成長率將達到 32.8%。

人工智慧驅動的通訊系統正在將傳統的網路管理轉變為更智慧、自動化和自適應的環境。借助機器學習演算法和即時分析，營運商可以預測效能瓶頸、識別異常情況、最佳化頻寬分配並最大限度地減少服務中斷。人工智慧透過更快的回應速度、智慧故障排除和客製化服務選項來提升客戶滿意度。隨著 5G、物聯網設備和不斷成長的資料流量對傳統系統造成壓力，自動化可確保穩定的速度、低延遲和強大的安全性。這些智慧網路可降低營運成本、提高能源效率，並實現網路切片和自主服務監控等進階功能。

根據英偉達發布的《2024 年通訊業人工智慧現況報告》（基於對 400 多名通訊專業人士的全球調查），95% 的通訊公司正在使用或計劃在其營運中採用人工智慧。

數據流量不斷成長和5G網路的擴展

網路使用量的不斷成長和5G的部署是推動人工智慧驅動型通訊網路發展的關鍵因素。智慧型手機、雲端應用和物聯網設備產生大量數據，使得傳統的網路管理效率低。人工智慧工具能夠自動管理頻寬、預測網路擁塞並最佳化網路路由，從而維持低延遲和穩定的效能。工業自動化、互聯移動和智慧基礎設施等5G賦能的創新需要高度反應和智慧化的網路。通訊業者可以利用人工智慧來最大限度地減少服務中斷、確保無縫效能並提升客戶服務水準。隨著數據消費量逐年成長，基於人工智慧的自動化對於高效處理流量和支援下一代數位服務至關重要。

高昂的實施和整合成本

建構人工智慧驅動的通訊網路需要對軟體授權、智慧硬體、雲端伺服器和專業人員進行大量投資。維修或升級現有網路系統會進一步增加成本。小規模的通訊業者面臨預算限制，難以大規模部署人工智慧。員工還需要接受培訓才能操作自動化工具和分析平台，這會產生額外的成本。向人工智慧驅動環境的轉型需要先進的IT基礎設施、資料安全系統和持續的系統維護。這些高昂的初始成本和營運成本阻礙了許多營運商，尤其是在發展中市場的營運商採用人工智慧解決方案，從而減緩了整個產業的成長。

對自主網路運作的需求日益成長

電信業者正朝著智慧網路轉型，這些網路能夠自主管理，最大限度地減少人工干預。人工智慧 (AI) 可實現自動故障排除、頻寬調整、預測性維護和即時系統監控，從而降低營運風險並加快問題解決速度。自主網路也有助於增強安全性、減少服務中斷並提供穩定的服務品質。隨著數位流量的成長，通訊公司正在尋求減少人力投入和控制成本的解決方案。雲端基礎平台、虛擬化核心網路和邊緣基礎設施進一步推動了對智慧自動化的需求。鑑於這些優勢，採用人工智慧驅動的自主網路營運為全球技術開發商和通訊服務供應商帶來了巨大的市場機會。

供應商依賴和專有技術

許多人工智慧通訊平台依賴專有工具、專利軟體和客製化硬體。這可能導致通訊業者在升級、支援和安全補丁方面被鎖定在特定供應商的生態系統中。這降低了營運商選擇技術合作夥伴的靈活性，並增加了長期成本。遷移到新供應商會因資料相容性和整合問題而變得複雜。此外，專有系統會在整合多家供應商解決方案的網路中造成互通性差距。如果供應商改變政策、提高價格或停止產品支持，通訊業者將面臨服務風險和財務壓力。因此，過度依賴少數技術供應商會對市場穩定構成嚴重威脅。

新冠疫情的影響：

隨著全球數位依賴程度的加深，新冠疫情為人工智慧驅動的通訊網路提供了強勁的推動力。遠距辦公、視訊會議、遠端醫療和串流媒體服務導致網路負載激增，亟需更高程度的自動化。人工智慧透過最佳化流量、預測故障以及在高峰時段提升服務質量，為營運商提供了支援。由於現場人員有限，遠距離診斷和智慧監控對於關鍵基礎設施的運作至關重要。疫情凸顯了對無需人工干預即可擴展的自主、彈性網路系統的迫切需求。儘管經濟放緩導致部分計劃延期，但總體而言，疫情帶來了積極影響，鼓勵對基於人工智慧的通訊創新進行長期投資。

在預測期內，雲端基礎市場將佔據最大的市場佔有率。

由於其高擴充性、靈活的整合以及降低營運商的基礎設施負擔，預計在預測期內，雲端基礎市場將佔據最大的市場佔有率。雲端系統使通訊業者能夠快速部署人工智慧功能、自動化網路功能並分析即時流量，而無需大規模的實體部署。它們還支援集中監控、遠端故障排除以及隨著需求成長而無縫擴展容量。雲端環境也支援虛擬化網路功能、邊緣連接和持續軟體更新，從而提高服務效率。隨著對 5G、物聯網和數位服務的依賴性日益增強，基於雲端的人工智慧平台具有成本節約、快速創新和強大效能等優勢，使其成為通訊業應用最廣泛的部署方式。

機器學習領域在預測期內將實現最高的複合年成長率。

預計在預測期內，機器學習領域將以最高速度成長，因為它能夠使網路從數據中學習並做出無需人工干預的智慧決策。通訊業者正在利用機器學習工具進行擁塞預測、故障檢測和即時性能最佳化。隨著物聯網設備、5G 服務和數位應用產生的資料量不斷成長，機器學習能夠為流量管理、網路安全和服務客製化提供精準的洞察。它還能促進網路各層的自動化，降低營運複雜性並提高可靠性。其多功能性和處理大型動態資料集的能力正在加速其應用，使機器學習成為成長最快的領域。

佔比最大的地區：

由於北美擁有成熟的基礎設施、快速的5G部署以及通訊業者對人工智慧技術的早期整合，預計該地區將在預測期內佔據最大的市場佔有率。該地區完善的研究生態系統、對下一代網路的巨額投資以及有利的政策，正在推動人工智慧在營運、服務個人化和網路彈性方面的應用。該地區的領先營運商正在網路堆疊中全面採用自動化、巨量資料分析和機器學習技術，領先全球競爭對手。隨著數據量的指數級成長和網路複雜性的不斷提高，北美先進的能力和充分的準備使其在利用基於人工智慧的通訊解決方案方面擁有顯著優勢，並有望佔據最大的區域市場佔有率。

年複合成長率最高的地區：

預計亞太地區在預測期內將實現最高的複合年成長率，這主要得益於5G部署的不斷擴展和龐大的行動用戶群。中國、印度、日本和韓國等國家正迅速將人工智慧融入通訊營運，以實現自動化、智慧流量管理和智慧客戶支援。為了應對激增的數據需求和物聯網連接，區域通訊業者正在利用雲端平台、分析技術和基於人工智慧的編配升級其網路。政府推動數位轉型、提供價格合理的優質服務和建設先進基礎設施的措施也進一步推動了這一進程。在日益激烈的競爭和對下一代網路的大量投資的推動下，亞太地區有望在該領域實現最高的成長率。

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  • 公司簡介
    • 對最多三家其他公司進行全面分析
    • 對主要企業進行SWOT分析（最多3家公司）
  • 區域分類
    • 根據客戶興趣對主要國家進行市場估算、預測和複合年成長率分析（註：基於可行性檢查）
  • 競爭基準化分析
    • 基於產品系列、地域覆蓋和策略聯盟對主要企業基準化分析

目錄

第1章執行摘要

第2章 引言

  • 概述
  • 相關利益者
  • 分析範圍
  • 分析方法
  • 分析材料

第3章 市場趨勢分析

  • 介紹
  • 促進要素
  • 抑制因素
  • 市場機遇
  • 威脅
  • 技術分析
  • 應用分析
  • 終端用戶分析
  • 新興市場
  • 新冠疫情的感染疾病

第4章 波特五力分析

  • 供應商的議價能力
  • 買方議價能力
  • 替代產品的威脅
  • 新參與企業的威脅
  • 公司間的競爭

第5章 全球人工智慧通訊網路市場（按產品/服務分類）

  • 介紹
  • 解決方案
  • 服務
    • 託管服務
    • 專業服務

第6章 全球人工智慧通訊網路市場（依部署方式分類）

  • 介紹
  • 雲端基礎的
  • 本地部署

7. 全球人工智慧通訊網路市場（按技術分類）

  • 介紹
  • 機器學習
  • 自然語言處理（NLP）
  • 深度學習
  • 巨量資料分析

第8章 全球人工智慧通訊網路市場（按應用分類）

  • 介紹
  • 自主網路最佳化
  • 預測性故障檢測與修復
  • 人工智慧驅動的客戶體驗平台
  • 通訊詐騙偵測系統
  • 虛擬代理聊天機器人介面
  • AIOps（人工智慧驅動的IT運維）
  • 智慧CRM和宣傳活動自動化
  • 基於人工智慧的無線存取網效能分析

第9章 全球人工智慧通訊網路市場（按最終用戶分類）

  • 介紹
  • 通訊業者
  • 公司

第10章：全球人工智慧通訊網路市場（按地區分類）

  • 介紹
  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 亞太其他地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美洲國家
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第11章：主要趨勢

  • 合約、商業夥伴關係和合資企業
  • 企業合併（M&A）
  • 新產品發布
  • 業務拓展
  • 其他關鍵策略

第12章：公司簡介

  • 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.