查看購物車
  • English
  • Japanese
  • Korean
封面
市場調查報告書
商品編碼
2024143

面向 5G 網路的 AI 市場預測（至 2034 年）—按組件、部署模式、5G 頻段、技術、應用、最終用戶和地區分類的全球分析

AI in 5G Networks Market Forecasts to 2034 - Global Analysis By Component (Solutions, Platforms and Services), Deployment, 5G Spectrum Band, Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，預計 2026 年全球 5G 網路人工智慧市場規模將達到 84 億美元，並在預測期內以 10.4% 的複合年成長率成長，到 2034 年將達到 186 億美元。

5G網路中的人工智慧是指將機器學習、深度強化學習、聯邦學習和人工智慧驅動的自動化技術整合到5G網路架構的各個元件中，例如無線接取網路管理、核心網路編配、網路切片最佳化、頻譜管理、預測性干擾抑制、自主故障偵測和智慧流量控制系統。這使得通訊業者能夠動態最佳化網路效能、降低營運成本，並在獨立組網（SA）和非獨立組網（NSA）的5G網路部署中提供全新的AI原生服務功能。

5G網路複雜性管理

5G網路架構的複雜度呈指數級成長，大規模MIMO天線陣列、異構多頻段頻譜管理以及動態網路切片配置等特性已超出人工操作員的能力範圍。因此，對於大規模部署5G網路的通訊業者，採用人工智慧至關重要。人工智慧驅動的無線接取網路最佳化能夠降低能耗、提高頻譜效率，進而顯著降低營運成本。這帶來了遠超過傳統網路管理系統成本的可靠投資報酬率，充分證明了投資人工智慧網路管理平台的合理性。

電信業的AI整合成本

將人工智慧網路管理系統整合到現有的開源營運支援系統(OSS) 和業務支撐系統 (BSS) 環境中成本高昂，且會限制部署速度。這是因為，在傳統的 OSS/BSS 架構中，為人工智慧最佳化演算法提供有效管理網路效能所需的即時網路遙測輸入，需要大規模的API 開發和資料管道設計。整合的複雜性導致部署週期長達數年，從而延緩了供應商和通訊業者實現 5G 人工智慧平台商業化的進程。

開放式無線接取網人工智慧最佳化

開放式無線接取網路（Open RAN）架構的採用，建立了標準化的AI介面規範，為市場拓展帶來了巨大的機會。 Open RAN的xApp和rApp AI應用生態系統支援在多廠商RAN環境中以獨立於通訊設備的方式部署AI最佳化。通訊業者的Open RAN投資計劃，在實現AI驅動的網路最佳化的同時，也打破了廠商鎖定，為傳統網路設備供應商生態系統之外的AI原生RAN智慧平台供應商創造了新的市場准入機會。

網路虛擬化的安全風險

人工智慧管理的虛擬化5G核心網路和無線接取網路網路軟體中的安全漏洞會帶來網路攻擊風險。在對安全要求較高的應用中，一旦人工智慧管理系統遭到入侵，網路可能被操控，導致流量攔截、服務中斷或未經授權的網路存取，進而影響關鍵通訊基礎設施，最終可能限制政府和企業部署人工智慧最佳化的5G網路。

新冠疫情的影響：

新冠疫情凸顯了5G網路的戰略重要性。疫情期間，遠距辦公、遠端醫療和數位化服務交付的需求激增，暴露了傳統4G基礎設施的頻寬局限性，並加速了政府對5G部署的投資。人工智慧驅動的網路最佳化能夠最大限度地利用已部署的5G頻寬容量，對於應對封鎖期間前所未有的流量成長至關重要。疫情後數位經濟的擴張以及企業私有5G網路的部署，持續推動人工智慧網路管理的需求。

在預測期內，服務業預計將成為規模最大的產業。

在預測期內，服務領域預計將佔據最大的市場佔有率。這主要歸功於通訊業者對人工智慧網路管理部署服務、無線接取網最佳化諮詢、網路人工智慧模型訓練和部署以及持續的人工智慧網路營運管理服務的龐大需求。這些需求的驅動力源自於複雜5G人工智慧平台的部署，而這些平台需要先進的網路專業知識和持續的人工智慧模型效能管理，以適應不斷演變的5G網路配置和不斷變化的流量模式。

在預測期內，雲端業務板塊預計將呈現最高的複合年成長率。

在預測期內，雲端領域預計將呈現最高的成長率。這主要歸功於通訊業者採用雲端原生5G核心網路架構。該架構透過基於雲端的網路AI服務，實現了AI驅動的網路主導、預測性擴展和智慧流量管理，從而為大規模5G網路編配提供了處理大量即時網路遙測資料流所需的運算彈性，以實現AI驅動的最佳化。

市佔率最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率。這主要歸功於美國Verizon、AT&T和T-Mobile等主要營運商完成了大規模的5G獨立組網部署，從而產生了對人工智慧網路管理平台的巨大需求；同時，高通、英特爾和英偉達等領先的通訊人工智慧供應商和半導體公司也透過與現有通訊業者的合作關係，從北美的人工智慧5G技術中獲得了可觀的收入。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率。這是因為中國已完成全球規模最大的5G網路部署，並實現了全球最高的5G基地台密度，因此需要先進的AI網路最佳化技術；日本和韓國正在大力推廣5G獨立組網（SA）網路架構；而印度正在都市區地區實施大規模5G部署計劃，從而對AI網路管理平台產生了巨大的需求。

免費客製化服務：

所有購買此報告的客戶均可享受以下免費自訂選項之一：

  • 企業概況
    • 對其他市場參與者（最多 3 家公司）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
    • 應客戶要求，我們提供主要國家和地區的市場估算和預測，以及複合年成長率（註：需進行可行性檢查）。
  • 競爭性標竿分析
    • 根據產品系列、地理覆蓋範圍和策略聯盟對主要企業進行基準分析。

目錄

第1章執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要企業市佔率分析
  • 產品基準評效和效能比較

第5章：全球5G網路人工智慧市場：按組件分類

  • 解決方案
  • 平台
  • 服務

第6章：全球5G網路人工智慧市場：依部署方式分類

  • 雲
  • 現場
  • 邊緣
  • 混合部署
  • 集中式無線存取網（C-RAN）
  • 虛擬化無線存取網（vRAN）

第7章：全球5G網路中的人工智慧市場：依5G頻段分類

  • 低頻寬（低於 1 GHz）
  • 中頻段（1 GHz 至 7 GHz）
  • 高頻段（毫米波，24 GHz 至 100 GHz）

第8章：全球5G網路人工智慧市場：按技術分類

  • 生成式人工智慧（GenAI）
  • 強化學習
  • 電腦視覺
  • 預測分析
  • 聯邦學習

第9章：全球5G網路人工智慧市場：按應用領域分類

  • 網路最佳化
  • 預測性保護
  • 資源分配
  • 安全管理

第10章：全球5G網路人工智慧市場：按最終用戶分類

  • 溝通
  • 車
  • 衛生保健
  • 媒體與娛樂
  • 其他最終用戶

第11章：全球5G網路人工智慧市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第12章 策略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第13章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第14章：公司簡介

  • Ericsson
  • Nokia Corporation
  • Huawei Technologies
  • ZTE Corporation
  • Samsung Electronics
  • Cisco Systems Inc.
  • Qualcomm Inc.
  • Intel Corporation
  • IBM Corporation
  • Microsoft Corporation
  • Google LLC
  • Amazon Web Services Inc.
  • NEC Corporation
  • Fujitsu Limited
  • VMware Inc.
  • Oracle Corporation
  • Hewlett Packard Enterprise
Product Code: SMRC35354

According to Stratistics MRC, the Global AI in 5G Networks Market is accounted for $8.4 billion in 2026 and is expected to reach $18.6 billion by 2034 growing at a CAGR of 10.4% during the forecast period. AI in 5G networks refers to the integration of machine learning, deep reinforcement learning, federated learning, and AI-powered automation into 5G network architecture components including radio access network management, core network orchestration, network slicing optimization, spectrum management, predictive interference mitigation, autonomous fault detection, and intelligent traffic steering systems that enable telecom operators to deliver dynamic network performance optimization, reduced operational costs, and new AI-native service capabilities across 5G standalone and non-standalone network deployments.

Market Dynamics:

Driver:

5G Network Complexity Management

Exponentially increasing 5G network architecture complexity across massive MIMO antenna arrays, heterogeneous multi-frequency spectrum management, and dynamic network slicing configuration demands surpass human operator management capacity, creating mandatory AI adoption requirements for telecom operators deploying 5G networks at commercial scale. AI-powered radio access network optimization reducing energy consumption and improving spectral efficiency delivers measurable operational cost savings that justify AI network management platform investment with documented returns exceeding conventional network management system costs.

Restraint:

Telecom AI Integration Costs

Substantial integration costs for AI network management systems within existing telecom operational support system and business support system environments constrain deployment pace as legacy OSS/BSS architectures require extensive API development and data pipeline engineering to provide the real-time network telemetry inputs that AI optimization algorithms require for effective network performance management. Integration complexity creates multi-year implementation timelines that delay AI 5G platform revenue realization for both vendors and operators.

Opportunity:

Open RAN AI Optimization

Open Radio Access Network architecture adoption creating standardized AI interface specifications represents a major market expansion opportunity as Open RAN xApp and rApp AI application ecosystems enable telecom equipment-agnostic AI optimization deployment across multi-vendor RAN environments. Telecom operator Open RAN investment programs eliminating vendor lock-in while enabling AI-powered network optimization are creating new market entry opportunities for AI-native RAN intelligence platform vendors beyond traditional network equipment provider ecosystems.

Threat:

Network Virtualization Security Risks

AI-managed virtualized 5G core network and radio access network software security vulnerabilities create cyberattack exposure risks that may constrain government and enterprise adoption of AI-optimized 5G network deployments in security-sensitive applications where network manipulation through AI management system compromise could enable traffic interception, service disruption, or unauthorized network access affecting critical communications infrastructure.

Covid-19 Impact:

COVID-19 demonstrated 5G network strategic importance as pandemic-era remote work, telemedicine, and digital service delivery demands exposed bandwidth limitations of legacy 4G infrastructure and accelerated government 5G deployment investment. AI-powered network optimization enabling maximum utilization of deployed 5G spectrum capacity proved essential for accommodating unprecedented traffic growth during lockdown periods. Post-pandemic digital economy expansion and enterprise private 5G network deployment continue driving AI network management demand.

The services segment is expected to be the largest during the forecast period

The services segment is expected to account for the largest market share during the forecast period, due to substantial telecom operator demand for AI network management implementation services, RAN optimization consulting, network AI model training and deployment, and ongoing managed AI network operations services that accompany complex 5G AI platform deployments requiring deep network expertise and continuous AI model performance management across evolving 5G network configurations and traffic pattern changes.

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

Over the forecast period, the cloud segment is predicted to witness the highest growth rate, driven by telecom operator adoption of cloud-native 5G core network architectures enabling AI-powered network function orchestration, predictive scaling, and intelligent traffic management through cloud-delivered network AI services that provide the computational elasticity required to process massive real-time network telemetry streams for AI-driven optimization across large-scale 5G network deployments.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, due to the United States completing large-scale 5G standalone network deployments across major carriers including Verizon, AT&T, and T-Mobile generating substantial AI network management platform procurement demand, combined with leading telecom AI vendors and semiconductor companies including Qualcomm, Intel, and NVIDIA generating significant North American AI 5G technology revenue from established operator relationships.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, due to China completing the world's largest 5G network deployment with the highest 5G base station density globally requiring sophisticated AI network optimization, Japan and South Korea advancing 5G standalone network architecture adoption, and India implementing large-scale 5G rollout programs across urban and rural coverage areas creating extensive AI network management platform procurement demand.

Key players in the market

Some of the key players in AI in 5G Networks Market include Ericsson, Nokia Corporation, Huawei Technologies, ZTE Corporation, Samsung Electronics, Cisco Systems Inc., Qualcomm Inc., Intel Corporation, IBM Corporation, Microsoft Corporation, Google LLC, Amazon Web Services Inc., NEC Corporation, Fujitsu Limited, VMware Inc., Oracle Corporation, and Hewlett Packard Enterprise.

Key Developments:

In February 2026, Nokia Corporation introduced MantaRay Network Intelligence AI platform expansion with automated 5G network slicing optimization and predictive capacity management capabilities for enterprise private network operators.

In January 2026, Samsung Electronics secured a major 5G Open RAN AI deployment contract with a North American tier-one operator implementing AI-powered radio resource management across its nationwide 5G standalone network infrastructure.

In November 2025, NEC Corporation launched an AI-powered 5G core network orchestration platform enabling telecom operators to autonomously manage virtual network function scaling and service quality optimization across hybrid cloud deployments.

Components Covered:

  • Solutions
  • Platforms
  • Services

Deployments Covered:

  • Cloud
  • On-Premise
  • Edge
  • Hybrid Deployment
  • Centralized RAN (C-RAN)
  • Virtualized RAN (vRAN)

5G Spectrum Bands Covered:

  • Low-Band (Sub-1 GHz)
  • Mid-Band (1 GHz - 7 GHz)
  • High-Band (mmWave, 24 GHz - 100 GHz)

Technologies Covered:

  • Generative AI (GenAI)
  • Reinforcement Learning
  • Computer Vision
  • Predictive Analytics
  • Federated Learning

Applications Covered:

  • Network Optimization
  • Predictive Maintenance
  • Resource Allocation
  • Security Management

End Users Covered:

  • Telecom
  • Automotive
  • Healthcare
  • Media & Entertainment
  • Other End Users

Regions Covered:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Netherlands
    • Belgium
    • Sweden
    • Switzerland
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Indonesia
    • Thailand
    • Malaysia
    • Singapore
    • Vietnam
    • Rest of Asia Pacific
  • South America
    • Brazil
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of South America
  • Rest of the World (RoW)
    • Middle East
  • Saudi Arabia
  • United Arab Emirates
  • Qatar
  • Israel
  • Rest of Middle East
    • Africa
  • South Africa
  • Egypt
  • Morocco
  • Rest of 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 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
  • 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

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global AI in 5G Networks Market, By Component

  • 5.1 Solutions
  • 5.2 Platforms
  • 5.3 Services

6 Global AI in 5G Networks Market, By Deployment

  • 6.1 Cloud
  • 6.2 On-Premise
  • 6.3 Edge
  • 6.4 Hybrid Deployment
  • 6.5 Centralized RAN (C-RAN)
  • 6.6 Virtualized RAN (vRAN)

7 Global AI in 5G Networks Market, By 5G Spectrum Band

  • 7.1 Low-Band (Sub-1 GHz)
  • 7.2 Mid-Band (1 GHz - 7 GHz)
  • 7.3 High-Band (mmWave, 24 GHz - 100 GHz)

8 Global AI in 5G Networks Market, By Technology

  • 8.1 Generative AI (GenAI)
  • 8.2 Reinforcement Learning
  • 8.3 Computer Vision
  • 8.4 Predictive Analytics
  • 8.5 Federated Learning

9 Global AI in 5G Networks Market, By Application

  • 9.1 Network Optimization
  • 9.2 Predictive Maintenance
  • 9.3 Resource Allocation
  • 9.4 Security Management

10 Global AI in 5G Networks Market, By End User

  • 10.1 Telecom
  • 10.2 Automotive
  • 10.3 Healthcare
  • 10.4 Media & Entertainment
  • 10.5 Other End Users

11 Global AI in 5G Networks Market, By Geography

  • 11.1 North America
    • 11.1.1 United States
    • 11.1.2 Canada
    • 11.1.3 Mexico
  • 11.2 Europe
    • 11.2.1 United Kingdom
    • 11.2.2 Germany
    • 11.2.3 France
    • 11.2.4 Italy
    • 11.2.5 Spain
    • 11.2.6 Netherlands
    • 11.2.7 Belgium
    • 11.2.8 Sweden
    • 11.2.9 Switzerland
    • 11.2.10 Poland
    • 11.2.11 Rest of Europe
  • 11.3 Asia Pacific
    • 11.3.1 China
    • 11.3.2 Japan
    • 11.3.3 India
    • 11.3.4 South Korea
    • 11.3.5 Australia
    • 11.3.6 Indonesia
    • 11.3.7 Thailand
    • 11.3.8 Malaysia
    • 11.3.9 Singapore
    • 11.3.10 Vietnam
    • 11.3.11 Rest of Asia Pacific
  • 11.4 South America
    • 11.4.1 Brazil
    • 11.4.2 Argentina
    • 11.4.3 Colombia
    • 11.4.4 Chile
    • 11.4.5 Peru
    • 11.4.6 Rest of South America
  • 11.5 Rest of the World (RoW)
    • 11.5.1 Middle East
      • 11.5.1.1 Saudi Arabia
      • 11.5.1.2 United Arab Emirates
      • 11.5.1.3 Qatar
      • 11.5.1.4 Israel
      • 11.5.1.5 Rest of Middle East
    • 11.5.2 Africa
      • 11.5.2.1 South Africa
      • 11.5.2.2 Egypt
      • 11.5.2.3 Morocco
      • 11.5.2.4 Rest of Africa

12 Strategic Market Intelligence

  • 12.1 Industry Value Network and Supply Chain Assessment
  • 12.2 White-Space and Opportunity Mapping
  • 12.3 Product Evolution and Market Life Cycle Analysis
  • 12.4 Channel, Distributor, and Go-to-Market Assessment

13 Industry Developments and Strategic Initiatives

  • 13.1 Mergers and Acquisitions
  • 13.2 Partnerships, Alliances, and Joint Ventures
  • 13.3 New Product Launches and Certifications
  • 13.4 Capacity Expansion and Investments
  • 13.5 Other Strategic Initiatives

14 Company Profiles

  • 14.1 Ericsson
  • 14.2 Nokia Corporation
  • 14.3 Huawei Technologies
  • 14.4 ZTE Corporation
  • 14.5 Samsung Electronics
  • 14.6 Cisco Systems Inc.
  • 14.7 Qualcomm Inc.
  • 14.8 Intel Corporation
  • 14.9 IBM Corporation
  • 14.10 Microsoft Corporation
  • 14.11 Google LLC
  • 14.12 Amazon Web Services Inc.
  • 14.13 NEC Corporation
  • 14.14 Fujitsu Limited
  • 14.15 VMware Inc.
  • 14.16 Oracle Corporation
  • 14.17 Hewlett Packard Enterprise

List of Tables

  • Table 1 Global AI in 5G Networks Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI in 5G Networks Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global AI in 5G Networks Market Outlook, By Solutions (2023-2034) ($MN)
  • Table 4 Global AI in 5G Networks Market Outlook, By Platforms (2023-2034) ($MN)
  • Table 5 Global AI in 5G Networks Market Outlook, By Services (2023-2034) ($MN)
  • Table 6 Global AI in 5G Networks Market Outlook, By Deployment (2023-2034) ($MN)
  • Table 7 Global AI in 5G Networks Market Outlook, By Cloud (2023-2034) ($MN)
  • Table 8 Global AI in 5G Networks Market Outlook, By On-Premise (2023-2034) ($MN)
  • Table 9 Global AI in 5G Networks Market Outlook, By Edge (2023-2034) ($MN)
  • Table 10 Global AI in 5G Networks Market Outlook, By Hybrid Deployment (2023-2034) ($MN)
  • Table 11 Global AI in 5G Networks Market Outlook, By Centralized RAN (C-RAN) (2023-2034) ($MN)
  • Table 12 Global AI in 5G Networks Market Outlook, By Virtualized RAN (vRAN) (2023-2034) ($MN)
  • Table 13 Global AI in 5G Networks Market Outlook, By 5G Spectrum Band (2023-2034) ($MN)
  • Table 14 Global AI in 5G Networks Market Outlook, By Low-Band (Sub-1 GHz) (2023-2034) ($MN)
  • Table 15 Global AI in 5G Networks Market Outlook, By Mid-Band (1 GHz - 7 GHz) (2023-2034) ($MN)
  • Table 16 Global AI in 5G Networks Market Outlook, By High-Band (mmWave, 24 GHz - 100 GHz) (2023-2034) ($MN)
  • Table 17 Global AI in 5G Networks Market Outlook, By Technology (2023-2034) ($MN)
  • Table 18 Global AI in 5G Networks Market Outlook, By Generative AI (GenAI) (2023-2034) ($MN)
  • Table 19 Global AI in 5G Networks Market Outlook, By Reinforcement Learning (2023-2034) ($MN)
  • Table 20 Global AI in 5G Networks Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 21 Global AI in 5G Networks Market Outlook, By Predictive Analytics (2023-2034) ($MN)
  • Table 22 Global AI in 5G Networks Market Outlook, By Federated Learning (2023-2034) ($MN)
  • Table 23 Global AI in 5G Networks Market Outlook, By Application (2023-2034) ($MN)
  • Table 24 Global AI in 5G Networks Market Outlook, By Network Optimization (2023-2034) ($MN)
  • Table 25 Global AI in 5G Networks Market Outlook, By Predictive Maintenance (2023-2034) ($MN)
  • Table 26 Global AI in 5G Networks Market Outlook, By Resource Allocation (2023-2034) ($MN)
  • Table 27 Global AI in 5G Networks Market Outlook, By Security Management (2023-2034) ($MN)
  • Table 28 Global AI in 5G Networks Market Outlook, By End User (2023-2034) ($MN)
  • Table 29 Global AI in 5G Networks Market Outlook, By Telecom (2023-2034) ($MN)
  • Table 30 Global AI in 5G Networks Market Outlook, By Automotive (2023-2034) ($MN)
  • Table 31 Global AI in 5G Networks Market Outlook, By Healthcare (2023-2034) ($MN)
  • Table 32 Global AI in 5G Networks Market Outlook, By Media & Entertainment (2023-2034) ($MN)
  • Table 33 Global AI in 5G Networks Market Outlook, By Other End Users (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) Regions are also represented in the same manner as above.