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1700096

2032 年雲端 AI 市場預測：按組件、部署類型、組織規模、技術、最終用戶和地區進行的全球分析

Cloud AI Market Forecasts to 2032 - Global Analysis by Component (Hardware, Software and Services), Deployment Mode (Public Cloud, Private Cloud and Hybrid Cloud), Organization Size, Technology, 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 的數據，全球雲端 AI 市場預計在 2025 年達到 1,021 億美元，到 2032 年將達到 6,586 億美元，預測期內的複合年成長率為 30.5%。

雲端AI是指在雲端運算環境中整合人工智慧（AI）功能。這使得企業和開發人員無需內部基礎設施即可使用機器學習、自然語言處理和電腦視覺等人工智慧服務。來自 Google Cloud AI、AWS AI 和 Microsoft Azure AI 等供應商的雲端 AI 平台提供可擴展的運算能力、預訓練模型和 API，可加速 AI 的採用。利用雲端運算，企業可以處理大型資料集、提高自動化程度並有效部署人工智慧主導的應用程式。雲端 AI 廣泛應用於醫療保健、金融和零售等產業的預測分析和智慧自動化。

據 IBM 稱，98% 的組織計劃採用多重雲端架構，但只有 41% 的組織制定了多重雲端管理策略，只有 38% 的組織擁有在這種環境中運行所需的程式和工具。

對人工智慧服務的需求不斷成長

對人工智慧服務不斷成長的需求正在推動雲端人工智慧市場的發展，使企業能夠提高效率、擴充性和決策能力。雲端 AI 解決方案使企業能夠獲得自動化流程、即時資訊和經濟實惠的處理能力。醫療保健、金融和零售等行業的人工智慧應用熱潮推動了自然語言處理和預測分析等人工智慧驅動應用的創新。隨著企業擴大將人工智慧融入其雲端平台以推動全球數位轉型，預計市場將快速成長。

基礎設施挑戰

由於可擴展性有限、延遲增加以及營運成本上升，基礎設施挑戰是雲端 AI 市場成長的主要障礙。網路頻寬不足、資料中心過時以及缺乏強大的邊緣運算基礎設施正在減緩人工智慧模型的部署和即時處理。舊有系統和雲端平台之間較差的互通性進一步增加了採用的複雜性。此外，安全漏洞和監管合規問題也成為企業發展的障礙，降低了對雲端 AI 解決方案的信任和投資，最終減緩了市場擴張和創新。

人工智慧技術的進步

人工智慧技術的進步正在透過提高自動化程度、擴充性和效率來推動雲端人工智慧市場的發展。人工智慧雲端解決方案可實現即時資料分析、預測分析和智慧自動化，從而增強各領域的決策能力。透過人工智慧主導的安全性、機器學習和自然語言處理的進步，雲端的效能和可靠性得到了提高。這些發展使企業能夠加速數位轉型，在日益資料主導的世界中創新並獲得競爭優勢。

監理與合規問題

監管和合規問題透過實施嚴格的資料隱私法、安全標準和跨境資料傳輸限制阻礙了雲端 AI 市場的發展。遵守 GDPR 和 CCPA 等不斷發展的法規會增加營運成本和複雜性。人工智慧管治的不確定性、道德問題和法律責任將進一步減緩其採用。醫療保健、金融和政府領域的嚴格行業特定法規成為限制雲端 AI 供應商創新、可擴展性和全球市場擴張的障礙。

COVID-19的影響

隨著企業接受數位轉型，實現遠距工作、自動化和資料主導的決策，COVID-19 疫情加速了雲端 AI 的採用。人工智慧驅動的創新在醫療保健、電子商務和網路安全領域取得了長足的進步。但供應鏈中斷和經濟不確定性最初抑制了投資。疫情過後，受可擴展性、效率和改善客戶體驗的需求推動，對人工智慧雲端解決方案的需求持續成長。

預計製造業將成為預測期內最大的產業

由於雲端 AI 能夠透過先進的機器學習演算法實現即時監控、生產流程最佳化和品管改進，因此製造業預計將在預測期內佔據最大的市場佔有率。透過整合人工智慧主導的機器人和物聯網解決方案，製造商可以降低成本、提高生產力並簡化供應鏈管理。這種轉變將加速創新、促進永續性、增強競爭力，使製造業成為雲端 AI 市場成長的關鍵貢獻者。

預計軟體產業在預測期內將實現最高的複合年成長率。

預計軟體部門將在預測期內實現最高的成長率。這是因為人工智慧軟體解決方案加速了數位轉型，同時提高了成本效益、擴充性和效率。隨著機器學習演算法、自然語言處理和預測分析的不斷改進，該軟體推動了虛擬助理、詐騙偵測和客製化建議等雲端人工智慧應用的創新。隨著越來越多的企業使用人工智慧軟體來提升競爭優勢和業務敏捷性，雲端人工智慧市場正在迅速擴張。

占比最大的地區

在預測期內，由於數位轉型的不斷推進、雲端運算應用的不斷成長以及政府支持人工智慧發展的舉措，預計亞太地區將佔據最大的市場佔有率。各行各業的公司都在轉向人工智慧雲端解決方案來提高效率、實現流程自動化和推動創新。智慧城市、金融科技以及醫療人工智慧的興起正在進一步加速市場擴張。憑藉對人工智慧研究和雲端基礎設施的大力投資，該地區有望成為人工智慧主導的成長和經濟發展的全球中心。

複合年成長率最高的地區：

預計北美地區在預測期內將呈現最高的複合年成長率。這是因為企業採用人工智慧雲端解決方案來實現預測分析、個人化客戶體驗和提高業務效率。該地區強大的技術生態系統，加上對人工智慧主導的雲端運算不斷增加的投資，將加速數位轉型。雲端 AI 將推動可擴展性、成本節約和資料主導的洞察力，使醫療保健、金融和零售等行業受益。隨著應用的不斷推進，北美將繼續成為人工智慧進步的領導者，推動競爭優勢和經濟成長。

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  • 公司簡介
    • 對其他市場公司（最多 3 家公司）進行全面分析
    • 主要企業的SWOT分析（最多3家公司）
  • 地理細分
    • 根據客戶興趣對主要國家市場進行估計、預測和複合年成長率（註：基於可行性檢查）
  • 競爭基準化分析
    • 根據產品系列、地理分佈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 前言

  • 概述
  • 相關利益者
  • 研究範圍
  • 調查方法
    • 資料探勘
    • 資料分析
    • 資料檢驗
    • 研究途徑
  • 研究材料
    • 主要研究資料
    • 次級研究資訊來源
    • 先決條件

第3章市場走勢分析

  • 驅動程式
  • 限制因素
  • 機會
  • 威脅
  • 技術分析
  • 最終用戶分析
  • 新興市場
  • COVID-19的影響

第4章 波特五力分析

  • 供應商的議價能力
  • 買家的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

第5章 全球雲端 AI 市場（按組件）

  • 硬體
  • 軟體
  • 服務

第6章 全球雲端 AI 市場（依部署類型）

  • 公共雲端
  • 私有雲端
  • 混合雲端

第7章 全球雲端 AI 市場（依組織規模）

  • 中小型企業
  • 大型企業

第8章 全球雲端人工智慧市場（按技術）

  • 機器學習 (ML) 與深度學習
  • 自然語言處理（NLP）
  • 電腦視覺
  • 語音辨識
  • 其他技術

第9章 全球雲端人工智慧市場（按最終用戶）

  • 銀行、金融服務和保險（BFSI）
  • 醫療保健和生命科學
  • 零售與電子商務
  • 資訊科技/通訊
  • 製造業
  • 政府和國防
  • 能源與公共產業
  • 媒體與娛樂
  • 汽車與運輸
  • 其他最終用戶

第 10 章全球雲端 AI 市場（按地區）

  • 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
    • 其他歐洲國家
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地區
  • 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 重大進展

  • 協議、夥伴關係、合作和合資企業
  • 收購與合併
  • 新產品發布
  • 業務擴展
  • 其他關鍵策略

第12章 公司概況

  • Amazon Web Services（AWS）
  • Microsoft
  • Google
  • IBM
  • Oracle
  • NVIDIA
  • Salesforce
  • SAP
  • Alibaba Cloud
  • Intel
  • Hewlett Packard Enterprise（HPE）
  • Tencent Cloud
  • H2O.ai
  • OpenAI
  • Baidu
  • DataRobot
  • Huawei
  • C3 AI
  • Cloudera
Product Code: SMRC28999

According to Stratistics MRC, the Global Cloud AI Market is accounted for $102.1 billion in 2025 and is expected to reach $658.6 billion by 2032 growing at a CAGR of 30.5% during the forecast period. Cloud AI refers to the integration of artificial intelligence (AI) capabilities within cloud computing environments. It enables businesses and developers to access AI-powered services, such as machine learning, natural language processing, and computer vision, without the need for on-premises infrastructure. Cloud AI platforms, offered by providers like Google Cloud AI, AWS AI, and Microsoft Azure AI, offer scalable computing power, pre-trained models, and APIs to accelerate AI adoption. By leveraging the cloud, organizations can process large datasets, enhance automation, and deploy AI-driven applications efficiently. Cloud AI is widely used in industries like healthcare, finance, and retail for predictive analytics and intelligent automation.

According to IBM, while 98% of organizations plan to adopt multi-cloud architectures, only 41% have a multi-cloud management strategy and 38% have the necessary procedures and tools to operate in such an environment.

Market Dynamics:

Driver:

Rising Demand for AI Services

The growing demand for AI services is propelling the Cloud AI market, allowing businesses to improve efficiency, scalability, and decision-making. Cloud AI solutions give enterprises access to automated processes, real-time information, and affordable processing capacity. Innovation in AI-driven applications, such natural language processing and predictive analytics, is fueled by this adoption boom in industries like healthcare, finance, and retail. The market is expected to grow faster as businesses incorporate AI more and more into cloud platforms, promoting digital transformation on a worldwide scale.

Restraint:

Infrastructure Challenges

Infrastructure challenges significantly hinder the growth of the cloud AI market by limiting scalability, increasing latency, and raising operational costs. Insufficient network bandwidth, outdated data centers, and lack of robust edge computing infrastructure slow AI model deployment and real-time processing. Poor interoperability between legacy systems and cloud platforms further complicates adoption. Additionally, security vulnerabilities and regulatory compliance issues create barriers for businesses, reducing trust and investment in cloud AI solutions, ultimately slowing market expansion and innovation.

Opportunity:

Advancements in AI Technologies

Advancements in AI technologies are propelling the Cloud AI market forward by improving automation, scalability, and efficiency. Real-time data analysis, predictive analytics, and intelligent automation are made possible by AI-powered cloud solutions, which enhance decision-making in a variety of sectors. Cloud performance and dependability are being improved by advancements in AI-driven security, machine learning, and natural language processing. These developments enable companies to innovate and obtain a competitive edge in a world that is becoming more and more data-driven by speeding up digital transformation.

Threat:

Regulatory and Compliance Issues

Regulatory and compliance issues hinder the Cloud AI market by imposing strict data privacy laws, security standards, and cross-border data transfer restrictions. Compliance with evolving regulations like GDPR and CCPA increases operational costs and complexity. Uncertainty in AI governance, ethical concerns, and legal liabilities further slow adoption. Stringent industry-specific rules in healthcare, finance, and government sectors create barriers, limiting innovation, scalability, and global market expansion for Cloud AI providers.

Covid-19 Impact

The COVID-19 pandemic accelerated the adoption of Cloud AI as businesses embraced digital transformation to enable remote work, automation, and data-driven decision-making. Healthcare, e-commerce, and cybersecurity sectors saw significant AI-driven innovations. However, supply chain disruptions and economic uncertainty initially slowed investments. Post-pandemic, demand for AI-powered cloud solutions continue to rise, driven by the need for scalability, efficiency, and enhanced customer experiences.

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

The manufacturing segment is expected to account for the largest market share during the forecast period, as Cloud AI enables real-time monitoring, optimizing production processes, and improving quality control through advanced machine learning algorithms. By integrating AI-driven robotics and IoT solutions, manufacturers achieve cost savings, increased productivity, and streamlined supply chain management. This transformation accelerates innovation, fosters sustainability, and strengthens competitiveness, making manufacturing a major contributor to the Cloud AI market's growth.

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

Over the forecast period, the software segment is predicted to witness the highest growth rate, because software solutions driven by AI improve cost-effectiveness, scalability, and efficiency while speeding up digital transformation. Software propels innovation in cloud AI applications like virtual assistants, fraud detection, and tailored recommendations with ongoing improvements in machine learning algorithms, natural language processing, and predictive analytics. The market for cloud AI is expanding rapidly as more businesses use AI-powered software, which increases competitive advantage and business agility.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share due to increasing digital transformation, expanding cloud adoption, and government initiatives supporting AI development. Businesses across industries leverage AI-powered cloud solutions to enhance efficiency, automate processes, and drive innovation. The rise of smart cities, fintech advancements, and healthcare AI further accelerates market expansion. With strong investments in AI research and cloud infrastructure, the region is poised to become a global hub for AI-driven growth and economic progress.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR, as businesses leverage AI-powered cloud solutions for predictive analytics, personalized customer experiences, and improved operational productivity. The region's strong tech ecosystem, coupled with increasing investments in AI-driven cloud computing, accelerates digital transformation. Cloud AI fosters scalability, cost savings, and data-driven insights, benefiting sectors like healthcare, finance, and retail. As adoption grows, North America remains a leader in AI advancements, driving competitive advantage and economic growth.

Key players in the market

Some of the key players profiled in the Cloud AI Market include Amazon Web Services (AWS), Microsoft, Google, IBM, Oracle, NVIDIA, Salesforce, SAP, Alibaba Cloud, Intel, Hewlett Packard Enterprise (HPE), Tencent Cloud, H2O.ai, OpenAI, Baidu, DataRobot, Huawei, C3 AI and Cloudera.

Key Developments:

In March 2025, Google announced it has signed a definitive agreement to acquire Wiz, Inc., This acquisition represents an investment by Google Cloud to accelerate two large and growing trends in the AI era: improved cloud security and the ability to use multiple clouds (multicloud).

In October 2024, IBM has launched Granite 3.0, an open-source AI model tailored for enterprise applications. It includes general-purpose models with 2 billion and 8 billion parameters, as well as specialized Mixture-of-Experts (MoE) models. IBM also introduced Granite Guardian models, focusing on AI safety and security.

In September 2024, Oracle and Amazon Web Services, Inc. (AWS) announced the launch of Oracle Database@AWS, a new offering that allows customers to access Oracle Autonomous Database on dedicated infrastructure and Oracle Exadata Database Service within AWS.

Components Covered:

  • Hardware
  • Software
  • Services

Deployment Modes Covered:

  • Public Cloud
  • Private Cloud
  • Hybrid Cloud

Organization Sizes Covered:

  • Small & Medium Enterprises (SMEs)
  • Large Enterprises

Technologies Covered:

  • Machine Learning (ML) & Deep Learning
  • Natural Language Processing (NLP)
  • Computer Vision
  • Speech Recognition
  • Other Technologies

End Users Covered:

  • Banking, Financial Services, and Insurance (BFSI)
  • Healthcare & Life Sciences
  • Retail & E-commerce
  • IT & Telecom
  • Manufacturing
  • Government & Defense
  • Energy & Utilities
  • Media & Entertainment
  • Automotive & Transportation
  • Other End Users

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 End User Analysis
  • 3.8 Emerging Markets
  • 3.9 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 Cloud AI Market, By Component

  • 5.1 Introduction
  • 5.2 Hardware
  • 5.3 Software
  • 5.4 Services

6 Global Cloud AI Market, By Deployment Mode

  • 6.1 Introduction
  • 6.2 Public Cloud
  • 6.3 Private Cloud
  • 6.4 Hybrid Cloud

7 Global Cloud AI Market, By Organization Size

  • 7.1 Introduction
  • 7.2 Small & Medium Enterprises (SMEs)
  • 7.3 Large Enterprises

8 Global Cloud AI Market, By Technology

  • 8.1 Introduction
  • 8.2 Machine Learning (ML) & Deep Learning
  • 8.3 Natural Language Processing (NLP)
  • 8.4 Computer Vision
  • 8.5 Speech Recognition
  • 8.6 Other Technologies

9 Global Cloud AI Market, By End User

  • 9.1 Introduction
  • 9.2 Banking, Financial Services, and Insurance (BFSI)
  • 9.3 Healthcare & Life Sciences
  • 9.4 Retail & E-commerce
  • 9.5 IT & Telecom
  • 9.6 Manufacturing
  • 9.7 Government & Defense
  • 9.8 Energy & Utilities
  • 9.9 Media & Entertainment
  • 9.10 Automotive & Transportation
  • 9.11 Other End Users

10 Global Cloud AI 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 Amazon Web Services (AWS)
  • 12.2 Microsoft
  • 12.3 Google
  • 12.4 IBM
  • 12.5 Oracle
  • 12.6 NVIDIA
  • 12.7 Salesforce
  • 12.8 SAP
  • 12.9 Alibaba Cloud
  • 12.10 Intel
  • 12.11 Hewlett Packard Enterprise (HPE)
  • 12.12 Tencent Cloud
  • 12.13 H2O.ai
  • 12.14 OpenAI
  • 12.15 Baidu
  • 12.16 DataRobot
  • 12.17 Huawei
  • 12.18 C3 AI
  • 12.19 Cloudera

List of Tables

  • Table 1 Global Cloud AI Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global Cloud AI Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global Cloud AI Market Outlook, By Hardware (2024-2032) ($MN)
  • Table 4 Global Cloud AI Market Outlook, By Software (2024-2032) ($MN)
  • Table 5 Global Cloud AI Market Outlook, By Services (2024-2032) ($MN)
  • Table 6 Global Cloud AI Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 7 Global Cloud AI Market Outlook, By Public Cloud (2024-2032) ($MN)
  • Table 8 Global Cloud AI Market Outlook, By Private Cloud (2024-2032) ($MN)
  • Table 9 Global Cloud AI Market Outlook, By Hybrid Cloud (2024-2032) ($MN)
  • Table 10 Global Cloud AI Market Outlook, By Organization Size (2024-2032) ($MN)
  • Table 11 Global Cloud AI Market Outlook, By Small & Medium Enterprises (SMEs) (2024-2032) ($MN)
  • Table 12 Global Cloud AI Market Outlook, By Large Enterprises (2024-2032) ($MN)
  • Table 13 Global Cloud AI Market Outlook, By Technology (2024-2032) ($MN)
  • Table 14 Global Cloud AI Market Outlook, By Machine Learning (ML) & Deep Learning (2024-2032) ($MN)
  • Table 15 Global Cloud AI Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
  • Table 16 Global Cloud AI Market Outlook, By Computer Vision (2024-2032) ($MN)
  • Table 17 Global Cloud AI Market Outlook, By Speech Recognition (2024-2032) ($MN)
  • Table 18 Global Cloud AI Market Outlook, By Other Technologies (2024-2032) ($MN)
  • Table 19 Global Cloud AI Market Outlook, By End User (2024-2032) ($MN)
  • Table 20 Global Cloud AI Market Outlook, By Banking, Financial Services, and Insurance (BFSI) (2024-2032) ($MN)
  • Table 21 Global Cloud AI Market Outlook, By Healthcare & Life Sciences (2024-2032) ($MN)
  • Table 22 Global Cloud AI Market Outlook, By Retail & E-commerce (2024-2032) ($MN)
  • Table 23 Global Cloud AI Market Outlook, By IT & Telecom (2024-2032) ($MN)
  • Table 24 Global Cloud AI Market Outlook, By Manufacturing (2024-2032) ($MN)
  • Table 25 Global Cloud AI Market Outlook, By Government & Defense (2024-2032) ($MN)
  • Table 26 Global Cloud AI Market Outlook, By Energy & Utilities (2024-2032) ($MN)
  • Table 27 Global Cloud AI Market Outlook, By Media & Entertainment (2024-2032) ($MN)
  • Table 28 Global Cloud AI Market Outlook, By Automotive & Transportation (2024-2032) ($MN)
  • Table 29 Global Cloud AI Market Outlook, By Other End Users (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.