查看购物车
  • Traditional Chinese
  • English
  • Japanese
封面
市场调查报告书
商品编码
1700096

2032 年云端 AI 市场预测：按组件、部署类型、组织规模、技术、最终用户和地区进行的全球分析

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个工作天内

价格

根据 Stratistics MRC 的数据，全球云端 AI 市场预计在 2025 年达到 1,021 亿美元，到 2032 年将达到 6,586 亿美元，预测期内的复合年增长率为 30.5%。

云端AI是指在云端运算环境中整合人工智慧（AI）功能。这使得企业和开发人员无需内部基础设施即可使用机器学习、自然语言处理和电脑视觉等人工智慧服务。来自 Google Cloud AI、AWS AI 和 Microsoft Azure AI 等供应商的云端 AI 平台提供可扩展的运算能力、预训练模型和 API，可加速 AI 的采用。利用云端运算，企业可以处理大型资料集、提高自动化程度并有效部署人工智慧主导的应用程式。云端 AI 广泛应用于医疗保健、金融和零售等产业的预测分析和智慧自动化。

据 IBM 称，98% 的组织计划采用多重云端架构，但只有 41% 的组织制定了多重云端管理策略，只有 38% 的组织拥有在这种环境中运行所需的程式和工具。

对人工智慧服务的需求不断增长

对人工智慧服务不断增长的需求正在推动云端人工智慧市场的发展，使企业能够提高效率、扩充性和决策能力。云端 AI 解决方案使企业能够获得自动化流程、即时资讯和经济实惠的处理能力。医疗保健、金融和零售等行业的人工智慧应用热潮推动了自然语言处理和预测分析等人工智慧驱动应用的创新。随着企业越来越多地将人工智慧融入其云端平台以推动全球数位转型，预计市场将快速成长。

基础设施挑战

由于可扩展性有限、延迟增加以及营运成本上升，基础设施挑战是云端 AI 市场成长的主要障碍。网路频宽不足、资料中心过时以及缺乏强大的边缘运算基础设施正在减缓人工智慧模型的部署和即时处理。旧有系统和云端平台之间较差的互通性进一步增加了采用的复杂性。此外，安全漏洞和监管合规问题也成为企业发展的障碍，降低了对云端 AI 解决方案的信任和投资，最终减缓了市场扩张和创新。

人工智慧技术的进步

人工智慧技术的进步正在透过提高自动化程度、扩充性和效率来推动云端人工智慧市场的发展。人工智慧云端解决方案可实现即时资料分析、预测分析和智慧自动化，从而增强各领域的决策能力。透过人工智慧主导的安全性、机器学习和自然语言处理的进步，云端的效能和可靠性得到了提高。这些发展使企业能够加速数位转型，在日益资料主导的世界中创新并获得竞争优势。

监理与合规问题

监管和合规问题透过实施严格的资料隐私法、安全标准和跨境资料传输限制阻碍了云端 AI 市场的发展。遵守 GDPR 和 CCPA 等不断发展的法规会增加营运成本和复杂性。人工智慧管治的不确定性、道德问题和法律责任将进一步减缓其采用。医疗保健、金融和政府领域的严格行业特定法规成为限制云端 AI 供应商创新、可扩展性和全球市场扩张的障碍。

COVID-19的影响

随着企业接受数位转型，实现远距工作、自动化和资料主导的决策，COVID-19 疫情加速了云端 AI 的采用。人工智慧驱动的创新在医疗保健、电子商务和网路安全领域取得了长足的进步。但供应链中断和经济不确定性最初抑制了投资。疫情过后，受可扩展性、效率和改善客户体验的需求推动，对人工智慧云端解决方案的需求持续成长。

预计製造业将成为预测期内最大的产业

由于云端 AI 能够透过先进的机器学习演算法实现即时监控、生产流程优化和品管改进，因此製造业预计将在预测期内占据最大的市场占有率。透过整合人工智慧主导的机器人和物联网解决方案，製造商可以降低成本、提高生产力并简化供应链管理。这种转变将加速创新、促进永续性、增强竞争力，使製造业成为云端 AI 市场成长的关键贡献者。

预计软体产业在预测期内将实现最高的复合年增长率。

预计软体部门将在预测期内实现最高的成长率。这是因为人工智慧软体解决方案加速了数位转型，同时提高了成本效益、扩充性和效率。随着机器学习演算法、自然语言处理和预测分析的不断改进，该软体推动了虚拟助理、诈骗侦测和客製化建议等云端人工智慧应用的创新。随着越来越多的企业使用人工智慧软体来提升竞争优势和业务敏捷性，云端人工智慧市场正在迅速扩张。

比最大的地区

在预测期内，由于数位转型的不断推进、云端运算应用的不断增长以及政府支持人工智慧发展的倡议，预计亚太地区将占据最大的市场占有率。各行各业的公司都在转向人工智慧云端解决方案来提高效率、实现流程自动化和推动创新。智慧城市、金融科技以及医疗人工智慧的兴起正在进一步加速市场扩张。凭藉对人工智慧研究和云端基础设施的大力投资，该地区有望成为人工智慧主导的成长和经济发展的全球中心。

复合年增长率最高的地区：

预计北美地区在预测期内将呈现最高的复合年增长率。这是因为企业采用人工智慧云端解决方案来实现预测分析、个人化客户体验和提高业务效率。该地区强大的技术生态系统，加上对人工智慧主导的云端运算不断增加的投资，将加速数位转型。云端 AI 将推动可扩展性、成本节约和资料主导的洞察力，使医疗保健、金融和零售等行业受益。随着应用的不断推进，北美将继续成为人工智慧进步的领导者，推动竞争优势和经济成长。

免费客製化服务

订阅此报告的客户可享有以下免费自订选项之一：

  • 公司简介
    • 对其他市场公司（最多 3 家公司）进行全面分析
    • 主要企业的SWOT分析（最多3家公司）
  • 地理细分
    • 根据客户兴趣对主要国家市场进行估计、预测和复合年增长率（註：基于可行性检查）
  • 竞争基准化分析
    • 根据产品系列、地理分布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 前言

  • 概述
  • 相关利益者
  • 研究范围
  • 调查方法
    • 资料探勘
    • 资料分析
    • 资料检验
    • 研究途径
  • 研究材料
    • 主要研究资料
    • 次级研究资讯来源
    • 先决条件

第三章市场走势分析

  • 驱动程式
  • 限制因素
  • 机会
  • 威胁
  • 技术分析
  • 最终用户分析
  • 新兴市场
  • COVID-19的影响

第四章 波特五力分析

  • 供应商的议价能力
  • 买家的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

第五章 全球云端 AI 市场（按组件）

  • 硬体
  • 软体
  • 服务

第六章 全球云端 AI 市场（依部署类型）

  • 公共云端
  • 私有云端
  • 混合云端

第七章 全球云端 AI 市场（依组织规模）

  • 中小型企业
  • 大型企业

第八章 全球云端人工智慧市场（按技术）

  • 机器学习 (ML) 与深度学习
  • 自然语言处理（NLP）
  • 电脑视觉
  • 语音辨识
  • 其他技术

第九章 全球云端人工智慧市场（按最终用户）

  • 银行、金融服务和保险（BFSI）
  • 医疗保健和生命科学
  • 零售与电子商务
  • 资讯科技/通讯
  • 製造业
  • 政府和国防
  • 能源与公共产业
  • 媒体与娱乐
  • 汽车与运输
  • 其他最终用户

第 10 章全球云端 AI 市场（按地区）

  • 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
    • 其他欧洲国家
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 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 重大进展

  • 协议、伙伴关係、合作和合资企业
  • 收购与合併
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第十二章 公司概况

  • 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.