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市场调查报告书
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1716649

人工智慧即服务 (AIaaS) 市场机会、成长动力、产业趋势分析及 2025 - 2034 年预测

Artificial Intelligence as a Service (AIaaS) Market Opportunity, Growth Drivers, Industry Trend Analysis, and Forecast 2025 - 2034
出版日期: | 出版商: Global Market Insights Inc. | 英文 170 Pages | 商品交期: 2-3个工作天内

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简介目录

2024 年全球人工智慧即服务市场价值为 127 亿美元，预计 2025 年至 2034 年期间的复合年增长率为 30.6%。随着人工智慧成为数位转型的基石，各行各业的企业都在迅速采用 AIaaS 解决方案，以保持竞争力和敏捷性。对自动化、数据驱动决策和增强客户体验的不断增长的需求正在推动 AIaaS 平台的广泛采用。这些解决方案使公司无需建造昂贵的内部基础设施即可存取先进的 AI 工具，从而使 AI 整合更加可行且可扩展。

人工智慧即服务 (AIaaS) 市场 - IMG1

AIaaS 平台正在透过帮助企业简化流程、优化营运和减少重复性任务中的人为干预来彻底改变产业。随着人工智慧能力的进步，越来越多的公司利用 AIaaS 开发创新应用程式、个人化客户互动并获得即时洞察以便做出更好的决策。企业越来越多地寻求能够提供灵活、经济高效的模型的 AIaaS 供应商，以解决越来越多的用例——从智慧聊天机器人和诈欺检测到预测分析和供应链优化。对数位化的日益重视和持续创新的需求，使得 AIaaS 成为业务成长和卓越营运的关键推动因素，吸引了大型企业和中小型企业。

市场范围
起始年份 2024
预测年份 2025-2034
起始值 127亿美元
预测值 1787亿美元
复合年增长率 30.6%

推动 AIaaS 市场成长的主要因素是各个垂直产业对自动化的需求不断增长。 AIaaS 为组织提供了提高生产力、优化客户服务、高效管理大量资料和降低营运成本的基本工具。随着企业寻求更聪明的方法来提高效能和简化运营，对 AIaaS 平台的需求持续激增。工业自动化和控制系统市场的不断扩大进一步加速了人工智慧驱动技术的采用。医疗保健、零售、金融、製造和物流等行业的公司正在采用 AIaaS 来自动化客户支援、资料输入、库存管理等关键功能，从而提高效率并推动创新。

AIaaS 市场按技术细分为机器学习 (ML)、自然语言处理 (NLP)、电脑视觉等。其中，机器学习领域占据主导地位，占 40%，到 2024 年将创造 50 亿美元的产值。机器学习是众多人工智慧应用的支柱，包括推荐引擎、诈欺侦测系统、预测分析和流程自动化。随着企业寻求更深入的洞察力并实现决策自动化，ML 跨多个行业的适应性使其成为 AIaaS 产品不可或缺的一部分。

就产品而言，市场分为基础设施即服务、平台即服务和软体即服务 (SaaS)。 2024 年，SaaS 领域占据 46% 的市场份额，为企业提供基于订阅的强大 AI 工具存取权限，无需对内部部署解决方案进行高额的前期投资。 SaaS 模型提供了无与伦比的可扩展性和灵活性，使各种规模的组织更容易采用 AI，从而进一步推动 AIaaS 市场的成长。

2024年，北美占据全球AIaaS市场的34%。美国凭藉其先进的云端运算生态系统脱颖而出，成为AIaaS部署的支柱。美国领先的供应商提供强大、可扩展的云端平台，使企业能够无缝整合 AI 解决方案，降低成本并扩大各行各业对尖端 AI 工具的存取。

目录

第一章：方法论与范围

第二章：执行摘要

第三章：行业洞察

  • 产业生态系统分析
  • 供应商格局
    • AIaaS 供应商
    • 技术整合商和顾问
    • 最终用途
  • 利润率分析
  • 技术与创新格局
  • 专利分析
  • 重要新闻和倡议
  • 监管格局
  • 衝击力
    • 成长动力
      • 人工智慧技术的进步
      • 自动化需求不断成长
      • 提高成本效率和可扩展性
      • 云端采用率不断上升
      • 对个人化客户体验的需求日益增长
    • 产业陷阱与挑战
      • 资料隐私和安全问题
      • 缺乏内部专业知识
  • 成长潜力分析
  • 波特的分析
  • PESTEL分析

第四章：竞争格局

  • 介绍
  • 公司市占率分析
  • 竞争定位矩阵
  • 战略展望矩阵

第五章：市场估计与预测：依技术分类，2021 - 2034 年

  • 主要趋势
  • 机器学习（ML）
  • 电脑视觉
  • 自然语言处理（NLP）
  • 其他的

第六章：市场估计与预测：按云类型，2021 - 2034 年

  • 主要趋势
  • 民众
  • 杂交种
  • 私人的

第七章：市场估计与预测：依组织规模，2021 - 2034 年

  • 主要趋势
  • 中小企业
  • 大型企业

第八章：市场估计与预测：按供应量，2021 - 2034 年

  • 主要趋势
  • 基础设施即服务
  • 平台即服务
  • 软体即服务

第九章：市场估计与预测：依产业垂直，2021 - 2034 年

  • 主要趋势
  • 银行、金融和保险（BFSI）
  • 医疗保健和生命科学
  • 零售
  • 资讯科技和电信
  • 政府和国防
  • 製造业
  • 能源与公用事业
  • 其他的

第十章：市场估计与预测：按地区，2021 - 2034 年

  • 主要趋势
  • 北美洲
    • 我们
    • 加拿大
  • 欧洲
    • 英国
    • 德国
    • 法国
    • 义大利
    • 西班牙
    • 俄罗斯
    • 北欧人
  • 亚太地区
    • 中国
    • 印度
    • 日本
    • 澳洲
    • 韩国
    • 东南亚
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 阿根廷
  • MEA
    • 阿联酋
    • 南非
    • 沙乌地阿拉伯

第 11 章：公司简介

  • Alibaba Cloud
  • Amazon Web Services
  • Baidu Cloud
  • BigML
  • C3.ai
  • Datarobot
  • Fair Isaac
  • Google
  • H2O.ai
  • IBM
  • Intel
  • Iris.ai
  • Meta AI
  • Microsoft
  • NVIDIA
  • Oracle
  • Salesforce
  • SAP
  • Siemens
  • Palantir
  • Yellow.ai
简介目录
Product Code: 5714

The Global Artificial Intelligence as a Service Market was valued at USD 12.7 billion in 2024 and is expected to grow at a CAGR of 30.6% between 2025 and 2034. As AI becomes a cornerstone of digital transformation, businesses across industries are rapidly embracing AIaaS solutions to remain competitive and agile. The rising demand for automation, data-driven decision-making, and enhanced customer experiences is fueling the widespread adoption of AIaaS platforms. These solutions allow companies to access advanced AI tools without building expensive in-house infrastructure, making AI integration more feasible and scalable.

Artificial Intelligence as a Service (AIaaS) Market - IMG1

AIaaS platforms are revolutionizing industries by helping businesses streamline processes, optimize operations, and reduce human intervention in repetitive tasks. As AI capabilities advance, more companies are leveraging AIaaS to develop innovative applications, personalize customer interactions, and gain real-time insights for better decision-making. Enterprises are increasingly seeking AIaaS providers that offer flexible, cost-efficient models to address a growing range of use cases-from intelligent chatbots and fraud detection to predictive analytics and supply chain optimization. The growing emphasis on digitalization and the need for continuous innovation have positioned AIaaS as a critical enabler of business growth and operational excellence, attracting both large enterprises and small to mid-sized businesses.

Market Scope
Start Year2024
Forecast Year2025-2034
Start Value$12.7 Billion
Forecast Value$178.7 Billion
CAGR30.6%

The primary factor driving the AIaaS market's growth is the rising demand for automation across various industry verticals. AIaaS gives organizations the essential tools to enhance productivity, optimize customer service, manage large data volumes efficiently, and reduce operational costs. As businesses look for smarter ways to improve performance and streamline operations, the demand for AIaaS platforms continues to surge. The expanding market for industrial automation and control systems is further accelerating the adoption of AI-driven technologies. Companies across sectors such as healthcare, retail, finance, manufacturing, and logistics are embracing AIaaS to automate key functions like customer support, data entry, inventory management, and more, thereby improving efficiency and driving innovation.

The AIaaS market is segmented by technology into machine learning (ML), natural language processing (NLP), computer vision, and others. Among these, the machine learning segment dominated with a 40% share, generating USD 5 billion in 2024. ML forms the backbone of numerous AI applications, including recommendation engines, fraud detection systems, predictive analytics, and process automation. Its adaptability across multiple industries makes ML an indispensable part of AIaaS offerings, as companies seek to unlock deeper insights and automate decision-making.

In terms of offerings, the market is divided into infrastructure as a service, platform as a service, and software as a service (SaaS). The SaaS segment led with a 46% market share in 2024, providing businesses with subscription-based access to powerful AI tools without the high upfront investment of on-premises solutions. SaaS models offer unparalleled scalability and flexibility, making AI adoption more accessible for organizations of all sizes, further propelling AIaaS market growth.

North America held a 34% share of the global AIaaS market in 2024. The U.S. stands out with its advanced cloud computing ecosystem, which serves as a backbone for AIaaS deployment. Leading U.S.-based providers offer robust, scalable cloud platforms that enable businesses to seamlessly integrate AI solutions, driving down costs and expanding access to cutting-edge AI tools across industries.

Table of Contents

Chapter 1 Methodology & Scope

  • 1.1 Research design
    • 1.1.1 Research approach
    • 1.1.2 Data collection methods
  • 1.2 Base estimates & calculations
    • 1.2.1 Base year calculation
    • 1.2.2 Key trends for market estimation
  • 1.3 Forecast model
  • 1.4 Primary research and validation
    • 1.4.1 Primary sources
    • 1.4.2 Data mining sources
  • 1.5 Market scope & definition

Chapter 2 Executive Summary

  • 2.1 Industry 3600 synopsis, 2021 - 2034

Chapter 3 Industry Insights

  • 3.1 Industry ecosystem analysis
  • 3.2 Supplier landscape
    • 3.2.1 AIaaS Providers
    • 3.2.2 Technology integrators and consultants
    • 3.2.3 End Use
  • 3.3 Profit margin analysis
  • 3.4 Technology & innovation landscape
  • 3.5 Patent analysis
  • 3.6 Key news & initiatives
  • 3.7 Regulatory landscape
  • 3.8 Impact forces
    • 3.8.1 Growth drivers
      • 3.8.1.1 Advancements in AI technologies
      • 3.8.1.2 Increasing demand for automation
      • 3.8.1.3 Improved cost efficiency and scalability
      • 3.8.1.4 Rising cloud adoption
      • 3.8.1.5 Growing need for personalized customer experiences
    • 3.8.2 Industry pitfalls & challenges
      • 3.8.2.1 Data privacy and security concerns
      • 3.8.2.2 Lack of in-house expertise
  • 3.9 Growth potential analysis
  • 3.10 Porter's analysis
  • 3.11 PESTEL analysis

Chapter 4 Competitive Landscape, 2024

  • 4.1 Introduction
  • 4.2 Company market share analysis
  • 4.3 Competitive positioning matrix
  • 4.4 Strategic outlook matrix

Chapter 5 Market Estimates & Forecast, By Technology, 2021 - 2034 ($Bn)

  • 5.1 Key trends
  • 5.2 Machine Learning (ML)
  • 5.3 Computer vision
  • 5.4 Natural Language Processing (NLP)
  • 5.5 Others

Chapter 6 Market Estimates & Forecast, By Cloud Type, 2021 - 2034 ($Bn)

  • 6.1 Key trends
  • 6.2 Public
  • 6.3 Hybrid
  • 6.4 Private

Chapter 7 Market Estimates & Forecast, By Organization Size, 2021 - 2034 ($Bn)

  • 7.1 Key trends
  • 7.2 SME
  • 7.3 Large enterprise

Chapter 8 Market Estimates & Forecast, By Offering, 2021 - 2034 ($Bn)

  • 8.1 Key trends
  • 8.2 Infrastructure as a service
  • 8.3 Platform as a service
  • 8.4 Software as a service

Chapter 9 Market Estimates & Forecast, By Industry Vertical, 2021 - 2034 ($Bn)

  • 9.1 Key trends
  • 9.2 Banking, Financial, and Insurance (BFSI)
  • 9.3 Healthcare and Life Sciences
  • 9.4 Retail
  • 9.5 IT & Telecommunication
  • 9.6 Government and defense
  • 9.7 Manufacturing
  • 9.8 Energy & Utility
  • 9.9 Others

Chapter 10 Market Estimates & Forecast, By Region, 2021 - 2034 ($Bn)

  • 10.1 Key trends
  • 10.2 North America
    • 10.2.1 U.S.
    • 10.2.2 Canada
  • 10.3 Europe
    • 10.3.1 UK
    • 10.3.2 Germany
    • 10.3.3 France
    • 10.3.4 Italy
    • 10.3.5 Spain
    • 10.3.6 Russia
    • 10.3.7 Nordics
  • 10.4 Asia Pacific
    • 10.4.1 China
    • 10.4.2 India
    • 10.4.3 Japan
    • 10.4.4 Australia
    • 10.4.5 South Korea
    • 10.4.6 Southeast Asia
  • 10.5 Latin America
    • 10.5.1 Brazil
    • 10.5.2 Mexico
    • 10.5.3 Argentina
  • 10.6 MEA
    • 10.6.1 UAE
    • 10.6.2 South Africa
    • 10.6.3 Saudi Arabia

Chapter 11 Company Profiles

  • 11.1 Alibaba Cloud
  • 11.2 Amazon Web Services
  • 11.3 Baidu Cloud
  • 11.4 BigML
  • 11.5 C3.ai
  • 11.6 Datarobot
  • 11.7 Fair Isaac
  • 11.8 Google
  • 11.9 H2O.ai
  • 11.10 IBM
  • 11.11 Intel
  • 11.12 Iris.ai
  • 11.13 Meta AI
  • 11.14 Microsoft
  • 11.15 NVIDIA
  • 11.16 Oracle
  • 11.17 Salesforce
  • 11.18 SAP
  • 11.19 Siemens
  • 11.20 Palantir
  • 11.21 Yellow.ai