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市场调查报告书
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1698334

企业生成人工智慧市场机会、成长动力、产业趋势分析及 2025-2034 年预测

Enterprise Generative AI Market Opportunity, Growth Drivers, Industry Trend Analysis, and Forecast 2025-2034
出版日期: | 出版商: Global Market Insights Inc. | 英文 175 Pages | 商品交期: 2-3个工作天内

价格
简介目录

2024 年全球企业生成式人工智慧市场价值为 41 亿美元，预计 2025 年至 2034 年期间的复合年增长率将达到 33.2%。企业越来越多地利用生成式人工智能进行内容生成、客户参与、软件开发和财务分析。透过自动执行重复性任务，人工智慧可以提高效率、降低营运成本并优化生产力。自动化工具可以产生商业信函、行销材料、法律文件甚至软体应用程序，从而简化各行业的工作流程。报告显示，近四分之三的企业已经将人工智慧驱动的自动化融入营运中，其中超过一半的企业计划在 2026 年之前进一步投资。

企业生成式人工智慧市场 - IMG1

各组织也正在部署人工智慧来监控网路安全威胁、侦测诈欺并降低安全风险。人工智慧系统分析交易、网路流量和用户行为以识别可疑活动。金融、电子商务和网路安全领域正在利用深度学习演算法来加强安全框架。该公司正在积极开发由人工智慧驱动的解决方案来增强网路安全防御，并结合检测深度伪造和保护敏感资料的工具。

市场范围
起始年份 2024
预测年份 2025-2034
起始值 41亿美元
预测值 674亿美元
复合年增长率 33.2％

就组件而言，市场分为软体和服务。 2024 年，软体领域占据了超过 65% 的市场份额，预计到 2034 年将超过 350 亿美元。行业特定的人工智慧应用在金融、医疗保健和零售领域越来越受欢迎，可提供客製化的见解并支援复杂的决策。金融领域正在采用人工智慧驱动的合规解决方案来实现报告自动化，确保遵守法规并降低风险。企业越来越多地寻求可自订的人工智慧软体，促使供应商增强 API、引入无程式码平台并开发需要最低限度技术专长的自学习演算法。这些创新正在加速人工智慧在各个领域的应用。

CRM 和 ERP 系统等企业工具正在整合生成式 AI 来提取见解并实现关键流程的自动化。人工智慧分析平台透过产生报告、追踪趋势和提供预测分析来促进数据驱动的决策。金融机构利用人工智慧进行即时交易监控、诈欺侦测和风险评估。人工智慧模型评估信用度、产生合规报告并识别洗钱活动，使金融流程更加安全、有效率。人工智慧虚拟助理和聊天机器人可以管理客户互动、提供财务指导并自动化索赔处理，从而提高客户服务和营运效率。银行和保险公司利用人工智慧进行监管报告和审计，提高准确性，同时最大限度地减少人工工作。

根据应用，市场分为内容创作、产品开发、客户支援、行销和供应链管理。内容创作领域引领市场，2024 年创造的价值超过 10 亿美元。该公司正在使用人工智慧製作有针对性的行销文案、SEO 优化文章和数位内容，从而大幅降低成本并提高产量。人工智慧工具使企业能够快速产生用于广告和电子商务的高品质视觉和书面内容。人工智慧驱动的平台支援企业管理内容策略、自动化工作流程和增强数位参与度。

在部署方面，市场分为内部部署和基于云端的解决方案。 2024 年，云端运算领域占据了近 70% 的市场。企业青睐基于云端的人工智慧，因为它具有灵活性、成本效益和可扩展性。云端託管的 AI 模型可无缝存取高级 AI 功能，而无需大量的现场基础设施​​。企业利用基于云端的人工智慧工具来自动产生报告、执行商业智慧任务并简化协作。人工智慧驱动的系统处理大量结构化和非结构化资料，优化企业营运。

零售、金融和医疗保健领域的组织正在整合人工智慧聊天机器人和虚拟助手，以实现个人化客户互动。

北美仍然是人工智慧的领先采用者，美国企业利用人工智慧进行风险评估、自动索赔处理和预测分析。金融和医疗保健行业正在采用人工智慧来检测诈欺行为、简化合规性并加强患者护理。领先的云端供应商正在提供 AI 即服务解决方案，使各种规模的企业都可以使用生成性 AI。对人工智慧新创企业的投资持续推动创新，加速各行各业对生成式人工智慧的应用。

目录

第一章：方法论与范围

  • 研究设计
    • 研究方法
    • 资料收集方法
  • 基础估算与计算
    • 基准年计算
    • 市场评估的主要趋势
  • 预测模型
  • 初步研究和验证
    • 主要来源
    • 资料探勘来源
  • 市场范围和定义

第二章：执行摘要

第三章：行业洞察

  • 产业生态系统分析
    • 人工智慧软体供应商
    • 服务提供者
    • 数据提供者
    • 系统整合商
    • 最终用途
  • 供应商格局
  • 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
  • 产生人工智慧对劳动力和工作角色的影响
  • 案例研究
  • 衝击力
    • 成长动力
      • 越来越多的企业采用人工智慧驱动的自动化
      • 人工智慧内容创作的进步
      • 对人工智慧驱动的客户支援和虚拟助理的需求不断增长
      • 生成式人工智慧与云端运算的日益融合
    • 产业陷阱与挑战
      • 人工智慧生成内容面临的道德和监管挑战
      • 高运算成本和基础设施需求
  • 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：按组件，2021 - 2034 年

  • 主要趋势
  • 软体
  • 服务

第六章：市场估计与预测：依部署模型，2021 - 2034 年

  • 主要趋势
  • 本地
  • 云

第七章：市场估计与预测：依模型，2021 - 2034 年

  • 主要趋势
  • 文字
  • 影像
  • 声音的
  • 程式码

第八章：市场估计与预测：依技术分类，2021 - 2034 年

  • 主要趋势
  • 生成对抗网路 (GAN)
  • 变形金刚模型
  • 变分自编码器
  • 扩散模型
  • 其他的

第九章：市场估计与预测：按应用，2021 - 2034

  • 主要趋势
  • 内容创作
  • 产品设计与开发
  • 客户服务与支援
  • 行销与个人化
  • 供应链管理
  • 其他的

第十章：市场估计与预测：依最终用途，2021 - 2034 年

  • 主要趋势
  • 卫生保健
  • 零售与电子商务
  • 製造业
  • 金融服务业
  • 媒体和娱乐
  • 汽车
  • IT与电信
  • 其他的

第 11 章：市场估计与预测：按地区，2021 年至 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
  • 中东及非洲
    • 阿联酋
    • 南非
    • 沙乌地阿拉伯

第十二章：公司简介

  • Accenture
  • Adobe
  • AWS
  • Baidu
  • C3.ai
  • DeepMind Technologies
  • Google
  • H20.ai
  • IBM
  • Intel
  • Jasper.ai
  • Microsoft
  • Nvidia
  • NVIDIA
  • OpenAI
  • Oracle
  • Qualcomm
  • Salesforce
  • SAP
  • UiPath
简介目录
Product Code: 13161

The Global Enterprise Generative AI Market was valued at USD 4.1 billion in 2024 and is projected to expand at a CAGR of 33.2% between 2025 and 2034. Businesses are increasingly leveraging generative AI for content generation, customer engagement, software development, and financial analysis. By automating repetitive tasks, AI enhances efficiency, reduces operational costs, and optimizes productivity. Automated tools generate business correspondence, marketing materials, legal documents, and even software applications, streamlining workflows across industries. Reports indicate that nearly three-quarters of enterprises have already integrated AI-driven automation into their operations, with more than half planning further investments by 2026.

Enterprise Generative AI Market - IMG1

Organizations are also deploying AI to monitor cybersecurity threats, detect fraud, and mitigate security risks. AI-powered systems analyze transactions, network traffic, and user behavior to identify suspicious activities. The financial, e-commerce, and cybersecurity sectors are utilizing deep learning algorithms to fortify security frameworks. Companies are actively developing AI-driven solutions to enhance cybersecurity defenses, incorporating tools that detect deepfakes and secure sensitive data.

Market Scope
Start Year2024
Forecast Year2025-2034
Start Value$4.1 Billion
Forecast Value$67.4 Billion
CAGR33.2%

In terms of components, the market is categorized into software and services. The software segment accounted for over 65% of the market in 2024 and is anticipated to surpass USD 35 billion by 2034. Industry-specific AI applications are gaining traction in finance, healthcare, and retail, offering tailored insights and enabling complex decision-making. AI-driven compliance solutions are being adopted in the financial sector to automate reporting, ensuring regulatory adherence while reducing risks. Businesses are increasingly seeking customizable AI software, prompting providers to enhance APIs, introduce no-code platforms, and develop self-learning algorithms that require minimal technical expertise. These innovations are accelerating AI adoption across diverse sectors.

Enterprise tools such as CRM and ERP systems are integrating generative AI to extract insights and automate key processes. AI analytics platforms facilitate data-driven decision-making by generating reports, tracking trends, and offering predictive analytics. Financial institutions leverage AI for real-time transaction monitoring, fraud detection, and risk assessment. AI models assess creditworthiness, generate compliance reports, and identify money laundering activities, making financial processes more secure and efficient. AI-powered virtual assistants and chatbots manage client interactions, provide financial guidance, and automate claims processing, improving both customer service and operational efficiency. Banks and insurance firms utilize AI for regulatory reporting and audits, enhancing accuracy while minimizing manual effort.

By application, the market is segmented into content creation, product development, customer support, marketing, and supply chain management. The content creation segment led the market, generating over USD 1 billion in 2024. Companies are using AI to produce targeted marketing copy, SEO-optimized articles, and digital content, significantly cutting costs while increasing output. AI-powered tools enable businesses to rapidly generate high-quality visual and written content for advertising and e-commerce. AI-driven platforms support enterprises in managing content strategies, automating workflows, and enhancing digital engagement.

In terms of deployment, the market is divided into on-premises and cloud-based solutions. The cloud segment accounted for nearly 70% of the market in 2024. Companies prefer cloud-based AI due to its flexibility, cost-effectiveness, and scalability. Cloud-hosted AI models provide seamless access to advanced AI capabilities without requiring extensive on-site infrastructure. Businesses utilize cloud-based AI tools for automating reports, executing business intelligence tasks, and streamlining collaboration. AI-driven systems process vast amounts of structured and unstructured data, optimizing enterprise operations.

Organizations across retail, finance, and healthcare are integrating AI-powered chatbots and virtual assistants to personalize customer interactions.

North America remains a leading adopter, with businesses in the United States leveraging AI for risk assessment, automated claims processing, and predictive analytics. The financial and healthcare sectors are incorporating AI to detect fraud, streamline compliance, and enhance patient care. Leading cloud providers are offering AI-as-a-Service solutions, making generative AI accessible to enterprises of all sizes. Investments in AI startups continue to drive innovation, accelerating the adoption of generative AI 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 synopsis, 2021 - 2034

Chapter 3 Industry Insights

  • 3.1 Industry ecosystem analysis
    • 3.1.1 AI software providers
    • 3.1.2 Service providers
    • 3.1.3 Data providers
    • 3.1.4 System integrators
    • 3.1.5 End use
  • 3.2 Supplier landscape
  • 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 of generative AI on workforce and job roles
  • 3.9 Case studies
  • 3.10 Impact forces
    • 3.10.1 Growth drivers
      • 3.10.1.1 Increasing enterprise adoption of AI-driven automation
      • 3.10.1.2 Advancements in AI-powered content creation
      • 3.10.1.3 Rising demand for AI-driven customer support and virtual assistants
      • 3.10.1.4 Growing integration of generative AI with cloud computing
    • 3.10.2 Industry pitfalls & challenges
      • 3.10.2.1 Ethical and regulatory challenges surrounding AI-generated content
      • 3.10.2.2 High computational costs and infrastructure requirements
  • 3.11 Growth potential analysis
  • 3.12 Porter's analysis
  • 3.13 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 Component, 2021 - 2034 ($Bn)

  • 5.1 Key trends
  • 5.2 Software
  • 5.3 Services

Chapter 6 Market Estimates & Forecast, By Deployment Model, 2021 - 2034 ($Bn)

  • 6.1 Key trends
  • 6.2 On-premises
  • 6.3 Cloud

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

  • 7.1 Key trends
  • 7.2 Text
  • 7.3 Image
  • 7.4 Audio
  • 7.5 Code

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

  • 8.1 Key trends
  • 8.2 Generative Adversarial Networks (GANs)
  • 8.3 Transformers model
  • 8.4 Variational auto-encoders
  • 8.5 Diffusion models
  • 8.6 Others

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

  • 9.1 Key trends
  • 9.2 Content creation
  • 9.3 Product design & development
  • 9.4 Customer service & support
  • 9.5 Marketing & personalization
  • 9.6 Supply chain management
  • 9.7 Others

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

  • 10.1 Key trends
  • 10.2 Healthcare
  • 10.3 Retail and e-commerce
  • 10.4 Manufacturing
  • 10.5 BFSI
  • 10.6 Media and entertainment
  • 10.7 Automotive
  • 10.8 IT & telecom
  • 10.9 Others

Chapter 11 Market Estimates & Forecast, By Region, 2021 - 2034 ($Bn, Units)

  • 11.1 Key trends
  • 11.2 North America
    • 11.2.1 U.S.
    • 11.2.2 Canada
  • 11.3 Europe
    • 11.3.1 UK
    • 11.3.2 Germany
    • 11.3.3 France
    • 11.3.4 Italy
    • 11.3.5 Spain
    • 11.3.6 Russia
    • 11.3.7 Nordics
  • 11.4 Asia Pacific
    • 11.4.1 China
    • 11.4.2 India
    • 11.4.3 Japan
    • 11.4.4 Australia
    • 11.4.5 South Korea
    • 11.4.6 Southeast Asia
  • 11.5 Latin America
    • 11.5.1 Brazil
    • 11.5.2 Mexico
    • 11.5.3 Argentina
  • 11.6 MEA
    • 11.6.1 UAE
    • 11.6.2 South Africa
    • 11.6.3 Saudi Arabia

Chapter 12 Company Profiles

  • 12.1 Accenture
  • 12.2 Adobe
  • 12.3 AWS
  • 12.4 Baidu
  • 12.5 C3.ai
  • 12.6 DeepMind Technologies
  • 12.7 Google
  • 12.8 H20.ai
  • 12.9 IBM
  • 12.10 Intel
  • 12.11 Jasper.ai
  • 12.12 Microsoft
  • 12.13 Nvidia
  • 12.14 NVIDIA
  • 12.15 OpenAI
  • 12.16 Oracle
  • 12.17 Qualcomm
  • 12.18 Salesforce
  • 12.19 SAP
  • 12.20 UiPath