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市场调查报告书
商品编码
1797786

人工智慧代理市场机会、成长动力、产业趋势分析及 2025 - 2034 年预测

AI Agents 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，全球人工智慧代理市场规模达59亿美元，预计2034年将以38.5%的复合年增长率成长，达到1,056亿美元。这一爆炸式增长反映了市场对能够自主处理任务、以自然语言互动并跨复杂数位生态系统扩展的智慧数位解决方案日益增长的需求。随着企业逐渐意识到人工智慧代理不仅仅是技术工具，其培训和部署已成为一项策略重点。如今，企业正转向将这些平台与更广泛的组织目标结合，确保员工和系统能够有效地利用这些代理商。基础模型、自然语言理解和人工智慧编排领域的快速创新，正在将代理平台转变为跨行业的关键基础设施。

人工智慧代理市场 - IMG1

曾经的技术专业化如今已成为组织的当务之急。企业正在从一次性的智慧代理实施转向持续学习的环境，这种环境优先考虑性能、适应性和创造性的问题解决能力。随着人工智慧技术的成熟，成功越来越依赖跨职能协作。 IT、营运、人力资源和客户体验团队之间的整合对于最大化人工智慧智慧代理的价值至关重要。培训计画正在全球扩展，重点是提供实践操作、场景驱动的学习。这些措施支持不同职位的技能提升，并帮助组织为长期采用人工智慧做好准备。

市场范围
起始年份 2024
预测年份 2025-2034
起始值 59亿美元
预测值 1056亿美元
复合年增长率 38.5%

根据代理类型，市场可分为对话代理、自主代理、具身人工智慧代理、多代理系统和任务执行代理。其中，对话代理商占最大市场份额，2024 年约为 44%，预计到 2034 年将以超过 41% 的复合年增长率成长。这些旨在模拟人类对话的代理正广泛应用于客户支援、员工入职和知识管理等各个领域。企业青睐它们，因为它们能够透过上下文理解和意图识别来处理大量查询。目前已有结构化模组可供使用，透过持续的学习週期来增强对话流程、情绪侦测和使用者参与度。

人工智慧代理市场按技术细分，可分为自然语言处理 (NLP)、机器学习 (ML) 和深度学习、强化学习 (RL)、电脑视觉、语音识别与生成以及大型语言模型 (LLM)。其中，NLP 市场在 2024 年将占据 38% 的市场份额，预计 2025 年至 2034 年的复合年增长率将超过 43%。 NLP 的成长源自于人工智慧系统需要理解、处理并回应多种语言和方言的人类语言。金融、医疗、教育和零售等行业正越来越多地采用 NLP 的功能来增强人机互动、从非结构化文字中提取含义以及自动化文件处理流程。

就部署模式而言，市场细分为基于云端、本地部署和边缘运算整合。云端部署占据主导地位并持续成长，这得益于对可扩展且灵活的解决方案的需求，这些解决方案能够适应不断变化的业务需求。这种模式使企业能够跨地区、跨部门和跨监管环境快速部署AI代理。它支援集中控制、快速更新以及与现有企业系统的无缝整合。云端基础设施还支援持续训练和代理监控，帮助团队更有效率地协作并更快地进行创新。

从地理分布来看，美国在2024年占据北美人工智慧代理市场最高份额，贡献了约77%的市场份额，创造了约22亿美元的收入。凭藉其强大的先进云端基础设施、广泛的企业人工智慧整合以及创新驱动的生态系统，美国已成为该领域的全球领导者。美国庞大且多样化的用户群体积极利用人工智慧代理，从智慧通讯到自动化运营，再到数据驱动的决策，无所不包。

塑造 AI 代理商格局的领先公司包括微软、OpenAI、Google、Anthropic、UiPath、IBM（Watson）、NVIDIA、亚马逊、Meta 和 Automation Anywhere。这些公司正在大力投资平台开发、用户培训和部署技术，以满足不断变化的业务需求。他们专注于研究和产品创新，并不断突破 AI 代理商在实际企业环境中的极限。

目录

第一章：方法论

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

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
    • 利润率分析
    • 成本结构
    • 每个阶段的增值
    • 影响价值链的因素
    • 中断
  • 产业衝击力
    • 成长动力
      • 客户服务自动化需求不断成长
      • 自然语言处理 (NLP) 和大型语言模型的进步
      • 云端运算和人工智慧即服务的采用日益增多
      • 与新兴科技的融合
      • 监管支持和数位转型倡议
    • 产业陷阱与挑战
      • 缺乏上下文理解和准确性
      • 初期实施成本高
    • 市场机会
      • 无代码代理建构器培训
      • 企业代理商治理模组
      • 与边缘和物联网设备的集成
      • 具身和实体人工智慧代理的进步
  • 成长潜力分析
  • 监管格局
    • 北美洲
    • 欧洲
    • 亚太地区
    • 拉丁美洲
    • 中东和非洲
  • 波特的分析
  • PESTEL分析
  • 科技与创新格局
    • 代理AI架构的演变
    • 大型语言模型集成
    • 自主决策能力
  • 专利分析
  • 永续性和环境方面
    • 永续实践
    • 减少废弃物的策略
    • 生产中的能源效率
    • 环保倡议
    • 碳足迹考量
  • 用例
  • 最佳情况

第四章：竞争格局

  • 介绍
  • 公司市占率分析
    • 北美洲
    • 欧洲
    • 亚太地区
    • 拉丁美洲
    • MEA
  • 主要市场参与者的竞争分析
  • 竞争定位矩阵
  • 战略展望矩阵
  • 关键进展
    • 併购
    • 伙伴关係与合作
    • 新产品发布
    • 扩张计划和资金

第五章：市场估计与预测：依代理商划分，2021 年至 2034 年

  • 主要趋势
  • 对话代理
  • 自主代理
  • 具身人工智慧代理
  • 多智能体系统
  • 任务执行代理

第六章：市场估计与预测：依技术分类，2021 - 2034 年

  • 主要趋势
  • 自然语言处理（NLP）
  • 机器学习 (ML) 与深度学习
  • 强化学习（RL）
  • 电脑视觉
  • 语音辨识与生成
  • 大型语言模型（LLM）

第七章：市场估计与预测：依部署模式，2021 - 2034 年

  • 主要趋势
  • 基于云端
  • 本地
  • 边缘运算集成

第八章：市场估计与预测：按应用，2021 - 2034 年

  • 主要趋势
  • 客户服务自动化
  • 流程自动化
  • 私人助理
  • 卫生保健
  • 教育与电子学习
  • 金融
  • 电子商务与零售
  • 媒体与娱乐
  • 网路安全
  • 自动驾驶汽车和机器人

第九章：市场估计与预测：依最终用途，2021 - 2034 年

  • 主要趋势
  • 医疗保健和生命科学
  • 银行、金融服务和保险（BFSI）
  • 零售和消费品
  • 製造业和汽车业
  • 科技与电信
  • 政府和公共部门
  • 教育与研究
  • 媒体与娱乐

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
    • 韩国
    • 澳新银行
    • 菲律宾
    • 越南
    • 印尼
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 阿根廷
  • MEA
    • 阿联酋
    • 沙乌地阿拉伯
    • 南非

第 11 章：公司简介

  • Adept AI
  • Amazon
  • Anthropic
  • Apple
  • Automation Anywhere
  • Baidu
  • Character.ai
  • Cognigy
  • Google
  • Hugging Face
  • IBM (Watson)
  • Inflection AI
  • Meta
  • Microsoft
  • NVIDIA
  • OpenAI
  • Replika
  • Runway
  • UiPath
  • xAI
简介目录
Product Code: 14416

The Global AI Agents Market was valued at USD 5.9 billion in 2024 and is estimated to grow at a CAGR of 38.5% to reach USD 105.6 billion by 2034. This explosive growth reflects the rising demand for intelligent digital solutions that can handle tasks autonomously, interact in natural language, and scale across complex digital ecosystems. As enterprises recognize AI agents as more than just technical tools, their training and deployment have evolved into a strategic priority. There is now a shift toward aligning these platforms with broader organizational goals, ensuring that employees and systems can leverage these agents effectively. Rapid innovations in foundational models, natural language understanding, and AI orchestration are turning agent platforms into critical infrastructure across industries.

AI Agents Market - IMG1

What used to be a technical specialization is now becoming an organizational imperative. Companies are moving from one-time agent implementation to continuous learning environments that prioritize performance, adaptability, and creative problem-solving. As AI technologies mature, success increasingly depends on cross-functional collaboration. Integration across IT, operations, HR, and customer experience teams is essential to maximize the value of AI agents. Training programs are expanding globally, with a focus on providing hands-on, scenario-driven learning. These initiatives support upskilling across different job roles and help prepare organizations for long-term AI adoption.

Market Scope
Start Year2024
Forecast Year2025-2034
Start Value$5.9 Billion
Forecast Value$105.6 Billion
CAGR38.5%

By agent type, the market is categorized into conversational agent, autonomous agent, embodied AI agent, multi-agent systems, and task execution agent. Among these, conversational agents held the largest market share at around 44% in 2024 and are projected to grow at a CAGR of over 41% through 2034. These agents, designed to simulate human conversation, are being widely used across sectors for functions like customer support, employee onboarding, and knowledge management. Organizations prefer them for their ability to handle large volumes of queries with contextual understanding and intent recognition. Structured modules are now available to enhance dialogue flow, sentiment detection, and user engagement through continuous learning cycles.

The AI agents market, based on technology, is segmented into natural language processing (NLP), machine learning (ML) and deep learning, reinforcement learning (RL), computer vision, speech recognition and generation, and large language models (LLMs). Among these, NLP leads with a 38% share in 2024 and is expected to expand at a CAGR of over 43% from 2025 to 2034. NLP's growth is driven by the need for AI systems to understand, process, and respond to human language across multiple languages and dialects. Its capabilities are increasingly being adopted in sectors such as finance, healthcare, education, and retail to enhance human-machine interactions, extract meaning from unstructured text, and automate documentation processes.

In terms of deployment mode, the market is segmented into cloud-based, on-premises, and edge computing integration. Cloud-based deployment dominates and continues to grow, driven by the need for scalable and flexible solutions that can adapt to changing business requirements. This model enables businesses to deploy AI agents across regions, departments, and regulatory environments quickly. It allows centralized control, rapid updates, and seamless integration with existing enterprise systems. Cloud infrastructure also supports continuous training and agent monitoring, helping teams collaborate more efficiently and innovate faster.

Geographically, the United States accounted for the highest share in the North American AI agents market in 2024, contributing around 77% and generating approximately USD 2.2 billion in revenue. The strong presence of advanced cloud infrastructure, widespread enterprise AI integration, and an innovation-driven ecosystem have made the US a global leader in this space. The country's large and diverse user base actively utilizes AI-powered agents for everything from intelligent communication to automated operations and data-driven decision-making.

Leading companies shaping the AI agents landscape include Microsoft, OpenAI, Google, Anthropic, UiPath, IBM (Watson), NVIDIA, Amazon, Meta, and Automation Anywhere. These players are investing heavily in platform development, user training, and deployment technologies to meet evolving business demands. Their focus on research and product innovation continues to push the boundaries of what AI agents can do in real-world enterprise settings.

Table of Contents

Chapter 1 Methodology

  • 1.1 Market scope and definition
  • 1.2 Research design
    • 1.2.1 Research approach
    • 1.2.2 Data collection methods
  • 1.3 Data mining sources
    • 1.3.1 Global
    • 1.3.2 Regional/Country
  • 1.4 Base estimates and calculations
    • 1.4.1 Base year calculation
    • 1.4.2 Key trends for market estimation
  • 1.5 Primary research and validation
    • 1.5.1 Primary sources
  • 1.6 Forecast model
  • 1.7 Research assumptions and limitations

Chapter 2 Executive Summary

  • 2.1 Industry 3600 synopsis, 2021 - 2034
  • 2.2 Key market trends
    • 2.2.1 Regional
    • 2.2.2 Agents
    • 2.2.3 Technology
    • 2.2.4 Deployment Mode
    • 2.2.5 Application
    • 2.2.6 End Use
  • 2.3 TAM Analysis, 2025-2034
  • 2.4 CXO perspectives: Strategic imperatives
    • 2.4.1 Executive decision points
    • 2.4.2 Critical success factors
  • 2.5 Future outlook and strategic recommendations

Chapter 3 Industry Insights

  • 3.1 Industry ecosystem analysis
    • 3.1.1 Supplier landscape
    • 3.1.2 Profit margin analysis
    • 3.1.3 Cost structure
    • 3.1.4 Value addition at each stage
    • 3.1.5 Factor affecting the value chain
    • 3.1.6 Disruptions
  • 3.2 Industry impact forces
    • 3.2.1 Growth drivers
      • 3.2.1.1 Increasing demand for automation in customer service
      • 3.2.1.2 Advancements in natural language processing (NLP) and large language models
      • 3.2.1.3 Growing adoption of cloud computing and AI-as-a-service
      • 3.2.1.4 Integration with emerging technologies
      • 3.2.1.5 Regulatory support and digital transformation initiatives
    • 3.2.2 Industry pitfalls and challenges
      • 3.2.2.1 Lack of contextual understanding and accuracy
      • 3.2.2.2 High initial implementation costs
    • 3.2.3 Market opportunities
      • 3.2.3.1 No-code agent builder training
      • 3.2.3.2 Enterprise agent governance modules
      • 3.2.3.3 Integration with edge and IoT devices
      • 3.2.3.4 Advancement of embodied and physical AI agents
  • 3.3 Growth potential analysis
  • 3.4 Regulatory landscape
    • 3.4.1 North America
    • 3.4.2 Europe
    • 3.4.3 Asia Pacific
    • 3.4.4 Latin America
    • 3.4.5 Middle East & Africa
  • 3.5 Porter's analysis
  • 3.6 PESTEL analysis
  • 3.7 Technology and Innovation landscape
    • 3.7.1 Agentic AI architecture evolution
    • 3.7.2 Large language model integration
    • 3.7.3 Autonomous decision-making capabilities
  • 3.8 Patent analysis
  • 3.9 Sustainability and environmental aspects
    • 3.9.1 Sustainable practices
    • 3.9.2 Waste reduction strategies
    • 3.9.3 Energy efficiency in production
    • 3.9.4 Eco-friendly Initiatives
    • 3.9.5 Carbon footprint considerations
  • 3.10 Use cases
  • 3.11 Best-case scenario

Chapter 4 Competitive Landscape, 2024

  • 4.1 Introduction
  • 4.2 Company market share analysis
    • 4.2.1 North America
    • 4.2.2 Europe
    • 4.2.3 Asia Pacific
    • 4.2.4 LATAM
    • 4.2.5 MEA
  • 4.3 Competitive analysis of major market players
  • 4.4 Competitive positioning matrix
  • 4.5 Strategic outlook matrix
  • 4.6 Key developments
    • 4.6.1 Mergers & acquisitions
    • 4.6.2 Partnerships & collaborations
    • 4.6.3 New Product Launches
    • 4.6.4 Expansion Plans and funding

Chapter 5 Market Estimates & Forecast, By Agents, 2021 - 2034 ($Mn)

  • 5.1 Key trends
  • 5.2 Conversational agents
  • 5.3 Autonomous agents
  • 5.4 Embodied AI agents
  • 5.5 Multi-agent systems
  • 5.6 Task execution agents

Chapter 6 Market Estimates & Forecast, By Technology, 2021 - 2034 ($Mn)

  • 6.1 Key trends
  • 6.2 Natural language processing (NLP)
  • 6.3 Machine learning (ML) & deep learning
  • 6.4 Reinforcement learning (RL)
  • 6.5 Computer vision
  • 6.6 Speech recognition & generation
  • 6.7 Large language models (LLMs)

Chapter 7 Market Estimates & Forecast, By Deployment Mode, 2021 - 2034 ($Mn)

  • 7.1 Key trends
  • 7.2 Cloud-based
  • 7.3 On-premises
  • 7.4 Edge computing integration

Chapter 8 Market Estimates & Forecast, By Application, 2021 - 2034 ($Mn)

  • 8.1 Key trends
  • 8.2 Customer Service Automation
  • 8.3 Process automation
  • 8.4 Personal assistants
  • 8.5 Healthcare
  • 8.6 Education & E-learning
  • 8.7 Finance
  • 8.8 E-commerce & retail
  • 8.9 Media & entertainment
  • 8.10 Cybersecurity
  • 8.11 Autonomous vehicles & robotics

Chapter 9 Market Estimates & Forecast, By End Use, 2021 - 2034 ($Mn)

  • 9.1 Key trends
  • 9.2 Healthcare and life sciences
  • 9.3 Banking, financial services, and insurance (BFSI)
  • 9.4 Retail and consumer goods
  • 9.5 Manufacturing and automotive
  • 9.6 Technology and telecommunications
  • 9.7 Government and public sector
  • 9.8 Education and research
  • 9.9 Media and entertainment

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

  • 10.1 Key trends
  • 10.2 North America
    • 10.2.1 US
    • 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 South Korea
    • 10.4.5 ANZ
    • 10.4.6 Philippines
    • 10.4.7 Vietnam
    • 10.4.8 Indonesia
  • 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 Saudi Arabia
    • 10.6.3 South Africa

Chapter 11 Company Profiles

  • 11.1 Adept AI
  • 11.2 Amazon
  • 11.3 Anthropic
  • 11.4 Apple
  • 11.5 Automation Anywhere
  • 11.6 Baidu
  • 11.7 Character.ai
  • 11.8 Cognigy
  • 11.9 Google
  • 11.10 Hugging Face
  • 11.11 IBM (Watson)
  • 11.12 Inflection AI
  • 11.13 Meta
  • 11.14 Microsoft
  • 11.15 NVIDIA
  • 11.16 OpenAI
  • 11.17 Replika
  • 11.18 Runway
  • 11.19 UiPath
  • 11.20 xAI