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1797786

人工智慧代理市場機會、成長動力、產業趨勢分析及 2025 - 2034 年預測

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個工作天內

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簡介目錄

2024年，全球人工智慧代理市場規模達59億美元，預計2034年將以38.5%的複合年成長率成長，達到1,056億美元。這一爆炸式成長反映了市場對能夠自主處理任務、以自然語言互動並跨複雜數位生態系統擴展的智慧數位解決方案日益成長的需求。隨著企業逐漸意識到人工智慧代理不僅僅是技術工具，其培訓和部署已成為一項策略重點。如今，企業正轉向將這些平台與更廣泛的組織目標結合，確保員工和系統能夠有效地利用這些代理商。基礎模型、自然語言理解和人工智慧編排領域的快速創新，正在將代理平台轉變為跨行業的關鍵基礎設施。

人工智慧代理市場 - IMG1

曾經的技術專業化如今已成為組織的當務之急。企業正在從一次性的智慧代理實施轉向持續學習的環境，這種環境優先考慮性能、適應性和創造性的問題解決能力。隨著人工智慧技術的成熟，成功越來越依賴跨職能協作。 IT、營運、人力資源和客戶體驗團隊之間的整合對於最大化人工智慧智慧代理的價值至關重要。培訓計畫正在全球擴展，重點是提供實踐操作、場景驅動的學習。這些措施支持不同職位的技能提升，並幫助組織為長期採用人工智慧做好準備。

市場範圍
起始年份 2024
預測年份 2025-2034
起始值 59億美元
預測值 1056億美元
複合年成長率 38.5%

根據代理類型，市場可分為對話代理、自主代理、具身人工智慧代理、多代理系統和任務執行代理。其中，對話代理商佔最大市場佔有率，2024 年約為 44%，預計到 2034 年將以超過 41% 的複合年成長率成長。這些旨在模擬人類對話的代理正廣泛應用於客戶支援、員工入職和知識管理等各個領域。企業青睞它們，因為它們能夠透過上下文理解和意圖識別來處理大量查詢。目前已有結構化模組可供使用，透過持續的學習週期來增強對話流程、情緒偵測和使用者參與度。

人工智慧代理市場按技術細分，可分為自然語言處理 (NLP)、機器學習 (ML) 和深度學習、強化學習 (RL)、電腦視覺、語音識別與生成以及大型語言模型 (LLM)。其中，NLP 市場在 2024 年將佔據 38% 的市場佔有率，預計 2025 年至 2034 年的複合年成長率將超過 43%。 NLP 的成長源自於人工智慧系統需要理解、處理並回應多種語言和方言的人類語言。金融、醫療、教育和零售等行業正擴大採用 NLP 的功能來增強人機互動、從非結構化文字中提取含義以及自動化文件處理流程。

就部署模式而言，市場細分為基於雲端、本地部署和邊緣運算整合。雲端部署佔據主導地位並持續成長，這得益於對可擴展且靈活的解決方案的需求，這些解決方案能夠適應不斷變化的業務需求。這種模式使企業能夠跨地區、跨部門和跨監管環境快速部署AI代理。它支援集中控制、快速更新以及與現有企業系統的無縫整合。雲端基礎設施還支援持續訓練和代理監控，幫助團隊更有效率地協作並更快地進行創新。

從地理分佈來看，美國在2024年佔據北美人工智慧代理市場最高佔有率，貢獻了約77%的市場佔有率，創造了約22億美元的收入。憑藉其強大的先進雲端基礎設施、廣泛的企業人工智慧整合以及創新驅動的生態系統，美國已成為該領域的全球領導者。美國龐大且多樣化的用戶群體積極利用人工智慧代理，從智慧通訊到自動化營運，再到數據驅動的決策，無所不包。

塑造 AI 代理商格局的領先公司包括微軟、OpenAI、Google、Anthropic、UiPath、IBM（Watson）、NVIDIA、亞馬遜、Meta 和 Automation Anywhere。這些公司正在大力投資平台開發、用戶培訓和部署技術，以滿足不斷變化的業務需求。他們專注於研究和產品創新，並不斷突破 AI 代理商在實際企業環境中的極限。

目錄

第1章：方法論

  • 市場範圍和定義
  • 研究設計
    • 研究方法
    • 資料收集方法
  • 資料探勘來源
    • 全球的
    • 地區/國家
  • 基礎估算與計算
    • 基準年計算
    • 市場評估的主要趨勢
  • 初步研究和驗證
    • 主要來源
  • 預測模型
  • 研究假設和局限性

第2章：執行摘要

第3章：行業洞察

  • 產業生態系統分析
    • 供應商格局
    • 利潤率分析
    • 成本結構
    • 每個階段的增值
    • 影響價值鏈的因素
    • 中斷
  • 產業衝擊力
    • 成長動力
      • 客戶服務自動化需求不斷成長
      • 自然語言處理 (NLP) 和大型語言模型的進步
      • 雲端運算和人工智慧即服務的採用日益增多
      • 與新興科技的融合
      • 監管支持和數位轉型舉措
    • 產業陷阱與挑戰
      • 缺乏上下文理解和準確性
      • 初期實施成本高
    • 市場機會
      • 無代碼代理建構器培訓
      • 企業代理商治理模組
      • 與邊緣和物聯網設備的整合
      • 具身和實體人工智慧代理的進步
  • 成長潛力分析
  • 監管格局
    • 北美洲
    • 歐洲
    • 亞太地區
    • 拉丁美洲
    • 中東和非洲
  • 波特的分析
  • PESTEL分析
  • 科技與創新格局
    • 代理AI架構的演變
    • 大型語言模型整合
    • 自主決策能力
  • 專利分析
  • 永續性和環境方面
    • 永續實踐
    • 減少廢棄物的策略
    • 生產中的能源效率
    • 環保舉措
    • 碳足跡考量
  • 用例
  • 最佳情況

第4章：競爭格局

  • 介紹
  • 公司市佔率分析
    • 北美洲
    • 歐洲
    • 亞太地區
    • 拉丁美洲
    • MEA
  • 主要市場參與者的競爭分析
  • 競爭定位矩陣
  • 戰略展望矩陣
  • 關鍵進展
    • 併購
    • 夥伴關係與合作
    • 新產品發布
    • 擴張計劃和資金

第5章：市場估計與預測：依代理商分類，2021 年至 2034 年

  • 主要趨勢
  • 對話代理
  • 自主代理
  • 具身人工智慧代理
  • 多智慧體系統
  • 任務執行代理

第6章：市場估計與預測：依技術分類，2021 - 2034 年

  • 主要趨勢
  • 自然語言處理（NLP）
  • 機器學習 (ML) 與深度學習
  • 強化學習（RL）
  • 電腦視覺
  • 語音辨識與生成
  • 大型語言模型（LLM）

第7章：市場估計與預測：依部署模式，2021 - 2034 年

  • 主要趨勢
  • 基於雲端
  • 本地
  • 邊緣運算整合

第8章：市場估計與預測：按應用，2021 - 2034 年

  • 主要趨勢
  • 客戶服務自動化
  • 流程自動化
  • 私人助理
  • 衛生保健
  • 教育與電子學習
  • 金融
  • 電子商務與零售
  • 媒體與娛樂
  • 網路安全
  • 自動駕駛汽車和機器人

第9章：市場估計與預測：依最終用途，2021 - 2034 年

  • 主要趨勢
  • 醫療保健和生命科學
  • 銀行、金融服務和保險（BFSI）
  • 零售和消費品
  • 製造業和汽車業
  • 科技與電信
  • 政府和公共部門
  • 教育與研究
  • 媒體與娛樂

第10章：市場估計與預測：按地區，2021 - 2034 年

  • 主要趨勢
  • 北美洲
    • 美國
    • 加拿大
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 俄羅斯
    • 北歐人
  • 亞太地區
    • 中國
    • 印度
    • 日本
    • 韓國
    • 澳新銀行
    • 菲律賓
    • 越南
    • 印尼
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 阿根廷
  • MEA
    • 阿拉伯聯合大公國
    • 沙烏地阿拉伯
    • 南非

第 11 章：公司簡介

  • 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
簡介目錄
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