기업용 에이전트형 AI 시장 : 제공, 에이전트 시스템, 기술, 전개, 비즈니스 펑션, 조직 규모, 최종 이용 산업별 - 시장 규모, 업계 역학, 기회 분석 및 예측(2026년-2035년)

The global enterprise agentic AI market is experiencing exceptionally strong demand and rapid expansion, reflecting a major transformation in how organizations are adopting artificial intelligence for business operations. In 2025, the market is valued at approximately USD 2.42 billion, and it is projected to grow dramatically to around USD 105.7 billion by 2035. This represents a robust compound annual growth rate (CAGR) of about 45.89% during the forecast period from 2026 to 2035, highlighting the accelerating pace of enterprise adoption and the increasing strategic importance of autonomous AI systems across industries.

This remarkable growth is being driven by a fundamental shift in the nature of AI deployment within enterprises. The market is moving beyond traditional generative chatbots, which primarily focus on responding to user prompts with static or conversational outputs, toward more advanced goal-oriented autonomous AI agents. These next-generation systems are designed to operate with a higher degree of independence, enabling them to understand objectives, interpret contextual information, and execute complex multi-step workflows without continuous human intervention.

Noteworthy Market Developments

The enterprise agentic AI market is currently dominated by a small group of leading technology players that are shaping the direction of autonomous enterprise systems through advanced platforms, frameworks, and large-scale AI infrastructure. Microsoft holds a dominant position in the market by leveraging its Copilot Studio and AutoGen framework to embed agentic AI workflows directly into its widely used enterprise software ecosystem.

Salesforce is another major leader, particularly in the customer service and CRM-driven segments of the market. Through its Agentforce platform, the company has positioned itself at the forefront of AI-powered customer engagement and service automation. OpenAI plays a foundational role in the ecosystem by providing the underlying large-scale AI models and enterprise APIs that power a wide range of agentic applications.

Google maintains a strong competitive position through its Vertex AI Agent Builder and the deployment of multimodal Gemini-based agents. IBM continues to hold a significant share of the market, particularly within highly regulated industries, through its watsonx platform.

Core Growth Drivers

Enterprises operating within the enterprise agentic AI market are demonstrating rapidly increasing demand for automated customer service deflection tools, as organizations seek to reduce operational costs while improving service efficiency and scalability. Customer support functions have traditionally been one of the most resource-intensive areas for businesses, requiring large teams of human agents to manage high volumes of incoming queries across multiple communication channels. However, with rising customer expectations for instant and accurate responses, companies are under pressure to transform their support models using AI-driven automation.

Emerging Opportunity Trends

The advancement of core artificial intelligence technologies is emerging as a significant opportunity driving growth in the enterprise agentic AI market. In particular, the rapid maturation of Large Language Models (LLMs), retrieval-augmented generation (RAG) frameworks, and multimodal AI systems is fundamentally reshaping how autonomous agents operate within enterprise environments. These technologies are enabling a new generation of AI systems that are not only capable of understanding and generating human language but can also reason over complex information, interact with external systems, and execute real-time actions across digital workflows.

Barriers to Optimization

Fragmented data environments and legacy technology infrastructure present a significant challenge to the growth of the enterprise agentic AI market. Despite rapid advancements in AI capabilities, many organizations continue to operate on outdated systems that were not originally designed for modern, interconnected, and API-driven architectures. These legacy systems often lack standardization, interoperability, and real-time data accessibility, making it difficult for AI agents to function effectively across enterprise workflows. A core requirement for agentic AI systems is seamless integration through robust application programming interfaces (APIs) that enable continuous data exchange between different platforms, applications, and services.

Detailed Market Segmentation

By agent system, the single-agent segment maintained a strong and dominant position in the enterprise agentic AI market, accounting for approximately 54.80% of the total market share in 2025. This leadership reflects the continued preference among organizations for simpler, more controllable AI architectures that are easier to integrate into existing enterprise systems. Single-agent frameworks are widely adopted because they provide a clear, centralized decision-making structure, which aligns well with traditional IT environments that prioritize stability, predictability, and operational consistency.

By technology, Natural Language Processing (NLP) and Large Language Models (LLMs) dominate the enterprise agentic AI market, accounting for an overwhelming 68.30% share. This leadership reflects a fundamental shift in how enterprises design and interact with artificial intelligence systems, moving away from rigid rule-based automation toward highly adaptive, language-driven intelligence. In 2026, organizations are increasingly prioritizing systems that can interpret, generate, and reason through human language with high accuracy, enabling seamless interaction between users, data systems, and autonomous AI agents.

By deployment, cloud infrastructure maintains a clear and decisive dominance within the enterprise agentic AI market, accounting for approximately 63.20% of the total market share. This strong position reflects the fundamental role that cloud environments play in enabling the large-scale execution of autonomous AI systems, which require significant computational power, storage capacity, and real-time processing capabilities. As enterprises increasingly shift toward AI-driven operational models, the cloud has become the preferred deployment foundation due to its scalability, flexibility, and ability to support continuous, high-volume workloads.

By business function, customer service has emerged as the leading segment within the enterprise agentic AI market, accounting for approximately 24% of the total market share in 2025. This strong position reflects the growing prioritization by enterprises of enhancing customer experience while simultaneously reducing operational costs and improving service efficiency. As customer expectations continue to rise in terms of speed, accuracy, and availability, organizations are increasingly turning to agentic AI systems capable of delivering real-time, autonomous support across multiple communication channels.

Segment Breakdown

By Offering

• Software / Platforms
• Agent Platforms
• Pre-Built Functional Agents
• Services
• Professional
• Managed

By Agent System

• Single-Agent
• Multi-Agent Systems

By Technology

• Machine Learning
• NLP / Large Language Models
• Orchestration Frameworks
• RAG / Knowledge Integration

By Deployment

• Cloud
• On-Premises
• Hybrid

By Business Function

• Customer Service
• IT Operations
• Sales & Marketing
• Finance & Accounting
• Human Resources
• Supply Chain
• Workplace / Employee Experience

By Organization Size

• Large Enterprises
• Small & Medium Enterprises

By End-Use Industry

• BFSI
• IT & Telecom
• Healthcare
• Retail & E-commerce
• Manufacturing
• Government
• Others

By Region

• North America
• The U.S.
• Canada
• Mexico
• Europe
• Western Europe
• The UK
• Germany
• France
• Italy
• Spain
• Rest of Western Europe
• Eastern Europe
• Poland
• Russia
• Rest of Eastern Europe
• Asia Pacific
• China
• India
• Japan
• Australia & New Zealand
• South Korea
• ASEAN
• Rest of Asia Pacific
• Middle East & Africa (MEA)
• Saudi Arabia
• South Africa
• UAE
• Rest of MEA
• South America
• Argentina
• Brazil
• Rest of South America

Geography Breakdown

• North America holds the largest share of the global enterprise agentic AI market in 2026, driven by a powerful combination of technological leadership, advanced infrastructure, and strong enterprise adoption across key industries. The region's dominance is largely anchored in the concentration of leading technology conglomerates headquartered in the United States, including Microsoft, Google, Anthropic, and Nvidia.
• A key factor supporting North America's leadership is its highly developed digital infrastructure, particularly in cloud computing and AI-optimized hardware. Companies such as Microsoft and Google provide hyperscale cloud environments that support continuous AI processing, real-time decision-making, and seamless orchestration of multiple autonomous agents.
• Enterprise adoption across major sectors further strengthens demand in the North American market. Industries such as finance, healthcare, retail, and logistics are increasingly integrating agentic AI systems to automate complex decision-making processes, optimize operations, and enhance customer engagement. Financial institutions are using autonomous agents for fraud detection, risk analysis, and trading optimization, while healthcare providers are leveraging AI for diagnostics support and administrative automation.
• Leading Market Participants
• Accenture
• Capgemini
• Celonis
• Dataiku
• qBotica
• NVIDIA Corporation
• SAP SE
• Oracle
• Shield AI
• Other Prominent Players

Table of Content

Chapter 1. Executive Summary: Global Enterprise Agentic AI Market

Chapter 2. Research Methodology & Research Framework

• 2.1. Research Objective
• 2.2. Product Overview
• 2.3. Market Segmentation
• 2.4. Qualitative Research
  • 2.4.1. Primary & Secondary Sources
• 2.5. Quantitative Research
  • 2.5.1. Primary & Secondary Sources
• 2.6. Breakdown of Primary Research Respondents, By Region
• 2.7. Assumption for Study
• 2.8. Market Size Estimation
• 2.9. Data Triangulation

Chapter 3. Global Enterprise Agentic AI Market Overview

• 3.1. Industry Value Chain Analysis
  • 3.1.1. AI Infrastructure & Compute Providers (GPUs, Cloud)
  • 3.1.2. Foundation Model & LLM Developers
  • 3.1.3. Agent Platform & Orchestration Framework Vendors
  • 3.1.4. Pre-Built Functional Agent & Application Developers
  • 3.1.5. System Integrators & Professional / Managed Service Providers
  • 3.1.6. Enterprise End Users (BFSI, IT & Telecom, Healthcare, Retail, Manufacturing)
• 3.2. Industry Outlook
  • 3.2.1. Overview of the Global Enterprise AI & Autonomous Agent Industry
  • 3.2.2. Shift from Generative Assistants to Autonomous Multi-Agent Workflows
  • 3.2.3. Governance, Security & ROI Considerations in Agent Deployment
• 3.3. PESTLE Analysis
• 3.4. Porter's Five Forces Analysis
  • 3.4.1. Bargaining Power of Suppliers
  • 3.4.2. Bargaining Power of Buyers
  • 3.4.3. Threat of Substitutes
  • 3.4.4. Threat of New Entrants
  • 3.4.5. Degree of Competition
• 3.5. Market Growth and Outlook
  • 3.5.1. Market Revenue Estimates and Forecast (US$ Mn), 2020-2035
  • 3.5.2. Price Trend Analysis, By Offering

Chapter 4. Global Enterprise Agentic AI Market Analysis

• 4.1. Competition Dashboard
  • 4.1.1. Market Concentration Rate
  • 4.1.2. Company Market Share Analysis (Value %), 2025
  • 4.1.3. Competitor Mapping & Benchmarking

Chapter 5. Global Enterprise Agentic AI Market Analysis

• 5.1. Market Dynamics and Trends
  • 5.1.1. Growth Drivers
  • 5.1.2. Restraints
  • 5.1.3. Opportunity
  • 5.1.4. Key Trends
• 5.2. Market Size and Forecast, 2020-2035 (US$ Mn)
  • 5.2.1. By Offering
    • 5.2.1.1. Key Insights
      • 5.2.1.1.1. Software / Platforms
        • 5.2.1.1.1.1. Agent Platforms
        • 5.2.1.1.1.2. Pre-Built Functional Agents
      • 5.2.1.1.2. Services
        • 5.2.1.1.2.1. Professional
        • 5.2.1.1.2.2. Managed
  • 5.2.2. By Agent System
    • 5.2.2.1. Key Insights
      • 5.2.2.1.1. Single-Agent
      • 5.2.2.1.2. Multi-Agent Systems
  • 5.2.3. By Technology
    • 5.2.3.1. Key Insights
      • 5.2.3.1.1. Machine Learning
      • 5.2.3.1.2. NLP / Large Language Models
      • 5.2.3.1.3. Orchestration Frameworks
      • 5.2.3.1.4. RAG / Knowledge Integration
  • 5.2.4. By Deployment
    • 5.2.4.1. Key Insights
      • 5.2.4.1.1. Cloud
      • 5.2.4.1.2. On-Premises
      • 5.2.4.1.3. Hybrid
  • 5.2.5. By Business Function
    • 5.2.5.1. Key Insights
      • 5.2.5.1.1. Customer Service
      • 5.2.5.1.2. IT Operations
      • 5.2.5.1.3. Sales & Marketing
      • 5.2.5.1.4. Finance & Accounting
      • 5.2.5.1.5. Human Resources
      • 5.2.5.1.6. Supply Chain
      • 5.2.5.1.7. Workplace / Employee Experience
  • 5.2.6. By Organization Size
    • 5.2.6.1. Key Insights
      • 5.2.6.1.1. Large Enterprises
      • 5.2.6.1.2. Small & Medium Enterprises
  • 5.2.7. By End-Use Industry
    • 5.2.7.1. Key Insights
      • 5.2.7.1.1. BFSI
      • 5.2.7.1.2. IT & Telecom
      • 5.2.7.1.3. Healthcare
      • 5.2.7.1.4. Retail & E-commerce
      • 5.2.7.1.5. Manufacturing
      • 5.2.7.1.6. Government
      • 5.2.7.1.7. Others
  • 5.2.8. By Region
    • 5.2.8.1. Key Insights
      • 5.2.8.1.1. North America
        • 5.2.8.1.1.1. The U.S.
        • 5.2.8.1.1.2. Canada
        • 5.2.8.1.1.3. Mexico
      • 5.2.8.1.2. Europe
        • 5.2.8.1.2.1. Western Europe
          • 5.2.8.1.2.1.1. The UK
          • 5.2.8.1.2.1.2. Germany
          • 5.2.8.1.2.1.3. France
          • 5.2.8.1.2.1.4. Italy
          • 5.2.8.1.2.1.5. Spain
          • 5.2.8.1.2.1.6. Rest of Western Europe
        • 5.2.8.1.2.2. Eastern Europe
          • 5.2.8.1.2.2.1. Poland
          • 5.2.8.1.2.2.2. Russia
          • 5.2.8.1.2.2.3. Rest of Eastern Europe
      • 5.2.8.1.3. Asia Pacific
        • 5.2.8.1.3.1. China
        • 5.2.8.1.3.2. India
        • 5.2.8.1.3.3. Japan
        • 5.2.8.1.3.4. Australia & New Zealand
        • 5.2.8.1.3.5. South Korea
        • 5.2.8.1.3.6. ASEAN
        • 5.2.8.1.3.7. Rest of Asia Pacific
      • 5.2.8.1.4. Middle East & Africa (MEA)
        • 5.2.8.1.4.1. Saudi Arabia
        • 5.2.8.1.4.2. South Africa
        • 5.2.8.1.4.3. UAE
        • 5.2.8.1.4.4. Rest of MEA
      • 5.2.8.1.5. South America
        • 5.2.8.1.5.1. Argentina
        • 5.2.8.1.5.2. Brazil
        • 5.2.8.1.5.3. Rest of South America

Chapter 6. North America Market Analysis

• 6.1. Market Dynamics and Trends
  • 6.1.1. Growth Drivers
  • 6.1.2. Restraints
  • 6.1.3. Opportunity
  • 6.1.4. Key Trends
• 6.2. Market Size and Forecast, 2020-2035 (US$ Mn)
  • 6.2.1. Key Insights
    • 6.2.1.1. By Offering
    • 6.2.1.2. By Agent System
    • 6.2.1.3. By Technology
    • 6.2.1.4. By Deployment
    • 6.2.1.5. By Business Function
    • 6.2.1.6. By Organization Size
    • 6.2.1.7. By End-Use Industry
    • 6.2.1.8. By Country

Chapter 7. Europe Market Analysis

• 7.1. Market Dynamics and Trends
  • 7.1.1. Growth Drivers
  • 7.1.2. Restraints
  • 7.1.3. Opportunity
  • 7.1.4. Key Trends
• 7.2. Market Size and Forecast, 2020-2035 (US$ Mn)
  • 7.2.1. Key Insights
    • 7.2.1.1. By Offering
    • 7.2.1.2. By Agent System
    • 7.2.1.3. By Technology
    • 7.2.1.4. By Deployment
    • 7.2.1.5. By Business Function
    • 7.2.1.6. By Organization Size
    • 7.2.1.7. By End-Use Industry
    • 7.2.1.8. By Country

Chapter 8. Asia Pacific Market Analysis

• 8.1. Market Dynamics and Trends
  • 8.1.1. Growth Drivers
  • 8.1.2. Restraints
  • 8.1.3. Opportunity
  • 8.1.4. Key Trends
• 8.2. Market Size and Forecast, 2020-2035 (US$ Mn)
  • 8.2.1. Key Insights
    • 8.2.1.1. By Offering
    • 8.2.1.2. By Agent System
    • 8.2.1.3. By Technology
    • 8.2.1.4. By Deployment
    • 8.2.1.5. By Business Function
    • 8.2.1.6. By Organization Size
    • 8.2.1.7. By End-Use Industry
    • 8.2.1.8. By Country

Chapter 9. Middle East & Africa Market Analysis

• 9.1. Market Dynamics and Trends
  • 9.1.1. Growth Drivers
  • 9.1.2. Restraints
  • 9.1.3. Opportunity
  • 9.1.4. Key Trends
• 9.2. Market Size and Forecast, 2020-2035 (US$ Mn)
  • 9.2.1. Key Insights
    • 9.2.1.1. By Offering
    • 9.2.1.2. By Agent System
    • 9.2.1.3. By Technology
    • 9.2.1.4. By Deployment
    • 9.2.1.5. By Business Function
    • 9.2.1.6. By Organization Size
    • 9.2.1.7. By End-Use Industry
    • 9.2.1.8. By Country

Chapter 10. South America Market Analysis

• 10.1. Market Dynamics and Trends
  • 10.1.1. Growth Drivers
  • 10.1.2. Restraints
  • 10.1.3. Opportunity
  • 10.1.4. Key Trends
• 10.2. Market Size and Forecast, 2020-2035 (US$ Mn)
  • 10.2.1. Key Insights
    • 10.2.1.1. By Offering
    • 10.2.1.2. By Agent System
    • 10.2.1.3. By Technology
    • 10.2.1.4. By Deployment
    • 10.2.1.5. By Business Function
    • 10.2.1.6. By Organization Size
    • 10.2.1.7. By End-Use Industry
    • 10.2.1.8. By Country

Chapter 11. Company Profile (Company Overview, Financial Matrix, Key Product landscape, Key Personnel, Key Competitors, Contact Address, and Business Strategy Outlook)

• 11.1. Accenture
• 11.2. Capgemini
• 11.3. Celonis
• 11.4. Dataiku
• 11.5. qBotica
• 11.6. NVIDIA Corporation
• 11.7. SAP SE
• 11.8. Oracle
• 11.9. Shield AI
• 11.10. Other Prominent Players

Chapter 12. Annexure

• 12.1. List of Secondary Sources
• 12.2. Key Country Markets- Macro Economic Outlook/Indicators