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企業級基於代理的人工智慧市場：按產品、代理系統、技術、部署方式、業務功能、組織規模和最終用戶行業分類——市場規模、行業動態、機會分析和預測（2026-2035 年）

Enterprise Agentic AI Market: By Offering, Agent System, Technology, Deployment, Business Function, Organization Size, End-Use Industry - Market Size, Industry Dynamics, Opportunity Analysis and Forecast For 2026-2035
出版日期: | 出版商: Astute Analytica | 英文 290 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

全球企業級基於代理的人工智慧市場正經歷著極其強勁的需求和快速擴張，這反映出企業在業務營運中部署人工智慧的方式正在發生重大轉變。該市場在2025年的估值約為24.2億美元，預計到2035年將大幅成長至約1,057億美元。這意味著在2026年至2035年的預測期內，該市場將維持約45.89%的強勁複合年成長率，凸顯了企業加速採用人工智慧的趨勢，以及自主人工智慧系統在各行業日益成長的戰略重要性。

這一顯著成長源自於企業人工智慧應用方式的根本性轉變。市場正從傳統的生成式聊天機器人（主要專注於使用靜態或對話式輸出響應用戶指令）轉向更先進、目標導向、自主運行的人工智慧代理。這些新一代系統旨在以更高的自主性運行，使其能夠理解目標、解讀上下文訊息，並在無需持續人工干預的情況下執行複雜的多階段工作流程。

顯著的市場趨勢

目前，企業級基於代理的人工智慧市場由少數幾家領先的科技公司主導，它們透過先進的平台、框架和大規模人工智慧基礎設施，引領著自主企業系統的發展方向。微軟憑藉其 Copilot Studio 和 AutoGen 框架，並將基於代理的人工智慧工作流程直接整合到其廣泛使用的企業軟體生態系統中，確立了市場主導地位。

銷售團隊也是業界領導者，尤其是在客戶服務和 CRM主導的市場領域。憑藉其 Agentforce 平台，該公司在人工智慧驅動的客戶參與和服務自動化方面處於領先地位。 OpenAI 在該生態系統中扮演著至關重要的角色，它提供大規模人工智慧模型和企業級 API，為各種基於代理的應用程式提供支援。

谷歌憑藉其 Vertex AI Agent Builder 和基於 Gemini 的多模態代理部署，保持著強大的競爭優勢。 IBM 則憑藉其 WatsonX 平台，繼續佔相當大的市場佔有率，尤其是在監管嚴格的行業。

主要成長要素

企業級基於代理的人工智慧市場正面臨著對自動化客戶服務傳輸工具的快速成長的需求。這是因為企業希望在提高服務效率和擴充性的同時降低營運成本。客戶支援歷來是企業資源消耗最大的領域之一，需要大規模的人工客服團隊來處理大量透過多種溝通管道湧入的諮詢。然而，隨著客戶對即時準確回​​應的期望不斷提高，企業正被迫透過利用人工智慧驅動的自動化來轉型其支援模式。

新機會的趨勢

核心人工智慧技術的進步正成為推動企業級基於代理的人工智慧市場成長的關鍵機會。特別是，大規模語言模型（LLM）、搜尋增強生成（RAG）框架和多模態人工智慧系統的快速成熟，正在從根本上改變自主代理在企業環境中的運作方式。這些技術正在催生新一代人工智慧系統，它們不僅能夠理解和產生人類語言，還能推斷複雜資訊、與外部系統交互，並在數位化工作流程中即時採取行動。

最佳化障礙

數據環境碎片化和傳統技術基礎設施為企業級基於代理的人工智慧市場成長帶來了巨大挑戰。儘管人工智慧能力快速發展，許多組織仍在使用過時的系統，這些系統並非為現代互聯互通且API驅動的架構而設計。這些舊有系統通常缺乏標準化、互通性和即時數據訪問，導致人工智慧代理難以在企業工作流程中高效運作。基於代理的人工智慧系統的一個關鍵需求是透過強大的應用程式介面（API）實現無縫整合，從而在不同平台、應用程式和服務之間進行持續的資料交換。

目錄

第1章摘要整理：全球企業級基於代理的人工智慧市場

第2章：調查方法與研究框架

  • 研究目標
  • 產品概述
  • 市場區隔
  • 定性研究
    • 一手和二手資訊
  • 量化研究
    • 一手和二手資訊
  • 主要調查受訪者組成：按地區分類
  • 本研究的前提
  • 市場規模估算
  • 數據三角測量

第3章：全球企業級基於代理的人工智慧市場概述

  • 產業價值鏈分析
  • 產業展望
    • 全球企業人工智慧和自主代理產業概覽
    • 從生成式助理過渡到自主多智慧體工作流程
    • 部署代理時需要考慮的管治、安全性和投資報酬率問題
  • PESTLE分析
  • 波特五力分析
  • 市場成長及前景
    • 2020-2035年市場收入估算與預測
    • 價格趨勢分析：透過報價

第4章：全球企業級基於代理的人工智慧市場分析

  • 競爭對手儀表板
    • 市場集中度
    • 企業市場占有率分析，2025 年
    • 競爭對手分析與基準測試

第5章：全球企業級基於代理的人工智慧市場分析

  • 市場動態和趨勢
    • 成長要素
    • 抑制因子
    • 機會
    • 主要趨勢
  • 市場規模及預測，2020-2035年
    • 報價
      • 關鍵見解
        • 軟體/平台
          • 代理平台
          • 預先建置的功能代理
        • 服務
          • 專業的
          • 管理
    • 代理系統
      • 關鍵見解
        • 單代理
        • 多智慧體系統
    • 透過技術
      • 關鍵見解
        • 機器學習
        • 自然語言處理/大規模語言模型
        • 編配框架
        • RAG / 知識整合
    • 不同的發展
      • 關鍵見解
        • 雲
        • 現場
        • 混合
    • 一個
      • 關鍵見解
        • 客戶服務
        • IT營運
        • 銷售與行銷
        • 財會
        • 人力資源
        • 供應鏈
        • 工作場所/員工體驗
    • 按組織規模
      • 關鍵見解
        • 大公司
        • 小型企業
    • 按最終用途行業分類
      • 關鍵見解
        • BFSI
        • 資訊科技/通訊
        • 衛生保健
        • 零售與電子商務
        • 製造業
        • 政府
        • 其他
    • 按地區
      • 關鍵見解
        • 北美洲
          • 美國
          • 加拿大
          • 墨西哥
        • 歐洲
          • 西歐
            • 英國
            • 德國
            • 法國
            • 義大利
            • 西班牙
            • 其他西歐國家
          • 東歐
            • 波蘭
            • 俄羅斯
            • 其他東歐國家
        • 亞太地區
          • 中國
          • 印度
          • 日本
          • 澳洲和紐西蘭
          • 韓國
          • ASEAN
          • 其他亞太國家
        • 中東和非洲（MEA）
          • 沙烏地阿拉伯
          • 南非
          • UAE
          • 其他中東和非洲國家
        • 南美洲
          • 阿根廷
          • 巴西
          • 其他南美國家

第6章：北美市場分析

第7章：歐洲市場分析

第8章：亞太市場分析

第9章：中東和非洲市場分析

第10章：南美市場分析

第11章：公司簡介

  • Accenture
  • Capgemini
  • Celonis
  • Dataiku
  • qBotica
  • NVIDIA Corporation
  • SAP SE
  • Oracle
  • Shield AI
  • Other Prominent Players

第12章附錄

簡介目錄
Product Code: AA06261826

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