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1889460

人工智慧驅動的自動化市場預測至2032年：按自動化類型、技術、應用、最終用戶和地區分類的全球分析

AI-Driven Automation Market Forecasts to 2032 - Global Analysis By Automation Type (Cognitive Automation, Agentic Automation, Intelligent Process Automation and Event-driven / API-based Automation), Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

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根據 Stratistics MRC 的一項研究，預計到 2025 年，全球人工智慧驅動的自動化市場規模將達到 237.4 億美元，到 2032 年將達到 783.6 億美元，預測期內的複合年成長率為 18.6%。

人工智慧驅動的自動化正在改變眾多產業，它使軟體和機器能夠執行傳統上由人工管理的流程。借助機器學習、自然語言理解和機器人流程自動化 (RPA) 等技術，企業可以最佳化工作流程、最大限度地減少錯誤並提高效率。這使得企業能夠即時決策、進行預測性維護並有效管理大型資料集。自動化重複性和常規性任務使員工能夠專注於具有策略意義的增值措施。醫療保健、金融、物流和製造業等行業正因人工智慧自動化而發生顯著變化。人工智慧的應用提高了可擴展性、營運績效和成本效益，同時確保了準確性、合規性和更佳的客戶體驗，從而為企業帶來競爭優勢。

根據 ODSC 2025 年人工智慧趨勢和應用調查，92% 的專業人士認為人工智慧將在他們的整個職業生涯中發揮作用，84% 的人表示他們已經在使用人工智慧來探索和理解新的概念和想法。

對即時決策的需求日益成長

對即時決策日益成長的需求正在推動人工智慧驅動的自動化市場的發展。各行各業的企業都面臨著大量數據，需要即時分析才能保持競爭力。人工智慧自動化能夠快速檢驗複雜訊息，並提供可執行的洞察和建議。透過預測建模和機器學習，企業可以預測趨勢、評估風險並做出及時、數據驅動的決策。即時決策能力能夠提升營運柔軟性、應對力和整體績效。尤其是在零售、醫療保健、金融和物流等行業，即時洞察能夠改善客戶體驗、最佳化供應鏈並最大限度地減少營運延誤，所有這些都有助於提高盈利、效率和市場競爭力。

高昂的實施成本

人工智慧驅動的自動化市場成長面臨的主要限制因素之一是高昂的實施成本。實施人工智慧技術需要對軟體、硬體、基礎設施和專業人員進行投資。對於中小企業而言，這些初始成本可能成為採用人工智慧技術的障礙。將人工智慧整合到現有業務流程中通常十分複雜，需要客製化解決方案和大量的培訓。此外，持續的維護和更新也會帶來額外的財務負擔。儘管人工智慧自動化具有提高效率和生產力的潛力，但高昂的初始成本和持續成本，尤其是在成本敏感型產業，是重要的障礙，使得許多企業儘管能夠獲得長期的營運效益，卻仍然無法利用人工智慧驅動的解決方案。

對個人化客戶體驗的需求日益成長

對個人化客戶體驗日益成長的需求正在推動人工智慧驅動的自動化市場的發展機會。企業正利用人工智慧分析客戶偏好、行為和購買趨勢等數據，從而提供客製化的產品、服務和推薦。自動化人工智慧系統能夠實現即時客戶回應，提升客戶滿意度與參與度。在零售、銀行、醫療保健和電信等行業，增強個人化服務能夠提高客戶忠誠度和收入。透過人工智慧驅動的自動化，企業可以有效處理大規模資料集、細分客戶群並預測未來需求。這種對高度個人化的關注為人工智慧解決方案供應商提供了巨大的機遇，使其能夠開發出既能提升客戶體驗，又能提高營運效率和整體業務績效的創新工具。

監理和合規挑戰

合規和監管障礙對人工智慧驅動的自動化市場成長構成重大威脅。世界各國政府都已實施了關於資料隱私、人工智慧倫理和自動化決策流程的嚴格規定。不遵守規定可能導致罰款、訴訟和品牌聲譽受損。企業必須密切關注監管動態，實施強力的合規策略，並使自身的人工智慧系統與不斷發展的標準保持一致。在醫療保健、金融和公共服務等行業，這些要求尤其嚴格。管理不同的區域性法規會導致成本增加、實施延誤和擴張受限，因此，對於全球採用人工智慧驅動的自動化技術的企業而言，監管合規始終是一項挑戰。

新冠疫情的影響：

新冠疫情對人工智慧驅動的自動化市場產生了顯著影響，加速了數位化和自動化解決方案的普及。封鎖、勞動力限制和保持社交定序等措施促使企業減少人工操作，並加速採用人工智慧工具以維持生產力。醫療保健、物流、零售和製造業等行業加速了人工智慧在遠端監控、預測性維護和工作流程自動化方面的應用。儘管經濟的不確定性和預算限制暫時減緩了一些投資，但疫情凸顯了建構能夠有效應對各種干擾的彈性擴充性系統的必要性。總而言之，新冠疫情既是人工智慧驅動自動化的挑戰，也是其發展的驅動力，重塑了產業實踐，並凸顯了其在後疫情時代成長中的戰略作用。

預計在預測期內，智慧流程自動化（IPA）細分市場將佔據最大的市場佔有率。

預計在預測期內，智慧流程自動化 (IPA) 細分市場將佔據最大的市場佔有率。 IPA 融合了機器人流程自動化 (RPA) 和人工智慧 (AI) 技術，包括機器學習和自然語言處理。 IPA 使企業能夠自動化處理複雜且認知性強的任務，而不僅僅是簡單的重複性任務，從而提高效率和準確性。醫療保健、金融和製造業等行業正在利用 IPA 來簡化工作流程、減少錯誤並增強策略決策能力。 IPA 之所以高效，是因為它能夠分析資料、識別模式並適應不斷變化的環境。透過將 IPA 整合到業務流程中，企業能夠提高生產力、降低營運成本並獲得競爭優勢，從而成為人工智慧驅動的自動化市場中最突出的細分市場。

預計在預測期內，汽車和運輸業將實現最高的複合年成長率。

由於自動駕駛汽車、互聯出行和智慧物流系統的日益普及，預計汽車和運輸領域在預測期內將實現最高成長率。人工智慧技術能夠實現即時監控、預測性維護和精簡的供應鏈營運，從而提高效率並降低成本。汽車製造商和運輸業者正擴大採用人工智慧驅動的分析、機器學習和自動化技術，以提高安全性、最大限度地減少停機時間並提升客戶滿意度。此外，電動車的廣泛應用、智慧交通管理以及自動駕駛技術的進步也推動了人工智慧自動化技術的應用。這些趨勢共同創造了高成長環境，使汽車和運輸產業成為人工智慧驅動自動化市場中成長最快的領域。

佔比最大的地區：

由於北美擁有強大的技術生態系統、較高的AI普及率以及眾多關鍵產業參與者，預計在預測期內，北美將佔據最大的市場佔有率。大量的研發投入、完善的IT基礎設施以及有利的法規結構，都為創新和市場擴張提供了支持。醫療保健、金融、製造和電信等行業正擴大採用AI自動化技術來最佳化流程、降低成本並提升決策能力。美國和加拿大擁有許多領先的AI技術公司和創新Start-Ups，這促進了AI技術的快速普及。北美積極擁抱AI解決方案並採取早期應用策略，使其成為全球AI驅動自動化市場的主導地區。

預計年複合成長率最高的地區：

由於技術的快速發展、人工智慧的日益普及以及對智慧數位基礎設施的大規模投資，亞太地區預計將在預測期內實現最高的複合年成長率。中國、印度、日本和韓國等主要經濟體正著力發展工業4.0、智慧製造和機器人技術，這推動了對自動化的需求。該地區受益於新興Start-Ups、政府支持的創新舉措以及不斷擴大的工業活動，所有這些因素都促進了市場成長。醫療保健、汽車、物流和製造等行業的企業正在加速採用人工智慧驅動的自動化技術，以提高效率、降低成本並最佳化生產力。這些趨勢使亞太地區成為全球人工智慧驅動自動化市場成長最快的地區。

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目錄

第1章執行摘要

第2章 前言

  • 摘要
  • 相關利益者
  • 調查範圍
  • 調查方法
  • 研究材料

第3章 市場趨勢分析

  • 促進要素
  • 抑制因素
  • 機會
  • 威脅
  • 技術分析
  • 應用分析
  • 終端用戶分析
  • 新興市場
  • 新冠疫情的感染疾病

第4章 波特五力分析

  • 供應商的議價能力
  • 買方的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

5. 全球人工智慧驅動自動化市場（按自動化類型分類）

  • 認知自動化
  • 基於代理的自動化
  • 智慧流程自動化（IPA）
  • 事件驅動/基於 API 的自動化

6. 全球人工智慧驅動自動化市場（按技術分類）

  • 機器學習
    • 監督式學習
    • 無監督學習
    • 強化學習
    • 深度學習
      • 卷積類神經網路（CNN）
      • 遞迴神經網（RNN）
      • 生成對抗網路（GAN）
  • 自然語言處理（NLP）
  • 電腦視覺

7. 全球人工智慧驅動自動化市場（按應用領域分類）

  • 財會
  • 營運和供應鏈
  • 行銷和客戶體驗
  • 生產/品管
  • 臨床和醫療服務
  • 能源基礎設施與公共產業

8. 全球人工智慧驅動自動化市場（以最終用戶分類）

  • 電訊和資訊技術
  • 汽車與運輸
  • 製藥和診斷
  • 食品/飲料
  • 能源與化工
  • 先進材料

9. 全球人工智慧驅動自動化市場（按地區分類）

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 亞太其他地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美國家
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第10章：重大進展

  • 協議、夥伴關係、合作和合資企業
  • 併購
  • 新產品發布
  • 業務拓展
  • 其他關鍵策略

第11章 企業概況

  • UiPath
  • Automation Anywhere
  • IBM watsonx
  • Infosys
  • HCLTech
  • Tata Consultancy Services（TCS）
  • Wipro
  • Persistent Systems
  • Fractal Analytics
  • Addverb Technologies
  • Uncanny Vision
  • Unbox Robotics
  • Ripik.AI
  • Jidoka Technologies
  • Haber
Product Code: SMRC32752

According to Stratistics MRC, the Global AI-Driven Automation Market is accounted for $23.74 billion in 2025 and is expected to reach $78.36 billion by 2032 growing at a CAGR of 18.6% during the forecast period. AI-powered automation is transforming various sectors by enabling software and machines to execute processes that were once managed by humans. Through technologies like machine learning, natural language understanding, and robotic process automation, organizations can optimize workflows, minimize mistakes, and boost efficiency. It facilitates instant decision-making, predictive upkeep, and effective management of massive data sets. By automating repetitive and routine operations, employees are freed to concentrate on strategic, value-added initiatives. Sectors including healthcare, finance, logistics, and manufacturing are experiencing notable changes with AI automation. Incorporating AI enhances scalability, operational performance, and cost efficiency, while ensuring precision, compliance, and improved customer experiences, giving businesses a competitive edge.

According to the ODSC AI Trends and Adoption Survey 2025, data shows that 92% of professionals believe AI will help them throughout their careers, while 84% already use AI to explore and understand new concepts and ideas.

Market Dynamics:

Driver:

Rising demand for real-time decision making

Increasing demand for real-time decision-making is fueling growth in the AI-driven automation market. Businesses across sectors handle enormous amounts of data and require immediate analysis to remain competitive. AI automation allows rapid examination of complex information, providing actionable insights and recommendations. Through predictive modeling and machine learning, organizations can foresee trends, assess risks, and make timely, data-backed decisions. Real-time decision capabilities improve operational flexibility, responsiveness, and overall performance. Industries like retail, healthcare, finance, and logistics particularly gain from instant insights, enabling enhanced customer experiences, optimized supply chains, and minimized operational delays, which collectively contribute to better profitability, efficiency, and a stronger competitive market position.

Restraint:

High implementation costs

A key limitation restraining the growth of the AI-driven automation market is the substantial expense involved in implementation. Introducing AI technologies necessitates investments in software, hardware, infrastructure, and skilled workforce. For small and medium-sized enterprises, these initial costs can be prohibitive, restricting adoption. Integrating AI into existing business processes is often complicated, requiring tailored solutions and extensive training. Moreover, ongoing maintenance and updates add additional financial pressure. Even though AI automation can enhance efficiency and productivity, the high upfront and recurring costs pose a significant barrier, particularly for cost-conscious industries, preventing many organizations from leveraging AI-driven solutions despite their long-term operational advantages.

Opportunity:

Increasing demand for personalized customer experiences

Growing demand for personalized customer experiences is driving opportunities in the AI-driven automation market. Organizations are using AI to examine customer data, including preferences, behaviors, and purchase trends, to offer customized products, services, and recommendations. Automated AI systems enable real-time customer interactions, increasing satisfaction and engagement. Sectors like retail, banking, healthcare, and telecom benefit from enhanced personalization, which boosts loyalty and revenue. Through AI-driven automation, companies can efficiently process large datasets, segment customers, and anticipate future needs. This focus on hyper-personalization offers a significant chance for AI solution providers to develop innovative tools that enhance customer experiences while simultaneously improving operational efficiency and overall business performance.

Threat:

Regulatory and compliance challenges

Compliance and regulatory hurdles represent a major threat to the growth of the AI-driven automation market. Governments across the globe are implementing stringent rules on data privacy, AI ethics, and automated decision-making processes. Failure to comply may lead to penalties, lawsuits, and damage to brand reputation. Businesses must stay informed about regulatory updates, implement strong compliance strategies, and adjust AI systems to align with evolving standards. These requirements are especially strict in sectors like healthcare, finance, and public services. Managing diverse regional regulations can increase costs, delay implementation, and restrict expansion, making regulatory compliance a persistent challenge for companies deploying AI-driven automation technologies worldwide.

Covid-19 Impact:

The COVID-19 pandemic had a notable impact on the AI-driven automation market, driving accelerated adoption of digital and automated solutions. Lockdowns, workforce restrictions, and social distancing measures prompted organizations to reduce manual operations and implement AI-powered tools to sustain productivity. Industries such as healthcare, logistics, retail, and manufacturing increasingly leveraged AI for remote monitoring, predictive maintenance, and workflow automation. While economic uncertainty and budget limitations temporarily slowed some investments, the pandemic underscored the necessity for resilient, scalable systems capable of managing disruptions effectively. Overall, COVID-19 acted as a dual force-both challenging and propelling AI-driven automation-reshaping industry practices and highlighting its strategic role in post-pandemic growth.

The intelligent process automation (IPA) segment is expected to be the largest during the forecast period

The intelligent process automation (IPA) segment is expected to account for the largest market share during the forecast period as it merges robotic process automation with AI technologies, including machine learning and natural language processing. IPA allows companies to automate complex, cognitive tasks that go beyond simple, repetitive operations, improving efficiency and accuracy. Industries such as healthcare, finance, and manufacturing utilize IPA to streamline workflows, minimize errors, and enhance strategic decision-making. Its ability to analyze data, recognize patterns, and adjust to evolving scenarios makes IPA highly effective. By embedding IPA into business processes, organizations achieve increased productivity, reduced operational costs, and stronger competitive positioning, establishing it as the most prominent segment in the AI-driven automation market.

The automotive & transportation segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the automotive & transportation segment is predicted to witness the highest growth rate due to rising adoption of autonomous vehicles, connected mobility, and intelligent logistics systems. AI technologies allow real-time monitoring, predictive maintenance, and streamlined supply chain operations, boosting efficiency and lowering costs. Vehicle manufacturers and transportation operators increasingly implement AI-driven analytics, machine learning, and automation to enhance safety, minimize downtime, and elevate customer satisfaction. Additionally, the expansion of electric vehicles, smart traffic management, and self-driving technologies is fueling AI automation adoption. Collectively, these trends create a high-growth environment, making Automotive & Transportation the fastest-growing segment in the AI-driven automation market.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share due to its strong technological ecosystem, high AI adoption, and concentration of key industry players. Significant investments in research, well-established IT infrastructure, and favorable regulatory frameworks support innovation and market expansion. Industries including healthcare, finance, manufacturing, and telecommunications increasingly utilize AI automation to optimize processes, lower costs, and enhance decision-making capabilities. The presence of prominent AI technology companies and innovative startups in the U.S. and Canada contributes to rapid adoption. North America's proactive embrace of AI solutions and early deployment strategies establish it as the dominant region in the global AI-driven automation landscape.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR owing to rapid technological advancement, rising AI adoption, and significant investments in smart and digital infrastructure. Key countries, including China, India, Japan, and South Korea, are emphasizing Industry 4.0, intelligent manufacturing, and robotics, which fuel automation demand. The region benefits from emerging startups, government-backed innovation initiatives, and expanding industrial activities, all contributing to market growth. Organizations in sectors like healthcare, automotive, logistics, and manufacturing increasingly deploy AI-driven automation to boost efficiency, reduce costs, and optimize productivity. These trends position Asia-Pacific as the fastest-growing region in the global AI-driven automation landscape.

Key players in the market

Some of the key players in AI-Driven Automation Market include UiPath, Automation Anywhere, IBM watsonx, Infosys , HCLTech, Tata Consultancy Services (TCS), Wipro, Persistent Systems, Fractal Analytics, Addverb Technologies, Uncanny Vision, Unbox Robotics, Ripik.AI, Jidoka Technologies and Haber.

Key Developments:

In November 2025, Automation Anywhere and enterprise AI, has announced the acquisition of Aisera, a top provider of agentic AI solutions for autonomous IT. The deal unites two pioneers in automation and conversational AI to create the industry's most complete agentic automation platform, designed to deliver fully autonomous operations across departments such as IT, HR, and customer service.

In October 2025, UiPath has announced collaboration with NVIDIA to integrate advanced AI capabilities into enterprise automation, enabling high-trust applications such as fraud detection and healthcare management. The partnership combines UiPath's agentic automation expertise with NVIDIA's open Nemotron models and NVIDIA NIM, allowing organizations to deploy enterprise-ready AI models as microservices - including natural language processing, image understanding, and predictive analytics.

In October 2025, Infosys has landed a £1.2 billion contract from the NHS Business Services Authority (NHSBSA) to modernise its workforce management system in England and Wales, marking one of the largest deals in recent times amid a challenging macroeconomic environment. The mega deal comes nearly two years after the company won a $1.64 billion contract from Liberty Global. So far, the $3.2 billion deal with Germany's Daimler - which was signed during the pandemic in 2020 - is the biggest for Infosys.

Automation Types Covered:

  • Cognitive Automation
  • Agentic Automation
  • Intelligent Process Automation (IPA)
  • Event-driven / API-based Automation

Technologies Covered:

  • Machine Learning
  • Natural Language Processing (NLP)
  • Computer Vision

Applications Covered:

  • Finance & Accounting
  • Operations & Supply Chain
  • Marketing & Customer Experience
  • Manufacturing & Quality Control
  • Clinical & Healthcare Operations
  • Energy Infrastructure & Utilities

End Users Covered:

  • Telecom & IT
  • Automotive & Transportation
  • Pharma & Diagnostics
  • Food & Beverages
  • Energy & Chemicals
  • Advanced Materials

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Technology Analysis
  • 3.7 Application Analysis
  • 3.8 End User Analysis
  • 3.9 Emerging Markets
  • 3.10 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global AI-Driven Automation Market, By Automation Type

  • 5.1 Introduction
  • 5.2 Cognitive Automation
  • 5.3 Agentic Automation
  • 5.4 Intelligent Process Automation (IPA)
  • 5.5 Event-driven / API-based Automation

6 Global AI-Driven Automation Market, By Technology

  • 6.1 Introduction
  • 6.2 Machine Learning
    • 6.2.1 Supervised Learning
    • 6.2.2 Unsupervised Learning
    • 6.2.3 Reinforcement Learning
    • 6.2.4 Deep Learning
      • 6.2.4.1 Convolutional Neural Networks (CNN)
      • 6.2.4.2 Recurrent Neural Networks (RNN)
      • 6.2.4.3 Generative Adversarial Networks (GANs)
  • 6.3 Natural Language Processing (NLP)
  • 6.4 Computer Vision

7 Global AI-Driven Automation Market, By Application

  • 7.1 Introduction
  • 7.2 Finance & Accounting
  • 7.3 Operations & Supply Chain
  • 7.4 Marketing & Customer Experience
  • 7.5 Manufacturing & Quality Control
  • 7.6 Clinical & Healthcare Operations
  • 7.7 Energy Infrastructure & Utilities

8 Global AI-Driven Automation Market, By End User

  • 8.1 Introduction
  • 8.2 Telecom & IT
  • 8.3 Automotive & Transportation
  • 8.4 Pharma & Diagnostics
  • 8.5 Food & Beverages
  • 8.6 Energy & Chemicals
  • 8.7 Advanced Materials

9 Global AI-Driven Automation Market, By Geography

  • 9.1 Introduction
  • 9.2 North America
    • 9.2.1 US
    • 9.2.2 Canada
    • 9.2.3 Mexico
  • 9.3 Europe
    • 9.3.1 Germany
    • 9.3.2 UK
    • 9.3.3 Italy
    • 9.3.4 France
    • 9.3.5 Spain
    • 9.3.6 Rest of Europe
  • 9.4 Asia Pacific
    • 9.4.1 Japan
    • 9.4.2 China
    • 9.4.3 India
    • 9.4.4 Australia
    • 9.4.5 New Zealand
    • 9.4.6 South Korea
    • 9.4.7 Rest of Asia Pacific
  • 9.5 South America
    • 9.5.1 Argentina
    • 9.5.2 Brazil
    • 9.5.3 Chile
    • 9.5.4 Rest of South America
  • 9.6 Middle East & Africa
    • 9.6.1 Saudi Arabia
    • 9.6.2 UAE
    • 9.6.3 Qatar
    • 9.6.4 South Africa
    • 9.6.5 Rest of Middle East & Africa

10 Key Developments

  • 10.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 10.2 Acquisitions & Mergers
  • 10.3 New Product Launch
  • 10.4 Expansions
  • 10.5 Other Key Strategies

11 Company Profiling

  • 11.1 UiPath
  • 11.2 Automation Anywhere
  • 11.3 IBM watsonx
  • 11.4 Infosys
  • 11.5 HCLTech
  • 11.6 Tata Consultancy Services (TCS)
  • 11.7 Wipro
  • 11.8 Persistent Systems
  • 11.9 Fractal Analytics
  • 11.10 Addverb Technologies
  • 11.11 Uncanny Vision
  • 11.12 Unbox Robotics
  • 11.13 Ripik.AI
  • 11.14 Jidoka Technologies
  • 11.15 Haber

List of Tables

  • Table 1 Global AI-Driven Automation Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI-Driven Automation Market Outlook, By Automation Type (2024-2032) ($MN)
  • Table 3 Global AI-Driven Automation Market Outlook, By Cognitive Automation (2024-2032) ($MN)
  • Table 4 Global AI-Driven Automation Market Outlook, By Agentic Automation (2024-2032) ($MN)
  • Table 5 Global AI-Driven Automation Market Outlook, By Intelligent Process Automation (IPA) (2024-2032) ($MN)
  • Table 6 Global AI-Driven Automation Market Outlook, By Event-driven / API-based Automation (2024-2032) ($MN)
  • Table 7 Global AI-Driven Automation Market Outlook, By Technology (2024-2032) ($MN)
  • Table 8 Global AI-Driven Automation Market Outlook, By Machine Learning (2024-2032) ($MN)
  • Table 9 Global AI-Driven Automation Market Outlook, By Supervised Learning (2024-2032) ($MN)
  • Table 10 Global AI-Driven Automation Market Outlook, By Unsupervised Learning (2024-2032) ($MN)
  • Table 11 Global AI-Driven Automation Market Outlook, By Reinforcement Learning (2024-2032) ($MN)
  • Table 12 Global AI-Driven Automation Market Outlook, By Deep Learning (2024-2032) ($MN)
  • Table 13 Global AI-Driven Automation Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
  • Table 14 Global AI-Driven Automation Market Outlook, By Computer Vision (2024-2032) ($MN)
  • Table 15 Global AI-Driven Automation Market Outlook, By Application (2024-2032) ($MN)
  • Table 16 Global AI-Driven Automation Market Outlook, By Finance & Accounting (2024-2032) ($MN)
  • Table 17 Global AI-Driven Automation Market Outlook, By Operations & Supply Chain (2024-2032) ($MN)
  • Table 18 Global AI-Driven Automation Market Outlook, By Marketing & Customer Experience (2024-2032) ($MN)
  • Table 19 Global AI-Driven Automation Market Outlook, By Manufacturing & Quality Control (2024-2032) ($MN)
  • Table 20 Global AI-Driven Automation Market Outlook, By Clinical & Healthcare Operations (2024-2032) ($MN)
  • Table 21 Global AI-Driven Automation Market Outlook, By Energy Infrastructure & Utilities (2024-2032) ($MN)
  • Table 22 Global AI-Driven Automation Market Outlook, By End User (2024-2032) ($MN)
  • Table 23 Global AI-Driven Automation Market Outlook, By Telecom & IT (2024-2032) ($MN)
  • Table 24 Global AI-Driven Automation Market Outlook, By Automotive & Transportation (2024-2032) ($MN)
  • Table 25 Global AI-Driven Automation Market Outlook, By Pharma & Diagnostics (2024-2032) ($MN)
  • Table 26 Global AI-Driven Automation Market Outlook, By Food & Beverages (2024-2032) ($MN)
  • Table 27 Global AI-Driven Automation Market Outlook, By Energy & Chemicals (2024-2032) ($MN)
  • Table 28 Global AI-Driven Automation Market Outlook, By Advanced Materials (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.