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市场调查报告书
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企业人工智慧市场 - 全球产业规模、份额、趋势、机会和预测，按部署类型、按技术、按行业、按地区和竞争划分，2019-2029F

Enterprise Artificial Intelligence Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Deployment Type, By Technology By Industry Vertical By Region & Competition, 2019-2029F
出版日期: | 出版商: TechSci Research | 英文 181 Pages | 商品交期: 2-3个工作天内

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简介目录

2023年全球企业人工智慧市场价值为114.9亿美元，预计2029年预测期间年复合成长率为34.59%。

市场概况
预测期 2025-2029
2023 年市场规模 114.9亿美元
2029 年市场规模 689.1亿美元
2024-2029 年复合年增长率 34.59%
成长最快的细分市场 BFSI
最大的市场 北美洲

近年来，在各行业广泛采用的推动下，全球企业人工智慧市场经历了显着扩张。自动驾驶汽车、医疗保健、零售和製造等关键产业已经认识到资料标籤解决方案在开发精确的人工智慧和机器学习模型中的重要性，最终提高业务成果。

更严格的监管框架以及对生产力和效率的日益关注促使组织对先进的资料标籤技术进行了大量投资。领先的资料註释平台供应商推出了创新产品，具有处理多个来源的资料、协作工作流程管理和智慧专案监督等功能。这些增强功能显着提高了资料註释的品质和可扩展性。

电脑视觉、自然语言处理和行动资料收集等技术的整合正在彻底改变资料标籤解决方案的功能。先进的解决方案现在提供自动註释帮助、即时分析和对专案进度的洞察。这使企业能够更好地监督资料质量，从资料资产中提取更大的价值，并加快人工智慧的开发週期。

该公司正在积极与资料註释专家建立合作伙伴关係，以设计满足其特定资料和用例要求的客製化解决方案。此外，对数据驱动决策的日益重视正在为各行业垂直领域带来新的前景。

随着自动驾驶汽车、医疗保健和零售等行业的数位转型倡议继续获得动力，企业人工智慧市场已做好持续成长的准备。全球对新功能的持续投资预计将透过提供大规模、高品质的註释的训练资料来增强市场支援人工智慧和机器学习的能力，最终塑造其长期前景。

主要市场驱动因素

数据扩散和可访问性

人工智慧技术的进步

竞争优势与市场动态

主要市场挑战

数据品质和可用性

道德和监管考虑

主要市场趋势

采用可解释的人工智慧

人工智慧与边缘运算的融合

关注负责任的人工智慧和道德考虑

细分市场洞察

依部署类型见解

区域洞察

目录

第 1 章：服务概述

  • 市场定义
  • 市场范围
    • 涵盖的市场
    • 考虑学习的年份
    • 主要市场区隔

第 2 章：研究方法

第 3 章：执行摘要

第 4 章：客户之声

第 5 章：全球企业人工智慧市场概述

第 6 章：全球企业人工智慧市场展望

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 依部署类型（云端、本机）
    • 按技术（机器学习、自然语言处理、电脑视觉、语音辨识、其他）
    • 按行业垂直（IT 和电信、BFSI、汽车、医疗保健、政府和国防、零售、其他）
    • 按地区
  • 按公司划分 (2023)
  • 市场地图

第 7 章：北美企业人工智慧市场展望

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 依部署类型
    • 依技术
    • 按行业垂直
    • 按国家/地区
  • 北美：国家分析
    • 美国
    • 加拿大
    • 墨西哥

第 8 章：欧洲企业人工智慧市场展望

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 依部署类型
    • 依技术
    • 按行业垂直
    • 按国家/地区
  • 欧洲：国家分析
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙

第9章：亚太企业人工智慧市场展望

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 依部署类型
    • 依技术
    • 按行业垂直
    • 按国家/地区
  • 亚太地区：国家分析
    • 中国
    • 印度
    • 日本
    • 韩国
    • 澳洲

第10章：南美洲企业人工智慧市场展望

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 依部署类型
    • 依技术
    • 按行业垂直
    • 按国家/地区
  • 南美洲：国家分析
    • 巴西
    • 阿根廷
    • 哥伦比亚

第11章：中东和非洲企业人工智慧市场展望

  • 市场规模及预测
    • 按价值
  • 市占率及预测
    • 依部署类型
    • 依技术
    • 按行业垂直
    • 按国家/地区
  • MEA：国家分析
    • 南非
    • 沙乌地阿拉伯
    • 阿联酋
    • 科威特
    • 土耳其
    • 埃及

第 12 章：市场动态

  • 司机
  • 挑战

第 13 章：市场趋势与发展

第 14 章：公司简介

  • Intel Corporation
  • IBM Corporation
  • Amazon Web Services, Inc
  • Google, LLC
  • Microsoft Corporation
  • SAS Institute Inc
  • SAP SE
  • Salesforce, Inc.
  • Fair Isaac Corporation.
  • Oracle Corporation

第 15 章：策略建议

第16章调查会社について・免责事项

简介目录
Product Code: 20264

Global Enterprise Artificial Intelligence market was valued at USD 11.49 billion in 2023 and is projected to register a compound annual growth rate of 34.59% during the forecast period through 2029.

Market Overview
Forecast Period2025-2029
Market Size 2023USD 11.49 Billion
Market Size 2029USD 68.91 Billion
CAGR 2024-202934.59%
Fastest Growing SegmentBFSI
Largest MarketNorth America

The global Enterprise Artificial Intelligence market has experienced significant expansion in recent times, driven by its widespread adoption across a variety of industries. Key sectors, including autonomous vehicles, healthcare, retail, and manufacturing, have come to recognize the importance of data labeling solutions in the development of precise Artificial Intelligence and Machine Learning models, ultimately enhancing business outcomes.

Stricter regulatory frameworks and an increased focus on productivity and efficiency have prompted organizations to make substantial investments in advanced data labeling technologies. Leading providers of data annotation platforms have introduced innovative offerings, featuring capabilities such as handling data from multiple sources, collaborative workflow management, and intelligent project oversight. These enhancements have markedly improved the quality and scalability of data annotation.

The integration of technologies such as computer vision, natural language processing, and mobile data collection is revolutionizing the capabilities of data labeling solutions. Advanced solutions now offer automated annotation assistance, real-time analytics, and insights into project progression. This empowers businesses to better oversee data quality, extract greater value from data assets, and expedite the development cycles of Artificial Intelligence.

Companies are actively forming partnerships with data annotation specialists to devise tailored solutions that cater to their specific data and use case requirements. Furthermore, the growing emphasis on data-driven decision-making is generating new prospects across various industry verticals.

The Enterprise Artificial Intelligence market is well-positioned for sustained growth as digital transformation initiatives continue to gain momentum in sectors such as autonomous vehicles, healthcare, and retail, among others. The persistent global investments in new capabilities are expected to bolster the market's capacity to support Artificial Intelligence and Machine Learning through the provision of large-scale, high-quality annotated training data, ultimately shaping its long-term prospects.

Key Market Drivers

Data Proliferation and Accessibility

In the age of digital transformation, data has become the lifeblood of enterprises. The exponential growth of data generated from a myriad of sources, such as sensors, social media, and connected devices, has created a treasure trove of information waiting to be harnessed. This vast and diverse dataset availability is the first driver propelling the Enterprise AI market.

The advent of big data has ushered in a new era of opportunities and challenges. Enterprises can now tap into previously unimaginable volumes of data to gain insights, optimize processes, and drive innovation. AI, with its sophisticated algorithms, offers the means to extract actionable insights from these colossal datasets, providing organizations with a competitive edge.

The democratization of data access through cloud computing and data-sharing platforms has empowered businesses of all sizes to leverage AI. Small and medium-sized enterprises (SMEs) can now access AI capabilities that were once reserved for tech giants, fostering a more level playing field in the market.

AI-powered analytics enable organizations to gain a deeper understanding of customer preferences and behaviors. This allows for the delivery of highly personalized experiences, which is particularly crucial in industries like e-commerce, marketing, and retail. As consumers increasingly expect tailored offerings, AI-driven insights are a potent tool for customer retention and revenue growth.

Advancements in AI Technologies

The second driver fueling the Enterprise AI market is the relentless advancement of AI technologies themselves. AI is no longer confined to basic automation; it has evolved into a sophisticated toolkit with the potential to revolutionize how businesses operate.

Machine Learning (ML) and Deep Learning (DL) are at the forefront of AI innovation. These technologies enable computers to learn and make decisions without explicit programming. Businesses are deploying ML and DL algorithms for tasks ranging from predictive maintenance in manufacturing to fraud detection in finance.

NLP, a branch of AI that focuses on human language understanding, has opened up opportunities for chatbots, virtual assistants, and sentiment analysis. These applications enhance customer service, streamline communication, and provide valuable insights from unstructured text data.

Computer vision allows machines to interpret and understand visual information from the world, making it invaluable in sectors like healthcare for medical image analysis, in retail for cashier-less checkout, and in autonomous vehicles for object recognition and navigation.

The integration of AI at the edge, closer to where data is generated (e.g., IoT devices), reduces latency and enhances real-time decision-making. This is especially critical in applications like autonomous vehicles, smart cities, and industrial automation.

Competitive Advantage and Market Dynamics

The third driver for the Enterprise AI market is the relentless pursuit of competitive advantage in a rapidly changing business environment. As organizations recognize the transformative potential of AI, they are driven by several dynamics to adopt and invest in AI solutions.

In many industries, AI is becoming a disruptive force. Companies that fail to embrace AI risk becoming obsolete as competitors leverage AI to improve operational efficiency, enhance customer experiences, and introduce innovative products and services.

AI-driven automation streamlines workflows and reduces operational costs. Businesses can automate repetitive tasks, optimize supply chains, and make data-driven decisions, resulting in improved productivity and profitability. AI empowers organizations to make data-driven decisions with greater accuracy and speed. This is particularly valuable in sectors where timely decision-making is critical, such as finance, healthcare, and cybersecurity. Businesses are increasingly adopting customer-centric approaches, and AI plays a pivotal role in delivering personalized experiences. This not only improves customer satisfaction but also drives loyalty and revenue growth.

The Enterprise AI market is on a trajectory of remarkable growth, driven by the proliferation of data, advancements in AI technologies, and the pursuit of competitive advantage in the dynamic business landscape. Organizations that strategically harness the power of AI stand to gain a substantial edge in their respective markets. As these drivers continue to evolve, businesses must adapt and innovate to stay ahead in the era of AI-driven transformation.

Key Market Challenges

Data Quality and Availability

One of the significant challenges facing the Enterprise Artificial Intelligence market is the quality and availability of data. AI algorithms heavily rely on large volumes of high-quality data to train and make accurate predictions. However, many organizations struggle with data quality issues such as incomplete, inconsistent, or biased data. Poor data quality can lead to inaccurate AI models and unreliable insights, undermining the effectiveness of AI implementation.

Moreover, data availability can be a challenge, especially for organizations that lack a centralized data infrastructure or have fragmented data sources. Data silos and lack of integration across systems can hinder the accessibility and availability of data for AI initiatives. This can limit the scope and impact of AI applications within the enterprise.

Addressing these challenges requires organizations to invest in robust data management strategies, including data cleansing, normalization, and enrichment processes. It is crucial to establish data governance frameworks that ensure data quality and integrity throughout its lifecycle. Additionally, organizations need to prioritize data integration efforts to consolidate data from various sources and make it readily available for AI applications.

Ethical and Regulatory Considerations

Another significant challenge in the Enterprise Artificial Intelligence market is navigating the ethical and regulatory considerations associated with AI implementation. As AI technologies become more sophisticated and pervasive, concerns around privacy, bias, transparency, and accountability arise.

Ethical considerations revolve around the responsible use of AI and ensuring that AI systems do not perpetuate biases or discriminate against certain groups. Organizations need to be mindful of the potential ethical implications of AI algorithms and ensure that they align with societal values and norms.

Regulatory challenges come into play as governments and regulatory bodies introduce new laws and regulations to govern AI technologies. Compliance with data protection regulations, such as the General Data Protection Regulation (GDPR), becomes crucial when dealing with sensitive customer data. Organizations need to navigate these regulatory landscapes and ensure that their AI implementations adhere to the necessary legal requirements.

To address these challenges, organizations should adopt ethical AI frameworks and guidelines that promote fairness, transparency, and accountability. They should also invest in robust data privacy and security measures to protect sensitive information. Collaboration with regulatory bodies and industry associations can help organizations stay updated on evolving regulations and ensure compliance with ethical and legal standards.

Key Market Trends

Adoption of Explainable AI

One of the prominent trends in the Enterprise Artificial Intelligence market is the adoption of Explainable AI (XAI). As AI systems become more complex and make critical decisions that impact businesses and individuals, there is a growing need for transparency and interpretability. Explainable AI techniques aim to provide insights into how AI models arrive at their decisions, enabling stakeholders to understand the underlying factors and reasoning. This trend is driven by the desire to build trust in AI systems, especially in highly regulated industries such as finance, healthcare, and legal. By adopting Explainable AI, organizations can ensure compliance, mitigate bias, and enhance accountability, ultimately fostering greater acceptance and adoption of AI technologies.

Integration of AI with Edge Computing

Another significant trend in the Enterprise Artificial Intelligence market is the integration of AI with edge computing. Edge computing refers to the processing and analysis of data at or near the source, rather than relying on centralized cloud infrastructure. This trend is driven by the need for real-time decision-making, reduced latency, and enhanced data privacy. By deploying AI models directly on edge devices, such as IoT devices, edge servers, or gateways, organizations can leverage the power of AI to process and analyze data locally. This enables faster response times, improved operational efficiency, and cost savings by reducing the need for data transmission to the cloud. The integration of AI with edge computing also addresses concerns related to data privacy and security, as sensitive data can be processed and analyzed locally without being transmitted to external servers. This trend is particularly relevant in industries such as manufacturing, transportation, and healthcare, where real-time insights and immediate actions are crucial.

Focus on Responsible AI and Ethical Considerations

A significant trend shaping the Enterprise Artificial Intelligence market is the increasing focus on responsible AI and ethical considerations. As AI technologies become more pervasive, there is a growing recognition of the potential risks and challenges associated with their deployment. Organizations are placing greater emphasis on ensuring that AI systems are developed and deployed in a responsible and ethical manner. This includes addressing issues such as bias, fairness, transparency, and accountability. Responsible AI practices involve considering the societal impact of AI applications, ensuring fairness and inclusivity, and safeguarding against unintended consequences. Organizations are adopting frameworks and guidelines, such as the AI Ethics Principles, to guide the development and deployment of AI systems. Additionally, collaborations between industry, academia, and regulatory bodies are being formed to establish standards and best practices for responsible AI. This trend is driven by the need to build trust among stakeholders, comply with regulations, and mitigate potential reputational and legal risks associated with unethical AI practices.

Segmental Insights

By Deployment Type Insights

In 2023, the cloud deployment segment dominated the Enterprise Artificial Intelligence (AI) Market and is expected to maintain its dominance during the forecast period. The cloud deployment model involves hosting AI applications and infrastructure on cloud platforms provided by third-party service providers. This dominance can be attributed to several factors that highlight the advantages of cloud deployment in the context of enterprise AI.

The cloud deployment model offers scalability and flexibility, allowing organizations to easily scale their AI infrastructure and resources based on their needs. This is particularly beneficial in the context of AI, where large amounts of data and computational power are required for training and inference tasks. Cloud platforms provide on-demand access to computing resources, enabling organizations to efficiently handle the resource-intensive nature of AI workloads.

The cloud deployment model offers cost-effectiveness and reduced upfront investment. By leveraging cloud services, organizations can avoid the need for significant upfront investments in hardware, software, and infrastructure. Instead, they can pay for the resources they consume on a pay-as-you-go basis, resulting in cost savings and improved financial flexibility. This makes AI more accessible to a wider range of organizations, including small and medium-sized enterprises (SMEs), who may not have the resources to invest in on-premises infrastructure.

Furthermore, the cloud deployment model provides ease of implementation and management. Cloud service providers offer pre-configured AI services and tools that simplify the deployment and management of AI applications. This reduces the complexity and technical expertise required to set up and maintain AI infrastructure, enabling organizations to focus on developing and deploying AI models rather than managing the underlying infrastructure.

Looking ahead, the cloud deployment segment is expected to maintain its dominance in the Enterprise AI Market during the forecast period. The increasing adoption of cloud computing across industries, advancements in cloud technologies, and the growing availability of AI-specific services and tools on cloud platforms will continue to drive the preference for cloud deployment. Additionally, the ongoing digital transformation initiatives and the need for agility and scalability in AI implementations will further fuel the demand for cloud-based AI solutions..

Regional Insights

In 2023, North America dominated the Enterprise Artificial Intelligence (AI) Market and is expected to maintain its dominance during the forecast period. North America's dominance can be attributed to several factors that highlight the region's strong position in the AI industry.

North America has been at the forefront of AI research and development, with leading technology companies, research institutions, and startups driving innovation in the field. The region is home to major AI hubs such as Silicon Valley, which has fostered a culture of technological advancement and entrepreneurship. This ecosystem has facilitated the availability of cutting-edge AI solutions and attracted investments from businesses across various industries.

North America has a robust infrastructure and technological capabilities that support the implementation and adoption of AI technologies. The region has advanced cloud computing infrastructure, high-speed internet connectivity, and a mature ecosystem of AI service providers. This enables organizations in North America to leverage AI technologies effectively and integrate them into their business processes.

North America has a diverse range of industries that heavily rely on AI technologies, such as healthcare, finance, retail, and manufacturing. These industries recognize the potential of AI in improving operational efficiency, enhancing customer experiences, and gaining a competitive edge. The demand for AI solutions in North America is driven by the need to leverage data-driven insights, automate processes, and drive innovation.

Looking ahead, North America is expected to maintain its dominance in the Enterprise AI Market during the forecast period. The region's strong AI ecosystem, technological capabilities, and industry demand for AI solutions will continue to drive the market. Additionally, ongoing investments in AI research and development, collaborations between academia and industry, and favorable government policies further contribute to North America's leadership position in the Enterprise AI Market. As businesses across industries continue to embrace AI technologies, the demand for advanced AI solutions in North America will remain strong, solidifying its dominance in the market.

Key Market Players

  • Intel Corporation
  • IBM Corporation
  • Amazon Web Services, Inc
  • Google, LLC
  • Microsoft Corporation
  • SAP SE
  • Salesforce, Inc.
  • Fair Isaac Corporation
  • SAS Institute Inc
  • Oracle Corporation

Report Scope:

In this report, the Global Enterprise Artificial Intelligence Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

Enterprise Artificial Intelligence Market, By Deployment Type:

  • Cloud
  • On-premises

Enterprise Artificial Intelligence Market, By Technology:

  • Machine learning
  • Natural language processing
  • Computer vision
  • Speech recognition
  • Others

Enterprise Artificial Intelligence Market, By Industry Vertical:

  • IT and telecom
  • BFSI
  • Automotive
  • Healthcare
  • Government and Defense
  • Retail
  • Others

Enterprise Artificial Intelligence Market, By Region:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • France
    • United Kingdom
    • Italy
    • Germany
    • Spain
  • Asia-Pacific
    • China
    • India
    • Japan
    • Australia
    • South Korea
  • South America
    • Brazil
    • Argentina
    • Colombia
  • Middle East & Africa
    • South Africa
    • Saudi Arabia
    • UAE
    • Kuwait
    • Turkey
    • Egypt

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global Enterprise Artificial Intelligence Market.

Available Customizations:

Global Enterprise Artificial Intelligence Market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

  • Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1. Service Overview

  • 1.1. Market Definition
  • 1.2. Scope of the Market
    • 1.2.1. Markets Covered
    • 1.2.2. Years Considered for Study
    • 1.2.3. Key Market Segmentations

2. Research Methodology

  • 2.1. Objective of the Study
  • 2.2. Baseline Methodology
  • 2.3. Formulation of the Scope
  • 2.4. Assumptions and Limitations
  • 2.5. Types of Research
    • 2.5.1. Secondary Research
    • 2.5.2. Primary Research
  • 2.6. Approach for the Market Study
    • 2.6.1. The Bottom-Up Approach
    • 2.6.2. The Top-Down Approach
  • 2.7. Methodology Followed for Calculation of Market Size & Market Shares
  • 2.8. Forecasting Methodology
    • 2.8.1. Data Triangulation & Validation

3. Executive Summary

4. Voice of Customer

5. Global Enterprise Artificial Intelligence Market Overview

6. Global Enterprise Artificial Intelligence Market Outlook

  • 6.1. Market Size & Forecast
    • 6.1.1. By Value
  • 6.2. Market Share & Forecast
    • 6.2.1. By Deployment Type (Cloud, On-premises)
    • 6.2.2. By Technology (Machine learning, Natural language processing, Computer vision, Speech recognition, Others)
    • 6.2.3. By Industry Vertical (IT and telecom, BFSI, Automotive, Healthcare, Government and Defense, Retail, Others)
    • 6.2.4. By Region
  • 6.3. By Company (2023)
  • 6.4. Market Map

7. North America Enterprise Artificial Intelligence Market Outlook

  • 7.1. Market Size & Forecast
    • 7.1.1. By Value
  • 7.2. Market Share & Forecast
    • 7.2.1. By Deployment Type
    • 7.2.2. By Technology
    • 7.2.3. By Industry Vertical
    • 7.2.4. By Country
  • 7.3. North America: Country Analysis
    • 7.3.1. United States Enterprise Artificial Intelligence Market Outlook
      • 7.3.1.1. Market Size & Forecast
        • 7.3.1.1.1. By Value
      • 7.3.1.2. Market Share & Forecast
        • 7.3.1.2.1. By Deployment Type
        • 7.3.1.2.2. By Technology
        • 7.3.1.2.3. By Industry Vertical
    • 7.3.2. Canada Enterprise Artificial Intelligence Market Outlook
      • 7.3.2.1. Market Size & Forecast
        • 7.3.2.1.1. By Value
      • 7.3.2.2. Market Share & Forecast
        • 7.3.2.2.1. By Deployment Type
        • 7.3.2.2.2. By Technology
        • 7.3.2.2.3. By Industry Vertical
    • 7.3.3. Mexico Enterprise Artificial Intelligence Market Outlook
      • 7.3.3.1. Market Size & Forecast
        • 7.3.3.1.1. By Value
      • 7.3.3.2. Market Share & Forecast
        • 7.3.3.2.1. By Deployment Type
        • 7.3.3.2.2. By Technology
        • 7.3.3.2.3. By Industry Vertical

8. Europe Enterprise Artificial Intelligence Market Outlook

  • 8.1. Market Size & Forecast
    • 8.1.1. By Value
  • 8.2. Market Share & Forecast
    • 8.2.1. By Deployment Type
    • 8.2.2. By Technology
    • 8.2.3. By Industry Vertical
    • 8.2.4. By Country
  • 8.3. Europe: Country Analysis
    • 8.3.1. Germany Enterprise Artificial Intelligence Market Outlook
      • 8.3.1.1. Market Size & Forecast
        • 8.3.1.1.1. By Value
      • 8.3.1.2. Market Share & Forecast
        • 8.3.1.2.1. By Deployment Type
        • 8.3.1.2.2. By Technology
        • 8.3.1.2.3. By Industry Vertical
    • 8.3.2. United Kingdom Enterprise Artificial Intelligence Market Outlook
      • 8.3.2.1. Market Size & Forecast
        • 8.3.2.1.1. By Value
      • 8.3.2.2. Market Share & Forecast
        • 8.3.2.2.1. By Deployment Type
        • 8.3.2.2.2. By Technology
        • 8.3.2.2.3. By Industry Vertical
    • 8.3.3. Italy Enterprise Artificial Intelligence Market Outlook
      • 8.3.3.1. Market Size & Forecast
        • 8.3.3.1.1. By Value
      • 8.3.3.2. Market Share & Forecast
        • 8.3.3.2.1. By Deployment Type
        • 8.3.3.2.2. By Technology
        • 8.3.3.2.3. By Industry Vertical
    • 8.3.4. France Enterprise Artificial Intelligence Market Outlook
      • 8.3.4.1. Market Size & Forecast
        • 8.3.4.1.1. By Value
      • 8.3.4.2. Market Share & Forecast
        • 8.3.4.2.1. By Deployment Type
        • 8.3.4.2.2. By Technology
        • 8.3.4.2.3. By Industry Vertical
    • 8.3.5. Spain Enterprise Artificial Intelligence Market Outlook
      • 8.3.5.1. Market Size & Forecast
        • 8.3.5.1.1. By Value
      • 8.3.5.2. Market Share & Forecast
        • 8.3.5.2.1. By Deployment Type
        • 8.3.5.2.2. By Technology
        • 8.3.5.2.3. By Industry Vertical

9. Asia-Pacific Enterprise Artificial Intelligence Market Outlook

  • 9.1. Market Size & Forecast
    • 9.1.1. By Value
  • 9.2. Market Share & Forecast
    • 9.2.1. By Deployment Type
    • 9.2.2. By Technology
    • 9.2.3. By Industry Vertical
    • 9.2.4. By Country
  • 9.3. Asia-Pacific: Country Analysis
    • 9.3.1. China Enterprise Artificial Intelligence Market Outlook
      • 9.3.1.1. Market Size & Forecast
        • 9.3.1.1.1. By Value
      • 9.3.1.2. Market Share & Forecast
        • 9.3.1.2.1. By Deployment Type
        • 9.3.1.2.2. By Technology
        • 9.3.1.2.3. By Industry Vertical
    • 9.3.2. India Enterprise Artificial Intelligence Market Outlook
      • 9.3.2.1. Market Size & Forecast
        • 9.3.2.1.1. By Value
      • 9.3.2.2. Market Share & Forecast
        • 9.3.2.2.1. By Deployment Type
        • 9.3.2.2.2. By Technology
        • 9.3.2.2.3. By Industry Vertical
    • 9.3.3. Japan Enterprise Artificial Intelligence Market Outlook
      • 9.3.3.1. Market Size & Forecast
        • 9.3.3.1.1. By Value
      • 9.3.3.2. Market Share & Forecast
        • 9.3.3.2.1. By Deployment Type
        • 9.3.3.2.2. By Technology
        • 9.3.3.2.3. By Industry Vertical
    • 9.3.4. South Korea Enterprise Artificial Intelligence Market Outlook
      • 9.3.4.1. Market Size & Forecast
        • 9.3.4.1.1. By Value
      • 9.3.4.2. Market Share & Forecast
        • 9.3.4.2.1. By Deployment Type
        • 9.3.4.2.2. By Technology
        • 9.3.4.2.3. By Industry Vertical
    • 9.3.5. Australia Enterprise Artificial Intelligence Market Outlook
      • 9.3.5.1. Market Size & Forecast
        • 9.3.5.1.1. By Value
      • 9.3.5.2. Market Share & Forecast
        • 9.3.5.2.1. By Deployment Type
        • 9.3.5.2.2. By Technology
        • 9.3.5.2.3. By Industry Vertical

10. South America Enterprise Artificial Intelligence Market Outlook

  • 10.1. Market Size & Forecast
    • 10.1.1. By Value
  • 10.2. Market Share & Forecast
    • 10.2.1. By Deployment Type
    • 10.2.2. By Technology
    • 10.2.3. By Industry Vertical
    • 10.2.4. By Country
  • 10.3. South America: Country Analysis
    • 10.3.1. Brazil Enterprise Artificial Intelligence Market Outlook
      • 10.3.1.1. Market Size & Forecast
        • 10.3.1.1.1. By Value
      • 10.3.1.2. Market Share & Forecast
        • 10.3.1.2.1. By Deployment Type
        • 10.3.1.2.2. By Technology
        • 10.3.1.2.3. By Industry Vertical
    • 10.3.2. Argentina Enterprise Artificial Intelligence Market Outlook
      • 10.3.2.1. Market Size & Forecast
        • 10.3.2.1.1. By Value
      • 10.3.2.2. Market Share & Forecast
        • 10.3.2.2.1. By Deployment Type
        • 10.3.2.2.2. By Technology
        • 10.3.2.2.3. By Industry Vertical
    • 10.3.3. Colombia Enterprise Artificial Intelligence Market Outlook
      • 10.3.3.1. Market Size & Forecast
        • 10.3.3.1.1. By Value
      • 10.3.3.2. Market Share & Forecast
        • 10.3.3.2.1. By Deployment Type
        • 10.3.3.2.2. By Technology
        • 10.3.3.2.3. By Industry Vertical

11. Middle East and Africa Enterprise Artificial Intelligence Market Outlook

  • 11.1. Market Size & Forecast
    • 11.1.1. By Value
  • 11.2. Market Share & Forecast
    • 11.2.1. By Deployment Type
    • 11.2.2. By Technology
    • 11.2.3. By Industry Vertical
    • 11.2.4. By Country
  • 11.3. MEA: Country Analysis
    • 11.3.1. South Africa Enterprise Artificial Intelligence Market Outlook
      • 11.3.1.1. Market Size & Forecast
        • 11.3.1.1.1. By Value
      • 11.3.1.2. Market Share & Forecast
        • 11.3.1.2.1. By Deployment Type
        • 11.3.1.2.2. By Technology
        • 11.3.1.2.3. By Industry Vertical
    • 11.3.2. Saudi Arabia Enterprise Artificial Intelligence Market Outlook
      • 11.3.2.1. Market Size & Forecast
        • 11.3.2.1.1. By Value
      • 11.3.2.2. Market Share & Forecast
        • 11.3.2.2.1. By Deployment Type
        • 11.3.2.2.2. By Technology
        • 11.3.2.2.3. By Industry Vertical
    • 11.3.3. UAE Enterprise Artificial Intelligence Market Outlook
      • 11.3.3.1. Market Size & Forecast
        • 11.3.3.1.1. By Value
      • 11.3.3.2. Market Share & Forecast
        • 11.3.3.2.1. By Deployment Type
        • 11.3.3.2.2. By Technology
        • 11.3.3.2.3. By Industry Vertical
    • 11.3.4. Kuwait Enterprise Artificial Intelligence Market Outlook
      • 11.3.4.1. Market Size & Forecast
        • 11.3.4.1.1. By Value
      • 11.3.4.2. Market Share & Forecast
        • 11.3.4.2.1. By Deployment Type
        • 11.3.4.2.2. By Technology
        • 11.3.4.2.3. By Industry Vertical
    • 11.3.5. Turkey Enterprise Artificial Intelligence Market Outlook
      • 11.3.5.1. Market Size & Forecast
        • 11.3.5.1.1. By Value
      • 11.3.5.2. Market Share & Forecast
        • 11.3.5.2.1. By Deployment Type
        • 11.3.5.2.2. By Technology
        • 11.3.5.2.3. By Industry Vertical
    • 11.3.6. Egypt Enterprise Artificial Intelligence Market Outlook
      • 11.3.6.1. Market Size & Forecast
        • 11.3.6.1.1. By Value
      • 11.3.6.2. Market Share & Forecast
        • 11.3.6.2.1. By Deployment Type
        • 11.3.6.2.2. By Technology
        • 11.3.6.2.3. By Industry Vertical

12. Market Dynamics

  • 12.1. Drivers
  • 12.2. Challenges

13. Market Trends & Developments

14. Company Profiles

  • 14.1. Intel Corporation
    • 14.1.1. Business Overview
    • 14.1.2. Key Revenue and Financials
    • 14.1.3. Recent Developments
    • 14.1.4. Key Personnel/Key Contact Person
    • 14.1.5. Key Product/Services Offered
  • 14.2. IBM Corporation
    • 14.2.1. Business Overview
    • 14.2.2. Key Revenue and Financials
    • 14.2.3. Recent Developments
    • 14.2.4. Key Personnel/Key Contact Person
    • 14.2.5. Key Product/Services Offered
  • 14.3. Amazon Web Services, Inc
    • 14.3.1. Business Overview
    • 14.3.2. Key Revenue and Financials
    • 14.3.3. Recent Developments
    • 14.3.4. Key Personnel/Key Contact Person
    • 14.3.5. Key Product/Services Offered
  • 14.4. Google, LLC
    • 14.4.1. Business Overview
    • 14.4.2. Key Revenue and Financials
    • 14.4.3. Recent Developments
    • 14.4.4. Key Personnel/Key Contact Person
    • 14.4.5. Key Product/Services Offered
  • 14.5. Microsoft Corporation
    • 14.5.1. Business Overview
    • 14.5.2. Key Revenue and Financials
    • 14.5.3. Recent Developments
    • 14.5.4. Key Personnel/Key Contact Person
    • 14.5.5. Key Product/Services Offered
  • 14.6. SAS Institute Inc
    • 14.6.1. Business Overview
    • 14.6.2. Key Revenue and Financials
    • 14.6.3. Recent Developments
    • 14.6.4. Key Personnel/Key Contact Person
    • 14.6.5. Key Product/Services Offered
  • 14.7. SAP SE
    • 14.7.1. Business Overview
    • 14.7.2. Key Revenue and Financials
    • 14.7.3. Recent Developments
    • 14.7.4. Key Personnel/Key Contact Person
    • 14.7.5. Key Product/Services Offered
  • 14.8. Salesforce, Inc.
    • 14.8.1. Business Overview
    • 14.8.2. Key Revenue and Financials
    • 14.8.3. Recent Developments
    • 14.8.4. Key Personnel/Key Contact Person
    • 14.8.5. Key Product/Services Offered
  • 14.9. Fair Isaac Corporation.
    • 14.9.1. Business Overview
    • 14.9.2. Key Revenue and Financials
    • 14.9.3. Recent Developments
    • 14.9.4. Key Personnel/Key Contact Person
    • 14.9.5. Key Product/Services Offered
  • 14.10. Oracle Corporation
    • 14.10.1. Business Overview
    • 14.10.2. Key Revenue and Financials
    • 14.10.3. Recent Developments
    • 14.10.4. Key Personnel/Key Contact Person
    • 14.10.5. Key Product/Services Offered

15. Strategic Recommendations

16. About Us & Disclaimer