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1613921

分析市場中的產生人工智慧 - 全球產業規模、佔有率、趨勢、機會和預測，按部署、技術、應用、地區和競爭細分，2019-2029F

Generative AI in Analytics Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Deployment, By Technology, By Application, By Region & Competition, 2019-2029F
出版日期: | 出版商: TechSci Research | 英文 185 Pages | 商品交期: 2-3個工作天內

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簡介目錄

2023年，全球生成式人工智慧分析市值為9.2875億美元，預計2029年將達到40.0877億美元，2029年複合年成長率為27.60%。

市場概況
預測期 2025-2029
2023 年市場規模 9.2875億美元
2029 年市場規模 400877萬美元
2024-2029 年複合年成長率 27.60%
成長最快的細分市場 雲
最大的市場 北美洲

分析中的生成式人工智慧是指人工智慧技術的應用，它根據從現有資料中學到的模式創建新的資料或見解。這涉及複雜的機器學習演算法，包括深度學習和自然語言處理 (NLP)，以產生預測模型、自動解釋資料並提供可行的見解。與主要專注於解釋歷史資料的傳統分析不同，生成式人工智慧可以模擬各種場景，預測未來趨勢，並透過綜合新資料提出最佳行動建議。此功能透過提供更動態、準確和個人化的見解來徹底改變分析市場，從而顯著增強跨行業的決策。由於幾個關鍵因素，分析中的生成人工智慧市場可望大幅成長。跨部門產生的資料量和複雜性不斷增加，推動了對能夠有效處理和理解大量資料集的高級分析解決方案的需求。企業越來越認知到數據驅動策略的價值，這刺激了對更複雜的分析工具的需求，這些工具可以提供更深入、更可行的見解。人工智慧技術和運算能力的進步使生成模型更容易獲得且更具成本效益，鼓勵大型企業和中小型企業更廣泛地採用。對個人化客戶體驗和即時決策的日益重視正在推動組織採用生成式人工智慧解決方案，以提供量身定做的建議和即時見解。隨著越來越多的行業將生成式人工智慧整合到其分析流程中，在持續的技術進步、資料複雜性的增加以及對決策精度和效率不斷成長的需求的推動下，市場將繼續擴大。隨著組織尋求利用生成式人工智慧來保持競爭優勢並最佳化其營運，這種動態成長軌跡預計將加速。

主要市場促進因素

數據量和複雜度不斷增加

越來越重視數據驅動的決策

對即時和個人化見解的需求

先進分析解決方案的成本效益與效率

主要市場挑戰

人工智慧模型中的偏見與公平性

資料隱私和安全問題

整合和實施複雜性

主要市場趨勢

不同產業擴大採用生成式人工智慧

生成模型和演算法的進步

產生人工智慧與雲端運算的整合

細分市場洞察

技術洞察

區域洞察

目錄

第 1 章：服務概述

  • 市場定義
  • 市場範圍
    • 涵蓋的市場
    • 研究年份
    • 主要市場區隔

第 2 章：研究方法

第 3 章：執行摘要

第 4 章：客戶之聲

第 5 章：全球生成式人工智慧分析市場概述

第 6 章：全球生成式人工智慧分析市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按部署（基於雲端、本地）
    • 按技術（自然語言處理、機器學習、深度學習、其他）
    • 按應用（預測和預測、自動報告、異常檢測、個人化）
    • 按地區（北美、歐洲、南美、中東和非洲、亞太地區）
  • 按公司分類 (2023)
  • 市場地圖

第 7 章：北美生成式人工智慧分析市場前景

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按部署
    • 依技術
    • 按申請
    • 按國家/地區
  • 北美：國家分析
    • 美國
    • 加拿大
    • 墨西哥

第 8 章：歐洲生成式人工智慧分析市場前景

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按部署
    • 依技術
    • 按申請
    • 按國家/地區
  • 歐洲：國家分析
    • 德國
    • 法國
    • 英國
    • 義大利
    • 西班牙
    • 比利時

第 9 章：亞太地區生成式人工智慧分析市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按部署
    • 依技術
    • 按申請
    • 按國家/地區
  • 亞太地區：國家分析
    • 中國
    • 印度
    • 日本
    • 韓國
    • 澳洲
    • 印尼
    • 越南

第 10 章：南美洲生成式人工智慧分析市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按部署
    • 依技術
    • 按申請
    • 按國家/地區
  • 南美洲：國家分析
    • 巴西
    • 哥倫比亞
    • 阿根廷
    • 智利

第 11 章：中東和非洲生成式人工智慧分析市場前景

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按部署
    • 依技術
    • 按申請
    • 按國家/地區
  • 中東和非洲：國家分析
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 南非
    • 土耳其
    • 以色列

第 12 章：市場動態

  • 促進要素
  • 挑戰

第 13 章：市場趨勢與發展

第 14 章：公司簡介

  • OpenAI OpCo, LLC
  • IBM Corporation
  • Microsoft Corporation
  • Google LLC
  • Amazon Web Services, Inc.
  • NVIDIA Corporation
  • Salesforce, Inc.
  • SAP SE
  • Oracle Corporation
  • Palantir Technologies Inc.
  • DataRobot, Inc.
  • H2O.ai, Inc.

第 15 章：策略建議

第16章調查會社について,免責事項

簡介目錄
Product Code: 24916

The Global generative AI in analytics market was valued at USD 928.75 million in 2023 and is expected to reach USD 4008.77 million by 2029 with a CAGR of 27.60% through 2029.

Market Overview
Forecast Period2025-2029
Market Size 2023USD 928.75 Million
Market Size 2029USD 4008.77 Million
CAGR 2024-202927.60%
Fastest Growing SegmentCloud
Largest MarketNorth America

Generative AI in analytics refers to the application of artificial intelligence technologies that create new data or insights based on patterns learned from existing data. This involves sophisticated machine learning algorithms, including deep learning and natural language processing (NLP), to generate predictive models, automate data interpretation, and provide actionable insights. Unlike traditional analytics, which primarily focuses on interpreting historical data, generative AI can simulate various scenarios, forecast future trends, and suggest optimal actions by synthesizing new data. This capability is revolutionizing the analytics market by offering more dynamic, accurate, and personalized insights that can significantly enhance decision-making across industries. The market for generative AI in analytics is poised for substantial growth due to several key factors. The increasing volume and complexity of data being generated across sectors drive the need for advanced analytics solutions that can handle and make sense of vast datasets efficiently. Businesses are increasingly recognizing the value of data-driven strategies, which fuels demand for more sophisticated analytical tools that can provide deeper and more actionable insights. Advancements in AI technologies and computational power are making generative models more accessible and cost-effective, encouraging wider adoption among both large enterprises and small to medium-sized businesses. The growing emphasis on personalized customer experiences and real-time decision-making is pushing organizations to adopt generative AI solutions that offer tailored recommendations and immediate insights. As more industries integrate generative AI into their analytics processes, the market will continue to expand, driven by ongoing technological advancements, increasing data complexity, and a rising demand for precision and efficiency in decision-making. This dynamic growth trajectory is expected to accelerate as organizations seek to leverage generative AI to maintain competitive advantage and optimize their operations.

Key Market Drivers

Increasing Volume and Complexity of Data

The growing volume and complexity of data being generated across various industries is a primary driver for the rise of generative artificial intelligence in analytics. As businesses and organizations collect vast amounts of data from diverse sources such as social media, sensors, transactional systems, and customer interactions, traditional analytical methods often struggle to keep up with the sheer scale and intricacy of this information. Generative artificial intelligence leverages advanced algorithms to process and analyze large datasets more effectively, enabling organizations to extract meaningful insights from complex data structures. By employing machine learning models that can generate new data points or synthesize existing data in innovative ways, generative artificial intelligence helps businesses manage and interpret the growing influx of information. This capability is crucial for industries such as healthcare, where patient data is increasingly detailed and voluminous, and financial services, where real-time market data requires sophisticated analysis. As data continues to expand in both scope and complexity, the demand for generative artificial intelligence solutions that can handle and make sense of this data is expected to grow, driving the market forward.

Growing Emphasis on Data-Driven Decision Making

The shift towards data-driven decision-making is significantly propelling the adoption of generative artificial intelligence in analytics. In today's competitive business environment, organizations recognize the importance of leveraging data to inform strategic decisions, optimize operations, and enhance customer experiences. Traditional analytics tools often provide valuable insights but may lack the capability to offer predictive or prescriptive recommendations. Generative artificial intelligence fills this gap by creating advanced models that can predict future trends, simulate various scenarios, and recommend actionable strategies based on data-driven insights. This technology enables businesses to move beyond reactive decision-making to a proactive approach, where decisions are informed by predictive analytics and tailored recommendations. As companies increasingly seek to harness the full potential of their data to gain a competitive edge, the demand for generative artificial intelligence solutions that provide deeper, more actionable insights is expected to rise, fueling growth in the market.

Demand for Real-Time and Personalized Insights

The increasing demand for real-time and personalized insights is driving the expansion of the generative artificial intelligence in analytics market. In an era where consumers expect instant and tailored experiences, businesses need to adopt technologies that can provide timely and relevant information to meet these expectations. Generative artificial intelligence excels in this regard by offering real-time analytics capabilities and generating personalized insights based on individual user data and behavior. For instance, in the retail sector, generative artificial intelligence can analyze customer interactions and preferences to recommend products or promotions in real-time, enhancing the overall shopping experience. Similarly, in the financial industry, it can provide instantaneous risk assessments and investment recommendations based on real-time market data. The ability to deliver personalized and timely insights helps organizations enhance customer satisfaction, improve operational efficiency, and make informed decisions swiftly. As the demand for such capabilities grows, so does the adoption of generative artificial intelligence in analytics, driving the market forward.

Cost-Effectiveness and Efficiency of Advanced Analytical Solutions

The cost-effectiveness and efficiency of advanced analytical solutions provided by generative artificial intelligence are key drivers of market growth. Traditionally, sophisticated analytics and data processing required substantial investments in hardware, software, and human resources. However, generative artificial intelligence solutions offer a more cost-efficient alternative by automating complex analytical tasks and reducing the need for extensive manual intervention. These solutions leverage advanced algorithms to perform tasks such as data generation, scenario simulation, and predictive modeling more rapidly and accurately than traditional methods. Additionally, as the technology matures and becomes more widely adopted, the costs associated with implementing generative artificial intelligence solutions are decreasing, making them more accessible to organizations of various sizes. This increased accessibility, combined with the ability to achieve more accurate and efficient results, drives the adoption of generative artificial intelligence in analytics. Organizations are increasingly investing in these solutions to optimize their analytical capabilities while managing costs effectively, contributing to the overall growth of the market.

Key Market Challenges

Bias and Fairness in AI Models

Bias and fairness in artificial intelligence models present a significant challenge in the generative artificial intelligence in analytics market. Generative artificial intelligence systems are trained on historical data, which may contain inherent biases reflecting societal or organizational prejudices. If these biases are not identified and corrected, they can be perpetuated and even amplified by the artificial intelligence models, leading to unfair or discriminatory outcomes. For example, a generative model used for predictive analytics in hiring might inadvertently favor certain demographic groups over others if the training data reflects historical biases in recruitment practices. Addressing bias requires a multi-faceted approach, including diversifying training datasets, implementing fairness algorithms, and conducting rigorous testing to identify and mitigate potential biases. Organizations must also establish clear guidelines and ethical standards for the use of generative artificial intelligence to ensure that the insights and recommendations provided by these models are equitable and non-discriminatory. Transparency in how models are trained and how their outputs are used is essential for fostering trust and ensuring that generative artificial intelligence is deployed in a fair and responsible manner.

Data Privacy and Security Concerns

Data privacy and security are significant challenges for the generative artificial intelligence in analytics market. As generative artificial intelligence systems rely heavily on large volumes of data to train models and produce insights, there is an inherent risk of sensitive information being exposed or misused. Organizations must ensure that their data handling practices comply with stringent regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), which mandate rigorous data protection measures. The use of generative artificial intelligence often involves processing and storing personal and proprietary data, raising concerns about potential breaches and unauthorized access. Additionally, the generation of synthetic data by these models must be managed carefully to prevent the inadvertent disclosure of real, identifiable information. Ensuring robust encryption, implementing access controls, and conducting regular security audits are essential measures to mitigate these risks. Furthermore, businesses must navigate the complexities of data ownership and consent, ensuring that they have appropriate agreements in place with data providers. Addressing these privacy and security concerns is critical for maintaining trust and compliance while leveraging the capabilities of generative artificial intelligence in analytics.

Integration and Implementation Complexity

The integration and implementation of generative artificial intelligence technologies pose considerable challenges for organizations looking to leverage these advanced analytics solutions. Implementing generative artificial intelligence requires substantial changes to existing data infrastructure, processes, and workflows. Organizations must ensure that their data architecture can support the high computational demands of generative models, which often necessitate advanced hardware and software resources. Additionally, integrating these models with existing systems and platforms can be complex and may require significant customization and development efforts. This complexity is compounded by the need for specialized expertise in artificial intelligence and machine learning, which can be scarce and costly. Businesses must also address potential disruptions to operations during the transition period, ensuring that they have contingency plans in place to manage any potential downtime or performance issues. Furthermore, ongoing maintenance and updates are required to keep generative artificial intelligence systems functioning effectively and securely. Organizations must invest in training their staff, upgrading their infrastructure, and managing the integration process carefully to fully realize the benefits of generative artificial intelligence while minimizing operational disruptions.

Key Market Trends

Increasing Adoption of Generative Artificial Intelligence in Diverse Industries

One of the most prominent trends in the generative artificial intelligence in analytics market is its expanding adoption across a diverse range of industries. Originally more common in technology-centric sectors, such as finance and e-commerce, generative artificial intelligence is now being increasingly utilized in industries such as healthcare, manufacturing, and logistics. In healthcare, for example, generative artificial intelligence is employed to develop predictive models for patient outcomes and personalized treatment plans. In manufacturing, it aids in optimizing supply chain management and predictive maintenance. This widespread adoption is driven by the technology's ability to provide tailored insights and predictive analytics that enhance operational efficiency and strategic decision-making. As industries recognize the value of generative artificial intelligence in analyzing complex datasets and generating actionable insights, the market is experiencing significant growth. Companies are investing in these technologies to gain a competitive edge and respond more effectively to changing market conditions and consumer demands. The trend towards broader industry adoption is likely to continue, with more sectors leveraging generative artificial intelligence to drive innovation and improve business performance.

Advancements in Generative Models and Algorithms

Another key trend in the generative artificial intelligence in analytics market is the rapid advancement in generative models and algorithms. Recent innovations in deep learning, neural networks, and natural language processing have significantly enhanced the capabilities of generative artificial intelligence. Modern generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), offer improved performance in generating synthetic data and simulating complex scenarios. These advancements enable more accurate and reliable predictive analytics, as well as the creation of highly realistic synthetic datasets for training other machine learning models. As research and development in artificial intelligence continue to evolve, the algorithms driving generative artificial intelligence are becoming more sophisticated, efficient, and capable of handling larger and more diverse datasets. This trend is fostering greater innovation within the market, allowing organizations to leverage cutting-edge technologies to gain deeper insights and make more informed decisions. The continuous improvement of generative models and algorithms is expected to drive further growth in the market and expand the range of applications for generative artificial intelligence.

Integration of Generative Artificial Intelligence with Cloud Computing

The integration of generative artificial intelligence with cloud computing is transforming the analytics landscape and is a significant trend in the market. Cloud computing offers scalable and flexible infrastructure that supports the computational demands of generative artificial intelligence models, which often require substantial processing power and storage capacity. By leveraging cloud platforms, organizations can access advanced generative artificial intelligence tools and resources without the need for extensive on-premises infrastructure investments. This integration enables businesses to deploy generative artificial intelligence solutions more rapidly and cost-effectively, facilitating the adoption of these technologies across various applications and industries. Additionally, cloud-based solutions offer the advantage of easy scalability, allowing organizations to adjust their resources based on their needs and access the latest advancements in generative artificial intelligence. The synergy between generative artificial intelligence and cloud computing is driving innovation and making advanced analytics more accessible to a broader range of businesses. As cloud technologies continue to evolve, the integration with generative artificial intelligence is expected to enhance the capabilities and reach of analytics solutions, further accelerating market growth.

Segmental Insights

Technology Insights

Natural Language Processing segment dominated the generative AI in analytics market in 2023 and is anticipated to maintain its leading position throughout the forecast period. Natural Language Processing (NLP) stands out due to its extensive application in transforming and understanding textual data, which is crucial for generating actionable insights and enhancing user interactions. NLP's ability to handle vast amounts of unstructured data from sources such as social media, customer feedback, and document repositories makes it invaluable for analytics applications. This technology enables advanced capabilities such as sentiment analysis, automated content generation, and contextual understanding, which are increasingly vital in a data-driven environment. Organizations are leveraging NLP to extract meaningful patterns and insights from textual information, driving its continued dominance in the market. As businesses strive to enhance customer experiences, improve decision-making processes, and automate routine tasks, the demand for sophisticated NLP solutions is growing. This trend is expected to persist as NLP technologies advance, offering more refined and accurate language processing capabilities. The integration of NLP with other technologies, such as machine learning and deep learning, further amplifies its impact and utility, reinforcing its dominant role in the generative artificial intelligence in analytics market. Consequently, NLP's broad applicability and continual evolution make it the foremost technology segment, set to lead the market through the forecast period.

Regional Insights

North America dominated the generative artificial intelligence in analytics market and is anticipated to sustain its dominance throughout the forecast period. This region's leadership can be attributed to several factors, including its advanced technological infrastructure, high concentration of key players, and substantial investment in artificial intelligence research and development. North America, particularly the United States, is home to numerous technology giants and innovative start-ups specializing in artificial intelligence and machine learning. The region benefits from a robust ecosystem that supports the rapid adoption and deployment of cutting-edge generative artificial intelligence solutions. Furthermore, North America's strong focus on innovation and digital transformation across various industries such as finance, healthcare, and retail, drives significant demand for advanced analytics technologies. The presence of leading technology companies and research institutions fosters an environment conducive to continuous advancement in generative artificial intelligence, further solidifying the region's dominant position.

Favorable government policies and substantial funding for artificial intelligence initiatives contribute to the region's market leadership. As businesses and organizations in North America increasingly prioritize data-driven strategies and seek to leverage generative artificial intelligence for enhanced decision-making, predictive analytics, and operational efficiency, the region is expected to maintain its dominance. The continued expansion of digital infrastructure, coupled with ongoing advancements in artificial intelligence technologies, ensures that North America remains at the forefront of the generative artificial intelligence in analytics market, driving innovation and shaping industry trends throughout the forecast period.

Key Market Players

  • OpenAI OpCo, LLC
  • IBM Corporation
  • Microsoft Corporation
  • Google LLC
  • Amazon Web Services, Inc.
  • NVIDIA Corporation
  • Salesforce, Inc.
  • SAP SE
  • Oracle Corporation
  • Palantir Technologies Inc.
  • DataRobot, Inc.
  • H2O.ai, Inc.

Report Scope:

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

Generative AI in Analytics Market, By Deployment:

  • Cloud-based
  • On-premises

Generative AI in Analytics Market, By Technology:

  • Natural Language Processing
  • Machine Learning
  • Deep Learning
  • Others

Generative AI in Analytics Market, By Application:

  • Forecasting and Predictions
  • Automated Reporting
  • Anomaly Detection
  • Personalization

Generative AI in Analytics Market, By Region:

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

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global Generative AI in Analytics Market.

Available Customizations:

Global Generative AI in Analytics 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. Sources 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 Generative AI in Analytics Market Overview

6. Global Generative AI in Analytics Market Outlook

  • 6.1. Market Size & Forecast
    • 6.1.1. By Value
  • 6.2. Market Share & Forecast
    • 6.2.1. By Deployment (Cloud-based, On-premises)
    • 6.2.2. By Technology (Natural Language Processing, Machine Learning, Deep Learning, Others)
    • 6.2.3. By Application (Forecasting and Predictions, Automated Reporting, Anomaly Detection, Personalization)
    • 6.2.4. By Region (North America, Europe, South America, Middle East & Africa, Asia Pacific)
  • 6.3. By Company (2023)
  • 6.4. Market Map

7. North America Generative AI in Analytics Market Outlook

  • 7.1. Market Size & Forecast
    • 7.1.1. By Value
  • 7.2. Market Share & Forecast
    • 7.2.1. By Deployment
    • 7.2.2. By Technology
    • 7.2.3. By Application
    • 7.2.4. By Country
  • 7.3. North America: Country Analysis
    • 7.3.1. United States Generative AI in Analytics 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
        • 7.3.1.2.2. By Technology
        • 7.3.1.2.3. By Application
    • 7.3.2. Canada Generative AI in Analytics 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
        • 7.3.2.2.2. By Technology
        • 7.3.2.2.3. By Application
    • 7.3.3. Mexico Generative AI in Analytics 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
        • 7.3.3.2.2. By Technology
        • 7.3.3.2.3. By Application

8. Europe Generative AI in Analytics Market Outlook

  • 8.1. Market Size & Forecast
    • 8.1.1. By Value
  • 8.2. Market Share & Forecast
    • 8.2.1. By Deployment
    • 8.2.2. By Technology
    • 8.2.3. By Application
    • 8.2.4. By Country
  • 8.3. Europe: Country Analysis
    • 8.3.1. Germany Generative AI in Analytics 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
        • 8.3.1.2.2. By Technology
        • 8.3.1.2.3. By Application
    • 8.3.2. France Generative AI in Analytics 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
        • 8.3.2.2.2. By Technology
        • 8.3.2.2.3. By Application
    • 8.3.3. United Kingdom Generative AI in Analytics 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
        • 8.3.3.2.2. By Technology
        • 8.3.3.2.3. By Application
    • 8.3.4. Italy Generative AI in Analytics 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
        • 8.3.4.2.2. By Technology
        • 8.3.4.2.3. By Application
    • 8.3.5. Spain Generative AI in Analytics 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
        • 8.3.5.2.2. By Technology
        • 8.3.5.2.3. By Application
    • 8.3.6. Belgium Generative AI in Analytics Market Outlook
      • 8.3.6.1. Market Size & Forecast
        • 8.3.6.1.1. By Value
      • 8.3.6.2. Market Share & Forecast
        • 8.3.6.2.1. By Deployment
        • 8.3.6.2.2. By Technology
        • 8.3.6.2.3. By Application

9. Asia Pacific Generative AI in Analytics Market Outlook

  • 9.1. Market Size & Forecast
    • 9.1.1. By Value
  • 9.2. Market Share & Forecast
    • 9.2.1. By Deployment
    • 9.2.2. By Technology
    • 9.2.3. By Application
    • 9.2.4. By Country
  • 9.3. Asia-Pacific: Country Analysis
    • 9.3.1. China Generative AI in Analytics 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
        • 9.3.1.2.2. By Technology
        • 9.3.1.2.3. By Application
    • 9.3.2. India Generative AI in Analytics 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
        • 9.3.2.2.2. By Technology
        • 9.3.2.2.3. By Application
    • 9.3.3. Japan Generative AI in Analytics 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
        • 9.3.3.2.2. By Technology
        • 9.3.3.2.3. By Application
    • 9.3.4. South Korea Generative AI in Analytics 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
        • 9.3.4.2.2. By Technology
        • 9.3.4.2.3. By Application
    • 9.3.5. Australia Generative AI in Analytics 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
        • 9.3.5.2.2. By Technology
        • 9.3.5.2.3. By Application
    • 9.3.6. Indonesia Generative AI in Analytics Market Outlook
      • 9.3.6.1. Market Size & Forecast
        • 9.3.6.1.1. By Value
      • 9.3.6.2. Market Share & Forecast
        • 9.3.6.2.1. By Deployment
        • 9.3.6.2.2. By Technology
        • 9.3.6.2.3. By Application
    • 9.3.7. Vietnam Generative AI in Analytics Market Outlook
      • 9.3.7.1. Market Size & Forecast
        • 9.3.7.1.1. By Value
      • 9.3.7.2. Market Share & Forecast
        • 9.3.7.2.1. By Deployment
        • 9.3.7.2.2. By Technology
        • 9.3.7.2.3. By Application

10. South America Generative AI in Analytics Market Outlook

  • 10.1. Market Size & Forecast
    • 10.1.1. By Value
  • 10.2. Market Share & Forecast
    • 10.2.1. By Deployment
    • 10.2.2. By Technology
    • 10.2.3. By Application
    • 10.2.4. By Country
  • 10.3. South America: Country Analysis
    • 10.3.1. Brazil Generative AI in Analytics 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
        • 10.3.1.2.2. By Technology
        • 10.3.1.2.3. By Application
    • 10.3.2. Colombia Generative AI in Analytics 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
        • 10.3.2.2.2. By Technology
        • 10.3.2.2.3. By Application
    • 10.3.3. Argentina Generative AI in Analytics 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
        • 10.3.3.2.2. By Technology
        • 10.3.3.2.3. By Application
    • 10.3.4. Chile Generative AI in Analytics Market Outlook
      • 10.3.4.1. Market Size & Forecast
        • 10.3.4.1.1. By Value
      • 10.3.4.2. Market Share & Forecast
        • 10.3.4.2.1. By Deployment
        • 10.3.4.2.2. By Technology
        • 10.3.4.2.3. By Application

11. Middle East & Africa Generative AI in Analytics Market Outlook

  • 11.1. Market Size & Forecast
    • 11.1.1. By Value
  • 11.2. Market Share & Forecast
    • 11.2.1. By Deployment
    • 11.2.2. By Technology
    • 11.2.3. By Application
    • 11.2.4. By Country
  • 11.3. Middle East & Africa: Country Analysis
    • 11.3.1. Saudi Arabia Generative AI in Analytics 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
        • 11.3.1.2.2. By Technology
        • 11.3.1.2.3. By Application
    • 11.3.2. UAE Generative AI in Analytics 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
        • 11.3.2.2.2. By Technology
        • 11.3.2.2.3. By Application
    • 11.3.3. South Africa Generative AI in Analytics 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
        • 11.3.3.2.2. By Technology
        • 11.3.3.2.3. By Application
    • 11.3.4. Turkey Generative AI in Analytics 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
        • 11.3.4.2.2. By Technology
        • 11.3.4.2.3. By Application
    • 11.3.5. Israel Generative AI in Analytics 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
        • 11.3.5.2.2. By Technology
        • 11.3.5.2.3. By Application

12. Market Dynamics

  • 12.1. Drivers
  • 12.2. Challenges

13. Market Trends and Developments

14. Company Profiles

  • 14.1. OpenAI OpCo, LLC
    • 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. Microsoft Corporation
    • 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. Amazon Web Services, Inc.
    • 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. NVIDIA Corporation
    • 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. Salesforce, Inc.
    • 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. SAP SE
    • 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. Oracle 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. Palantir Technologies Inc.
    • 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
  • 14.11. DataRobot, Inc.
    • 14.11.1. Business Overview
    • 14.11.2. Key Revenue and Financials
    • 14.11.3. Recent Developments
    • 14.11.4. Key Personnel/Key Contact Person
    • 14.11.5. Key Product/Services Offered
  • 14.12. H2O.ai, Inc.
    • 14.12.1. Business Overview
    • 14.12.2. Key Revenue and Financials
    • 14.12.3. Recent Developments
    • 14.12.4. Key Personnel/Key Contact Person
    • 14.12.5. Key Product/Services Offered

15. Strategic Recommendations

16. About Us & Disclaimer