分析领域生成式人工智慧市场－全球产业规模、份额、趋势、机会、预测：按部署、技术、应用、地区和竞争格局划分，2021-2031年

全球分析领域的生成式人工智慧市场预计将经历显着成长，从 2025 年的 13.9 亿美元成长到 2031 年的 60.6 亿美元，复合年增长率为 27.81%。

该领域利用先进的机器学习模型，透过自然语言介面自主解读资料集，产生程式码和洞见。其主要成长驱动因素包括“数据民主化”，使业务用户无需技术技能即可访问复杂资讯；以及对非结构化数据处理的需求，以支援快速战略规划。这些并非昙花一现的趋势，而是旨在满足企业营运的基本需求，最大限度地缩短从企业资料中提取价值所需的时间。

然而，市场在模型输出的准确性和资料管治风险方面面临着许多挑战，这可能会削弱企业的信心。企业难以确保自动化洞察的可靠性足以支援关键业务运营，这往往阻碍了规模化发展。儘管有这些挑战，业界的决心依然坚定不移。 IEEE 2024 年的报告显示，65% 的技术主管将人工智慧（包括生成式人工智慧）列为首要任务。这项数据凸显了业界克服现有障碍、推动未来应用的坚定决心。

市场驱动因素

透过自然语言介面实现数据存取的民主化，正在从根本上重塑市场格局，降低高阶分析的进入门槛。透过互动式提示对复杂资料集进行查询，企业能够让非技术人员无需依赖专业的资料科学团队即可获得可操作的洞察。这种转变加快了决策速度，并在各个业务职能部门中培养了以数据为中心的文化。根据Google云端于2024年4月发布的《2024年数据与人工智慧趋势报告》，84%的数据决策者认为生成式人工智慧将有助于更快地获取洞察，这凸显了建立更易于使用的分析框架的趋势。

此外，分析和提取非结构化资料价值的能力显着提升，是推动人工智慧发展的关键因素，使企业能够利用以往无法获得的资讯来源，例如客户回馈、电子邮件和合约。生成式人工智慧模型能够有效处理这些定性讯息，发现传统结构化工具难以发现的模式和异常情况，从而直接提升业务绩效。其影响是可以衡量的。凯捷研究院 (Capigemini Research Institute) 于 2024 年 9 月发布的报告《发挥生成式人工智慧的价值：第二版》显示，采用该技术的组织生产力提高了 7.8%。此外，IBM 在 2024 年发布的报告显示，42% 的企业级组织正在积极采用人工智慧，显示这项技术正日益融入企业营运。

市场挑战

全球生成式人工智慧分析市场的主要限制因素是模型输出准确性的固有不确定性以及重大的资料管治风险。在商业分析领域，准确性对策略至关重要，而生成式模型往往会产生看似合理但不准确的资讯（即所谓的「幻觉」），这导致其可靠性严重不足。这种可靠性的缺失削弱了速度和自动化的价值，迫使企业实施严格且耗时的人工检验流程。此外，对资料外洩的担忧以及缺乏透明的管治框架也阻碍了企业在敏感工作流程中应用这些工具，通常只能将其应用于试验计画，而无法进行全面部署。

行业数据显示专家普遍担忧，进一步加剧了这种担忧。根据ISACA 2024年的调查，81%的数位可信度专家认为，假资讯和错误资讯是人工智慧面临的最大风险。这种高度的不信任感阻碍了生成式分析技术在关键业务功能的应用。因此，由于企业优先考虑风险规避而非创新，在进行全公司范围部署之前，他们会等待模型可信度和安全性标准得到确立，从而阻碍了市场成长。

市场趋势

自主代理人工智慧的兴起正在将分析从被动的报告工具​​转变为主动的、自我纠错的运作层。与每一步都依赖人工指令的传统模型不同，这些代理能够自主规划、说明和执行程式码来清理资料集，并迭代改进输出以确保准确性。这种能力透过减少传统复杂资料处理所需的人工监控，满足了可靠自动化工作流程的需求。 Salesforce 于销售团队年 9 月发布的《基于代理的企业指数》清晰地反映了这项技术的快速普及，该指数显示，早期采用者在今年上半年创建人工智慧代理的数量激增了 119%，表明企业正在向自动化决策系统转型。

同时，合成资料的普及正在解决与隐私保护和模型训练限制相关的关键瓶颈。随着企业面临日益严格的管治通讯协定，合成资料集能够在不洩露敏感客户资讯或智慧财产权的情况下训练出强大的分析模型。这种方法不仅降低了合规风险，还弥补了历史数据的不足，并有助于更全面的场景模拟。金融业是这项隐私增强技术的主要应用领域。根据英伟达于2025年2月发布的《金融服务业人工智慧现况》报告，46%的受访机构已采用合成资料生成技术，凸显了其在安全检验分析策略方面日益重要的角色。

目录

第一章概述

第二章：调查方法

第三章执行摘要

第四章：客户心声

第五章：分析领域生成式人工智慧的全球市场展望

• 市场规模及预测
  • 按金额
• 市占率及预测
  • 部署方式（云端部署、本机部署）
  • 透过科技（自然语言处理、机器学习、深度学习等）
  • 依应用领域（预测、自动报告产生、异常检测、个人化）
  • 按地区
  • 按公司（2025 年）
• 市场地图

第六章：北美分析领域生成式人工智慧市场展望

• 市场规模及预测
• 市占率及预测
• 北美洲：国别分析
  • 我们
  • 加拿大
  • 墨西哥

第七章：欧洲分析领域生成式人工智慧市场展望

• 市场规模及预测
• 市占率及预测
• 欧洲：国别分析
  • 德国
  • 法国
  • 英国
  • 义大利
  • 西班牙

第八章：亚太地区分析领域生成式人工智慧市场展望

• 市场规模及预测
• 市占率及预测
• 亚太地区：国别分析
  • 中国
  • 印度
  • 日本
  • 韩国
  • 澳洲

第九章：中东和非洲分析​​领域生成式人工智慧市场展望

• 市场规模及预测
• 市占率及预测
• 中东与非洲：国别分析
  • 沙乌地阿拉伯
  • 阿拉伯聯合大公国
  • 南非

第十章：南美分析领域生成式人工智慧市场展望

• 市场规模及预测
• 市占率及预测
• 南美洲：国别分析
  • 巴西
  • 哥伦比亚
  • 阿根廷

第十一章 市场动态

• 促进因素
• 任务

第十二章 市场趋势与发展

• 併购
• 产品发布
• 近期趋势

第十三章：全球生成式人工智慧分析市场：SWOT分析

第十四章：波特五力分析

• 产业竞争
• 新进入者的潜力
• 供应商的议价能力
• 顾客权力
• 替代品的威胁

第十五章 竞争格局

• 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.

第十六章 策略建议

第十七章：关于研究公司及免责声明

The Global Generative AI in Analytics Market is projected to experience substantial growth, expanding from USD 1.39 Billion in 2025 to USD 6.06 Billion by 2031, reflecting a CAGR of 27.81%. This sector involves sophisticated machine learning models that utilize natural language interfaces to autonomously interpret datasets and generate code or insights. Growth is primarily driven by the push for data democratization, which enables business users to access complex information without technical skills, and the need to process unstructured data for rapid strategic planning. Rather than being fleeting trends, these drivers address the fundamental operational requirement of minimizing the time needed to extract value from enterprise information.

However, the market faces significant hurdles related to the accuracy of model outputs and data governance risks, which threaten to undermine organizational trust. Enterprises struggle to ensure that automated insights are sufficiently reliable for critical business operations, often impeding expansion. Despite these challenges, industry commitment remains steadfast. As reported by the IEEE in 2024, 65% of technology executives identified artificial intelligence, including generative forms, as a top priority. This statistic highlights a strong intent to overcome existing barriers and drive future implementation.

Market Driver

The democratization of data access via natural language interfaces is fundamentally reshaping the market by reducing the entry barriers for advanced analytics. By allowing users to query complex datasets using conversational prompts, organizations empower non-technical staff to obtain actionable intelligence without depending on specialized data science teams. This transition accelerates decision-making and cultivates a data-centric culture across business functions. According to Google Cloud's 'Data and AI Trends Report 2024' from April 2024, 84% of data decision-makers believe generative AI will facilitate faster access to insights, confirming the momentum toward more accessible analytical frameworks.

Additionally, the enhanced capability to analyze and extract value from unstructured data is a vital driver, enabling enterprises to utilize previously inaccessible sources such as customer feedback, emails, and contracts. Generative AI models effectively process this qualitative information to uncover patterns and anomalies that traditional structured tools miss, leading to direct operational improvements. The impact is measurable; the Capgemini Research Institute's 'Harnessing the value of generative AI: 2nd edition' report from September 2024 noted a 7.8% productivity increase among implementing organizations. Furthermore, IBM reported in 2024 that 42% of enterprise-scale organizations have actively deployed AI, demonstrating the technology's widening operational integration.

Market Challenge

The primary obstacle constraining the Global Generative AI in Analytics Market is the inherent uncertainty regarding the accuracy of model outputs alongside significant data governance risks. In the realm of business analytics, where precision is vital for strategy, the tendency of generative models to produce plausible but incorrect information-known as hallucinations-creates a major trust deficit. This unreliability undermines the value of speed and automation, as organizations are compelled to institute rigorous, time-consuming human validation processes. Moreover, concerns over data leakage and the lack of transparent governance frameworks prevent enterprises from applying these tools in sensitive workflows, often limiting their use to pilot programs rather than full deployment.

This apprehension is reinforced by industry data showing widespread caution among professionals. According to ISACA in 2024, 81% of digital trust professionals cited misinformation and disinformation as the most significant risks associated with artificial intelligence. This high level of distrust causes companies to delay adopting generative analytics for essential business functions. Consequently, market growth is throttled as organizations prioritize risk mitigation over innovation, waiting for established standards of model reliability and security before committing to enterprise-wide implementation.

Market Trends

The rise of autonomous agentic AI is shifting analytics from a passive reporting tool into an active, self-correcting operational layer. Unlike traditional models that rely on human prompts for each step, these agents can autonomously devise plans, write and execute code to clean datasets, and iteratively refine their outputs to ensure precision. This capability meets the demand for reliable automated workflows by reducing the manual oversight previously necessary for complex data tasks. The rapid adoption of this technology is evident in Salesforce's 'Agentic Enterprise Index' from September 2025, which noted a 119% surge in AI agent creation among early adopters in the first half of the year, signaling a move toward automated decision-making systems.

Simultaneously, the widespread use of synthetic data is resolving key bottlenecks related to privacy preservation and model training limitations. As enterprises encounter stricter governance protocols, synthetic datasets enable the training of robust analytical models without exposing sensitive customer information or intellectual property. This method not only lowers compliance risks but also bridges gaps in historical data, facilitating more comprehensive scenario simulations. The financial sector is a leading adopter of this privacy-enhancing technique; according to NVIDIA's 'State of AI in Financial Services' report from February 2025, 46% of surveyed organizations have adopted synthetic data generation, underscoring its growing role in securely validating analytical strategies.

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
  • 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

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. Product 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. Key Industry Partners
• 2.4. Major Association and Secondary Sources
• 2.5. Forecasting Methodology
• 2.6. Data Triangulation & Validation
• 2.7. Assumptions and Limitations

3. Executive Summary

• 3.1. Overview of the Market
• 3.2. Overview of Key Market Segmentations
• 3.3. Overview of Key Market Players
• 3.4. Overview of Key Regions/Countries
• 3.5. Overview of Market Drivers, Challenges, Trends

4. Voice of Customer

5. Global Generative AI in Analytics Market Outlook

• 5.1. Market Size & Forecast
  • 5.1.1. By Value
• 5.2. Market Share & Forecast
  • 5.2.1. By Deployment (Cloud-based, On-premises)
  • 5.2.2. By Technology (Natural Language Processing, Machine Learning, Deep Learning, Others)
  • 5.2.3. By Application (Forecasting and Predictions, Automated Reporting, Anomaly Detection, Personalization)
  • 5.2.4. By Region
  • 5.2.5. By Company (2025)
• 5.3. Market Map

6. North America 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
  • 6.2.2. By Technology
  • 6.2.3. By Application
  • 6.2.4. By Country
• 6.3. North America: Country Analysis
  • 6.3.1. United States Generative AI in Analytics Market Outlook
    • 6.3.1.1. Market Size & Forecast
      • 6.3.1.1.1. By Value
    • 6.3.1.2. Market Share & Forecast
      • 6.3.1.2.1. By Deployment
      • 6.3.1.2.2. By Technology
      • 6.3.1.2.3. By Application
  • 6.3.2. Canada Generative AI in Analytics Market Outlook
    • 6.3.2.1. Market Size & Forecast
      • 6.3.2.1.1. By Value
    • 6.3.2.2. Market Share & Forecast
      • 6.3.2.2.1. By Deployment
      • 6.3.2.2.2. By Technology
      • 6.3.2.2.3. By Application
  • 6.3.3. Mexico Generative AI in Analytics Market Outlook
    • 6.3.3.1. Market Size & Forecast
      • 6.3.3.1.1. By Value
    • 6.3.3.2. Market Share & Forecast
      • 6.3.3.2.1. By Deployment
      • 6.3.3.2.2. By Technology
      • 6.3.3.2.3. By Application

7. Europe 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. Europe: Country Analysis
  • 7.3.1. Germany 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. France 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. United Kingdom 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
  • 7.3.4. Italy Generative AI in Analytics Market Outlook
    • 7.3.4.1. Market Size & Forecast
      • 7.3.4.1.1. By Value
    • 7.3.4.2. Market Share & Forecast
      • 7.3.4.2.1. By Deployment
      • 7.3.4.2.2. By Technology
      • 7.3.4.2.3. By Application
  • 7.3.5. Spain Generative AI in Analytics Market Outlook
    • 7.3.5.1. Market Size & Forecast
      • 7.3.5.1.1. By Value
    • 7.3.5.2. Market Share & Forecast
      • 7.3.5.2.1. By Deployment
      • 7.3.5.2.2. By Technology
      • 7.3.5.2.3. By Application

8. Asia Pacific 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. Asia Pacific: Country Analysis
  • 8.3.1. China 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. India 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. Japan 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. South Korea 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. Australia 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

9. Middle East & Africa 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. Middle East & Africa: Country Analysis
  • 9.3.1. Saudi Arabia 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. UAE 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. South Africa 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

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

11. Market Dynamics

• 11.1. Drivers
• 11.2. Challenges

12. Market Trends & Developments

• 12.1. Merger & Acquisition (If Any)
• 12.2. Product Launches (If Any)
• 12.3. Recent Developments

13. Global Generative AI in Analytics Market: SWOT Analysis

14. Porter's Five Forces Analysis

• 14.1. Competition in the Industry
• 14.2. Potential of New Entrants
• 14.3. Power of Suppliers
• 14.4. Power of Customers
• 14.5. Threat of Substitute Products

15. Competitive Landscape

• 15.1. OpenAI OpCo, LLC
  • 15.1.1. Business Overview
  • 15.1.2. Products & Services
  • 15.1.3. Recent Developments
  • 15.1.4. Key Personnel
  • 15.1.5. SWOT Analysis
• 15.2. IBM Corporation
• 15.3. Microsoft Corporation
• 15.4. Google LLC
• 15.5. Amazon Web Services, Inc.
• 15.6. NVIDIA Corporation
• 15.7. Salesforce, Inc.
• 15.8. SAP SE
• 15.9. Oracle Corporation
• 15.10. Palantir Technologies Inc.
• 15.11. DataRobot, Inc.
• 15.12. H2O.ai, Inc.

16. Strategic Recommendations

17. About Us & Disclaimer