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市场调查报告书
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1916678

可解释人工智慧 (XAI) 市场，全球预测至 2032 年：按组件、模型类型、部署模式、技术、最终用户和地区划分

Explainable AI (XAI) Market Forecasts to 2032 - Global Analysis By Component (Software and Services), Model Type, Deployment Model, Technology, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的一项研究，全球可解释人工智慧 (XAI) 市场预计到 2025 年价值 91.9 亿美元，到 2032 年达到 292.8 亿美元，在预测期内以 18% 的复合年增长率增长。

可解释人工智慧 (XAI) 指的是一系列旨在使人工智慧模型决策、预测和行为对人类透明、可解释和可理解的技术和系统。与「黑箱」人工智慧系统不同，XAI 可以清晰地视觉化输入资料如何影响结果，使用户能够追踪推理过程并检验结果。 XAI 使相关人员能够评估人工智慧模型的公平性、可靠性和偏差，从而有助于建立信任、确保课责并支持监管合规。这在医疗保健、金融、国防和自主系统等高影响力领域尤其重要，因为在这些领域，理解人工智慧驱动的决策至关重要。

监管机构对人工智慧透明度的要求日益提高

政策制定者正在强制要求人工智慧系统具备可解释性，以确保课责和公平性。企业也越来越需要可解释人工智慧（XAI）框架来检验金融、医疗和政府应用中的决策。供应商正在将透明度模组整合到其人工智慧平台中，以加强合规性和信任度。对可解释模型日益增长的需求正在推动受监管行业的广泛应用。对透明度的追求正将可解释性从小众功能转变为人工智慧部署的主流要求。

实现可解释性的复杂性很高

开发既能保持准确性又不牺牲性能的可解释模型在技术上极具挑战性。由于资源限制，企业难以将可解释性整合到现有的人工智慧工作流程中。与拥有先进研发能力的大型企业相比，中小企业面临的障碍更大。供应商正在探索混合方法，以平衡透明度和效率。这种复杂性减缓了可解释性的应用，使其成为人工智慧创新领域一个充满挑战的前沿领域。

在受监管产业中扩大应用

金融服务业对透明人工智慧的需求日益增长，以支援信用评分、诈欺检测和合规性审核。医疗服务提供者正在将可解释模型融入诊断系统，以增强患者信任并获得监管部门的核准。各国政府正投资可解释人工智慧框架，以改善公共服务决策。供应商正在定制解决方案，以满足特定产业的合规标准。受监管产业不仅在推动可解释人工智慧（XAI）的应用，而且还将其定位为建立合乎伦理且值得信赖的人工智慧生态系统的关键基础。

缺乏标准化的可解释性框架

由于缺乏统一的指导方针，企业在选择合适的调查方法时面临不确定性。监管机构尚未制定统一的透明度标准，这使得合规变得更加复杂。供应商必须根据不同的地区和特定产业要求调整其解决方案。这种缺乏标准化的做法增加了供应商的成本，并减缓了扩充性。如果没有明确的框架，可解释性将持续存在不一致的风险，从而有可能损害全球市场对人工智慧系统的信任。

新冠疫情的影响：

新冠疫情加速了对可解释人工智慧的需求，因为企业对自动化系统的依赖性日益增强。同时，研发中断和计划延期减缓了透明度工具的普及。然而，医疗保健和公共领域对可信赖人工智慧日益增长的需求推动了其应用。各组织越来越依赖可解释模型来检验危机期间所做的决策。供应商将可解释性功能整合到其人工智慧平台中，以增强系统的韧性和合规性。疫情也凸显了透明度在保障人工智慧主导的决策在不确定环境下安全的重要性。

在预测期内，软体领域将占据最大的市场份额。

在预测期内，软体领域预计将占据最大的市场份额，这主要得益于人工智慧平台对整合透明度模组的需求。软体解决方案使企业能够将可解释性直接融入机器学习工作流程。供应商正在投资开发先进的视觉化和模型解释工具，以提高可用性。对可扩展和模组化解决方案日益增长的需求正在推动该领域的应用。企业发现，软体驱动的可解释性对于合规性和建立信任至关重要。软体的主导地位反映了其作为基础层的作用，该基础层能够实现各种人工智慧应用的透明度。

在预测期内，深度学习可解释性细分市场将呈现最高的复合年增长率。

预计在预测期内，深度学习可解释性领域将实现最高成长率，这主要得益于对复杂神经网路透明度日益增长的需求。深度学习模型通常以黑箱形式运行，这给课责带来了挑战。供应商正在将SHAP、LIME和基于注意力机制的方法等可解释性技术整合到其框架中。企业正在采用这些解决方案来增强对自主系统和高阶分析的信任。对深度学习应用的投资不断增加，也推动了该领域的需求。深度学习可解释性的成长凸显了在下一代人工智慧中兼顾效能和透明度的重要性。

占比最大的地区：

由于北美地区拥有成熟的人工智慧基础设施和对透明度的高度监管，预计该地区将在预测期内占据最大的市场份额。美国和加拿大的公司在投资可解释框架以满足合规标准方面处于主导。主要技术供应商的存在进一步巩固了该地区的领先地位。金融、医疗保健和政府部门对符合伦理的人工智慧日益增长的需求正在推动其应用。供应商正在整合先进的可解释性模组，以在竞争激烈的市场中脱颖而出。

复合年增长率最高的地区：

亚太地区预计将在预测期内实现最高的复合年增长率，这主要得益于快速的都市化、人工智慧应用的日益普及以及政府主导的数位化倡议。中国、印度和东南亚等国家正大力投资解释人工智慧，以支持其金融科技、医疗保健和智慧城市生态系统的发展。该地区的企业正在采用可解释人工智慧框架来加强合规性并满足消费者信任需求。本地Start-Ups正在为各行各业部署客製化、高性价比的解决方案。政府推行的旨在促进合乎伦理的人工智慧和透明度的计画正在加速人工智慧的普及应用。

免费客製化服务：

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  • 公司概况
    • 对其他市场参与者（最多 3 家公司）进行全面分析
    • 主要参与者（最多3家公司）的SWOT分析
  • 区域细分
    • 根据客户要求，提供主要国家的市场估算和预测以及复合年增长率（註：可行性需确认）。
  • 竞争标竿分析
    • 根据主要参与者的产品系列、地理覆盖范围和策略联盟进行基准分析

目录

第一章执行摘要

第二章 前言

  • 概括
  • 相关利益者
  • 调查范围
  • 调查方法
  • 研究材料

第三章 市场趋势分析

  • 司机
  • 抑制因素
  • 机会
  • 威胁
  • 技术分析
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的感染疾病

第四章 波特五力分析

  • 供应商的议价能力
  • 买方的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

5. 全球可解释人工智慧 (XAI) 市场（按组件划分）

  • 软体
    • XAI框架库
    • 模型解释和说明工具
    • 可视化和彙报解决方案
    • 人工智慧管治、审核和合规平台
  • 服务
    • 咨询和顾问服务
    • 整合和实施服务
    • 託管服务和平台支持

6. 全球可解释人工智慧 (XAI) 市场按模型类型划分

  • 监督学习中的可解释性
  • 无监督学习中的可解释性
  • 强化学习中的可解释性
  • 整合模型和混合模式

7. 全球可解释人工智慧 (XAI) 市场按部署模式划分

  • 本地部署
  • 基于云端的

8. 全球可解释人工智慧 (XAI) 市场（按技术划分）

  • 机器学习可解释性
  • 深度学习可解释性
  • 自然语言处理可解释性
  • 电脑视觉可解释性
  • 其他的

9. 全球可解释人工智慧 (XAI) 市场（按最终用户划分）

  • 医学与生命科​​学
  • 零售/电子商务
  • 资讯科技/通讯
  • 製造和工业自动化
  • 能源与公用事业
  • 政府/公共部门
  • 其他的

10. 全球可解释人工智慧 (XAI) 市场（按地区划分）

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 亚太其他地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美国家
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十一章 重大进展

  • 协议、伙伴关係、合作和合资企业
  • 併购
  • 新产品发布
  • 业务拓展
  • 其他关键策略

第十二章：企业概况

  • IBM Corporation
  • Microsoft Corporation
  • Oracle Corporation
  • SAP SE
  • SAS Institute Inc.
  • Google LLC
  • Amazon Web Services, Inc.
  • Fiddler AI, Inc.
  • DarwinAI Corp.
  • Kyndi, Inc.
  • H2O.ai, Inc.
  • DataRobot, Inc.
  • Seldon Technologies Ltd.
  • Peltarion AB
  • Zest AI
Product Code: SMRC33251

According to Stratistics MRC, the Global Explainable AI (XAI) Market is accounted for $9.19 billion in 2025 and is expected to reach $29.28 billion by 2032 growing at a CAGR of 18% during the forecast period. Explainable Artificial Intelligence (XAI) refers to a set of methods and systems designed to make the decisions, predictions, and behaviors of artificial intelligence models transparent, interpretable, and understandable to humans. Unlike "black-box" AI systems, XAI provides clear insights into how input data influences outputs, enabling users to trace reasoning processes and validate results. XAI helps build trust, ensures accountability, and supports regulatory compliance by allowing stakeholders to assess fairness, reliability, and bias in AI models. It is especially critical in high-impact domains such as healthcare, finance, defense, and autonomous systems, where understanding AI-driven decisions is essential.

Market Dynamics:

Driver:

Growing regulatory demand for AI transparency

Policymakers are mandating explainability in AI systems to ensure accountability and fairness. Enterprises increasingly require XAI frameworks to validate decisions in finance, healthcare, and government applications. Vendors are embedding transparency modules into AI platforms to strengthen compliance and trust. Rising demand for interpretable models is reinforcing adoption across regulated industries. The push for transparency is transforming explainability from a niche capability into a mainstream requirement for AI deployment.

Restraint:

High complexity of explainability implementation

Developing interpretable models that maintain accuracy without sacrificing performance is technically challenging. Enterprises struggle to integrate explainability into existing AI workflows due to resource constraints. Smaller firms face higher barriers compared to large incumbents with advanced R&D capabilities. Vendors are experimenting with hybrid approaches to balance transparency and efficiency. This complexity is slowing widespread adoption, making explainability a demanding frontier in AI innovation.

Opportunity:

Expanding use in regulated industries

Financial services increasingly require transparent AI to support credit scoring, fraud detection, and compliance audits. Healthcare providers are embedding explainable models into diagnostic systems to strengthen patient trust and regulatory approval. Governments are investing in interpretable AI frameworks to improve decision-making in public services. Vendors are tailoring solutions to meet industry-specific compliance standards. Regulated industries are not only driving adoption but positioning XAI as a critical enabler of ethical and trustworthy AI ecosystems.

Threat:

Lack of standardized explainability frameworks

Enterprises face uncertainty in selecting appropriate methodologies due to fragmented guidelines. Regulators have yet to establish unified benchmarks for transparency which complicates compliance. Vendors must adapt solutions to diverse regional and industry-specific requirements. This lack of standardization increases costs and slows scalability for providers. Without clear frameworks, explainability risks remaining inconsistent, undermining trust in AI systems across global markets.

Covid-19 Impact:

The Covid-19 pandemic accelerated demand for explainable AI as enterprises faced surging reliance on automated systems. On one hand, disruptions in R&D and delayed projects slowed deployment of transparency tools. On the other hand, rising demand for trustworthy AI in healthcare and public safety boosted adoption. Organizations increasingly relied on interpretable models to validate decisions during crisis conditions. Vendors embedded explainability features into AI platforms to strengthen resilience and compliance. The pandemic highlighted the importance of transparency as a safeguard for AI-driven decision-making in uncertain environments.

The software segment is expected to be the largest during the forecast period

The software segment is expected to account for the largest market share during the forecast period, driven by demand for integrated transparency modules in AI platforms. Software solutions enable enterprises to embed explainability directly into machine learning workflows. Vendors are investing in advanced visualization and model interpretation tools to improve usability. Rising demand for scalable and modular solutions is reinforcing adoption in this segment. Enterprises view software-driven explainability as critical for compliance and trust-building. The dominance of software reflects its role as the foundation layer enabling transparency across diverse AI applications.

The deep learning explainability segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the deep learning explainability segment is predicted to witness the highest growth rate, supported by rising demand for transparency in complex neural networks. Deep learning models often operate as black boxes, creating challenges for accountability. Vendors are embedding interpretability techniques such as SHAP, LIME, and attention-based methods into frameworks. Enterprises are adopting these solutions to strengthen trust in autonomous systems and advanced analytics. Rising investment in deep learning applications is reinforcing demand in this segment. The growth of deep learning explainability highlights its role in bridging performance with transparency in next-generation AI.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share by mature AI infrastructure and strong regulatory emphasis on transparency. Enterprises in the United States and Canada are leading investments in explainable frameworks to meet compliance standards. The presence of major technology vendors further strengthens regional dominance. Rising demand for ethical AI in finance, healthcare, and government is reinforcing adoption. Vendors are embedding advanced explainability modules to differentiate offerings in competitive markets.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, fueled by rapid urbanization, expanding AI adoption, and government-led digital initiatives. Countries such as China, India, and Southeast Asia are investing heavily in explainable AI to support fintech, healthcare, and smart city ecosystems. Enterprises in the region are adopting XAI frameworks to strengthen compliance and meet consumer trust requirements. Local startups are deploying cost-effective solutions tailored to diverse industries. Government programs promoting ethical AI and transparency are accelerating adoption.

Key players in the market

Some of the key players in Explainable AI (XAI) Market include IBM Corporation, Microsoft Corporation, Oracle Corporation, SAP SE, SAS Institute Inc., Google LLC, Amazon Web Services, Inc., Fiddler AI, Inc., DarwinAI Corp., Kyndi, Inc., H2O.ai, Inc., DataRobot, Inc., Seldon Technologies Ltd., Peltarion AB and Zest AI.

Key Developments:

In October 2023, SAP and Microsoft expanded their partnership to integrate SAP's responsible AI and data ethics capabilities with Microsoft's Azure OpenAI Service. This collaboration, announced at SAP TechEd, specifically aimed to provide greater transparency and control for generative AI models used in enterprise processes, embedding XAI principles into joint solutions.

In May 2022, Microsoft Research partnered with this MIT center to fund and conduct fundamental research on intelligence and cognition, which includes interdisciplinary work on making AI decision-making processes more transparent and aligned with human reasoning.

Components Covered:

  • Software
  • Services

Model Types Covered:

  • Supervised Learning Explainability
  • Unsupervised Learning Explainability
  • Reinforcement Learning Explainability
  • Ensemble & Hybrid Models

Deployment Models Covered:

  • On-Premise
  • Cloud-Based

Technologies Covered:

  • Machine Learning Explainability
  • Deep Learning Explainability
  • Natural Language Processing Explainability
  • Computer Vision Explainability
  • Other Technologies

End Users Covered:

  • Healthcare and Life Sciences
  • Retail and E-Commerce
  • IT and Telecommunications
  • Manufacturing and Industrial Automation
  • Energy and Utilities
  • Government and Public Sector
  • Other End Users

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 End User Analysis
  • 3.8 Emerging Markets
  • 3.9 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 Explainable AI (XAI) Market, By Component

  • 5.1 Introduction
  • 5.2 Software
    • 5.2.1 XAI Frameworks and Libraries
    • 5.2.2 Model Interpretation and Explanation Tools
    • 5.2.3 Visualization and Reporting Solutions
    • 5.2.4 AI Governance, Audit, and Compliance Platforms
  • 5.3 Services
    • 5.3.1 Consulting and Advisory Services
    • 5.3.2 Integration and Implementation Services
    • 5.3.3 Managed Services and Platform Support

6 Global Explainable AI (XAI) Market, By Model Type

  • 6.1 Introduction
  • 6.2 Supervised Learning Explainability
  • 6.3 Unsupervised Learning Explainability
  • 6.4 Reinforcement Learning Explainability
  • 6.5 Ensemble & Hybrid Models

7 Global Explainable AI (XAI) Market, By Deployment Model

  • 7.1 Introduction
  • 7.2 On-Premise
  • 7.3 Cloud-Based

8 Global Explainable AI (XAI) Market, By Technology

  • 8.1 Introduction
  • 8.2 Machine Learning Explainability
  • 8.3 Deep Learning Explainability
  • 8.4 Natural Language Processing Explainability
  • 8.5 Computer Vision Explainability
  • 8.8 Other Technologies

9 Global Explainable AI (XAI) Market, By End User

  • 9.1 Introduction
  • 9.2 Healthcare and Life Sciences
  • 9.3 Retail and E-Commerce
  • 9.4 IT and Telecommunications
  • 9.5 Manufacturing and Industrial Automation
  • 9.6 Energy and Utilities
  • 9.7 Government and Public Sector
  • 9.8 Other End Users

10 Global Explainable AI (XAI) Market, By Geography

  • 10.1 Introduction
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
    • 10.2.3 Mexico
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 Italy
    • 10.3.4 France
    • 10.3.5 Spain
    • 10.3.6 Rest of Europe
  • 10.4 Asia Pacific
    • 10.4.1 Japan
    • 10.4.2 China
    • 10.4.3 India
    • 10.4.4 Australia
    • 10.4.5 New Zealand
    • 10.4.6 South Korea
    • 10.4.7 Rest of Asia Pacific
  • 10.5 South America
    • 10.5.1 Argentina
    • 10.5.2 Brazil
    • 10.5.3 Chile
    • 10.5.4 Rest of South America
  • 10.6 Middle East & Africa
    • 10.6.1 Saudi Arabia
    • 10.6.2 UAE
    • 10.6.3 Qatar
    • 10.6.4 South Africa
    • 10.6.5 Rest of Middle East & Africa

11 Key Developments

  • 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 11.2 Acquisitions & Mergers
  • 11.3 New Product Launch
  • 11.4 Expansions
  • 11.5 Other Key Strategies

12 Company Profiling

  • 12.1 IBM Corporation
  • 12.2 Microsoft Corporation
  • 12.3 Oracle Corporation
  • 12.4 SAP SE
  • 12.5 SAS Institute Inc.
  • 12.6 Google LLC
  • 12.7 Amazon Web Services, Inc.
  • 12.8 Fiddler AI, Inc.
  • 12.9 DarwinAI Corp.
  • 12.10 Kyndi, Inc.
  • 12.11 H2O.ai, Inc.
  • 12.12 DataRobot, Inc.
  • 12.13 Seldon Technologies Ltd.
  • 12.14 Peltarion AB
  • 12.15 Zest AI

List of Tables

  • Table 1 Global Explainable AI (XAI) Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global Explainable AI (XAI) Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global Explainable AI (XAI) Market Outlook, By Software (2024-2032) ($MN)
  • Table 4 Global Explainable AI (XAI) Market Outlook, By XAI Frameworks and Libraries (2024-2032) ($MN)
  • Table 5 Global Explainable AI (XAI) Market Outlook, By Model Interpretation and Explanation Tools (2024-2032) ($MN)
  • Table 6 Global Explainable AI (XAI) Market Outlook, By Visualization and Reporting Solutions (2024-2032) ($MN)
  • Table 7 Global Explainable AI (XAI) Market Outlook, By AI Governance, Audit, and Compliance Platforms (2024-2032) ($MN)
  • Table 8 Global Explainable AI (XAI) Market Outlook, By Services (2024-2032) ($MN)
  • Table 9 Global Explainable AI (XAI) Market Outlook, By Consulting and Advisory Services (2024-2032) ($MN)
  • Table 10 Global Explainable AI (XAI) Market Outlook, By Integration and Implementation Services (2024-2032) ($MN)
  • Table 11 Global Explainable AI (XAI) Market Outlook, By Managed Services and Platform Support (2024-2032) ($MN)
  • Table 12 Global Explainable AI (XAI) Market Outlook, By Model Type (2024-2032) ($MN)
  • Table 13 Global Explainable AI (XAI) Market Outlook, By Supervised Learning Explainability (2024-2032) ($MN)
  • Table 14 Global Explainable AI (XAI) Market Outlook, By Unsupervised Learning Explainability (2024-2032) ($MN)
  • Table 15 Global Explainable AI (XAI) Market Outlook, By Reinforcement Learning Explainability (2024-2032) ($MN)
  • Table 16 Global Explainable AI (XAI) Market Outlook, By Ensemble & Hybrid Models (2024-2032) ($MN)
  • Table 17 Global Explainable AI (XAI) Market Outlook, By Deployment Model (2024-2032) ($MN)
  • Table 18 Global Explainable AI (XAI) Market Outlook, By On-Premise (2024-2032) ($MN)
  • Table 19 Global Explainable AI (XAI) Market Outlook, By Cloud-Based (2024-2032) ($MN)
  • Table 20 Global Explainable AI (XAI) Market Outlook, By Technology (2024-2032) ($MN)
  • Table 21 Global Explainable AI (XAI) Market Outlook, By Machine Learning Explainability (2024-2032) ($MN)
  • Table 22 Global Explainable AI (XAI) Market Outlook, By Deep Learning Explainability (2024-2032) ($MN)
  • Table 23 Global Explainable AI (XAI) Market Outlook, By Natural Language Processing Explainability (2024-2032) ($MN)
  • Table 24 Global Explainable AI (XAI) Market Outlook, By Computer Vision Explainability (2024-2032) ($MN)
  • Table 25 Global Explainable AI (XAI) Market Outlook, By Other Technologies (2024-2032) ($MN)
  • Table 26 Global Explainable AI (XAI) Market Outlook, By End User (2024-2032) ($MN)
  • Table 27 Global Explainable AI (XAI) Market Outlook, By Healthcare and Life Sciences (2024-2032) ($MN)
  • Table 28 Global Explainable AI (XAI) Market Outlook, By Retail and E-Commerce (2024-2032) ($MN)
  • Table 29 Global Explainable AI (XAI) Market Outlook, By IT and Telecommunications (2024-2032) ($MN)
  • Table 30 Global Explainable AI (XAI) Market Outlook, By Manufacturing and Industrial Automation (2024-2032) ($MN)
  • Table 31 Global Explainable AI (XAI) Market Outlook, By Energy and Utilities (2024-2032) ($MN)
  • Table 32 Global Explainable AI (XAI) Market Outlook, By Government and Public Sector (2024-2032) ($MN)
  • Table 33 Global Explainable AI (XAI) Market Outlook, By Other End Users (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.