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市場調查報告書
商品編碼
2069322

可解釋人工智慧市場預測至2034年—按交付類型、可解釋性方法、部署類型、組織規模、應用、最終用戶和地區分類的全球分析

Explainable AI Market Forecasts to 2034 - Global Analysis By Offering, Explainability Technique, Deployment, Organization Size, Application, End User, and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，全球可解釋人工智慧 (XAI) 市場預計將在 2026 年達到 18 億美元，並在預測期內以 19.9% 的複合年成長率成長，到 2034 年達到 79 億美元。

可解釋人工智慧 (XAI) 是一個統稱，指的是那些使人工智慧模型的決策過程可解釋、透明且易於人類使用者理解的技術和工具。隨著人工智慧系統在醫療保健、金融、自動駕駛汽車和刑事司法等領域的關鍵決策中發揮越來越重要的作用，模型缺乏透明度會導致信任缺失和監管合規方面的挑戰。 XAI 透過提供預測解釋、識別特徵的重要性以及明確決策邊界來應對這些挑戰。推動這一市場發展的因素包括監管壓力、人工智慧在高風險應用中的日益普及，以及全球各行各業對符合倫理、課責且可審計的人工智慧系統日益成長的需求。

加強人工智慧透明度和課責的監管要求

隨著各國政府和產業組織強制要求演算法具備可解釋性，這項因素正顯著推動可解釋人工智慧解決方案的普及。歐盟的《人工智慧法》將高風險人工智慧系統歸類為需要詳細文件和透明度的系統，金融監管機構也呼籲採用可解釋的信用評分模型。醫療機構要求診斷人工智慧為治療建議提供基礎。缺乏可解釋人工智慧（XAI）能力的機構可能面臨法律責任、罰款和市場進入限制。在全球監管環境不斷擴展的背景下，企業正積極採用可解釋人工智慧框架，以確保合規性、降低聲譽風險並增強相關人員對自動化決策系統的信心。

模型準確性和可解釋性之間的權衡

這一因素正顯著阻礙市場成長，因為企業難以在預測績效和可解釋性之間取得平衡。最精確的人工智慧模型，例如深度神經網路，由於擁有數百萬個參數，如黑盒子一般運行，難以產生有意義的解釋。為了提高可解釋性而簡化模型，往往會降低準確性，從而損害業務目標。 SHAP 和 LIME 等先進的可解釋人工智慧 (XAI) 技術可能會產生誤導，因為它們提供的是近似而非精確的解釋。在詐欺偵測和醫療診斷等關鍵應用中，為了提高可解釋性而犧牲準確性是不可接受的。另一方面，黑箱模型又不符合合規性要求，這使其應用面臨兩難困境。

將可解釋人工智慧與邊緣運算和即時系統整合

邊緣人工智慧的採用帶來了巨大的市場機遇，因為它需要在對延遲敏感和隱私要求極高的應用中實現設備端可解釋性。自動駕駛汽車需要能夠即時理解的導航決策依據，以滿足安全法規要求。利用人工智慧進行預測性維護的工業IoT系統在網路連接受限的情況下，也能受益於本地可解釋性。用於監測患者的醫療邊緣設備可以即時向臨床醫生提供警報背後的原因。隨著邊緣人工智慧晶片效能和能源效率的提升，將可解釋人工智慧（XAI）功能直接整合到推理硬體中，將為機器人、製造和醫療設備應用等雲端可解釋性生成難以實現的領域開闢新的市場。

針對解釋系統的敵對攻擊日益增多

這項因素對可解釋人工智慧（XAI）的可信度構成重大威脅，因為惡意攻擊者正在開發操縱人工智慧模型輸出及其相關解釋的技術。對抗性輸入可使模型產生看似合理的解釋，同時產生錯誤的預測，從而欺騙人類負責人。篡改解釋的攻擊可能透過利用XAI輸出對專有模型進行逆向工程或提取敏感的訓練數據，侵犯智慧財產權和隱私權。隨著XAI在受監管的應用中變得至關重要，攻擊面正在擴展到解釋機製本身。如果沒有針對特定解釋的對抗性技術的有力應對措施，人們對XAI系統的信心可能會受到損害，並可能減緩其市場普及速度。

新型冠狀病毒（COVID-19）的影響：

新冠疫情加速了醫療保健和供應鏈領域對可解釋人工智慧的需求，同時也暴露了現有人工智慧模型可靠性的不足。人工智慧在新冠診斷、病患分診和疫苗分發方面的快速部署，需要透明的決策過程才能贏得臨床醫生和公眾的信任。醫療機構緊急部署可解釋人工智慧（XAI）工具，以便在臨床應用前檢驗模型建議。供應鏈中斷迫使物流公司採用人工智慧進行路線重新規劃決策，這使得可解釋性在與相關人員溝通中至關重要。遠距辦公的普及增加了對自動化監控系統的依賴，因此需要對員工績效評估進行解釋說明。即使在疫情結束後，隨著各組織將透明度要求制度化，可解釋人工智慧的採用率仍然很高。

在預測期內，SHAP細分市場預計將佔據最大的市場佔有率。

憑藉其強大的理論基礎和廣泛的行業認可，SHAP（Shapley Additive exPlanations，沙普利加性解釋）預計將在預測期內佔據最大的市場佔有率。 SHAP基於合作博弈論，提供數學上一致的特徵重要性值，確保解釋在局部準確，且在不同模型間保持一致。其模型獨立性使其適用於任何機器學習演算法，從簡單的線性回歸到複雜的深度神經網路。在主流程式語言中的最佳化實現、與常用機器學習框架的整合以及豐富的社群文件降低了實施門檻。企業在需要可靠、可審計和可複現解釋的監管申報中青睞SHAP，這鞏固了SHAP的市場領導地位。

在預測期內，雲端業務板塊預計將呈現最高的複合年成長率。

在預測期內，雲端領域預計將呈現最高的成長率，這主要得益於可擴展的基礎設施、更低的預付成本以及與現有人工智慧開發平台的無縫整合。基於雲端的可解釋人工智慧 (XAI) 解決方案無需專用的本地硬體，使各種規模的組織都能在無需大量資本投入的情況下產生解釋。領先的雲端服務供應商將 XAI 作為整合服務整合到其機器學習 (ML) 平台中，從而在模型訓練和推理過程中實現自動生成解釋。雲端平台有助於集中管治解釋工件，這對於跨分散式團隊的監管審計至關重要。隨著越來越多的組織採用機器學習維運 (MLOps) 和雲端原生人工智慧開發，雲端部署正成為成長最快的領域。

市佔率最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率，這得益於其對人工智慧的早期應用、嚴格的法規環境以及集中的技術創新。美國在人工智慧研究和商業化可解釋人工智慧（XAI）部署方面均處於主導，並獲得了來自國防機構、金融機構和醫療保健提供者的大量投資。美國證券交易委員會（SEC）、食品藥物管理局（FDA）和聯邦貿易委員會（FTC）的監管措施日益強調演算法透明度，從而推動了企業需求。主要XAI軟體供應商、雲端服務供應商和人工智慧諮詢公司的存在，為解決方案部署建構了一個成熟的生態系統。此外，許多開發XAI底層技術的學術研究機構也集中在北美，進一步鞏固了該地區的市場主導地位。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於人工智慧在製造業、金融業和政府部門的快速應用，以及新法規結構的發展。中國、日本、韓國和印度等國家已實施人工智慧管治指南，強制要求公共部門和高風險應用具備可解釋性。該地區銀行業、醫療保健業和電子商務領域的大規模數位轉型措施正在產生大量資料集，這些資料集需要透明的人工智慧解釋。消費者和監管機構對倫理人工智慧的日益關注，以及海外對人工智慧合規解決方案投資的不斷增加，正在加速可解釋人工智慧（XAI）的普及。隨著國內人工智慧領先企業擴大服務規模，亞太地區正成為可解釋人工智慧技術成長最快的市場。

免費客製化服務：

所有購買此報告的客戶均可享受以下免費自訂選項之一：

  • 企業概況
    • 對其他市場參與者（最多 3 家公司）進行全面分析
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  • 區域分類
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    • 根據產品系列、地理覆蓋範圍和策略聯盟對領先公司進行基準分析。

目錄

第1章執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要公司市佔率分析
  • 產品基準評效和效能比較

第5章：全球解釋人工智慧市場：按產品/服務分類

  • 軟體
  • 服務

第6章：全球可解釋人工智慧市場：依可解釋性方法論分類

  • SHAP
  • LIME
  • 反事實解釋
  • 代理模型
  • 顯著性圖
  • 基於規則的方法
  • 可解釋的本地模型
  • 其他方法

第7章：全球可解釋人工智慧市場：以部署方式分類

  • 雲
  • 現場
  • 混合

第8章：全球可解釋人工智慧市場：按組織規模分類

  • 大公司
  • 小型企業

第9章：全球可解釋人工智慧市場：按應用領域分類

  • 詐欺偵測
  • 風險管理
  • 合規與審計
  • 支持醫療決策
  • 自主系統
  • 信用評分
  • 客戶分析
  • 模型監測
  • 其他用途

第10章：全球可解釋人工智慧市場：按最終用戶分類

  • BFSI
  • 衛生保健
  • 政府/國防
  • 零售與電子商務
  • 製造業
  • IT/通訊
  • 車
  • 其他最終用戶

第11章：全球可解釋人工智慧市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第12章 策略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第13章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第14章：公司簡介

  • IBM Corporation
  • Microsoft Corporation
  • Google LLC
  • Amazon Web Services, Inc.
  • SAS Institute Inc.
  • FICO
  • DataRobot, Inc.
  • H2O.ai, Inc.
  • Oracle Corporation
  • SAP SE
  • Salesforce, Inc.
  • Accenture plc
  • NVIDIA Corporation
  • OpenAI
  • Dataiku Inc.
  • C3.ai, Inc.
  • Intel Corporation
  • Deloitte Touche Tohmatsu Limited
  • Cognizant Technology Solutions Corporation
  • Capgemini SE
Product Code: SMRC37340

According to Stratistics MRC, the Global Explainable AI Market is accounted for $1.8 billion in 2026 and is expected to reach $7.9 billion by 2034 growing at a CAGR of 19.9% during the forecast period. Explainable AI (XAI) encompasses techniques and tools that make artificial intelligence model decisions interpretable, transparent, and understandable to human users. As AI systems increasingly influence critical decisions in healthcare, finance, autonomous vehicles, and criminal justice, the lack of model transparency creates trust deficits and regulatory compliance challenges. XAI addresses this by providing explanations for predictions, identifying feature importance, and revealing decision boundaries. The market is driven by regulatory pressure, rising AI adoption in high-stakes applications, and growing demand for ethical, accountable, and auditable AI systems across industries worldwide.

Market Dynamics:

Driver:

Increasing regulatory requirements for AI transparency and accountability

This factor is significantly driving adoption of explainable AI solutions as governments and industry bodies mandate algorithmic explainability. The European Union's AI Act categorizes high-risk AI systems requiring detailed documentation and transparency, while financial regulators demand explainable credit scoring models. Healthcare authorities require diagnostic AI to provide reasoning for treatment recommendations. Without XAI capabilities, organizations face legal liabilities, fines, and restricted market access. As the regulatory landscape expands globally, enterprises are proactively implementing XAI frameworks to ensure compliance, mitigate reputational risks, and build stakeholder confidence in automated decision-making systems.

Restraint:

Trade-off between model accuracy and explainability

This factor significantly restrains market growth as organizations struggle to balance predictive performance with interpretability. The most accurate AI models, such as deep neural networks, operate as black boxes with millions of parameters, making meaningful explanations difficult to generate. Simplifying models to improve explainability often reduces accuracy, compromising business objectives. Advanced XAI techniques like SHAP and LIME provide approximations rather than exact explanations, introducing potential misinterpretations. For critical applications such as fraud detection or medical diagnosis, sacrificing accuracy for explainability is unacceptable, while black-box models remain incompatible with compliance requirements, creating a challenging adoption dilemma.

Opportunity:

Integration of XAI with edge computing and real-time systems

This factor presents substantial opportunities for market expansion as edge AI deployments require on-device explainability for latency-sensitive and privacy-critical applications. Autonomous vehicles need immediate, understandable justifications for navigation decisions to satisfy safety regulators. Industrial IoT systems using AI for predictive maintenance benefit from localized explanations when network connectivity is limited. Healthcare edge devices monitoring patients can provide clinicians with immediate reasoning behind alerts. As edge AI chips become more powerful and energy-efficient, embedding XAI capabilities directly into inference hardware opens new markets in robotics, manufacturing, and medical devices where cloud-based explanation generation is impractical.

Threat:

Emergence of adversarial attacks on explanation systems

This factor poses a significant threat to XAI reliability as malicious actors develop techniques to manipulate both AI model outputs and their accompanying explanations. Adversarial inputs can cause models to produce incorrect predictions while generating seemingly plausible explanations, deceiving human reviewers. Explanation laundering attacks exploit XAI outputs to reverse-engineer proprietary models or extract sensitive training data, creating intellectual property and privacy violations. As XAI becomes mandatory for regulated applications, the attack surface expands to include explanation mechanisms themselves. Without robust countermeasures against explanation-specific adversarial techniques, trust in XAI systems could erode, slowing market adoption.

Covid-19 Impact:

The COVID-19 pandemic accelerated demand for explainable AI across healthcare and supply chain sectors while simultaneously exposing trust deficiencies in existing AI models. Rapid deployment of AI for COVID-19 diagnosis, patient triage, and vaccine distribution required transparent decision-making to gain clinician and public trust. Healthcare organizations urgently implemented XAI tools to validate model recommendations before clinical use. Supply chain disruptions forced logistics companies to adopt AI for rerouting decisions, with explainability becoming essential for stakeholder communication. Remote work environments increased reliance on automated monitoring systems, requiring explanations for employee performance assessments. Post-pandemic, XAI adoption remains elevated as organizations institutionalize transparency requirements.

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

The SHAP segment is expected to account for the largest market share during the forecast period, supported by its strong theoretical foundations and widespread industry acceptance. SHAP (SHapley Additive exPlanations) provides mathematically consistent feature importance values based on cooperative game theory, ensuring that explanations are locally accurate and globally consistent across models. Its model-agnostic nature allows application to any machine learning algorithm, from simple linear regression to complex deep neural networks. The availability of optimized implementations in major programming languages, integration with popular ML frameworks, and extensive community documentation reduces implementation barriers. Enterprises favor SHAP for regulatory submissions requiring robust, auditable, and reproducible explanations, cementing its market leadership.

The Cloud segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the Cloud segment is predicted to witness the highest growth rate, driven by scalable infrastructure, reduced upfront costs, and seamless integration with existing AI development platforms. Cloud-based XAI solutions eliminate the need for specialized on-premises hardware, allowing organizations of all sizes to generate explanations without significant capital investment. Major cloud providers offer XAI as integrated services within their ML platforms, enabling automatic explanation generation during model training and inference. The cloud facilitates centralized governance of explanation artifacts, essential for regulatory audits across distributed teams. As organizations increasingly adopt MLOps and cloud-native AI development, cloud deployment emerges as the fastest-growing segment.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, supported by early AI adoption, stringent regulatory environments, and concentrated technology innovation. The United States leads in both AI research and commercial XAI deployment, with significant investments from defense agencies, financial institutions, and healthcare providers. Regulatory actions from the SEC, FDA, and FTC increasingly mandate algorithmic transparency, driving enterprise demand. The presence of major XAI software vendors, cloud providers, and AI consultancies creates a mature ecosystem for solution implementation. Additionally, academic research institutions producing foundational XAI techniques are predominantly located in North America, sustaining regional market dominance.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, fueled by rapid AI adoption across manufacturing, finance, and government sectors combined with emerging regulatory frameworks. Countries including China, Japan, South Korea, and India are implementing AI governance guidelines requiring explainability for public-sector and high-risk applications. The region's massive digital transformation initiatives in banking, healthcare, and e-commerce generate vast datasets requiring transparent AI explanations. Growing awareness of ethical AI among consumers and regulators, alongside increasing foreign investment in AI compliance solutions, accelerates XAI deployment. As domestic AI champions scale their offerings, Asia Pacific emerges as the fastest-growing market for explainable AI technologies.

Key players in the market

Some of the key players in Explainable AI Market include IBM Corporation, Microsoft Corporation, Google LLC, Amazon Web Services, Inc., SAS Institute Inc., FICO, DataRobot, Inc., H2O.ai, Inc., Oracle Corporation, SAP SE, Salesforce, Inc., Accenture plc, NVIDIA Corporation, OpenAI, Dataiku Inc., C3.ai, Inc., Intel Corporation, Deloitte Touche Tohmatsu Limited, Cognizant Technology Solutions Corporation, and Capgemini SE.

Key Developments:

In May 2026, IBM and Red Hat launched Project Lightwell a $5 billion initiative deploying over 20,000 engineers-incorporating advanced agentic security methods and enterprise-grade validation layers to transparently track, audit, and patch vulnerabilities within complex software supply chains.

In May 2026, H2O.ai unveiled tabH2O at Dell Technologies World 2026, a specialized enterprise foundation model designed for tabular data that integrates automated feature engineering with built-in interpretability and prediction tracking.

In April 2026, Google Cloud introduced the Gemini Enterprise Agent Platform and eighth-generation TPUs at Cloud Next '26, integrating native governance and auditing tools to manage, monitor, and map out the multi-step reasoning pathways of autonomous AI agents.

Offerings Covered:

  • Software
  • Services

Explainability Techniques Covered:

  • SHAP
  • LIME
  • Counterfactual Explanations
  • Surrogate Models
  • Saliency Maps
  • Rule-Based Methods
  • Interpretable Native Models
  • Other Techniques

Deployments Covered:

  • Cloud
  • On-Premises
  • Hybrid

Organization Sizes Covered:

  • Large Enterprises
  • Small and Medium Enterprises

Applications Covered:

  • Fraud Detection
  • Risk Management
  • Compliance and Audit
  • Healthcare Decision Support
  • Autonomous Systems
  • Credit Scoring
  • Customer Analytics
  • Model Monitoring
  • Other Applications

End Users Covered:

  • BFSI
  • Healthcare
  • Government and Defense
  • Retail and E-Commerce
  • Manufacturing
  • IT and Telecom
  • Automotive
  • Other End Users

Regions Covered:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Netherlands
    • Belgium
    • Sweden
    • Switzerland
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Indonesia
    • Thailand
    • Malaysia
    • Singapore
    • Vietnam
    • Rest of Asia Pacific
  • South America
    • Brazil
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of South America
  • Rest of the World (RoW)
    • Middle East
  • Saudi Arabia
  • United Arab Emirates
  • Qatar
  • Israel
  • Rest of Middle East
    • Africa
  • South Africa
  • Egypt
  • Morocco
  • Rest of 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 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
  • 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

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global Explainable AI Market, By Offering

  • 5.1 Software
  • 5.2 Services

6 Global Explainable AI Market, By Explainability Technique

  • 6.1 SHAP
  • 6.2 LIME
  • 6.3 Counterfactual Explanations
  • 6.4 Surrogate Models
  • 6.5 Saliency Maps
  • 6.6 Rule-Based Methods
  • 6.7 Interpretable Native Models
  • 6.8 Other Techniques

7 Global Explainable AI Market, By Deployment

  • 7.1 Cloud
  • 7.2 On-Premises
  • 7.3 Hybrid

8 Global Explainable AI Market, By Organization Size

  • 8.1 Large Enterprises
  • 8.2 Small and Medium Enterprises

9 Global Explainable AI Market, By Application

  • 9.1 Fraud Detection
  • 9.2 Risk Management
  • 9.3 Compliance and Audit
  • 9.4 Healthcare Decision Support
  • 9.5 Autonomous Systems
  • 9.6 Credit Scoring
  • 9.7 Customer Analytics
  • 9.8 Model Monitoring
  • 9.9 Other Applications

10 Global Explainable AI Market, By End User

  • 10.1 BFSI
  • 10.2 Healthcare
  • 10.3 Government and Defense
  • 10.4 Retail and E-Commerce
  • 10.5 Manufacturing
  • 10.6 IT and Telecom
  • 10.7 Automotive
  • 10.8 Other End Users

11 Global Explainable AI Market, By Geography

  • 11.1 North America
    • 11.1.1 United States
    • 11.1.2 Canada
    • 11.1.3 Mexico
  • 11.2 Europe
    • 11.2.1 United Kingdom
    • 11.2.2 Germany
    • 11.2.3 France
    • 11.2.4 Italy
    • 11.2.5 Spain
    • 11.2.6 Netherlands
    • 11.2.7 Belgium
    • 11.2.8 Sweden
    • 11.2.9 Switzerland
    • 11.2.10 Poland
    • 11.2.11 Rest of Europe
  • 11.3 Asia Pacific
    • 11.3.1 China
    • 11.3.2 Japan
    • 11.3.3 India
    • 11.3.4 South Korea
    • 11.3.5 Australia
    • 11.3.6 Indonesia
    • 11.3.7 Thailand
    • 11.3.8 Malaysia
    • 11.3.9 Singapore
    • 11.3.10 Vietnam
    • 11.3.11 Rest of Asia Pacific
  • 11.4 South America
    • 11.4.1 Brazil
    • 11.4.2 Argentina
    • 11.4.3 Colombia
    • 11.4.4 Chile
    • 11.4.5 Peru
    • 11.4.6 Rest of South America
  • 11.5 Rest of the World (RoW)
    • 11.5.1 Middle East
      • 11.5.1.1 Saudi Arabia
      • 11.5.1.2 United Arab Emirates
      • 11.5.1.3 Qatar
      • 11.5.1.4 Israel
      • 11.5.1.5 Rest of Middle East
    • 11.5.2 Africa
      • 11.5.2.1 South Africa
      • 11.5.2.2 Egypt
      • 11.5.2.3 Morocco
      • 11.5.2.4 Rest of Africa

12 Strategic Market Intelligence

  • 12.1 Industry Value Network and Supply Chain Assessment
  • 12.2 White-Space and Opportunity Mapping
  • 12.3 Product Evolution and Market Life Cycle Analysis
  • 12.4 Channel, Distributor, and Go-to-Market Assessment

13 Industry Developments and Strategic Initiatives

  • 13.1 Mergers and Acquisitions
  • 13.2 Partnerships, Alliances, and Joint Ventures
  • 13.3 New Product Launches and Certifications
  • 13.4 Capacity Expansion and Investments
  • 13.5 Other Strategic Initiatives

14 Company Profiles

  • 14.1 IBM Corporation
  • 14.2 Microsoft Corporation
  • 14.3 Google LLC
  • 14.4 Amazon Web Services, Inc.
  • 14.5 SAS Institute Inc.
  • 14.6 FICO
  • 14.7 DataRobot, Inc.
  • 14.8 H2O.ai, Inc.
  • 14.9 Oracle Corporation
  • 14.10 SAP SE
  • 14.11 Salesforce, Inc.
  • 14.12 Accenture plc
  • 14.13 NVIDIA Corporation
  • 14.14 OpenAI
  • 14.15 Dataiku Inc.
  • 14.16 C3.ai, Inc.
  • 14.17 Intel Corporation
  • 14.18 Deloitte Touche Tohmatsu Limited
  • 14.19 Cognizant Technology Solutions Corporation
  • 14.20 Capgemini SE

List of Tables

  • Table 1 Global Explainable AI Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global Explainable AI Market Outlook, By Offering (2023-2034) ($MN)
  • Table 3 Global Explainable AI Market Outlook, By Software (2023-2034) ($MN)
  • Table 4 Global Explainable AI Market Outlook, By Services (2023-2034) ($MN)
  • Table 5 Global Explainable AI Market Outlook, By Explainability Technique (2023-2034) ($MN)
  • Table 6 Global Explainable AI Market Outlook, By SHAP (2023-2034) ($MN)
  • Table 7 Global Explainable AI Market Outlook, By LIME (2023-2034) ($MN)
  • Table 8 Global Explainable AI Market Outlook, By Counterfactual Explanations (2023-2034) ($MN)
  • Table 9 Global Explainable AI Market Outlook, By Surrogate Models (2023-2034) ($MN)
  • Table 10 Global Explainable AI Market Outlook, By Saliency Maps (2023-2034) ($MN)
  • Table 11 Global Explainable AI Market Outlook, By Rule-Based Methods (2023-2034) ($MN)
  • Table 12 Global Explainable AI Market Outlook, By Interpretable Native Models (2023-2034) ($MN)
  • Table 13 Global Explainable AI Market Outlook, By Other Techniques (2023-2034) ($MN)
  • Table 14 Global Explainable AI Market Outlook, By Deployment (2023-2034) ($MN)
  • Table 15 Global Explainable AI Market Outlook, By Cloud (2023-2034) ($MN)
  • Table 16 Global Explainable AI Market Outlook, By On-Premises (2023-2034) ($MN)
  • Table 17 Global Explainable AI Market Outlook, By Hybrid (2023-2034) ($MN)
  • Table 18 Global Explainable AI Market Outlook, By Organization Size (2023-2034) ($MN)
  • Table 19 Global Explainable AI Market Outlook, By Large Enterprises (2023-2034) ($MN)
  • Table 20 Global Explainable AI Market Outlook, By Small and Medium Enterprises (2023-2034) ($MN)
  • Table 21 Global Explainable AI Market Outlook, By Application (2023-2034) ($MN)
  • Table 22 Global Explainable AI Market Outlook, By Fraud Detection (2023-2034) ($MN)
  • Table 23 Global Explainable AI Market Outlook, By Risk Management (2023-2034) ($MN)
  • Table 24 Global Explainable AI Market Outlook, By Compliance and Audit (2023-2034) ($MN)
  • Table 25 Global Explainable AI Market Outlook, By Healthcare Decision Support (2023-2034) ($MN)
  • Table 26 Global Explainable AI Market Outlook, By Autonomous Systems (2023-2034) ($MN)
  • Table 27 Global Explainable AI Market Outlook, By Credit Scoring (2023-2034) ($MN)
  • Table 28 Global Explainable AI Market Outlook, By Customer Analytics (2023-2034) ($MN)
  • Table 29 Global Explainable AI Market Outlook, By Model Monitoring (2023-2034) ($MN)
  • Table 30 Global Explainable AI Market Outlook, By Other Applications (2023-2034) ($MN)
  • Table 31 Global Explainable AI Market Outlook, By End User (2023-2034) ($MN)
  • Table 32 Global Explainable AI Market Outlook, By BFSI (2023-2034) ($MN)
  • Table 33 Global Explainable AI Market Outlook, By Healthcare (2023-2034) ($MN)
  • Table 34 Global Explainable AI Market Outlook, By Government and Defense (2023-2034) ($MN)
  • Table 35 Global Explainable AI Market Outlook, By Retail and E-Commerce (2023-2034) ($MN)
  • Table 36 Global Explainable AI Market Outlook, By Manufacturing (2023-2034) ($MN)
  • Table 37 Global Explainable AI Market Outlook, By IT and Telecom (2023-2034) ($MN)
  • Table 38 Global Explainable AI Market Outlook, By Automotive (2023-2034) ($MN)
  • Table 39 Global Explainable AI Market Outlook, By Other End Users (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) Regions are also represented in the same manner as above.