설명 가능한 AI 시장 : 산업 동향 및 세계 예측(-2035년) : 컴포넌트 유형, 전개 유형, 용도, 최종사용자, 기업 유형, 지역별
Explainable AI Market, Till 2035: Distribution by Type of Component, Type of Deployment, Type of Application, End-User, Type of Enterprise and Geographical Regions: Industry Trends and Global Forecasts
상품코드:1787842
리서치사:Roots Analysis
발행일:On Demand Report
페이지 정보:영문 168 Pages
라이선스 & 가격 (부가세 별도)
한글목차
설명 가능한 AI 시장 : 개요
세계 설명 가능한 AI 시장 규모는 현재 801만 달러에서 예측 기간 동안 8.93%의 연평균 복합 성장률(CAGR)로 2035년에는 5,392만 달러로 성장할 것으로 예측됩니다.
설명 가능한 AI 시장 기회: 분류
구성요소 유형별
서비스
솔루션
전개 유형별
클라우드
On-Premise
용도별
신약 개발 및 진단
부정 및 이상 감지
아이덴티티 및 액세스 관리
예지보전
공급망 관리
기타
최종 사용자별
항공우주 및 방위 산업
자동차
헬스케어
IT 및 통신
공공 부문 및 유틸리티
소매 및 전자상거래
기업 유형별
대기업
중소기업
지역별
북미
미국
캐나다
멕시코
기타 북미 국가
유럽
오스트리아
벨기에
덴마크
프랑스
독일
아일랜드
이탈리아
네덜란드
노르웨이
러시아
스페인
스웨덴
스위스
영국
기타 유럽 국가
아시아
중국
인도
일본
싱가포르
한국
기타 아시아 국가
라틴아메리카
브라질
칠레
콜롬비아
베네수엘라
기타 라틴아메리카 국가
중동 및 북아프리카
이집트
이란
이라크
이스라엘
쿠웨이트
사우디아라비아
아랍에미리트(UAE)
기타 중동 및 북아프리카 국가
세계 기타 지역
호주
뉴질랜드
기타 국가
설명 가능한 AI 시장 : 성장과 동향
현재 AI 사용자 수는 2020년 이후 2배 이상 증가하여 전 세계적으로 약 3억 명에 달할 전망입니다. 이는 설명 가능한 컴퓨팅과 AI의 투명성 융합에 있어 중요한 이정표가 될 것으로 보입니다. 설명 가능한 AI(Explainable AI, XAI)는 AI 알고리즘을 이해할 수 있게 하고, 그 의사결정의 근거를 관계자가 파악할 수 있도록 하는 방법을 활용함으로써 다양한 분야를 변화시킬 것으로 예측됩니다. 설명 가능한 AI의 주요 장점으로는 모델 독립적 접근 방식, 사용자 이해를 촉진하고 의료 분야에서 신약 개발을 가속화할 수 있는 대화형 시각화 등이 있습니다.
또한, 설명 가능한 AI는 금융, 의료, 에너지, 제조 등 다양한 산업에서 더 깊은 인사이트력과 긴급한 문제에 대한 효과적인 해결책을 제공함으로써 비즈니스의 방식을 변화시키고 있습니다. 인터넷의 보급과 일반인들의 인식 개선을 배경으로 주요 분야에서 AI 활용이 빠르게 확대되고 있습니다.
설명 가능한 AI는 더 높은 업무 효율성을 달성하기 위한 혁신과 디지털 전환으로의 세계 전환에 있어 그 중요성이 커지고 있습니다. 자연어 처리와 해석 가능한 머신러닝은 설명 가능한 AI 시장의 잠재력을 극대화하는 데 중요한 역할을 하며, 에너지 효율성 향상과 신속한 대응에 기여하고 있습니다. 또한, SHAP 및 LIME과 같은 기법은 복잡한 AI 모델의 해석성을 높이고, AI 시스템에 대한 신뢰성을 향상시키고, 다양한 분야의 의사결정 프로세스 개선의 길을 열어주며, 현대의 중요한 발전을 보여주고 있습니다. 그 결과, 기술 발전과 투자 관심 증가를 배경으로 설명 가능한 AI 시장은 예측 기간 동안 큰 성장을 이룰 것으로 예측됩니다.
세계의 설명 가능한 AI(Explainable AI) 시장을 조사했으며, 시장 개요, 배경, 시장 영향요인 분석, 시장 규모 추이 및 예측, 각종 부문별/지역별 상세 분석, 경쟁 구도, 주요 기업 개요 등의 정보를 정리하여 전해드립니다.
목차
섹션 I : 보고서 개요
제1장 서문
제2장 조사 방법
제3장 시장 역학
제4장 거시경제 지표
섹션 II : 정성적 인사이트
제5장 주요 요약
제6장 서론
제7장 규제 시나리오
섹션 III : 시장 개요
제8장 주요 기업 종합적 데이터베이스
제9장 경쟁 구도
제10장 화이트 스페이스 분석
제11장 기업 경쟁력 분석
제12장 설명 가능한 AI 시장 스타트업 에코시스템
섹션 IV : 기업 개요
제13장 기업 개요
본 장의 개요
Alteryx
Amelia
Arthur.ai
AWS
BuildGroup
DarwinAI
DataRobot
Ditto.ai
Factmata
Google
IBM
Kyndi
Microsoft
Mphasis
NVIDIA
섹션 V : 시장 동향
제14장 메가트렌드 분석
제15장 미충족 요구 분석
제16장 특허 분석
제17장 최근 동향
섹션 VI : 시장 기회 분석
제18장 세계의 설명 가능한 AI 시장
제19장 설명 가능한 AI 시장 기회 : 컴포넌트 유형별
제20장 설명 가능한 AI 시장 기회 : 전개 유형별
제21장 설명 가능한 AI 시장 기회 : 용도별
제22장 설명 가능한 AI 시장 기회 : 최종사용자별
제23장 설명 가능한 AI 시장 기회 : 기업 형태별
제24장 설명 가능한 AI 시장 기회 : 북미
제25장 설명 가능한 AI 시장 기회 : 유럽
제26장 설명 가능한 AI 시장 기회 : 아시아
제27장 설명 가능한 AI 시장 기회 : 중동 및 북아프리카
제28장 설명 가능한 AI 시장 기회 : 라틴아메리카
제29장 설명 가능한 AI 시장 기회 : 세계 기타 지역
제30장 시장 집중 분석 : 주요 기업 분포
제31장 인접 시장 분석
섹션 VII : 전략 툴
제32장 승리의 열쇠가 되는 전략
제33장 Porter의 Five Forces 분석
제34장 SWOT 분석
제35장 밸류체인 분석
제36장 ROOTS 전략 제안
섹션 VIII : 기타 독점적 인사이트
제37장 1차 조사로부터 인사이트
제38장 보고서 결론
섹션 IX : 부록
제39장 표 형식 데이터
제40장 기업 및 단체 리스트
제41장 커스터마이즈 기회
제42장 ROOTS 구독 서비스
제43장 저자 상세
LSH
영문 목차
영문목차
Explainable AI Market Overview
As per Roots Analysis, the global explainable AI market size is estimated to grow from USD 8.01 million in the current year to USD 53.92 million by 2035, at a CAGR of 18.93% during the forecast period, till 2035.
The opportunity for explainable AI market has been distributed across the following segments:
Type of Component
Services
Solutions
Type of Deployment
Cloud
On-Premise
Type of Application
Drug Discovery & Diagnostics
Fraud and Anomaly Detection
Identity and Access Management
Predictive Maintenance
Supply Chain Management
Others
End-User
Aerospace & Defense
Automotive
Healthcare
IT & Telecommunication
Public Sector & Utilities
Retail and e-commerce
Type of Enterprise
Large Enterprises
Small and Medium Enterprises
Geographical Regions
North America
US
Canada
Mexico
Other North American countries
Europe
Austria
Belgium
Denmark
France
Germany
Ireland
Italy
Netherlands
Norway
Russia
Spain
Sweden
Switzerland
UK
Other European countries
Asia
China
India
Japan
Singapore
South Korea
Other Asian countries
Latin America
Brazil
Chile
Colombia
Venezuela
Other Latin American countries
Middle East and North Africa
Egypt
Iran
Iraq
Israel
Kuwait
Saudi Arabia
UAE
Other MENA countries
Rest of the World
Australia
New Zealand
Other countries
EXPLAINABLE AI MARKET: GROWTH AND TRENDS
As of now, the number of AI users has more than doubled since 2020, reaching approximately 300 million worldwide. This marks a significant milestone in the combination of explainable computing and transparency in artificial intelligence. It is important to note that explainable AI is set to transform various sectors by utilizing techniques that allow AI algorithms to be understood, enabling stakeholders to grasp the reasoning behind decisions made. Some of the key benefits of explainable AI include model-agnostic approaches and interactive visualizations that facilitate user comprehension and speed up drug discovery within the healthcare field.
Moreover, explainable AI has changed how businesses operate by offering deeper insights and more effective solutions to urgent challenges across multiple industries such as finance, healthcare, energy, and manufacturing. It should be emphasized that the use of AI in key sectors is increasing rapidly due to the widespread availability of the internet and growing public awareness.
The explainable AI is becoming crucial in the global transition towards innovation and digital transformation aimed at achieving greater work efficiency. Natural language processing and interpretable machine learning have been crucial in realizing the full potential of the explainable AI market, which contributes to improved energy efficiency and faster responses. Additionally, techniques like SHAP and LIME are enhancing the interpretability of intricate AI models, thereby fostering greater trust in AI systems and paving the way for improved decision-making processes across various sectors, indicating a significant modern development. As a result, with ongoing technological progress and increased investment interest, the explainable AI market is expected to experience significant growth during the forecast period.
EXPLAINABLE AI MARKET: KEY SEGMENTS
Market Share by Type of Component
Based on type of component, the global explainable AI market is segmented into software and services. According to our estimates, currently, software segment captures the majority share of the market. This can be attributed to the rising demand for transparency and accountability in AI systems, prompting organizations to adopt XAI solutions.
This helps to clarify the decision-making process, especially in critical areas like healthcare and finance, where compliance with explainable AI regulations is essential.
Market Share by Type of Deployment
Based on type of deployment, the explainable AI market is segmented into cloud and on-premise. According to our estimates, currently, the cloud computing segment captures the majority of the market. This can be attributed to its flexibility and scalability, which makes it appealing for businesses eager to utilize XAI without significant initial infrastructure costs.
However, the on-premise segment is expected to grow at a relatively higher CAGR during the forecast period. This can be attributed to its ability to enable businesses to retain full control over their sensitive data, thereby reducing the risks tied to data breaches that may occur with cloud solutions. Further, on-premise systems provide opportunities for customization and scalability, allowing organizations to adapt their AI frameworks to align with specific operational requirements.
Market Share by Type of Application
Based on type of application, the explainable AI market is segmented into drug discovery & diagnostics, fraud and anomaly detection, identity and access management, predictive maintenance, supply chain management and others. According to our estimates, currently, fraud detection segment captures the majority share of the market. This can be attributed to the growing demand for transparency and trust in automated decision-making, particularly in the realm of explainable AI in cybersecurity, where understanding AI-generated decisions is essential.
However, the drug discovery and diagnostics segment is expected to grow at a relatively higher CAGR during the forecast period. This can be attributed to the rising demand for AI technologies that improve diagnostic precision and facilitate personalized medicine. Such growth is driven by advancements in machine learning that optimize drug development procedures and enhance treatment results.
Market Share by End-User
Based on end-user, the explainable AI market is segmented into aerospace & defense, automotive, healthcare, IT & telecommunication, public sector & utilities and retail and e-commerce. According to our estimates, currently, the IT & telecommunication segment captures the majority share of the market. This growth can be attributed to the extensive data produced from various sources, which is crucial for training AI models and generating valuable insights.
However, the aerospace & defense sector is expected to grow at a relatively higher CAGR during the forecast period, owing to the rising demand for clarity and responsibility in decision-making, especially in critical contexts where AI plays a role in national security and public safety.
Market Share by Type of Enterprise
Based on type of enterprise, the explainable AI market is segmented into large and small and medium enterprise. According to our estimates, currently, the large enterprise segment captures the majority share of the market. Additionally, this segment is expected to grow at a higher CAGR during the forecast period. This can be attributed to their ability to invest in explainable AI technologies, leverage significant resources, enhance economies of scale, and foster business growth.
Market Share by Geographical Regions
Based on geographical regions, the explainable AI market is segmented into North America, Europe, Asia, Latin America, Middle East and North Africa, and the rest of the world. According to our estimates, currently, North America captures the majority share of the market. However, the market in Asia is expected to grow at a higher CAGR during the forecast period. This can be attributed to the significant investments, government initiatives, and an increasing demand for explainable AI in countries like China and India.
Example Players in Explainable AI Market
Alteryx
Amelia
Arthur.ai
AWS
BuildGroup
DarwinAI
DataRobot
Ditto.ai
Factmata
Google
IBM
Kyndi
Microsoft
Mphasis
NVIDIA
EXPLAINABLE AI MARKET: RESEARCH COVERAGE
The report on the explainable AI market features insights on various sections, including:
Market Sizing and Opportunity Analysis: An in-depth analysis of the explainable AI market, focusing on key market segments, including [A] type of component, [B] type of deployment, [C] type of application, [D] end-user, [E] type of enterprise and [F] geographical regions.
Competitive Landscape: A comprehensive analysis of the companies engaged in the Explainable AI market, based on several relevant parameters, such as [A] year of establishment, [B] company size, [C] location of headquarters and [D] ownership structure.
Company Profiles: Elaborate profiles of prominent players engaged in the Explainable AI market, providing details on [A] location of headquarters, [B] company size, [C] company mission, [D] company footprint, [E] management team, [F] contact details, [G] financial information, [H] operating business segments, [I] Explainable AI portfolio, [J] moat analysis, [K] recent developments, and an informed future outlook.
Megatrends: An evaluation of ongoing megatrends in Explainable AI industry.
Patent Analysis: An insightful analysis of patents filed / granted in the explainable AI domain, based on relevant parameters, including [A] type of patent, [B] patent publication year, [C] patent age and [D] leading players.
Recent Developments: An overview of the recent developments made in the explainable AI market, along with analysis based on relevant parameters, including [A] year of initiative, [B] type of initiative, [C] geographical distribution and [D] most active players.
Porter's Five Forces Analysis: An analysis of five competitive forces prevailing in the explainable AI market, including threats of new entrants, bargaining power of buyers, bargaining power of suppliers, threats of substitute products and rivalry among existing competitors.
SWOT Analysis: An insightful SWOT framework, highlighting the strengths, weaknesses, opportunities and threats in the domain. Additionally, it provides Harvey ball analysis, highlighting the relative impact of each SWOT parameter.
Value Chain Analysis: A comprehensive analysis of the value chain, providing information on the different phases and stakeholders involved in the Explainable AI market.
KEY QUESTIONS ANSWERED IN THIS REPORT
How many companies are currently engaged in explainable AI market?
Which are the leading companies in this market?
What factors are likely to influence the evolution of this market?
What is the current and future market size?
What is the CAGR of this market?
How is the current and future market opportunity likely to be distributed across key market segments?
REASONS TO BUY THIS REPORT
The report provides a comprehensive market analysis, offering detailed revenue projections of the overall market and its specific sub-segments. This information is valuable to both established market leaders and emerging entrants.
Stakeholders can leverage the report to gain a deeper understanding of the competitive dynamics within the market. By analyzing the competitive landscape, businesses can make informed decisions to optimize their market positioning and develop effective go-to-market strategies.
The report offers stakeholders a comprehensive overview of the market, including key drivers, barriers, opportunities, and challenges. This information empowers stakeholders to stay abreast of market trends and make data-driven decisions to capitalize on growth prospects.
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TABLE OF CONTENTS
SECTION I: REPORT OVERVIEW
1. PREFACE
1.1. Introduction
1.2. Market Share Insights
1.3. Key Market Insights
1.4. Report Coverage
1.5. Key Questions Answered
1.6. Chapter Outlines
2. RESEARCH METHODOLOGY
2.1. Chapter Overview
2.2. Research Assumptions
2.3. Database Building
2.3.1. Data Collection
2.3.2. Data Validation
2.3.3. Data Analysis
2.4. Project Methodology
2.4.1. Secondary Research
2.4.1.1. Annual Reports
2.4.1.2. Academic Research Papers
2.4.1.3. Company Websites
2.4.1.4. Investor Presentations
2.4.1.5. Regulatory Filings
2.4.1.6. White Papers
2.4.1.7. Industry Publications
2.4.1.8. Conferences and Seminars
2.4.1.9. Government Portals
2.4.1.10. Media and Press Releases
2.4.1.11. Newsletters
2.4.1.12. Industry Databases
2.4.1.13. Roots Proprietary Databases
2.4.1.14. Paid Databases and Sources
2.4.1.15. Social Media Portals
2.4.1.16. Other Secondary Sources
2.4.2. Primary Research
2.4.2.1. Introduction
2.4.2.2. Types
2.4.2.2.1. Qualitative
2.4.2.2.2. Quantitative
2.4.2.3. Advantages
2.4.2.4. Techniques
2.4.2.4.1. Interviews
2.4.2.4.2. Surveys
2.4.2.4.3. Focus Groups
2.4.2.4.4. Observational Research
2.4.2.4.5. Social Media Interactions
2.4.2.5. Stakeholders
2.4.2.5.1. Company Executives (CXOs)
2.4.2.5.2. Board of Directors
2.4.2.5.3. Company Presidents and Vice Presidents
2.4.2.5.4. Key Opinion Leaders
2.4.2.5.5. Research and Development Heads
2.4.2.5.6. Technical Experts
2.4.2.5.7. Subject Matter Experts
2.4.2.5.8. Scientists
2.4.2.5.9. Doctors and Other Healthcare Providers
2.4.2.6. Ethics and Integrity
2.4.2.6.1. Research Ethics
2.4.2.6.2. Data Integrity
2.4.3. Analytical Tools and Databases
3. MARKET DYNAMICS
3.1. Forecast Methodology
3.1.1. Top-Down Approach
3.1.2. Bottom-Up Approach
3.1.3. Hybrid Approach
3.2. Market Assessment Framework
3.2.1. Total Addressable Market (TAM)
3.2.2. Serviceable Addressable Market (SAM)
3.2.3. Serviceable Obtainable Market (SOM)
3.2.4. Currently Acquired Market (CAM)
3.3. Forecasting Tools and Techniques
3.3.1. Qualitative Forecasting
3.3.2. Correlation
3.3.3. Regression
3.3.4. Time Series Analysis
3.3.5. Extrapolation
3.3.6. Convergence
3.3.7. Forecast Error Analysis
3.3.8. Data Visualization
3.3.9. Scenario Planning
3.3.10. Sensitivity Analysis
3.4. Key Considerations
3.4.1. Demographics
3.4.2. Market Access
3.4.3. Reimbursement Scenarios
3.4.4. Industry Consolidation
3.5. Robust Quality Control
3.6. Key Market Segmentations
3.7. Limitations
4. MACRO-ECONOMIC INDICATORS
4.1. Chapter Overview
4.2. Market Dynamics
4.2.1. Time Period
4.2.1.1. Historical Trends
4.2.1.2. Current and Forecasted Estimates
4.2.2. Currency Coverage
4.2.2.1. Overview of Major Currencies Affecting the Market
4.2.2.2. Impact of Currency Fluctuations on the Industry
4.2.3. Foreign Exchange Impact
4.2.3.1. Evaluation of Foreign Exchange Rates and Their Impact on Market
4.2.3.2. Strategies for Mitigating Foreign Exchange Risk
4.2.4. Recession
4.2.4.1. Historical Analysis of Past Recessions and Lessons Learnt
4.2.4.2. Assessment of Current Economic Conditions and Potential Impact on the Market
4.2.5. Inflation
4.2.5.1. Measurement and Analysis of Inflationary Pressures in the Economy
4.2.5.2. Potential Impact of Inflation on the Market Evolution
4.2.6. Interest Rates
4.2.6.1. Overview of Interest Rates and Their Impact on the Market
4.2.6.2. Strategies for Managing Interest Rate Risk
4.2.7. Commodity Flow Analysis
4.2.7.1. Type of Commodity
4.2.7.2. Origins and Destinations
4.2.7.3. Values and Weights
4.2.7.4. Modes of Transportation
4.2.8. Global Trade Dynamics
4.2.8.1. Import Scenario
4.2.8.2. Export Scenario
4.2.9. War Impact Analysis
4.2.9.1. Russian-Ukraine War
4.2.9.2. Israel-Hamas War
4.2.10. COVID Impact / Related Factors
4.2.10.1. Global Economic Impact
4.2.10.2. Industry-specific Impact
4.2.10.3. Government Response and Stimulus Measures
4.2.10.4. Future Outlook and Adaptation Strategies
4.2.11. Other Indicators
4.2.11.1. Fiscal Policy
4.2.11.2. Consumer Spending
4.2.11.3. Gross Domestic Product (GDP)
4.2.11.4. Employment
4.2.11.5. Taxes
4.2.11.6. R&D Innovation
4.2.11.7. Stock Market Performance
4.2.11.8. Supply Chain
4.2.11.9. Cross-Border Dynamics
SECTION II: QUALITATIVE INSIGHTS
5. EXECUTIVE SUMMARY
6. INTRODUCTION
6.1. Chapter Overview
6.2. Overview of Explainable AI Market
6.2.1. Type of Component
6.2.2. Type of Deployment
6.2.3. Type of Application
6.2.4. Type of End-User
6.2.5. Type of Enterprise
6.3. Future Perspective
7. REGULATORY SCENARIO
SECTION III: MARKET OVERVIEW
8. COMPREHENSIVE DATABASE OF LEADING PLAYERS
9. COMPETITIVE LANDSCAPE
9.1. Chapter Overview
9.2. Explainable AI: Overall Market Landscape
9.2.1. Analysis by Year of Establishment
9.2.2. Analysis by Company Size
9.2.3. Analysis by Location of Headquarters
9.2.4. Analysis by Ownership Structure
10. WHITE SPACE ANALYSIS
11. COMPANY COMPETITIVENESS ANALYSIS
12. STARTUP ECOSYSTEM IN THE EXPLAINABLE AI MARKET
12.1. Explainable AI Market: Market Landscape of Startups
12.1.1. Analysis by Year of Establishment
12.1.2. Analysis by Company Size
12.1.3. Analysis by Company Size and Year of Establishment
12.1.4. Analysis by Location of Headquarters
12.1.5. Analysis by Company Size and Location of Headquarters
12.1.6. Analysis by Ownership Structure
12.2. Key Findings
SECTION IV: COMPANY PROFILES
13. COMPANY PROFILES
13.1. Chapter Overview
13.2. Alteryx *
13.2.1. Company Overview
13.2.2. Company Mission
13.2.3. Company Footprint
13.2.4. Management Team
13.2.5. Contact Details
13.2.6. Financial Performance
13.2.7. Operating Business Segments
13.2.8. Service / Product Portfolio (project specific)
13.2.9. MOAT Analysis
13.2.10. Recent Developments and Future Outlook
13.3. Amelia
13.4. Arthur.ai
13.5. AWS
13.6. BuildGroup
13.7. DarwinAI
13.8. DataRobot
13.9. Ditto.ai
13.10. Factmata
13.11. Google
13.12. IBM
13.13. Kyndi
13.14. Microsoft
13.15. Mphasis
13.16. NVIDIA
SECTION V: MARKET TRENDS
14. MEGA TRENDS ANALYSIS
15. UNMEET NEED ANALYSIS
16. PATENT ANALYSIS
17. RECENT DEVELOPMENTS
17.1. Chapter Overview
17.2. Recent Funding
17.3. Recent Partnerships
17.4. Other Recent Initiatives
SECTION VI: MARKET OPPORTUNITY ANALYSIS
18. GLOBAL EXPLAINABLE AI MARKET
18.1. Chapter Overview
18.2. Key Assumptions and Methodology
18.3. Trends Disruption Impacting Market
18.4. Demand Side Trends
18.5. Supply Side Trends
18.6. Global Explainable AI Market, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
18.7. Multivariate Scenario Analysis
18.7.1. Conservative Scenario
18.7.2. Optimistic Scenario
18.8. Investment Feasibility Index
18.9. Key Market Segmentations
19. EXPLAINABLE AI MARKET OPPORTUNITY BASED ON TYPE OF COMPONENT
19.1. Chapter Overview
19.2. Key Assumptions and Methodology
19.3. Revenue Shift Analysis
19.4. Market Movement Analysis
19.5. Penetration-Growth (P-G) Matrix
19.6. Explainable AI Market for Services: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
19.7. Explainable AI Market for Software: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
19.8. Data Triangulation and Validation
19.8.1. Secondary Sources
19.8.2. Primary Sources
19.8.3. Statistical Modeling
20. MARKET OPPORTUNITIES BASED ON TYPE OF DEPLOYMENT
20.1. Chapter Overview
20.2. Key Assumptions and Methodology
20.3. Revenue Shift Analysis
20.4. Market Movement Analysis
20.5. Penetration-Growth (P-G) Matrix
20.6. Explainable AI Market for Cloud: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
20.7. Explainable AI Market for On-Premise: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
20.8. Data Triangulation and Validation
20.8.1. Secondary Sources
20.8.2. Primary Sources
20.8.3. Statistical Modeling
21. MARKET OPPORTUNITIES BASED ON TYPE OF APPLICATION
21.1. Chapter Overview
21.2. Key Assumptions and Methodology
21.3. Revenue Shift Analysis
21.4. Market Movement Analysis
21.5. Penetration-Growth (P-G) Matrix
21.6. Explainable AI Market for Drug Discovery & Diagnostics: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
21.7. Explainable AI Market for Fraud and Anomaly Detection: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
21.8. Explainable AI Market for Identity and Access Management: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
21.9. Explainable AI Market for Predictive Maintenance: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
21.10. Explainable AI Market for Supply Chain Management: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
21.11. Explainable AI Market for Others: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
21.12. Data Triangulation and Validation
21.12.1. Secondary Sources
21.12.2. Primary Sources
21.12.3. Statistical Modeling
22. MARKET OPPORTUNITIES BASED ON END-USER
22.1. Chapter Overview
22.2. Key Assumptions and Methodology
22.3. Revenue Shift Analysis
22.4. Market Movement Analysis
22.5. Penetration-Growth (P-G) Matrix
22.6. Explainable AI Market for Aerospace & Defense: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
22.7. Explainable AI Market for Automotive: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
22.8. Explainable AI Market for Healthcare: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
22.9. Explainable AI Market for IT & Telecommunication: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
22.10. Explainable AI Market for Public Sector & Utilities: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
22.11. Explainable AI Market for Retail and E-commerce: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
22.12. Data Triangulation and Validation
22.12.1. Secondary Sources
22.12.2. Primary Sources
22.12.3. Statistical Modeling
23. MARKET OPPORTUNITIES BASED ON TYPE OF ENTERPRISE
23.1. Chapter Overview
23.2. Key Assumptions and Methodology
23.3. Revenue Shift Analysis
23.4. Market Movement Analysis
23.5. Penetration-Growth (P-G) Matrix
23.6. Explainable AI Market for Large: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
23.7. Explainable AI Market for Small and Medium Enterprise: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
23.8. Data Triangulation and Validation
23.8.1. Secondary Sources
23.8.2. Primary Sources
23.8.3. Statistical Modeling
24. MARKET OPPORTUNITIES FOR EXPLAINABLE AI IN NORTH AMERICA
24.1. Chapter Overview
24.2. Key Assumptions and Methodology
24.3. Revenue Shift Analysis
24.4. Market Movement Analysis
24.5. Penetration-Growth (P-G) Matrix
24.6. Explainable AI Market in North America: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
24.6.1. Explainable AI Market in the US: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
24.6.2. Explainable AI Market in Canada: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
24.6.3. Explainable AI Market in Mexico: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
24.6.4. Explainable AI Market in Other North American Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
24.7. Data Triangulation and Validation
25. MARKET OPPORTUNITIES FOR EXPLAINABLE AI IN EUROPE
25.1. Chapter Overview
25.2. Key Assumptions and Methodology
25.3. Revenue Shift Analysis
25.4. Market Movement Analysis
25.5. Penetration-Growth (P-G) Matrix
25.6. Explainable AI Market in Europe: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
25.6.1. Explainable AI Market in Austria: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
25.6.2. Explainable AI Market in Belgium: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
25.6.3. Explainable AI Market in Denmark: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
25.6.4. Explainable AI Market in France: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
25.6.5. Explainable AI Market in Germany: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
25.6.6. Explainable AI Market in Ireland: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
25.6.7. Explainable AI Market in Italy: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
25.6.8. Explainable AI Market in Netherlands: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
25.6.9. Explainable AI Market in Norway: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
25.6.10. Explainable AI Market in Russia: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
25.6.11. Explainable AI Market in Spain: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
25.6.12. Explainable AI Market in Sweden: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
25.6.13. Explainable AI Market in Sweden: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
25.6.14. Explainable AI Market in Switzerland: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
25.6.15. Explainable AI Market in the UK: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
25.6.16. Explainable AI Market in Other European Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
25.7. Data Triangulation and Validation
26. MARKET OPPORTUNITIES FOR EXPLAINABLE AI IN ASIA
26.1. Chapter Overview
26.2. Key Assumptions and Methodology
26.3. Revenue Shift Analysis
26.4. Market Movement Analysis
26.5. Penetration-Growth (P-G) Matrix
26.6. Explainable AI Market in Asia: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
26.6.1. Explainable AI Market in China: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
26.6.2. Explainable AI Market in India: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
26.6.3. Explainable AI Market in Japan: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
26.6.4. Explainable AI Market in Singapore: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
26.6.5. Explainable AI Market in South Korea: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
26.6.6. Explainable AI Market in Other Asian Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
26.7. Data Triangulation and Validation
27. MARKET OPPORTUNITIES FOR EXPLAINABLE AI IN MIDDLE EAST AND NORTH AFRICA (MENA)
27.1. Chapter Overview
27.2. Key Assumptions and Methodology
27.3. Revenue Shift Analysis
27.4. Market Movement Analysis
27.5. Penetration-Growth (P-G) Matrix
27.6. Explainable AI Market in Middle East and North Africa (MENA): Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
27.6.1. Explainable AI Market in Egypt: Historical Trends (Since 2019) and Forecasted Estimates (Till 205)
27.6.2. Explainable AI Market in Iran: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
27.6.3. Explainable AI Market in Iraq: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
27.6.4. Explainable AI Market in Israel: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
27.6.5. Explainable AI Market in Kuwait: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
27.6.6. Explainable AI Market in Saudi Arabia: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
27.6.7. Explainable AI Market in United Arab Emirates (UAE): Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
27.6.8. Explainable AI Market in Other MENA Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
27.7. Data Triangulation and Validation
28. MARKET OPPORTUNITIES FOR EXPLAINABLE AI IN LATIN AMERICA
28.1. Chapter Overview
28.2. Key Assumptions and Methodology
28.3. Revenue Shift Analysis
28.4. Market Movement Analysis
28.5. Penetration-Growth (P-G) Matrix
28.6. Explainable AI Market in Latin America: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
28.6.1. Explainable AI Market in Argentina: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
28.6.2. Explainable AI Market in Brazil: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
28.6.3. Explainable AI Market in Chile: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
28.6.4. Explainable AI Market in Colombia Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
28.6.5. Explainable AI Market in Venezuela: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
28.6.6. Explainable AI Market in Other Latin American Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
28.7. Data Triangulation and Validation
29. MARKET OPPORTUNITIES FOR EXPLAINABLE AI IN REST OF THE WORLD
29.1. Chapter Overview
29.2. Key Assumptions and Methodology
29.3. Revenue Shift Analysis
29.4. Market Movement Analysis
29.5. Penetration-Growth (P-G) Matrix
29.6. Explainable AI Market in Rest of the World: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
29.6.1. Explainable AI Market in Australia: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
29.6.2. Explainable AI Market in New Zealand: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
29.6.3. Explainable AI Market in Other Countries
29.7. Data Triangulation and Validation
30. MARKET CONCENTRATION ANALYSIS: DISTRIBUTION BY LEADING PLAYERS
