코절 AI 시장(-2035년) : 제공별, 전개 방식별, 응용 분야별, 기능별, 업계별, 기업 규모별, 주요 지역별, 산업 동향과 예측

Causal AI Market Till 2035: Distribution by Type of Offering, Type of Deployment Mode, Areas of Application, Type of Functionality, Type of Industry Vertical, Company Size and Key Geographical Regions: Industry Trends and Global Forecasts

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한글목차

코절 AI 시장 개요

세계 코절 AI 시장 규모는 현재 6,337만 달러에서 2035년까지 16억 2,843만 달러에 달할 것으로 예상되며, 2035년까지 예측 기간 동안 CAGR 38.35%를 보일 것으로 예측됩니다.

코절 AI 시장 : 성장과 동향

코절 AI는 데이터 세트 내 인과관계의 발견과 활용에 초점을 맞춘 AI와 머신러닝 분야의 중요한 돌파구를 의미합니다. 패턴을 인식하고 예측하기 위해 주로 상관관계 기반 기술에 의존하는 기존 AI 모델과 달리, 코절 AI는 기본적인 인과관계 메커니즘을 이해하는 것이 중요한 상황을 다룹니다. 코절 AI는 데이터에서 인과관계를 밝히는 것을 전문으로 하는 통계적, 철학적 분야인 인과추론의 원리를 도입하여 AI 기술의 분석 능력을 향상시킵니다.

코절 AI에 대한 수요는 다양한 요인으로 인해 급증하고 있습니다. 또한, 자연어로 대화할 수 있는 가상 비서 및 챗봇의 사용이 증가함에 따라 코절 AI 용도의 매력이 더욱 커지고 있습니다. 또한, 하드웨어, 클라우드 컴퓨팅, 데이터 스토리지 비용의 하락으로 인해 AI 기술은 더 많은 개인과 조직이 쉽게 이용할 수 있게 되었습니다. 특히, 이러한 경제적 접근성은 코절 AI 솔루션의 개발 및 통합을 촉진하고 있으며, 이러한 혁신이 일상적인 사용자들에게 더 가까이 다가갈 수 있도록 함으로써 예측 기간 동안 이 시장의 성장을 가속하고 있습니다.

세계의 코절 AI 시장에 대해 조사 분석했으며, 시장 규모 추정과 기회 분석, 경쟁 구도, 기업 프로파일, 최근 개발 동향 등의 정보를 전해드립니다.

목차

섹션 1 보고서 개요

제1장 서문

제2장 조사 방법

제3장 시장 역학

제4장 거시경제 지표

섹션 2 정성적인 지견

제5장 주요 요약

제6장 서론

제7장 규제 시나리오

섹션 3 시장 개요

제8장 주요 기업 종합 데이터베이스

제9장 경쟁 구도

제10장 화이트 스페이스 분석

제11장 기업 경쟁력 분석

제12장 코절 AI 시장 스타트업 에코시스템

섹션 4 기업 개요

제13장 기업 개요

• 본 장의 개요
• Aible
• Aitia
• Actable AI
• Alibaba
• Amazon Web Services
• Amelia.ai
• Beyond Limits
• Biotx.ai
• Blue Prism
• Causa
• Causality Link
• Cognizant
• Data Poem
• DataRobot
• Dataiku
• IBM
• Microsoft
• NVIDIA
• OpenAI

섹션 5 시장 동향

제14장 메가트렌드 분석

제15장 미충족 요구 분석

제16장 특허 분석

제17장 최근 발전

섹션 6 시장 기회 분석

제18장 세계의 코절 AI시장

제19장 시장 기회 : 제공별

제20장 시장 기회 : 전개 방식별

제21장 시장 기회 : 서비스별

제22장 시장 기회 : 애널리틱스별

제23장 시장 기회 : 기술별

제24장 시장 기회 : 컴포넌트별

제25장 시장 기회 : 응용 분야별

제26장 시장 기회 : 기능별

제27장 시장 기회 : 업계별

제28장 북미의 코절 AI 시장 기회

제29장 유럽의 코절 AI 시장 기회

제30장 아시아의 코절 AI 시장 기회

제31장 중동 및 북아프리카(MENA)의 코절 AI 시장 기회

제32장 라틴아메리카의 코절 AI 시장 기회

제33장 기타 지역의 코절 AI 시장 기회

제34장 시장 집중 분석 : 주요 기업별

제35장 인접 시장 분석

섹션 7 전략 툴

제36장 승리의 열쇠가 되는 전략

제37장 Porter의 Five Forces 분석

제38장 SWOT 분석

제39장 밸류체인 분석

제40장 Roots 전략적 제안

섹션 8 기타 독점적 지견

제41장 1차 조사로부터 지견

제42장 보고서 결론

섹션 9 부록

LSH

영문 목차

영문목차

Causal AI Market Overview

As per Roots Analysis, the global causal AI market size is estimated to grow from USD 63.37 million in the current year to USD 1,628.43 million by 2035, at a CAGR of 38.35% during the forecast period, till 2035.

The opportunity for causal AI market has been distributed across the following segments:

Type of Offering

• Services
• Software

Type of Deployment Mode

• Cloud
• Hybrid
• On-Premises

Type of Services

• Consulting
• Deployment & Integration
• Support and Maintenance
• Training

Type of Analytics

• Descriptive Analytics
• Predictive Analytics
• Prescriptive Analytics

Type of Technology

• Computer Vision
• Deep Learning
• Machine Learning
• Natural Language Processing

Type of Component

• Algorithms
• Frameworks
• Libraries

Areas of Application

• Customer Experience Management
• Fraud Detection
• Healthcare Diagnostics
• Marketing Optimization
• Predictive Maintenance
• Risk Management
• Supply Chain Optimization

Type of Functionality

• Causal Discovery
• Causal Inference
• Counterfactual Analysis

Type of Industry Vertical

• BFSI
• Financial Services
• Healthcare
• Manufacturing
• Retail
• Transportation & Logistics

Company Size

• 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

CAUSAL AI MARKET: GROWTH AND TRENDS

Causal AI signifies a significant breakthrough in the field of artificial intelligence and machine learning, focusing on the detection and application of cause-and-effect relationships within datasets. In contrast to the conventional AI models that primarily depend on correlation-based techniques to recognize patterns and make predictions, causal AI tackles situations where comprehending the fundamental causal mechanisms is crucial. By incorporating principles from causal inference, a statistical and philosophical field dedicated to uncovering causal relationships from data, causal AI improves the analytical capabilities of AI technologies.

The demand for causal AI is witnessing considerable surge driven by various factors. Further, the increasing use of virtual assistants and chatbots that can hold natural language conversations has heightened the appeal for causal AI applications. Moreover, the lower costs associated with hardware, cloud computing, and data storage have rendered AI technology more accessible to a broader spectrum of individuals and organizations. Notably, this financial accessibility has facilitated the development and integration of causal AI solutions, bringing these innovations closer to everyday users, thereby propelling the growth within this market, during the forecast period.

CAUSAL AI MARKET: KEY SEGMENTS

Market Share by Type of Offering

Based on type of offering, the global causal AI market is segmented services and software. According to our estimates, currently, services segment captures the majority share of the market. This can be attributed to the growing demand for consulting, integration, and continuous support as organizations aim to effectively implement causal AI solutions. However, the software segment is anticipated to grow at a relatively higher CAGR during the forecast period.

Market Share by Type of Deployment Mode

Based on type of deployment mode, the causal AI market is segmented into cloud, hybrid and on-premises. According to our estimates, currently, cloud segment captures the majority of the market. Further, this segment is expected to grow at a higher CAGR during the forecast period. This can be attributed to the benefits provided by cloud platforms, including scalability, accessibility, and reduced initial expenses relative to on-premises solutions.

The rising implementation of cloud technologies, coupled with the increasing demand for sophisticated analytics abilities across different sectors, is also driving market growth. Further, cloud-based solutions enable organizations to swiftly modify their resources according to demand, which is particularly advantageous for applications that need considerable computational power.

Market Share by Type of Service

Based on type of service, the causal AI market is segmented into consulting, deployment & integration, support & maintenance, and training. According to our estimates, currently, consulting segment captures the majority share of the market. This can be attributed to the important role that consulting plays in helping organizations implement and make the most of causal AI technologies. Consulting services assist businesses in comprehending how to apply causal AI to enhance decision-making processes and improve operational efficiency.

However, the support and maintenance sector is anticipated to grow at a relatively higher CAGR during the forecast period. This growth is driven by the increasing need for continuous support and training as organizations adopt causal AI solutions and seek help in optimizing their implementation and ensuring successful integration with existing systems.

Market Share by Type of Analytics

Based on type of analytics, the causal AI market is segmented into descriptive analytics, predictive analytics, and prescriptive analytics. According to our estimates, currently, predictive analytics segment captures the majority share of the market. This can be attributed to its extensive adoption by organizations to predict results based on past data and trends, making it a vital resource for decision-making across a range of industries.

In addition, the prescriptive analytics sector is projected to experience the highest CAGR during the forecast period. This is due to its capability to not only forecast results but also suggest actions to achieve intended outcomes. This feature is becoming increasingly important for companies looking to enhance their operations and strategies.

Market Share by Type of Technology

Based on type of technology, the causal AI market is segmented into computer vision, deep learning, machine learning, and natural language processing. According to our estimates, currently, machine learning segment captures the majority share of the market. This can be attributed to their capability to establish a foundation for various causal AI applications, which enables systems to learn from data and accurately discern cause-and-effect relationships.

Additionally, the natural language processing (NLP) sector is projected to experience the highest CAGR during the forecast period, owing to the rising demand for AI systems that can comprehend and interpret human language, facilitating more advanced interactions and insights from textual data.

Market Share by Type of Component

Based on type of component, the causal AI market is segmented into algorithms, frameworks, libraries. According to our estimates, currently, algorithms segment captures the majority share of the market. This can be attributed to the fact that algorithms serve as the foundation of causal AI models, allowing for the identification and examination of cause-and-effect relationships in data.

Additionally, the frameworks segment is projected to experience the highest CAGR during the forecast period. This is likely to be driven by the rising demand for strong frameworks that support the development and implementation of causal AI applications, enabling organizations to utilize these technologies more efficiently and effectively.

Market Share by Areas of Application

Based on areas of application, the causal AI market is segmented into customer experience management, fraud detection, healthcare diagnostics, marketing optimization, predictive maintenance, risk management, and supply chain optimization. According to our estimates, currently, healthcare diagnostics segment captures the majority share of the market. This can be attributed to the rising need for advanced analytics in the healthcare sector to enhance patient outcomes and improve operational efficiency.

Additionally, the fraud detection segment is projected to experience the highest CAGR during the forecast period. This increase can be linked to the growing demand for stronger security measures in financial services and other industries, as organizations aim to utilize causal AI to better identify and mitigate fraudulent activities. As a result, there is a heightened interest in causal AI within both healthcare and finance.

Market Share by Type of Functionality

Based on type of functionality, the causal AI market is segmented into causal discovery, causal inference, and counterfactual analysis. According to our estimates, currently, causal inference segment captures the majority share of the market. This can be attributed to the fact that it enables organizations to extract valuable insights about cause-and-effect relationships from data, which is crucial for making informed decisions across different industries.

Additionally, the growing awareness of its significance in improving decision-making processes, especially in areas such as marketing, healthcare, and operations, is significantly contributing to the growth of the market.

Market Share by Types of Industry Vertical

Based on types of industry vertical, the causal AI market is segmented into BFSI, financial services, healthcare, manufacturing, retail, transportation & logistics. According to our estimates, currently, healthcare segment captures the majority share of the market. This can be attributed to its capability to uncover causal connections among genetic, environmental, and lifestyle influences, as well as particular diseases, while offering valuable perspectives on intricate biological systems, disease pathways, and the effectiveness of treatments.

In addition, the manufacturing sector is projected to experience the highest CAGR during the forecast period. This surge can be linked to the rising implementation of causal AI in areas such as predictive maintenance, quality assurance, and supply chain optimization.

Market Share by Company Size

Based on company size, the causal AI market is segmented into large and small and medium enterprise. According to our estimates, currently, large enterprise segment captures the majority share of the market. However, the small and medium enterprise segment is expected to experience a comparatively higher growth rate during the forecast period. This growth can be attributed to their flexibility, innovation, emphasis on niche markets, and capability to adjust to evolving customer preferences and market dynamics.

Market Share by Geographical Regions

Based on geographical regions, the causal 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. This can be attributed to the presence of leading technology companies, academic institutions, and research organizations that are significantly contributing to advancements in causal AI and are engaged in pioneering research in AI algorithms, causal inference, and related fields..

Example Players in Causal AI Market

• Aible
• Aitia
• Actable AI
• Alibaba
• Amazon Web Services
• Amelia.ai
• Beyond Limits
• Biotx.ai
• Blue Prism
• Causa
• CausaAI
• CausaLens
• Causaly
• Causely
• Causality Link
• Cognizant
• CognitiveScale
• Data Poem
• DataRobot
• Dataiku
• Databricks
• Descartes Labs
• Dynatrace
• Element AI
• Ernst & Young
• Facebook
• Geminos
• Glencoe Software
• Howso
• H2O.ai
• IBM
• Impact Genome
• Incrmntl
• Intel
• Lifesight
• Logility
• Microsoft
• Modzy
• Nebula
• NVIDIA
• OpenAI
• Oracle
• Parabole.AI
• Pinterest
• PwC
• RapidMiner
• Restackio
• Salesforce
• SAP SE
• Scalnyx
• Seldon
• Shopify
• Slack
• Snowflake
• Symphony Ayasdi AI
• Taskade
• ThoughtSpot
• TikTok
• Trifacta
• Twitter
• Uber
• Unlearn.AI
• VELDT
• WeChat
• Wipro

CAUSAL AI MARKET: RESEARCH COVERAGE

The report on the causal AI market features insights on various sections, including:

• Market Sizing and Opportunity Analysis: An in-depth analysis of the causal AI market, focusing on key market segments, including [A] type of offering, [B] type of deployment mode, [C] type of services, [D] type of analytics, [E] type of technology, [F] type of component, [G] areas of application, [H] type of functionality, [I] type of industry vertical, [J] company size and [K] key geographical regions.
• Competitive Landscape: A comprehensive analysis of the companies engaged in the causal 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 causal 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] causal AI portfolio, [J] moat analysis, [K] recent developments, and an informed future outlook.
• Megatrends: An evaluation of ongoing megatrends in causal AI industry.
• Patent Analysis: An insightful analysis of patents filed / granted in the causal 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 causal 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 causal 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.

KEY QUESTIONS ANSWERED IN THIS REPORT

• How many companies are currently engaged in causal 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.

ADDITIONAL BENEFITS

• Complimentary Excel Data Packs for all Analytical Modules in the Report
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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 Causal AI Market
  • 6.2.1. Type of Offering
  • 6.2.2. Types of Deployment Mode
  • 6.2.3. Type of Services
  • 6.2.4. Type of Analytics
  • 6.2.5. Type of Technology
  • 6.2.6. Type of Component
  • 6.2.7. Areas of Application
  • 6.2.8. Type of Functionality
  • 6.2.9. Type of Industry Vertical
• 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. Causal AI Market: 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 CAUSAL AI MARKET

• 12.1. Causal 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. Aible*
  • 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. Aitia
• 13.4. Actable AI
• 13.5. Alibaba
• 13.6. Amazon Web Services
• 13.7. Amelia.ai
• 13.8. Beyond Limits
• 13.9. Biotx.ai
• 13.10. Blue Prism
• 13.11. Causa
• 13.12. Causality Link
• 13.13. Cognizant
• 13.14. Data Poem
• 13.15. DataRobot
• 13.16. Dataiku
• 13.17. IBM
• 13.18. Microsoft
• 13.19. NVIDIA
• 13.20. OpenAI

SECTION V: MARKET TRENDS

14. MEGA TRENDS ANALYSIS

15. UNMET 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 CAUSAL 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 Causal AI Market, Historical Trends (Since 2020) 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. MARKET OPPORTUNITIES BASED ON TYPE OF OFFERING

• 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. Causal AI Market for Services: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 19.7. Causal AI Market for Software: Historical Trends (Since 2020) 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 MODE

• 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. Causal AI Market for Cloud: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 20.7. Causal AI Market for Hybrid: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 20.8. Causal AI Market for On-Premises: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 20.9. Data Triangulation and Validation
  • 20.9.1. Secondary Sources
  • 20.9.2. Primary Sources
  • 20.9.3. Statistical Modeling

21. MARKET OPPORTUNITIES BASED ON TYPE OF SERVICES

• 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. Causal AI Market for Consulting: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 21.7. Causal AI Market for Deployment & Integration: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 21.8. Causal AI Market for Support and Maintenance: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 21.9. Causal AI Market for Training: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 21.10. Data Triangulation and Validation
  • 21.10.1. Secondary Sources
  • 21.10.2. Primary Sources
  • 21.10.3. Statistical Modeling

22. MARKET OPPORTUNITIES BASED ON TYPE OF ANALYTICS

• 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. Causal AI Market for Descriptive Analytics: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 22.7. Causal AI Market for Predictive Analytics: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 22.8. Causal AI Market for Prescriptive Analytics: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 22.9. Data Triangulation and Validation
  • 22.9.1. Secondary Sources
  • 22.9.2. Primary Sources
  • 22.9.3. Statistical Modeling

23. MARKET OPPORTUNITIES BASED ON TYPE OF TECHNOLOGY

• 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. Causal AI Market for Computer Vision: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 23.7. Causal AI Market for Deep Learning: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 23.8. Causal AI Market for Machine Learning: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 23.9. Causal AI Market for Natural Language Processing: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 23.10. Data Triangulation and Validation
  • 23.10.1. Secondary Sources
  • 23.10.2. Primary Sources
  • 23.10.3. Statistical Modeling

24. MARKET OPPORTUNITIES BASED ON TYPE OF COMPONENT

• 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. Causal AI Market for Algorithms: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 24.7. Causal AI Market for Frameworks: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 24.8. Causal AI Market for Libraries: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 24.9. Data Triangulation and Validation
  • 24.9.1. Secondary Sources
  • 24.9.2. Primary Sources
  • 24.9.3. Statistical Modeling

25. MARKET OPPORTUNITIES BASED ON AREAS OF APPLICATION

• 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. Causal AI Market for Customer Experience Management: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 25.7. Causal AI Market for Fraud Detection: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 25.8. Causal AI Market for Healthcare Diagnostics: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 25.9. Causal AI Market for Marketing Optimization: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 25.10. Causal AI Market for Predictive Maintenance: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 25.11. Causal AI Market for Risk Management: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 25.12. Causal AI Market for Supply Chain Optimization: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 25.13. Data Triangulation and Validation
  • 25.13.1. Secondary Sources
  • 25.13.2. Primary Sources
  • 25.13.3. Statistical Modeling

26. MARKET OPPORTUNITIES BASED ON TYPE OF FUNCTIONALITY

• 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. Causal AI Market for Causal Discovery: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 26.7. Causal AI Market for Causal Inference: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 26.8. Causal AI Market for Counterfactual Analysis: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 26.9. Data Triangulation and Validation
  • 26.9.1. Secondary Sources
  • 26.9.2. Primary Sources
  • 26.9.3. Statistical Modeling

27. MARKET OPPORTUNITIES BASED ON TYPE OF INDUSTRY VERTICAL

• 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. Causal AI Market for BFSI: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 27.7. Causal AI Market for Financial Services: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 27.8. Causal AI Market for Healthcare: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 27.9. Causal AI Market for Manufacturing: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 27.10. Causal AI Market for Retail: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 27.11. Causal AI Market for Transportation & Logistics: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 27.12. Data Triangulation and Validation
  • 27.12.1. Secondary Sources
  • 27.12.2. Primary Sources
  • 27.12.3. Statistical Modeling

28. MARKET OPPORTUNITIES FOR CAUSAL AI IN NORTH 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. Causal AI Market in North America: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 28.6.1. Causal AI Market in the US: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 28.6.2. Causal AI Market in Canada: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 28.6.3. Causal AI Market in Mexico: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 28.6.4. Causal AI Market in Other North American Countries: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 28.7. Data Triangulation and Validation

29. MARKET OPPORTUNITIES FOR CAUSAL AI IN EUROPE

• 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. Causal AI Market in Europe: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 29.6.1. Causal AI Market in Austria: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 29.6.2. Causal AI Market in Belgium: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 29.6.3. Causal AI Market in Denmark: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 29.6.4. Causal AI Market in France: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 29.6.5. Causal AI Market in Germany: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 29.6.6. Causal AI Market in Ireland: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 29.6.7. Causal AI Market in Italy: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 29.6.8. Causal AI Market in Netherlands: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 29.6.9. Causal AI Market in Norway: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 29.6.10. Causal AI Market in Russia: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 29.6.11. Causal AI Market in Spain: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 29.6.12. Causal AI Market in Sweden: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 29.6.13. Causal AI Market in Sweden: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 29.6.14. Causal AI Market in Switzerland: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 29.6.15. Causal AI Market in the UK: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 29.6.16. Causal AI Market in Other European Countries: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 29.7. Data Triangulation and Validation

30. MARKET OPPORTUNITIES FOR CAUSAL AI IN ASIA

• 30.1. Chapter Overview
• 30.2. Key Assumptions and Methodology
• 30.3. Revenue Shift Analysis
• 30.4. Market Movement Analysis
• 30.5. Penetration-Growth (P-G) Matrix
• 30.6. Causal AI Market in Asia: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 30.6.1. Causal AI Market in China: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 30.6.2. Causal AI Market in India: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 30.6.3. Causal AI Market in Japan: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 30.6.4. Causal AI Market in Singapore: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 30.6.5. Causal AI Market in South Korea: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 30.6.6. Causal AI Market in Other Asian Countries: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 30.7. Data Triangulation and Validation

31. MARKET OPPORTUNITIES FOR CAUSAL AI IN MIDDLE EAST AND NORTH AFRICA (MENA)

• 31.1. Chapter Overview
• 31.2. Key Assumptions and Methodology
• 31.3. Revenue Shift Analysis
• 31.4. Market Movement Analysis
• 31.5. Penetration-Growth (P-G) Matrix
• 31.6. Causal AI Market in Middle East and North Africa (MENA): Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 31.6.1. Causal AI Market in Egypt: Historical Trends (Since 2020) and Forecasted Estimates (Till 205)
  • 31.6.2. Causal AI Market in Iran: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 31.6.3. Causal AI Market in Iraq: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 31.6.4. Causal AI Market in Israel: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 31.6.5. Causal AI Market in Kuwait: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 31.6.6. Causal AI Market in Saudi Arabia: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 31.6.7. Causal AI Market in United Arab Emirates (UAE): Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 31.6.8. Causal AI Market in Other MENA Countries: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 31.7. Data Triangulation and Validation

32. MARKET OPPORTUNITIES FOR CAUSAL AI IN LATIN AMERICA

• 32.1. Chapter Overview
• 32.2. Key Assumptions and Methodology
• 32.3. Revenue Shift Analysis
• 32.4. Market Movement Analysis
• 32.5. Penetration-Growth (P-G) Matrix
• 32.6. Causal AI Market in Latin America: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 32.6.1. Causal AI Market in Argentina: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 32.6.2. Causal AI Market in Brazil: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 32.6.3. Causal AI Market in Chile: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 32.6.4. Causal AI Market in Colombia Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 32.6.5. Causal AI Market in Venezuela: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 32.6.6. Causal AI Market in Other Latin American Countries: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
• 32.7. Data Triangulation and Validation

33. MARKET OPPORTUNITIES FOR CAUSAL AI IN REST OF THE WORLD

• 33.1. Chapter Overview
• 33.2. Key Assumptions and Methodology
• 33.3. Revenue Shift Analysis
• 33.4. Market Movement Analysis
• 33.5. Penetration-Growth (P-G) Matrix
• 33.6. Causal AI Market in Rest of the World: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 33.6.1. Causal AI Market in Australia: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 33.6.2. Causal AI Market in New Zealand: Historical Trends (Since 2020) and Forecasted Estimates (Till 2035)
  • 33.6.3. Causal AI Market in Other Countries
• 33.7. Data Triangulation and Validation

34. MARKET CONCENTRATION ANALYSIS: DISTRIBUTION BY LEADING PLAYERS

• 34.1. Leading Player 1
• 34.2. Leading Player 2
• 34.3. Leading Player 3
• 34.4. Leading Player 4
• 34.5. Leading Player 5
• 34.6. Leading Player 6
• 34.7. Leading Player 7
• 34.8. Leading Player 8

35. ADJACENT MARKET ANALYSIS

SECTION VII: STRATEGIC TOOLS

36. KEY WINNING STRATEGIES

37. PORTER'S FIVE FORCES ANALYSIS

38. SWOT ANALYSIS

39. VALUE CHAIN ANALYSIS

40. ROOTS STRATEGIC RECOMMENDATIONS

• 40.1. Chapter Overview
• 40.2. Key Business-related Strategies
  • 40.2.1. Research & Development
  • 40.2.2. Product Manufacturing
  • 40.2.3. Commercialization / Go-to-Market
  • 40.2.4. Sales and Marketing
• 40.3. Key Operations-related Strategies
  • 40.3.1. Risk Management
  • 40.3.2. Workforce
  • 40.3.3. Finance
  • 40.3.4. Others

SECTION VIII: OTHER EXCLUSIVE INSIGHTS

41. INSIGHTS FROM PRIMARY RESEARCH

42. REPORT CONCLUSION

SECTION IX: APPENDIX

43. TABULATED DATA

44. LIST OF COMPANIES AND ORGANIZATIONS

45. CUSTOMIZATION OPPORTUNITIES

46. ROOTS SUBSCRIPTION SERVICES

47. AUTHOR DETAILS

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