Summary

Global Causal AI Market is valued approximately at USD 26.03 million in 2023 and is anticipated to grow with a healthy growth rate of more than 40.98% over the forecast period 2024-2032. Causal AI is a branch of artificial intelligence focused on understanding and modeling cause-and-effect relationships rather than just correlations. By identifying the underlying mechanisms driving observed phenomena, Causal AI enables more accurate predictions, better decision-making, and enhanced understanding of complex systems. It combines methods from statistics, machine learning, and domain-specific knowledge to uncover causality, offering insights that traditional AI approaches may miss. This technology is particularly valuable in fields such as healthcare, economics, and policy-making, where understanding causation is crucial for effective interventions and strategies.
The emergence of Causal AI as a solution to overcome the limitations of current AI models and the operationalizing of AI initiatives are primary drivers for market growth. In various fields, the importance of causal inference models is becoming increasingly recognized. For example, in healthcare, understanding causal relationships can significantly enhance patient outcomes and treatment efficacy. However, deriving causal inferences from complex data sets presents a substantial challenge, necessitating advanced algorithms and computational power.
The key regions considered for the market study includes Asia Pacific, North America, Europe, Latin America, and Rest of the World. In 2023, North America is poised to play a pivotal role in the advancement of causal AI. The increasing demand for sophisticated analytics solutions that provide deeper insights and improve decision-making capabilities is propelling the market forward. Governments in North America, particularly in the United States and Canada, are actively promoting the development and adoption of AI technologies through funding and resource allocation for research and innovation. The United States, through the National Institute of Standards and Technology (NIST), is working on establishing standards and guidelines for the application of AI across various industries, including healthcare and finance. Furthermore, the market in Asia Pacific is anticipated to develop at the fastest rate over the forecast period 2024-2032.
Major market player included in this report are:
IBM
CausaLens
Microsoft
Causaly
Google
Geminos
AWS
Aitia
Xplain Data
INCRMNTAL
Logility
Cognino.ai
The detailed segments and sub-segment of the market are explained below:
By Offering:
• Platform
• Services
By Vertical:
• Healthcare & Lifesciences
• BFSI
• Retail & eCommerce
• Transportation & Logistics
• Manufacturing
• Other Verticals
By Region:
North America
• U.S.
• Canada
Europe
• UK
• Germany
• France
• Spain
• Italy
• ROE
Asia Pacific
• China
• India
• Japan
• Australia
• South Korea
• RoAPAC
Latin America
• Brazil
• Mexico
• RoLA
Middle East & Africa
• Saudi Arabia
• South Africa
• RoMEA
Years considered for the study are as follows:
• Historical year – 2022
• Base year – 2023
• Forecast period – 2024 to 2032
Key Takeaways:
• Market Estimates & Forecast for 10 years from 2022 to 2032.
• Annualized revenues and regional level analysis for each market segment.
• Detailed analysis of geographical landscape with Country level analysis of major regions.
• Competitive landscape with information on major players in the market.
• Analysis of key business strategies and recommendations on future market approach.
• Analysis of competitive structure of the market.
• Demand side and supply side analysis of the market

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Table of Contents

Chapter 1. Global Causal AI Market Executive Summary
1.1. Global Causal AI Market Size & Forecast (2022-2032)
1.2. Regional Summary
1.3. Segmental Summary
1.3.1. By Offering
1.3.2. By Vertical
1.4. Key Trends
1.5. Recession Impact
1.6. Analyst Recommendation & Conclusion

Chapter 2. Global Causal AI Market Definition and Research Assumptions
2.1. Research Objective
2.2. Market Definition
2.3. Research Assumptions
2.3.1. Inclusion & Exclusion
2.3.2. Limitations
2.3.3. Supply Side Analysis
2.3.3.1. Availability
2.3.3.2. Infrastructure
2.3.3.3. Regulatory Environment
2.3.3.4. Market Competition
2.3.3.5. Economic Viability (Consumer’s Perspective)
2.3.4. Demand Side Analysis
2.3.4.1. Regulatory frameworks
2.3.4.2. Technological Advancements
2.3.4.3. Environmental Considerations
2.3.4.4. Consumer Awareness & Acceptance
2.4. Estimation Methodology
2.5. Years Considered for the Study
2.6. Currency Conversion Rates

Chapter 3. Global Causal AI Market Dynamics
3.1. Market Drivers
3.1.1. Importance of Causal Inference Models
3.1.2. Emergence of Causal AI
3.1.3. Operationalizing AI Initiatives
3.2. Market Challenges
3.2.1. Causal Inference from Complex Data Sets
3.3. Market Opportunities
3.3.1. Advancements in AI Technologies
3.3.2. Government Initiatives
3.3.3. Growing Investments

Chapter 4. Global Causal AI Market Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.1.6. Futuristic Approach to Porter’s 5 Force Model
4.1.7. Porter’s 5 Force Impact Analysis
4.2. PESTEL Analysis
4.2.1. Political
4.2.2. Economical
4.2.3. Social
4.2.4. Technological
4.2.5. Environmental
4.2.6. Legal
4.3. Top investment opportunity
4.4. Top winning strategies
4.5. Disruptive Trends
4.6. Industry Expert Perspective
4.7. Analyst Recommendation & Conclusion

Chapter 5. Global Causal AI Market Size & Forecasts by Offering 2022-2032
5.1. Segment Dashboard
5.2. Global Causal AI Market: Offering Revenue Trend Analysis, 2022 & 2032 (USD Million)
5.2.1. Platform
5.2.2. Services

Chapter 6. Global Causal AI Market Size & Forecasts by Vertical 2022-2032
6.1. Segment Dashboard
6.2. Global Causal AI Market: Vertical Revenue Trend Analysis, 2022 & 2032 (USD Million)
6.2.1. Healthcare & Lifesciences
6.2.2. BFSI
6.2.3. Retail & eCommerce
6.2.4. Transportation & Logistics
6.2.5. Manufacturing
6.2.6. Other Verticals

Chapter 7. Global Causal AI Market Size & Forecasts by Region 2022-2032
7.1. North America Causal AI Market
7.1.1. U.S. Causal AI Market
7.1.1.1. Offering breakdown size & forecasts, 2022-2032
7.1.1.2. Vertical breakdown size & forecasts, 2022-2032
7.1.2. Canada Causal AI Market
7.2. Europe Causal AI Market
7.2.1. U.K. Causal AI Market
7.2.2. Germany Causal AI Market
7.2.3. France Causal AI Market
7.2.4. Spain Causal AI Market
7.2.5. Italy Causal AI Market
7.2.6. Rest of Europe Causal AI Market
7.3. Asia-Pacific Causal AI Market
7.3.1. China Causal AI Market
7.3.2. India Causal AI Market
7.3.3. Japan Causal AI Market
7.3.4. Australia Causal AI Market
7.3.5. South Korea Causal AI Market
7.3.6. Rest of Asia Pacific Causal AI Market
7.4. Latin America Causal AI Market
7.4.1. Brazil Causal AI Market
7.4.2. Mexico Causal AI Market
7.4.3. Rest of Latin America Causal AI Market
7.5. Middle East & Africa Causal AI Market
7.5.1. Saudi Arabia Causal AI Market
7.5.2. South Africa Causal AI Market
7.5.3. Rest of Middle East & Africa Causal AI Market

Chapter 8. Competitive Intelligence
8.1. Key Company SWOT Analysis
8.1.1. Company 1
8.1.2. Company 2
8.1.3. Company 3
8.2. Top Market Strategies
8.3. Company Profiles
8.3.1. IBM
8.3.1.1. Key Information
8.3.1.2. Overview
8.3.1.3. Financial (Subject to Data Availability)
8.3.1.4. Product Summary
8.3.1.5. Market Strategies
8.3.2. CausaLens
8.3.3. Microsoft
8.3.4. Causaly
8.3.5. Google
8.3.6. Geminos
8.3.7. AWS
8.3.8. Aitia
8.3.9. Xplain Data
8.3.10. INCRMNTAL
8.3.11. Logility
8.3.12. Cognino.ai

Chapter 9. Research Process
9.1. Research Process
9.1.1. Data Mining
9.1.2. Analysis
9.1.3. Market Estimation
9.1.4. Validation
9.1.5. Publishing
9.2. Research Attributes