Global Causal AI Market size was valued at USD 27.2 Mn in 2022 and is expected to reach USD 301.37 Mn by 2029, at a CAGR of 41 %.Causal AI Market Overview
Causal Artificial Intelligence (Causal AI) is a rapidly evolving and cutting-edge field in artificial intelligence and machine learning. It emphasizes understanding and utilizing causal relationships within data to make predictions, explain phenomena and support decision-making processes. Unlike traditional machine learning approaches that mainly deal with correlations, Causal AI goes a step further by attempting to uncover the cause-and-effect relationships that underlie observed data patterns. Causal AI is rooted in the recognition that correlation does not imply causation. In other words, just because two variables are related in some way, it doesn't mean that changes in one cause changes in the other. Causal AI seeks to discern the true cause-and-effect relationships by conducting interventions and experiments. Causal AI has diverse Verticals across several fields. In healthcare, it helps identify the root causes of diseases and design more effective treatments. In economics, it informs policy decisions by understanding the impact of interventions. It's also valuable in marketing, where it can determine which strategies truly drive customer engagement.To know about the Research Methodology :- Request Free Sample Report Structural causal models (SCMs) serve as the bedrock of Causal AI, providing a formal mathematical framework to represent causal relationships. They incorporate variables, equations and directional arrows, elucidating how one variable directly influences another. Causal AI Market frequently employs interventions, wherein variables are deliberately altered to observe effects. For instance, in a medical study, administering a new drug to one group of patients and comparing their outcomes with those who did not receive the drug involves an intervention-based approach. Causal AI encounters challenges. It demands significant computational resources, extensive data, and domain expertise. Ethical considerations are particularly pronounced, especially in healthcare, where interventions impact patient well-being. Causal AI is progressively integrated into conventional machine learning models, endowing AI systems with the ability not only to predict outcomes but also to furnish insights into the underlying causative factors. This integration allows AI to unravel causality in machine learning, AI-driven causality, and the intricate interplay of AI and causality, fostering a deeper understanding of the cause-and-effect relationships that shape our data-driven world.
Causal AI Market Dynamics
Drivers
Causal AI Shapes Responsible Innovation in Healthcare and Finance Causal AI, a revolutionary domain within artificial intelligence, is taking center stage in ushering responsible innovation, with particular significance in the fields of healthcare and finance. This transformative technology places a strong focus on ethical considerations as it delves into causality and the intricate web of cause-and-effect relationships within complex systems. It operates on the essential principle that comprehending the root causes of events is as pivotal as predicting their outcomes, fostering a more responsible and informed approach to decision-making. In the healthcare sector, Causal AI is driving a paradigm shift in how medical practitioners approach patient care. By gaining a comprehensive understanding of the causative factors contributing to diseases, Causal AI paves the way for tailored treatments customized to individual patients, heralding the era of personalized medicine. This not only leads to enhanced patient outcomes but also mitigates the risks associated with one-size-fits-all medical approaches. The ethical implications of applying Causal AI in healthcare are profound. Interventions and experiments conducted in medical settings need a heightened level of responsibility, to ensure the well-being and equity of patients. The development of ethical guidelines and frameworks for the deployment of Causal AI in healthcare is pivotal, ensuring that innovations prioritize the health and safety of individuals while propelling advancements in the field. In the financial sector, Causal AI proves to be an equally transformative force. Unveiling causal relationships within economic systems empowers better decision-making in areas such as investments, risk management, and policy development. The ethical dimension here revolves around upholding fair and unbiased financial practices. Ethical considerations in financial Causal AI encompass risk mitigation and the prevention of discriminatory practices, ultimately culminating in more responsible and sustainable financial innovations. Across both healthcare and finance, Causal AI's unwavering emphasis on ethical and responsible AI Verticals is charting a path toward groundbreaking, yet ethically sound, innovations poised to reshape these critical industries and our understanding of causality and intelligence.Trends
Integration of Causal Inference into Mainstream AI Solutions Traditional machine learning models often work as "black boxes," making it challenging to understand why a particular prediction or decision was made. The integration of causal inference allows AI systems to provide not only predictions but also the causal factors influencing those predictions. This greatly improves the transparency and explainability of AI systems, making it easier for users, stakeholders, and regulatory bodies to comprehend the rationale behind AI-generated outcomes. In domains where AI plays a critical role, such as healthcare and finance, understanding causation is key for ethical decision-making. For instance, in healthcare, knowing the causal factors behind a patient's condition can lead to more personalized and effective treatments, improving patient care. In finance, understanding causal relationships within the economy leads to more informed investment and risk management decisions. The trend aligns with a growing prominence on responsible AI and ethical considerations. The capability to uncover causal relationships enhances the quality of decision support provided by AI systems. Instead of simply flagging correlations, AI systems equipped with causal inference have provided actionable insights and recommendations based on a deeper understanding of cause-and-effect relationships. This is particularly valuable in industries where the stakes are high including autonomous vehicles and healthcare diagnostics. Corporate AI investment includes funding for AI R&D, acquisitions, partnerships and infrastructure. Causal AI, a subset of AI, emphasizes understanding causal relationships in data. It has applications in healthcare, finance, and policy, with growth potential as organizations looking to uncover meaningful insights for decision-making based on causality.Global Total Corporate Artificial Intelligence (AI) Investment From 2017 To 2022 (USD BN)
Restraints
Complexity of Implementation and Expertise Requirement Causal AI systems are inherently intricate due to their capability to decipher causation from correlation in data. This complexity arises from the necessity to create and maintain comprehensive causal models, the requirement for high-quality and extensive datasets, and the computational resources necessary to process and analyze the information. Implementing these systems is a time-consuming and resource-intensive endeavor, which deters businesses, particularly smaller ones, from venturing into the causal AI domain. This complexity of integration into existing infrastructures also disrupts workflow and necessitates a substantial learning curve. Causal AI necessitates a specialized skill set that is not widely available. Building and fine-tuning causal models, interpreting their outcomes and integrating these insights into decision-making processes need a deep understanding of both the AI field and the specific industry or domain. Data scientists and AI experts with expertise in causality are still relatively scarce, making it a challenge for businesses to find and retain the talent required to leverage causal AI Market effectively. The expertise gap extends not only to technical aspects but also to domain-specific knowledge necessary to extract meaningful causative relationships from data.Causal AI Market Segment Analysis
Based on the Deployment Model, the market is segmented into On-Premises and Cloud-Based. Cloud Platforms dominated the Causal AI Market in 2022. Cloud platforms offer the ability to scale resources up or down based on demand. Causal AI Verticals often require significant computational resources, and cloud providers easily accommodate these needs, ensuring that organizations can adapt to changes in data volume and complexity. Cloud-based solutions are accessible from anywhere with an internet connection, making it convenient for users to access AI Causality tools and insights remotely. This is particularly important for organizations with distributed teams or the need for remote access. Cloud-based deployment often involves lower upfront costs compared to setting up and maintaining on-premises infrastructure. This is a significant advantage for organizations, especially smaller ones, as they have access powerful Causal AI tools without significant capital expenditures. Cloud-based solutions are typically quicker to deploy. Organizations can get up and running with Causal AI capabilities faster, which is essential for staying competitive in rapidly evolving markets. Cloud providers handle much of the maintenance, security, and updates, reducing the burden on internal IT departments. This allows organizations to focus on using Causal AI for their specific needs rather than managing the underlying infrastructure. Many cloud-based Causal AI solutions are designed to integrate easily with other cloud services and Verticals, making it easier to build comprehensive data ecosystems. This can lead to more holistic insights and decision-making capabilities. Leading cloud providers invest heavily in security and compliance measures. This often results in a higher level of security than smaller organizations can afford to implement on their own, making the cloud a safe option for handling sensitive data. Cloud providers have data centers in various regions worldwide, ensuring low-latency access to resources and compliance with data sovereignty regulations. With a pay-as-you-go model, cloud-based deployment allows organizations to pay only for the resources they use. This cost efficiency is particularly appealing as organizations have to avoid over-provisioning resources.Global Causal AI Market Share, by Deployment Mode in 2022 (%)
Causal AI Market Regional Insights
North America dominated the Causal AI Market in 2022 and is expected to continue its dominance over the forecast period. North America is home to some of the world's leading technology hubs such as Silicon Valley in California, which has been a global center for innovation and technology development. These hubs have attracted top AI talent and investments from around the world. North American universities and research institutions have been at the forefront of AI research and development. They have made substantial contributions to the field, which has driven innovation and the creation of AI-related startups. Many of the world's largest technology companies including Google, Amazon, Facebook (Meta), Microsoft, and IBM, are headquartered in North America. These companies have invested heavily in AI such as Causal AI, through research and development, acquisitions, and product development. North America has a vibrant startup ecosystem with a focus on AI. Startups have played a pivotal role in driving innovation and many have specialized in Causal AI and related fields. North America has attracted substantial investments in AI projects and startups. Venture capital firms and corporate investors have poured significant resources into AI research and development in the region. The North American market has shown a strong demand for AI technologies across various industries, including healthcare, finance, technology, and others. This demand has driven the development and adoption of AI solutions, including those related to causality.Causal AI Market Scope : Inquire Before Buying
Global Causal AI Market Report Coverage Details Base Year: 2022 Forecast Period: 2023-2029 Historical Data: 2018 to 2022 Market Size in 2022: US $ 27.2 Mn. Forecast Period 2023 to 2029 CAGR: 41% Market Size in 2029: US $ 301.37 Mn. Segments Covered: by Deployment Model On-Premises Cloud-Based by Vertical Healthcare and Life Sciences Finance and Banking Marketing and Advertising Supply Chain and Logistics Energy and Utilities Telecommunications Others Causal AI Market, by Region
North America (United States, Canada and Mexico) Europe (UK, France, Germany, Italy, Spain, Sweden, Austria and Rest of Europe) Asia Pacific (China, South Korea, Japan, India, Australia, Indonesia, Malaysia, Vietnam, Taiwan, Bangladesh, Pakistan and Rest of APAC) South America (Brazil, Argentina Rest of South America) Middle East & Africa (South Africa, GCC, Egypt, Nigeria and the Rest of ME&A)Causal AI Key Players
1. IBM (US) 2. CausaLens (UK) 3. Microsoft (US) 4. Causaly(UK) 5. Google (US) 6. Geminos (US) 7. AWS (US) 8. Aitia (US) 9. Xplain Data (Germany) 10. INCRMNTAL (Israel) 11. Logility (US) 12. Cognino.ai. (UK) 13. H2O.ai (US) 14. DataRobot (US) 15. Cognizant (US) 16. Scalnyx(France) 17. Causality Link (US) 18. Dynatrace (US) 19. Parabole.ai (US) 20. Datma (US)Frequently Asked Questions:
1] What segments are covered in the Global Causal AI Market report? Ans. The segments covered in the Global Causal AI Market report are based on Deployment Models, and Vertical. 2] Which region is expected to hold the highest share in the Global Causal AI Market? Ans. Asia Pacific is expected to hold the highest share of the Global Causal AI Market. 3] Who are the key players in the Global Causal AI Market? Ans. IBM (US), CausaLens (UK), Microsoft (US), Causaly(UK), Google (US), Geminos (US), AWS (US), Aitia (US), Xplain Data (Germany), INCRMNTAL (Israel), Logility (US) and others are the key players in the Global Causal AI Market. 4] Which segment hold the largest market share in the Global Causal AI market by 2029? Ans. The Deployment Model segment hold the largest market share in the Global Causal AI market by 2029. 5] What is the market size of the Global Causal AI market by 2029? Ans. The market size of the Global Causal AI market is USD 301.37 Mn. by 2029. 6] What was the market size of the Global Causal AI market in 2022? Ans. The market size of the Global Causal AI market was worth USD 7.2 Mn. in 2022.
1. Causal AI Market: Research Methodology 2. Causal AI Market Introduction 2.1 Study Assumption and Market Definition 2.2 Scope of the Study 2.3 Executive Summary 3. Causal AI Market: Dynamics 3.1 Causal AI Market Trends by Region 3.1.1 North America Causal AI Market Trends 3.1.2 Europe Causal AI Market Trends 3.1.3 Asia Pacific Causal AI Market Trends 3.1.4 Middle East and Africa Causal AI Market Trends 3.1.5 South America Causal AI Market Trends 3.2 Causal AI Market Dynamics by Region 3.2.1 North America 3.2.1.1 North America Causal AI Market Drivers 3.2.1.2 North America Causal AI Market Restraints 3.2.1.3 North America Causal AI Market Opportunities 3.2.1.4 North America Causal AI Market Challenges 3.2.2 Europe 3.2.2.1 Europe Causal AI Market Drivers 3.2.2.2 Europe Causal AI Market Restraints 3.2.2.3 Europe Causal AI Market Opportunities 3.2.2.4 Europe Causal AI Market Challenges 3.2.3 Asia Pacific 3.2.3.1 Asia Pacific Causal AI Market Market Drivers 3.2.3.2 Asia Pacific Causal AI Market Restraints 3.2.3.3 Asia Pacific Causal AI Market Opportunities 3.2.3.4 Asia Pacific Causal AI Market Challenges 3.2.4 Middle East and Africa 3.2.4.1 Middle East and Africa Causal AI Market Drivers 3.2.4.2 Middle East and Africa Causal AI Market Restraints 3.2.4.3 Middle East and Africa Causal AI Market Opportunities 3.2.4.4 Middle East and Africa Causal AI Market Challenges 3.2.5 South America 3.2.5.1 South America Causal AI Market Drivers 3.2.5.2 South America Causal AI Market Restraints 3.2.5.3 South America Causal AI Market Opportunities 3.2.5.4 South America Causal AI Market Challenges 3.3 PORTER’s Five Forces Analysis 3.3.1 Bargaining Power Of Suppliers 3.3.2 Bargaining Power Of Buyers 3.3.3 Threat Of New Entrants 3.3.4 Threat Of Substitutes 3.3.5 Intensity Of Rivalry 3.4 PESTLE Analysis 3.5 Regulatory Landscape by Region 3.5.1 North America 3.5.2 Europe 3.5.3 Asia Pacific 3.5.4 Middle East and Africa 3.5.5 South America 3.6 Analysis of Government Schemes and Initiatives For the Causal AI Industry 3.7 The Global Pandemic and Redefining of The Causal AI Industry Landscape 3.8 Technological Road Map 4. Global Causal AI Market: Global Market Size and Forecast by Segmentation (By Value) (2022-2029) 4.1 Global Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 4.1.1 On-Premises 4.1.2 Cloud-Based 4.2 Global Causal AI Market Size and Forecast, by Vertical (2022-2029) 4.2.1 Healthcare and Life Sciences 4.2.2 Finance and Banking 4.2.3 Marketing and Advertising 4.2.4 Supply Chain and Logistics 4.2.5 Energy and Utilities 4.2.6 Telecommunications 4.2.7 Others 4.3 Global Causal AI Market Size and Forecast, by Region (2022-2029) 4.3.1 North America 4.3.2 Europe 4.3.3 Asia Pacific 4.3.4 Middle East and Africa 4.3.5 South America 5. North America Causal AI Market Size and Forecast by Segmentation (By Value) (2022-2029) 5.1 North America Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 5.1.1 On-Premises 5.1.2 Cloud-Based 5.2 North America Causal AI Market Size and Forecast, by Vertical (2022-2029) 5.2.1 Healthcare and Life Sciences 5.2.2 Finance and Banking 5.2.3 Marketing and Advertising 5.2.4 Supply Chain and Logistics 5.2.5 Energy and Utilities 5.2.6 Telecommunications 5.2.7 Others 5.3 North America Causal AI Market Size and Forecast, by Country (2022-2029) 5.3.1 United States 5.3.1.1 United States Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 5.3.1.1.1 On-Premises 5.3.1.1.2 Cloud-Based 5.3.1.2 United States Causal AI Market Size and Forecast, by Vertical (2022-2029) 5.3.1.2.1 Healthcare and Life Sciences 5.3.1.2.2 Finance and Banking 5.3.1.2.3 Marketing and Advertising 5.3.1.2.4 Supply Chain and Logistics 5.3.1.2.5 Energy and Utilities 5.3.1.2.6 Telecommunications 5.3.1.2.7 Others 5.3.2 Canada 5.3.2.1 Canada Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 5.3.2.1.1 On-Premises 5.3.2.1.2 Cloud-Based 5.3.2.2 Canada Causal AI Market Size and Forecast, by Vertical (2022-2029) 5.3.2.2.1 Healthcare and Life Sciences 5.3.2.2.2 Finance and Banking 5.3.2.2.3 Marketing and Advertising 5.3.2.2.4 Supply Chain and Logistics 5.3.2.2.5 Energy and Utilities 5.3.2.2.6 Telecommunications 5.3.2.2.7 Others 5.3.3 Mexico 5.3.3.1 Mexico Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 5.3.3.1.1 On-Premises 5.3.3.1.2 Cloud-Based 5.3.3.2 Mexico Causal AI Market Size and Forecast, by Vertical (2022-2029) 5.3.3.2.1 Healthcare and Life Sciences 5.3.3.2.2 Finance and Banking 5.3.3.2.3 Marketing and Advertising 5.3.3.2.4 Supply Chain and Logistics 5.3.3.2.5 Energy and Utilities 5.3.3.2.6 Telecommunications 5.3.3.2.7 Others 6. Europe Causal AI Market Size and Forecast by Segmentation (By Value) (2022-2029) 6.1 Europe Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 6.2 Europe Causal AI Market Size and Forecast, by Vertical (2022-2029) 6.3 Europe Causal AI Market Size and Forecast, by End User (2022-2029) 6.4 Europe Causal AI Market Size and Forecast, by Country (2022-2029) 6.4.1 United Kingdom 6.4.1.1 United Kingdom Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 6.4.1.2 United Kingdom Causal AI Market Size and Forecast, by Vertical (2022-2029) 6.4.1.3 United Kingdom Causal AI Market Size and Forecast, by End User (2022-2029) 6.4.2 France 6.4.2.1 France Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 6.4.2.2 France Causal AI Market Size and Forecast, by Vertical (2022-2029) 6.4.2.3 France Causal AI Market Size and Forecast, by End User (2022-2029) 6.4.3 Germany 6.4.3.1 Germany Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 6.4.3.2 Germany Causal AI Market Size and Forecast, by Vertical (2022-2029) 6.4.3.3 Germany Causal AI Market Size and Forecast, by End User (2022-2029) 6.4.4 Italy 6.4.4.1 Italy Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 6.4.4.2 Italy Causal AI Market Size and Forecast, by Vertical (2022-2029) 6.4.4.3 Italy Causal AI Market Size and Forecast, by End User (2022-2029) 6.4.5 Spain 6.4.5.1 Spain Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 6.4.5.2 Spain Causal AI Market Size and Forecast, by Vertical (2022-2029) 6.4.5.3 Spain Causal AI Market Size and Forecast, by End User (2022-2029) 6.4.6 Sweden 6.4.6.1 Sweden Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 6.4.6.2 Sweden Causal AI Market Size and Forecast, by Vertical (2022-2029) 6.4.6.3 Sweden Causal AI Market Size and Forecast, by End User (2022-2029) 6.4.7 Austria 6.4.7.1 Austria Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 6.4.7.2 Austria Causal AI Market Size and Forecast, by Vertical (2022-2029) 6.4.7.3 Austria Causal AI Market Size and Forecast, by End User (2022-2029) 6.4.8 Rest of Europe 6.4.8.1 Rest of Europe Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 6.4.8.2 Rest of Europe Causal AI Market Size and Forecast, by Vertical (2022-2029). 6.4.8.3 Rest of Europe Causal AI Market Size and Forecast, by End User (2022-2029) 7. Asia Pacific Causal AI Market Size and Forecast by Segmentation (By Value) (2022-2029) 7.1 Asia Pacific Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 7.2 Asia Pacific Causal AI Market Size and Forecast, by Vertical (2022-2029) 7.3 Asia Pacific Causal AI Market Size and Forecast, by Country (2022-2029) 7.3.1 China 7.3.1.1 China Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 7.3.1.2 China Causal AI Market Size and Forecast, by Vertical (2022-2029) 7.3.2 South Korea 7.3.2.1 S Korea Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 7.3.2.2 S Korea Causal AI Market Size and Forecast, by Vertical (2022-2029) 7.3.3 Japan 7.3.3.1 Japan Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 7.3.3.2 Japan Causal AI Market Size and Forecast, by Vertical (2022-2029) 7.3.4 India 7.3.4.1 India Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 7.3.4.2 India Causal AI Market Size and Forecast, by Vertical (2022-2029) 7.3.5 Australia 7.3.5.1 Australia Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 7.3.5.2 Australia Causal AI Market Size and Forecast, by Vertical (2022-2029) 7.3.6 Indonesia 7.3.6.1 Indonesia Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 7.3.6.2 Indonesia Causal AI Market Size and Forecast, by Vertical (2022-2029) 7.3.7 Malaysia 7.3.7.1 Malaysia Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 7.3.7.2 Malaysia Causal AI Market Size and Forecast, by Vertical (2022-2029) 7.3.8 Vietnam 7.3.8.1 Vietnam Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 7.3.8.2 Vietnam Causal AI Market Size and Forecast, by Vertical (2022-2029) 7.3.9 Taiwan 7.3.9.1 Taiwan Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 7.3.9.2 Taiwan Causal AI Market Size and Forecast, by Vertical (2022-2029) 7.3.10 Bangladesh 7.3.10.1 Bangladesh Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 7.3.10.2 Bangladesh Causal AI Market Size and Forecast, by Vertical (2022-2029) 7.3.11 Pakistan 7.3.11.1 Pakistan Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 7.3.11.2 Pakistan Causal AI Market Size and Forecast, by Vertical (2022-2029) 7.3.12 Rest of Asia Pacific 7.3.12.1 Rest of Asia Pacific Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 7.3.12.2 Rest of Asia PacificCausal AI Market Size and Forecast, by Vertical (2022-2029) 8. Middle East and Africa Causal AI Market Size and Forecast by Segmentation (By Value) (2022-2029) 8.1 Middle East and Africa Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 8.2 Middle East and Africa Causal AI Market Size and Forecast, by Vertical (2022-2029) 8.3 Middle East and Africa Causal AI Market Size and Forecast, by Country (2022-2029) 8.3.1 South Africa 8.3.1.1 South Africa Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 8.3.1.2 South Africa Causal AI Market Size and Forecast, by Vertical (2022-2029) 8.3.2 GCC 8.3.2.1 GCC Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 8.3.2.2 GCC Causal AI Market Size and Forecast, by Vertical (2022-2029) 8.3.3 Egypt 8.3.3.1 Egypt Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 8.3.3.2 Egypt Causal AI Market Size and Forecast, by Vertical (2022-2029) 8.3.4 Nigeria 8.3.4.1 Nigeria Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 8.3.4.2 Nigeria Causal AI Market Size and Forecast, by Vertical (2022-2029) 8.3.5 Rest of ME&A 8.3.5.1 Rest of ME&A Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 8.3.5.2 Rest of ME&A Causal AI Market Size and Forecast, by Vertical (2022-2029) 9. South America Causal AI Market Size and Forecast by Segmentation (By Value) (2022-2029) 9.1 South America Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 9.2 South America Causal AI Market Size and Forecast, by Vertical (2022-2029) 9.3 South America Causal AI Market Size and Forecast, by Country (2022-2029) 9.3.1 Brazil 9.3.1.1 Brazil Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 9.3.1.2 Brazil Causal AI Market Size and Forecast, by Vertical (2022-2029) 9.3.2 Argentina 9.3.2.1 Argentina Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 9.3.2.2 Argentina Causal AI Market Size and Forecast, by Vertical (2022-2029) 9.3.3 Rest Of South America 9.3.3.1 Rest Of South America Causal AI Market Size and Forecast, by Deployment Model (2022-2029) 9.3.3.2 Rest Of South America Causal AI Market Size and Forecast, by End User (2022-2029) 10. Global Causal AI Market: Competitive Landscape 10.1 MMR Competition Matrix 10.2 Competitive Landscape 10.3 Key Players Benchmarking 10.3.1 Company Name 10.3.2 Service Segment 10.3.3 End User Segment 10.3.4 Revenue (2022) 10.3.5 Manufacturing Locations 10.4 Leading Causal AI Global Companies, by market capitalization 10.5 Market Structure 10.5.1 Market Leaders 10.5.2 Market Followers 10.5.3 Emerging Players 10.6 Mergers and Acquisitions Details 11. Company Profile: Key Players 11.1 IBM (US) 11.1.1 Company Overview 11.1.2 Business Portfolio 11.1.3 Financial Overview 11.1.4 SWOT Analysis 11.1.5 Strategic Analysis 11.1.6 Scale of Operation (small, medium, and large) 11.1.7 Details on Partnership 11.1.8 Regulatory Accreditations and Certifications Received by Them 11.1.9 Awards Received by the Firm 11.1.10 Recent Developments 11.2 CausaLens (UK) 11.3 Microsoft (US) 11.4 Causaly(UK) 11.5 Google (US) 11.6 Geminos (US) 11.7 AWS (US) 11.8 Aitia (US) 11.9 Xplain Data (Germany) 11.10 INCRMNTAL (Israel) 11.11 Logility (US) 11.12 Cognino.ai. (UK) 11.13 H2O.ai (US) 11.14 DataRobot (US) 11.15 Cognizant (US) 11.16 Scalnyx(France) 11.17 Causality Link (US) 11.18 Dynatrace (US) 11.19 Parabole.ai (US) 11.20 Datma (US) 12. Key Findings 13. Industry Recommendations 14. Terms and Glossary