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Global Causal AI Market by Offering (Platforms (Deployment (Cloud, On-premises)), Services), Vertical (Healthcare & Life Sciences, BFSI, Retail & eCommerce, Transportation & Logistics, Manufacturing) and Region - Forecast to 2030

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    Report

  • 201 Pages
  • May 2023
  • Region: Global
  • Markets and Markets
  • ID: 5805704
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High Demand for Platforms to Transfer Data from Physical Premises to Cloud

The global market for causal AI is projected to grow from USD 8.01 million in 2023 to USD 119.5 million by 2030, at a CAGR of 47.1% during the forecast period. Causal AI is the only technology that can reason and make choices such as humans do. It has the potential to revolutionize enterprise AI, making it more transparent, fair, and safe. The increasing demand for accurate predictions and decision-making is expected to drive the market.

The Healthcare and Lifesciences vertical is projected to be the largest market during the forecast period

The healthcare and life sciences industry is one of the fastest-growing sectors in the world, and the adoption of causal AI technology is on the rise. Causal AI and Causal ML is used in healthcare and life sciences for drug discovery, patient diagnosis, treatment, personalized medicine, and more. The high adoption of advanced technologies in the healthcare sector, the presence of several key players, and the growing demand for personalized medicine are some of the factors driving the growth of the market in North America. Europe is also expected to grow significantly, driven by the increasing adoption of AI technology and the growing demand for innovative healthcare solutions. The healthcare and life sciences industry is witnessing a surge in investments and acquisitions related to causal AI technology.

Among deployment, on-premises segment is registered to grow at the highest CAGR during the forecast period

On-premises deployment of causal AI platforms involves installing the software directly onto the organization's servers or hardware infrastructure. This deployment model provides maximum control over the data and the platform, as all data is stored within the organization's own network. On-premises deployment may be preferred by organizations with strict data privacy or regulatory compliance requirements, as it allows them to maintain complete control over their data. On-premises deployment also offers the potential for greater customization and integration with existing IT infrastructure.

Among training, support, and maintenance services segment is anticipated to account for the largest market size during the forecast period

Causal AI training, support, and maintenance services provide organizations with the ongoing support and expertise they need to effectively leverage causal inference tools and techniques. These services focus on providing the education, training, and technical support necessary to ensure organizations can get the most value from their causal inference solutions. Training services involve providing workshops or training sessions to help employees understand the basics of causal inference and how to use specific software solutions. Whereas support services provide ongoing technical support to help organizations troubleshoot problems or issues that arise with their causal inference solutions. Maintenance services involve regularly updating and maintaining software solutions to ensure they remain secure, reliable, and effective.

Rest of World is projected to witness the highest CAGR during the forecast period

The causal AI market is rapidly expanding globally, with a growing number of companies and governments investing in this emerging technology. In regions outside North America and Europe, the market is also experiencing significant growth, driven by various factors such as increasing demand for advanced data analytics, rising investments in AI research and development, and the adoption of AI-based solutions across various industries. One of the major trends in the causal AI market in these regions is the increasing adoption of AI-based solutions in sectors such as healthcare, finance, and retail.

Breakdown of Primaries

In-depth interviews were conducted with Chief Executive Officers (CEOs), innovation and technology directors, system integrators, and executives from various key organizations operating in the Causal AI market.

  • By Company: Tier I: 35%, Tier II: 45%, and Tier III: 20%
  • By Designation: C-Level Executives: 35%, Directors: 25%, and Others: 40%
  • By Region: APAC: 30%, Europe: 20%, North America: 45%, and RoW: 5%

Major vendors offering Causal AI solutions and services across the globe are IBM (US), CausaLens (England), Microsoft (US), Causaly (England), Google (US), Geminos (US), AWS (US), Aitia (US), INCRMNTAL (Israel), Logility (US), Cognino.ai. (England), H2O.ai (US), DataRobot (US), Cognizant (US), Scalnyx (France), Causality Link (US), Dynatrace (US), Parabole.ai (US), Causalis.ai (Israel), and Omics Data Automation (US).

Research Coverage

The market study covers Causal AI across segments. It aims at estimating the market size and the growth potential across different segments, such as offering, vertical, and region. It includes an in-depth competitive analysis of the key players in the market, along with their company profiles, key observations related to product and business offerings, recent developments, and key market strategies.

Key Benefits of Buying the Report

The report would provide the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall market for Causal AI and its subsegments. It would help stakeholders understand the competitive landscape and gain more insights better to position their business and plan suitable go-to-market strategies. It also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.

The report provides insights on the following pointers:

  • Analysis of key drivers (Importance of Causal Inference Models in Various Fields, Emergence of Causal AI as a Solution to Overcome the Limitations of Current AI, Operationalizing AI initiatives), restraints (Lack of interpretability & explainability and Acquiring & preparing high-quality data), opportunities (Causal AI is its potential to revolutionize the field of healthcare and Technological advancements in Causal AI), and challenges (Causal Inference from Complex Data Sets, Lack of Standardization and Ethical and Legal Issues) influencing the growth of the Causal AI market
  • Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the Causal AI market.
  • Market Development: Comprehensive information about lucrative markets - the report analyses the Causal AI market across varied regions
  • Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in Causal AI and Causal ML market strategies; the report also helps stakeholders understand the pulse of the Causal AI market and provides them with information on key market drivers, restraints, challenges, and opportunities
  • Competitive Assessment: In-depth assessment of market shares, growth strategies and service offerings of leading players such as IBM (US), Google (US), AWS (US), Microsoft (US) Cognizant (US) and Dynatrace (US) among others in the Causal AI market.

Table of Contents

1 Introduction
1.1 Study Objectives
1.2 Market Definition
1.3 Inclusions and Exclusions
1.4 Market Scope
1.4.1 Market Segmentation
1.4.2 Regions Covered
1.4.3 Years Considered
1.5 Currency Considered
Table 1 USD Exchange Rates, 2020-2022
1.6 Stakeholders

2 Research Methodology
2.1 Research Data
Figure 1 Causal AI Market: Research Design
2.1.1 Secondary Data
2.1.2 Primary Data
2.1.2.1 Primary Interviews
2.1.2.2 Breakup of Primary Profiles
2.1.2.3 Key Industry Insights
2.2 Data Triangulation
2.3 Market Size Estimation
Figure 2 Market: Top-Down and Bottom-Up Approaches
2.3.1 Top-Down Approach
2.3.2 Bottom-Up Approach
Figure 3 Approach 1 (Supply Side): Revenue from Offering of Causal AI Market
Figure 4 Approach 2 - Bottom-Up (Supply Side): Collective Revenue from Offering of Causal AI Players
Figure 5 Approach 3 - Bottom-Up (Supply Side): Revenue and Subsequent Market Estimation from Offering of Causal AI
Figure 6 Approach 4 - Bottom-Up (Demand Side): Share of Causal AI Offering Through Overall Causal AI Spending
2.4 Market Forecast
Table 2 Factor Analysis
2.5 Assumptions
Table 3 Research Assumptions
2.6 Limitations
2.7 Implication of Recession on Global Market
Table 4 Impact of Recession on Global Market

3 Executive Summary
Table 5 Causal AI Market Size and Growth Rate, 2020-2022 (USD Thousand, Y-O-Y)
Table 6 Market Size and Growth Rate, 2023-2030 (USD Thousand, Y-O-Y)
Figure 7 Causal AI Platforms to Account for Larger Market Than Services in 2023
Figure 8 Cloud Deployment to Account for Larger Market Share in 2023
Figure 9 Consulting Services to Account for Largest Market in 2023
Figure 10 Healthcare & Lifesciences Vertical to Account for Largest Market in 2023
Figure 11 North America Estimated to Account for Largest Share in 2023

4 Premium Insights
4.1 Attractive Opportunities for Players in Causal AI Market
Figure 12 High Demand for Platforms to Transfer Data from Physical Premises to Cloud
4.2 Market, by Vertical
Figure 13 Healthcare & Life Sciences to Account for Largest Size During Forecast Period
4.3 Market, by Region
Figure 14 North America to Account for Largest Share by 2028
4.4 Market, by Offering and Key Vertical
Figure 15 Platforms and Healthcare & Life Sciences Segments to Account for Significant Respective Shares by 2030

5 Market Overview and Industry Trends
5.1 Introduction
5.2 Market Dynamics
Figure 16 Causal AI Market: Drivers, Restraints, Opportunities, and Challenges
5.2.1 Drivers
5.2.1.1 Importance of Causal Inference Models in Various Fields
5.2.1.2 Emergence of Causal AI to Overcome Limitations of Current AI
5.2.1.3 Operationalizing AI Initiatives
5.2.2 Restraints
5.2.2.1 Lack of Interpretability and Explainability
5.2.2.2 Acquiring and Preparing High-Quality Data
5.2.3 Opportunities
5.2.3.1 Potential to Revolutionize Healthcare Field
5.2.3.2 Technological Advancements
5.2.4 Challenges
5.2.4.1 Causal Inference from Complex Data Sets
5.2.4.2 Lack of Standardization
5.3 Case Study Analysis
5.3.1 Accelerating Model Validation with Causal AI
5.3.2 Unlocking Revenue Growth with Causal AI-Powered Pricing and Promotion Optimization
5.3.3 Using Causal AI to Enhance Customer Retention Strategies
5.3.4 Revolutionizing Data Provider Industry with Causal AI
5.3.5 Use of Causal AI for Customer Segmentation
5.4 Ecosystem Analysis
Figure 17 Ecosystem Analysis
Table 7 Platform Providers
Table 8 Library Providers
Table 9 AI Framework Providers
Table 10 Regulatory Bodies
5.5 Key Steps in Using Causal AI
5.5.1 Data Collection & Preparation
5.5.2 Causal Inference
5.5.3 ML Models
5.5.4 Interpretability & Explainability
5.5.5 Validation & Testing
5.6 Correlation-based AI vs. Causal AI
Table 11 Correlation-based AI vs. Causal AI
5.7 Technology Analysis
5.7.1 Related Technologies
5.7.1.1 Supervised Learning
5.7.1.2 Unsupervised Learning
5.7.1.3 Natural Language Processing
5.7.1.4 Predictive Analytics
5.7.1.5 Deep Learning
5.7.1.6 AI Governance (Ethical, Explainable, and Responsible AI)
5.7.1.7 Bayesian Networks
5.7.2 Allied Technologies
5.7.2.1 Cloud Computing
5.7.2.2 Robotics
5.7.2.3 Federated Learning
5.7.2.4 Digital Twin
5.8 Best Practices in Causal AI Market
5.9 Future Directions of Causal AI Landscape
Table 12 Short-Term Roadmap, 2023-2025
Table 13 Mid-Term Roadmap, 2026-2028
Table 14 Long-Term Roadmap, 2029-2030
5.10 Brief History of Evolution of Causal AI
5.11 Value Chain Analysis
Figure 18 Market: Value Chain Analysis
5.11.1 Data Collection & Preparation
5.11.2 Algorithm Development
5.11.3 Model Training
5.11.4 Model Testing & Validation
5.11.5 Deployment & Integration
5.11.6 Maintenance & Support
5.12 Pricing Model Analysis
Table 15 Pricing Models
5.13 Patent Analysis
5.13.1 Methodology
5.13.2 Document Type
Table 16 Patents Filed, 2013-2023
5.13.3 Innovation & Patent Applications
Figure 19 Total Number of Patents Granted, 2013-2023
5.13.3.1 Top Applicants
Figure 20 Top Ten Companies with Highest Number of Patent Applications, 2013-2022
Table 17 US: Top 20 Patent Owners, 2013-2022
Table 18 List of Patents in Causal AI Market, 2021-2023
5.14 Porter's Five Forces Analysis
Figure 21 Porter's Five Forces Analysis
Table 19 Porter's Five Forces Analysis
5.14.1 Threat from New Entrants
5.14.2 Threat from Substitutes
5.14.3 Bargaining Power of Suppliers
5.14.4 Bargaining Power of Buyers
5.14.5 Intensity of Competitive Rivalry
5.15 Regulatory Landscape
5.15.1 Regulatory Bodies, Government Agencies, and Other Organizations
Table 20 North America: Regulatory Bodies, Government Agencies, and Other Organizations
Table 21 Europe: Regulatory Bodies, Government Agencies, and Other Organizations
Table 22 Asia-Pacific: List of Regulatory Bodies, Government Agencies, and Other Organizations
Table 23 RoW: Regulatory Bodies, Government Agencies, and Other Organizations
5.15.1.1 North America
5.15.1.1.1 US
5.15.1.1.2 Canada
5.15.1.2 Europe
5.15.1.3 Asia-Pacific
5.15.1.3.1 South Korea
5.15.1.3.2 China
5.15.1.3.3 India
5.15.1.4 Middle East & Africa
5.15.1.4.1 UAE
5.15.1.4.2 KSA
5.15.1.4.3 Bahrain
5.15.1.5 Latin America
5.15.1.5.1 Brazil
5.15.1.5.2 Mexico
5.16 Key Stakeholders and Buying Criteria
5.16.1 Key Stakeholders in Buying Process
Figure 22 Influence of Stakeholders on Buying Process in Top Three Verticals
Table 24 Influence of Stakeholders on Buying Process in Top Three Verticals
5.16.2 Buying Criteria
Figure 23 Key Buying Criteria in Top Three Verticals
Table 25 Key Buying Criteria in Top Three Verticals
5.17 Disruptions Impacting Buyers/Clients in Causal AI Market
Figure 24 Disruptions Impacting Buyers/Clients
5.18 Key Conferences & Events
Table 26 Detailed List of Conferences & Events, 2023-2024
5.19 Business Models of Causal AI
5.19.1 Potential Outcome Framework
5.19.2 Causal Graph Model
5.20 Approaches to Causal Inferences
5.20.1 Correlations
5.20.2 Causation
5.20.3 Interventions
5.20.4 Counterfactuals
5.20.5 System Modeling
5.21 Causal AI Techniques & Methods
5.21.1 Machine Learning Algorithms
5.21.1.1 Regression-based Methods
5.21.1.2 Decision Trees and Random Forests
5.21.1.3 K-Nearest Neighbor Algorithms
5.21.1.4 Other ML Algorithms
5.21.2 Bayesian Networks
5.21.2.1 Directed Acyclic Graphs (DAGs)
5.21.2.2 Structural Causal Models (SCMs)
5.21.2.3 Counterfactual DAGs
5.21.2.4 Other Bayesian Networks
5.21.3 Structural Equation Models
5.21.3.1 Path Analysis (DAGs)
5.21.3.2 Confirmatory Factor Analysis (CFA)
5.21.3.3 Partial Least Squares (PLS)
5.21.3.4 Other Structural Equation Models
5.21.4 Counterfactual Analysis
5.21.4.1 Propensity Score Matching (PSM)
5.21.4.2 Difference-In-Differences (DID)
5.21.4.3 Instrumental Variables (IV)
5.21.4.4 Regression Discontinuity Design (RDD)

6 Causal AI Market, by Offering
6.1 Introduction
6.1.1 Offering: Market Drivers
Figure 25 Causal AI Services Market to Grow at Highest CAGR During Forecast Period
Table 27 Market, by Offering, 2020-2022 (USD Thousand)
Table 28 Market, by Offering, 2023-2030 (USD Thousand)
6.2 Platforms
6.2.1 Demand for Data-Driven Decision-Making and More Accurate Predictions and Insights
Table 29 Platforms: Market, by Region, 2020-2022 (USD Thousand)
Table 30 Platforms: Market, by Region, 2023-2030 (USD Thousand)
6.2.2 Causal AI Platforms Market, by Deployment
Figure 26 On-Premise Platform Deployment to Witness Higher CAGR During Forecast Period
Table 31 Causal AI Platforms Market, by Deployment, 2020-2022 (USD Thousand)
Table 32 Causal AI Platforms Market, by Deployment, 2023-2030 (USD Thousand)
6.2.2.1 On-Premises
6.2.2.1.1 Potential for Greater Customization and Integration
Table 33 On-Premises: Causal AI Platforms Market, by Region, 2020-2022 (USD Thousand)
Table 34 On-Premises: Causal AI Platforms Market, by Region, 2023-2030 (USD Thousand)
6.2.2.2 Cloud
6.2.2.2.1 Potential for Greater Accessibility
Table 35 Cloud: Causal AI Platforms Market, by Region, 2020-2022 (USD Thousand)
Table 36 Cloud: Causal AI Platforms Market, by Region, 2023-2030 (USD Thousand)
6.3 Services
6.3.1 Valuable Resources Available for Those Lacking Internal Proficiency
Figure 27 Training, Support, and Maintenance Services to Account for Largest Market During Forecast Period
Table 37 Causal AI Market, by Service, 2020-2022 (USD Thousand)
Table 38 Market, by Service, 2023-2030 (USD Thousand)
6.3.2 Consulting Services
6.3.2.1 Expert Guidance to Make Informed Decisions and Achieve Better Results
Table 39 Consulting Services: Market, by Region, 2020-2022 (USD Thousand)
Table 40 Consulting Services: Market, by Region, 2023-2030 (USD Thousand)
6.3.3 Deployment & Integration
6.3.3.1 Focus on Practical Aspects of Implementing Causal Inference
Table 41 Deployment & Integration: Market, by Region, 2020-2022 (USD Thousand)
Table 42 Deployment & Integration: Market, by Region, 2023-2030 (USD Thousand)
6.3.4 Training, Support, and Maintenance
6.3.4.1 Need for Ongoing Training and Support to Ensure Optimal Model Performance and Accuracy
Table 43 Training, Support, and Maintenance: Market, by Region, 2020-2022 (USD Thousand)
Table 44 Training, Support, and Maintenance: Market, by Region, 2023-2030 (USD Thousand)

7 Causal AI Market, by Vertical
7.1 Introduction
7.1.1 Vertical: Market Drivers
Figure 28 Healthcare & Life Sciences Vertical to Grow at Highest CAGR During Forecast Period
Table 45 Market, by Vertical, 2020-2022 (USD Thousand)
Table 46 Market, by Vertical, 2023-2030 (USD Thousand)
7.2 BFSI
7.2.1 Highly Competitive with Several Operational Players
Table 47 BFSI: Market, by Region, 2020-2022 (USD Thousand)
Table 48 BFSI: Market, by Region, 2023-2030 (USD Thousand)
7.2.2 Use Cases: BFSI
7.3 Healthcare & Life Sciences
7.3.1 Investment by Startups in Developing Blood Tests for Early Cancer Detection
Table 49 Healthcare & Life Sciences: Market, by Region, 2020-2022 (USD Thousand)
Table 50 Healthcare & Life Sciences: Market, by Region, 2023-2030 (USD Thousand)
7.3.2 Use Cases: Healthcare & Life Sciences
7.4 Retail & eCommerce
7.4.1 Optimizing Product Inventory for Retailers and Discovery for Customers
Table 51 Retail & eCommerce: Causal AI Market, by Region, 2020-2022 (USD Thousand)
Table 52 Retail & eCommerce: Market, by Region, 2023-2030 (USD Thousand)
7.4.2 Use Cases: Retail & eCommerce
7.5 Manufacturing
7.5.1 Analyzing Data from Production Processes to Identify Defects and Quality Issues in Real Time
Table 53 Manufacturing: Market, by Region, 2020-2022 (USD Thousand)
Table 54 Manufacturing: Market, by Region, 2023-2030 (USD Thousand)
7.5.2 Use Cases: Manufacturing
7.6 Transportation & Logistics
7.6.1 Optimizing Vehicle Routes, Tracking Shipments in Real Time, and Improving Delivery Times
Table 55 Transportation & Logistics: Market, by Region, 2020-2022 (USD Thousand)
Table 56 Transportation & Logistics: Market, by Region, 2023-2030 (USD Thousand)
7.6.2 Use Cases: Transportation & Logistics
7.7 Other Verticals
Table 57 Other Verticals: Market, by Region, 2020-2022 (USD Thousand)
Table 58 Other Verticals: Market, by Region, 2023-2030 (USD Thousand)

8 Causal AI Market, by Region
8.1 Introduction
Figure 29 North America to be Largest Market During Forecast Period
Figure 30 Japan to Grow at Highest CAGR During Forecast Period
Table 59 Market, by Region, 2020-2022 (USD Thousand)
Table 60 Market, by Region, 2023-2030 (USD Thousand)
8.2 North America
8.2.1 North America: Market Drivers
8.2.2 North America: Impact of Recession
Figure 31 North America: Causal AI Market Snapshot
Table 61 North America: Market, by Offering, 2020-2022 (USD Thousand)
Table 62 North America: Market, by Offering, 2023-2030 (USD Thousand)
Table 63 North America: Causal AI Platforms Market, by Deployment, 2020-2022 (USD Thousand)
Table 64 North America: Causal AI Platforms Market, by Deployment, 2023-2030 (USD Thousand)
Table 65 North America: Market, by Service, 2020-2022 (USD Thousand)
Table 66 North America: Market, by Service, 2023-2030 (USD Thousand)
Table 67 North America: Market, by Vertical, 2020-2022 (USD Thousand)
Table 68 North America: Market, by Vertical, 2023-2030 (USD Thousand)
Table 69 North America: Market, by Country, 2020-2022 (USD Thousand)
Table 70 North America: Market, by Country, 2023-2030 (USD Thousand)
8.2.3 US
8.2.3.1 Research and Investment by Leading Universities and Organizations
8.2.4 Canada
8.2.4.1 Rise in Adoption of Machine Learning Applications in Various Industries
8.3 Europe
8.3.1 Europe: Market Drivers
8.3.2 Europe: Impact of Recession
Table 71 Europe: Causal AI Market, by Offering, 2020-2022 (USD Thousand)
Table 72 Europe: Market, by Offering, 2023-2030 (USD Thousand)
Table 73 Europe: Causal AI Platforms Market, by Deployment, 2020-2022 (USD Thousand)
Table 74 Europe: Causal AI Platforms Market, by Deployment, 2023-2030 (USD Thousand)
Table 75 Europe: Market, by Service, 2020-2022 (USD Thousand)
Table 76 Europe: Market, by Service, 2023-2030 (USD Thousand)
Table 77 Europe: Market, by Vertical, 2020-2022 (USD Thousand)
Table 78 Europe: Market, by Vertical, 2023-2030 (USD Thousand)
Table 79 Europe: Market, by Country, 2020-2022 (USD Thousand)
Table 80 Europe: Market, by Country, 2023-2030 (USD Thousand)
8.3.3 UK
8.3.3.1 Businesses Increasingly Seeking to Leverage Benefits of AI and ML
8.3.4 Germany
8.3.4.1 Strong IT Infrastructure and Robust Regulatory Framework
8.3.5 France
8.3.5.1 Thriving Startup Ecosystem
8.3.6 Rest of Europe
8.4 Rest of the World (RoW)
8.4.1 Rest of the World: Market Drivers
8.4.2 RoW: Impact of Recession
Table 81 RoW: Causal AI Market, by Offering, 2020-2022 (USD Thousand)
Table 82 RoW: Market, by Offering, 2023-2030 (USD Thousand)
Table 83 RoW: Causal AI Platforms Market, by Deployment, 2020-2022 (USD Thousand)
Table 84 RoW: Causal AI Platforms Market, by Deployment, 2023-2030 (USD Thousand)
Table 85 RoW: Market, by Service, 2020-2022 (USD Thousand)
Table 86 RoW: Market, by Service, 2023-2030 (USD Thousand)
Table 87 RoW: Market, by Vertical, 2020-2022 (USD Thousand)
Table 88 RoW: Market, by Vertical, 2023-2030 (USD Thousand)
Table 89 RoW: Market, by Country, 2020-2022 (USD Thousand)
Table 90 RoW: Market, by Country, 2023-2030 (USD Thousand)
8.4.3 Israel
8.4.3.1 Adoption of AI-based Solutions in Healthcare
8.4.4 China
8.4.4.1 Initiatives Such as Next Generation Artificial Intelligence Development Plan
8.4.5 Japan
8.4.5.1 Dedicated Research Initiatives Such as Artificial Intelligence Technology Strategy
8.4.6 Others in RoW

9 Competitive Landscape
9.1 Overview
9.2 Key Player Strategies
Table 91 Overview of Key Products Launched by Prominent Players in Market
9.3 Revenue Analysis
Figure 32 Revenue Analysis for Key Public Companies, 2020-2022 (USD Million)
9.4 Market Share Analysis
Figure 33 Market Share Analysis for Key Players, 2022
Table 92 Overview of Strategies Deployed by Key Players in Market
9.5 Company Evaluation Quadrant
9.5.1 Stars
9.5.2 Emerging Leaders
9.5.3 Pervasive Players
9.5.4 Participants
Figure 34 Key Causal AI Market Players, Company Evaluation Matrix, 2023
9.6 Competitive Benchmarking
Table 93 Competitive Benchmarking of Key Players, 2022
Table 94 Detailed List of Key Startups/SMEs
Table 95 Competitive Benchmarking of Startups/SMEs
9.7 Causal AI Product Landscape
9.7.1 Comparative Analysis of Causal AI Products
Table 96 Comparative Analysis of Causal AI Products
Figure 35 Comparative Analysis of Causal AI Products
9.7.2 Valuation and Financial Metrics of Key Causal AI Vendors
Figure 36 Financial Metrics of Key Causal AI Vendors
Figure 37 Ytd Price Total Return and Stock Beta of Key Causal AI Vendors
9.8 Competitive Scenario
9.8.1 Product Launches
Table 97 Product Launches, May 2021-February 2023
9.8.2 Deals
Table 98 Deals, October 2020-February 2023

10 Company Profiles
10.1 Introduction
(Business Overview, Products/Solutions/Services Offered, Recent Developments & Analyst's View)*
10.2 Key Players
10.2.1 IBM
Table 99 IBM: Business Overview
Figure 38 IBM: Company Snapshot
Table 100 IBM: Products/Solutions/Services Offered
Table 101 IBM: Product Launches
Table 102 IBM: Deals
10.2.2 Microsoft
Table 103 Microsoft: Business Overview
Figure 39 Microsoft: Company Snapshot
Table 104 Microsoft: Products/Solutions/Services Offered
Table 105 Microsoft: Product Launches
Table 106 Microsoft: Deals
10.2.3 Google
Table 107 Google: Business Overview
Figure 40 Google: Financial Overview
Table 108 Google: Products/Solutions/Services Offered
Table 109 Google: Product Launches
Table 110 Google: Deals
10.2.4 AWS
Table 111 AWS: Business Overview
Figure 41 AWS: Financial Overview
Table 112 AWS: Products/Solutions/Services Offered
Table 113 AWS: Product Launches
Table 114 AWS: Deals
10.2.5 Dynatrace
Table 115 Dynatrace: Business Overview
Figure 42 Dynatrace: Financial Overview
Table 116 Dynatrace: Products/Solutions/Services Offered
Table 117 Dynatrace: Product Launches
Table 118 Dynatrace: Deals
10.2.6 H2O.AI
Table 119 H2O.AI: Business Overview
Table 120 H2O.AI: Products/Solutions/Services Offered
Table 121 H2O.AI: Product Launches
Table 122 H2O.AI: Deals
10.2.7 Datarobot
Table 123 Datarobot: Business Overview
Table 124 Datarobot: Products/Solutions/Services Offered
Table 125 Datarobot: Deals
10.2.8 Causalens
Table 126 Causalens: Business Overview
Table 127 Causalens: Products/Solutions/Services Offered
Table 128 Causalens: Product Launches
Table 129 Causalens: Deals
10.2.9 Causality Link
Table 130 Causality Link: Business Overview
Table 131 Causality Link: Products/Solutions/Services Offered
Table 132 Causality Link: Product Launches
Table 133 Causality Link: Deals
10.2.10 Aitia
Table 134 Aitia: Business Overview
Table 135 Aitia: Products/Solutions/Services Offered
Table 136 Aitia: Product Launches
Table 137 Aitia: Deals
*Details on Business Overview, Products/Solutions/Services Offered, Recent Developments & Analyst's View Might Not be Captured in Case of Unlisted Companies.
10.3 Other Key Players
10.3.1 Parabole.AI
10.3.2 Causalis
10.3.3 Omics Data Automation
10.3.4 Incrmntal
10.3.5 Causaly
10.3.6 Logility
10.3.7 Cognino.AI
10.3.8 Cognizant
10.3.9 Scalnyx
10.3.10 Geminos

11 Adjacent and Related Markets
11.1 AI Governance Market
11.1.1 Market Definition
11.1.2 Market Overview
Table 138 AI Governance Market Size and Growth Rate, 2020-2026 (USD Million, Y-O-Y%)
11.1.3 AI Governance, by Component
Table 139 AI Governance Market, by Component, 2020-2026 (USD Million)
11.1.4 AI Governance Market, by Solution
Table 140 AI Governance Market, by Solution, 2020-2026 (USD Million)
11.1.5 AI Governance Market, by Deployment Mode
Table 141 AI Governance Market, by Deployment Mode, 2020-2026 (USD Million)
11.1.6 AI Governance Market, by Organization Size
Table 142 AI Governance Market, by Organization Size, 2020-2026 (USD Million)
11.1.7 AI Governance Market, by Vertical
Table 143 AI Governance Market, by Vertical, 2020-2026 (USD Million)
11.1.8 AI Governance Market, by Region
Table 144 AI Governance Market, by Region, 2020-2026 (USD Million)
11.2 Artificial Intelligence Market
11.2.1 Market Definition
11.2.2 Market Overview
11.2.3 Artificial Intelligence Market, by Offering
Table 145 Artificial Intelligence Market, by Offering, 2016-2021 (USD Billion)
Table 146 Artificial Intelligence Market, by Offering, 2022-2027 (USD Billion)
11.2.4 Artificial Intelligence Market, by Technology
Table 147 Artificial Intelligence Market, by Technology, 2016-2021 (USD Billion)
Table 148 Artificial Intelligence Market, by Technology, 2022-2027 (USD Billion)
11.2.5 Artificial Intelligence Market, by Deployment Mode
Table 149 Artificial Intelligence Market, by Deployment Mode, 2016-2021 (USD Billion)
Table 150 Artificial Intelligence Market, by Deployment Mode, 2022-2027 (USD Billion)
11.2.6 Artificial Intelligence Market, by Organization Size
Table 151 Artificial Intelligence Market, by Organization, 2016-2021 (USD Billion)
Table 152 Artificial Intelligence Market, by Organization, 2022-2027 (USD Billion)
11.2.7 Artificial Intelligence Market, by Business Function
Table 153 Artificial Intelligence Market, by Business Function, 2016-2021 (USD Billion)
Table 154 Artificial Intelligence Market, by Business Function, 2022-2027 (USD Billion)
11.2.8 Artificial Intelligence Market, by Vertical
Table 155 Artificial Intelligence Market, by Vertical, 2016-2021 (USD Billion)
Table 156 Artificial Intelligence Market, by Vertical, 2022-2027 (USD Billion)
11.2.9 Artificial Intelligence Market, by Region
Table 157 Artificial Intelligence Market, by Region, 2016-2021 (USD Billion)
Table 158 Artificial Intelligence Market, by Region, 2022-2027 (USD Billion)

12 Appendix
12.1 Discussion Guide
12.2 Knowledgestore: The Subscription Portal
12.3 Customization Options

Executive Summary

Companies Mentioned

  • Aitia
  • AWS
  • Causalens
  • Causalis
  • Causality Link
  • Causaly
  • Cognino.AI
  • Cognizant
  • Datarobot
  • Dynatrace
  • Geminos
  • Google
  • H2O.AI
  • IBM
  • Incrmntal
  • Logility
  • Microsoft
  • Omics Data Automation
  • Parabole.AI
  • Scalnyx

Methodology

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Table Information

Report AttributeDetails
No. of Pages201
PublishedMay 2023
Forecast Period2023 - 2030
Estimated Market Value ( USD ) in 2023 $ 8.01 Million
Forecasted Market Value ( USD ) by 2030 $ 119.5 Million
Compound Annual Growth Rate47.1%
Regions CoveredGlobal
No. of Companies Mentioned20

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About the Causal AI Market

The Causal AI market is a subset of the Artificial Intelligence (AI) industry that focuses on the development of AI systems that can identify and explain the cause and effect relationships between different variables. This type of AI technology is used to make predictions and decisions based on the data it collects. It can be used to identify patterns in data, detect anomalies, and make decisions based on the data it has collected. The Causal AI market is growing rapidly as businesses and organizations look to leverage AI technology to gain insights into their data and make better decisions. This technology is being used in a variety of industries, including healthcare, finance, retail, and manufacturing. Some of the companies in the Causal AI market include IBM, Microsoft, Google, Amazon, and Salesforce. These companies are developing AI systems that can identify and explain the cause and effect relationships between different variables. They are also developing AI systems that can make predictions and decisions based on the data they collect. Show Less Read more

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