According to Markntel Advisors Report, Causal Artificial Intelligence (AI) Market is expected to grow at a significant growth rate, and the analysis period is 2023-2028, considering the base year as 2022.Consistent monitoring and evaluating of market dynamics to stay informed and adapt your strategies accordingly. As a market research and consulting firm, we offer market research reports that focus on major parameters including Target Market Identification, Customer Needs and Preferences, Thorough Competitor Analysis, Market Size & Market Analysis, and other major factors. At the end, we do provide meaningful insights and actionable recommendations that inform decision-making and strategy development.

Causal Artificial Intelligence (AI) is a branch of AI that focuses on understanding & modeling causal relationships between variables or events. Unlike traditional AI methods, which are focused mainly on correlational relationships, causal AI involves techniques & methodologies that enable the identification, estimation, and analysis of causal relationships within complex systems.

Global Causal Artificial Intelligence (AI) Market Research Report & Summary:

The Global Causal Artificial Intelligence (AI) Market is still emerging and is anticipated to grow at a CAGR of around 38.46% during the forecast period, i.e., 2023-28.           

Time Period Captured in the Report:

·        Historical Years: 2018-21

·        Base Years: 2022

·        Forecast Years: 2023-28 

Who are the Key Players Operating in the Causal Artificial Intelligence (AI) Market?

The top companies of the Causal Artificial Intelligence (AI) Market ruling the industry are: 

IBM Corporation, Microsoft Corporation, Amazon Web Services, Causality Link, Aitia, DataRobot, Inc., causaLens, Google Corporation, H2O.ai, Dynatrace, Cognizant, Geminos, Cognino.ai, Omnics Data Automation, Logility and Others

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What is Included in Causal Artificial Intelligence (AI) Market Segmentation?

The Causal Artificial Intelligence (AI) Market explores the industry by emphasizing the growth parameters and categorizes including geographical segmentation, to offer a comprehensive understanding of the market dynamic.  The further bifurcations are as follows:

• By Offering
• Deployment (Platform) - Market Size & Forecast 2018-2028, (USD Million)
• Cloud - Market Size & Forecast 2018-2028, (USD Million)
• On-Premise - Market Size & Forecast 2018-2028, (USD Million)
• Services - Market Size & Forecast 2018-2028, (USD Million)
• Consulting Services
• Deployment & Integration
• Training, Support, and Maintenance
• By End-User
• BFSI- Market Size & Forecast 2018-2028, (USD Million)
• Healthcare and Life Sciences- Market Size & Forecast 2018-2028, (USD Million)
• Retail and E-commerce- Market Size & Forecast 2018-2028, (USD Million)
• Transportation and Logistics- Market Size & Forecast 2018-2028, (USD Million)
• Manufacturing- Market Size & Forecast 2018-2028, (USD Million)
• Others-- Market Size & Forecast 2018-2028, (USD Million)
• By Region
• North America
• South America
• Europe
• The Middle East & Africa
• Asia-Pacific

Explore the Complete Causal Artificial Intelligence (AI) Market Analysis Report – https://www.marknteladvisors.com/research-library/causal-artificial-intelligence-market.html

Global Causal Artificial Intelligence (AI) Market Dynamics:

Rapidly Growing Demand for Explainable AI - AI is becoming increasingly prevalent & crucial in several industries, owing to which there's a mounting demand for AI solutions that offer transparent & interpretable results. Causal AI has an immense potential to provide explanations for relationships & causal factors behind AI predictions & decisions, i.e., crucial in industries like healthcare, BFSI, and legal domains where trust, accountability, & regulatory compliance are critical. Hence, its ability to help understand the cause-and-effect relationships between variables is the most prominent driver for the Global Causal Artificial Intelligence (AI) Market.

Global Causal Artificial Intelligence (AI) Market Growth Restraint

Challenges Associated with Data Availability & Quality - Causal AI extensively relies on comprehensive & high-quality data for accurately identifying causal relationships. In several domains, it is challenging to obtain large-scale, diverse, & reliable data, and there are issues related to data quality, like missing or biased data, which can hinder the development & effectiveness of causal AI models. Hence, insufficient or low-quality data that can result in inaccurate or biased causal inferences can limit the practical applications & adoption of causal AI, i.e., the major growth restraint for the Global Causal Artificial Intelligence (AI) Market.

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