Causal AI Market Size, Projections of Share, Trends, and Growth for 2023-2030
Causal AI is a rapidly growing field that focuses on establishing cause-and-effect relationships between variables, ensuring the safety and fairness of AI predictions. Causal AI utilizes causality to go beyond narrow machine learning predictions and make choices like humans do. This technology is the future of decision-making, combining AI and causal reasoning to create a more transparent and safer approach to AI. Causal AI and Causal ML has the potential to reshape the world, particularly in the areas of health, development, and marketing.
The market for Causal AI is estimated to grow from USD 8010 thousand in 2023 to USD 119,500 thousand by 2030, at a CAGR of 47.1% during the forecast period, according to new research report by MarketsandMarkets™
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BFSI to account for higher CAGR during the forecast period
The BFSI (Banking, Financial Services, and Insurance) sector is one of the biggest adopters of causal AI technology. Causal AI is widely used in financial services for risk management, fraud detection, compliance, customer experience, and more. North America dominates the causal AI market in BFSI, followed by Europe and Asia-Pacific. The North American market hold the largest share in BFSI during the forecast period, due to the presence of several key players and the high adoption of AI technology in the region. The causal AI market in BFSI is highly competitive, with several players operating in the market. Some of the key players in this market include IBM, Microsoft, and Google. These players are focusing on partnerships, collaborations, and acquisitions to expand their market presence and strengthen their product portfolio.
Services Segment to account for higher CAGR during the forecast period
Causal AI services provide expert guidance, consulting, and support for organizations looking to implement causal inference tools and techniques. These services include Consulting Services, Deployment and Integration, Training, support, and maintenance. Causal AI services are particularly useful for organizations that lack the internal resources or expertise to implement causal inference on their own. They can help organizations identify and understand causal relationships in their data, improving the accuracy of predictions and data-driven decision making. Service providers may include data scientists, statisticians, software developers, and domain experts with expertise in causal inference. They may offer services on a project-by-project basis or provide ongoing support and consulting to organizations.
North America is expected to account for the largest market size in 2023
Causal AI has been gaining traction in North America, with both the United States and Canada making significant investments in AI research and development. The US government has launched several initiatives to promote the development of AI, such as the American AI Initiative, which aims to maintain the country's leadership in AI research and development. Canada has also been contributing to AI research, with several universities and research institutes working on developing AI technologies. The private sector in North America has also been investing heavily in AI research and development, with companies such as Google, Amazon, and Microsoft developing AI technologies for a wide range of applications. The healthcare industry has also been an area of focus for AI research and development, with several companies developing AI technologies to improve patient outcomes and reduce healthcare costs.
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Key Market Players
Major vendors in the global Causal AI market 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).
Causal AI Market Size, Projections of Share, Trends, and Growth for 2023-2030
Causal AI is a rapidly growing field that focuses on establishing cause-and-effect relationships between variables, ensuring the safety and fairness of AI predictions. Causal AI utilizes causality to go beyond narrow machine learning predictions and make choices like humans do. This technology is the future of decision-making, combining AI and causal reasoning to create a more transparent and safer approach to AI. Causal AI and Causal ML has the potential to reshape the world, particularly in the areas of health, development, and marketing.
The market for Causal AI is estimated to grow from USD 8010 thousand in 2023 to USD 119,500 thousand by 2030, at a CAGR of 47.1% during the forecast period, according to new research report by MarketsandMarkets™
Download Report Brochure @ https://www.marketsandmarkets.com/pdfdownloadNew.asp?id=162494083
BFSI to account for higher CAGR during the forecast period
The BFSI (Banking, Financial Services, and Insurance) sector is one of the biggest adopters of causal AI technology. Causal AI is widely used in financial services for risk management, fraud detection, compliance, customer experience, and more. North America dominates the causal AI market in BFSI, followed by Europe and Asia-Pacific. The North American market hold the largest share in BFSI during the forecast period, due to the presence of several key players and the high adoption of AI technology in the region. The causal AI market in BFSI is highly competitive, with several players operating in the market. Some of the key players in this market include IBM, Microsoft, and Google. These players are focusing on partnerships, collaborations, and acquisitions to expand their market presence and strengthen their product portfolio.
Services Segment to account for higher CAGR during the forecast period
Causal AI services provide expert guidance, consulting, and support for organizations looking to implement causal inference tools and techniques. These services include Consulting Services, Deployment and Integration, Training, support, and maintenance. Causal AI services are particularly useful for organizations that lack the internal resources or expertise to implement causal inference on their own. They can help organizations identify and understand causal relationships in their data, improving the accuracy of predictions and data-driven decision making. Service providers may include data scientists, statisticians, software developers, and domain experts with expertise in causal inference. They may offer services on a project-by-project basis or provide ongoing support and consulting to organizations.
North America is expected to account for the largest market size in 2023
Causal AI has been gaining traction in North America, with both the United States and Canada making significant investments in AI research and development. The US government has launched several initiatives to promote the development of AI, such as the American AI Initiative, which aims to maintain the country's leadership in AI research and development. Canada has also been contributing to AI research, with several universities and research institutes working on developing AI technologies. The private sector in North America has also been investing heavily in AI research and development, with companies such as Google, Amazon, and Microsoft developing AI technologies for a wide range of applications. The healthcare industry has also been an area of focus for AI research and development, with several companies developing AI technologies to improve patient outcomes and reduce healthcare costs.
Inquire Before Buying @ https://www.marketsandmarkets.com/Enquiry_Before_BuyingNew.asp?id=162494083
Key Market Players
Major vendors in the global Causal AI market 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).
