Causal AI Market worth $119,500 thousand by 2030 - Exclusive Report by MarketsandMarkets™

CHICAGO, May 17, 2023 /PRNewswire/ -- With rising usage, algorithmic improvements, integration with AI systems, industry-specific applications, ethical issues, changing regulations, and multidisciplinary research driving its growth, the Causal AI Market has a promising future. Causal AI will be crucial in allowing data-driven decision-making and opening up new opportunities across industries as organisations continue to recognise the significance of understanding causal linkages.

The Causal AI Market 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 a new report by MarketsandMarkets™. 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.

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157 - Tables
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195 - Pages

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Scope of the Report

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.

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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.

Top Key Companies in Causal AI Market:

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).

Recent Developments:

• In February 2023, Dynatrace introduced new capabilities to Grail that enable boundless exploratory analysis by adding new data types and unlocking support for graph analytics. These capabilities enable Davis, the Dynatrace causal AI engine, to gather even more insights.
• In January 2023, CausaLens released a new operating system for decision-making powered by causal AI. The system is designed to help organizations make more accurate predictions and optimize their business processes.
• In December 2022, Microsoft launched a causal AI suite (DoWhy, EconML, Causica, and ShowWhy) for decision-making that enables developers and data scientists to build models that provide causal explanations for their predictions. The suite includes the DoWhy, EconML, and CausalML libraries, and is integrated with Azure Machine Learning and Azure Databricks.
• In June 2022, Microsoft's collaboration with AWS to develop a new GitHub home for DoWhy will not only enhance the availability of the library but also help Microsoft gain a competitive edge in the causal machine learning space, showing a strategic move to leverage partnerships for growth.
• In, September 2021, IBM launched its Causal AI offering, the Causal Inference 360 Toolkit. This innovative toolkit provides users with a range of powerful tools and algorithms for performing causal inference tasks, allowing businesses and researchers to gain valuable insights into complex systems and make better decisions.

Causal AI Market Advantages:

• By identifying the correlations between variables that cause certain outcomes, causal AI helps companies and organisations make decisions that are more informed and precise. It goes beyond correlation and offers perceptions into the cause-and-effect connections that govern results. As a result, organisations can more confidently make data-driven decisions and better understand the underlying variables impacting their operations.
• In complicated datasets, causal AI algorithms can find patterns and causative elements, allowing for more precise predictions. Organisations can anticipate the effects of various actions or interventions and improve their projections of future outcomes by understanding the causal linkages between variables. This is especially useful in fields like banking, healthcare, and marketing where precise forecasts can result in notable advantages.
• Designing better policies and actions can be assisted by causal AI for governments, organisations, and policymakers. Policymakers can choose the most effective measures to produce desired results by analyzing historical data and discovering causal links. This can be extremely important in fields like public health, economics, and social policy, because creating successful interventions depends on an understanding of causal effects.
• By identifying previously unidentified causal linkages, causal AI creates new possibilities for innovation and discovery. Organisations might find unique ideas, new product features, or undiscovered market sectors by understanding how various variables interact and affect results. Increased market share, competitive advantages, and ground-breaking innovations may result from this.
• Causal AI can address problems with fairness and bias in the decision-making process. It assists in identifying the underlying factors that lead to biassed results by concentrating on causal linkages rather than just correlations. Through the identification and correction of systemic biases, organisations are able to make decisions that are fairer and more egalitarian.

Report Objectives

• To define, describe, and forecast the Causal AI Market based on offering, vertical, and region
• To provide detailed information about the major factors (drivers, restraints, opportunities, and challenges) influencing the market growth
• To analyze subsegments with respect to individual growth trends, prospects, and contributions to the total market
• To analyze opportunities in the market for stakeholders and provide the competitive landscape of the market
• To forecast the revenue of the market segments with respect to all the five major regions, namely, North America, Europe, Asia Pacific (APAC), the Middle East & Africa (MEA), and Latin America
• To profile the key players and comprehensively analyze the recent developments and their positioning related to the Causal AI Market
• To analyze competitive developments, such as mergers & acquisitions, product developments, and research & development (R&D) activities, in the market
• To analyze the impact of recession across all the regions across the Causal AI Market.

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