This research report categorizes the Causal AI market based on Offering, Vertical, and Region.
The market for Causal AI is estimated to grow from USD 26 million in 2023 to USD 293 million by 2030, at a CAGR of 40.9% during the forecast period, according to new research 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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Major The Key Players of Causal AI Market
IBM (US), CausaLens (UK), Microsoft (US), Causaly (UK), Google (US), Geminos (US), AWS (US), Aitia (US), Xplain Data (Germany), INCRMNTAL (Israel), Logility (US), Cognino.ai. (UK), H2O.ai (US), DataRobot (US), Cognizant (US), Scalnyx (France), Causality Link (US), Dynatrace (US), Parabole.ai (US) and datma (US).
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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Factors that Drive Causal AI Market
- Explainable AI is required since traditional machine learning algorithms are limited to predicting and correlating data; they are unable to identify causative linkages in the data. There is an increasing need for Causal AI models, which offer insights into why specific outcomes occur and how interventions may be made to obtain desired results, as businesses look for more transparent and interpretable AI solutions.
- Effect on Decision-Making: By comprehending the fundamental mechanisms underlying observed occurrences, causal AI empowers organizations to make better informed judgments. Organizations can more efficiently allocate resources, reduce risks, and optimize tactics to meet corporate objectives by determining the causal links between variables.
- Enhanced Predictive Accuracy: By eliminating biases and confounding variables that could skew predictions made only on the basis of correlation, the incorporation of causal inference techniques into AI models can increase predictive accuracy. Stronger and more accurate forecasts are made possible by causal AI, which improves results in industries including supply chain management, marketing, and finance.
- Causal AI has the ability to completely transform drug discovery, customized medicine, and clinical decision-making in the healthcare industry. Researchers and doctors can anticipate treatment outcomes, identify illness risk factors, and create customized interventions by examining causal linkages in patient data.
Regional Analysis for Causal AI Market
- Canada and the United States: Thanks to a robust ecosystem of cutting-edge startups, research centers, and technology businesses, North America is the leader in the Causal AI sector. The research and development of Causal AI is concentrated on major tech areas such as Silicon Valley, Seattle, and Toronto. Applications are used in many different areas, including as manufacturing, healthcare, finance, and marketing.
- Western Europe: The UK, Germany, France, and the Netherlands are leading the way in the implementation of Causal AI in Europe. Causal AI is being used by European companies and academic institutions to spur innovation in industries like healthcare, automotive, retail, and energy. Regulations like the GDPR, which place a strong emphasis on data protection and moral AI practices, have an impact on the adoption of causal AI. Eastern Europe: Although they’re still in the early stages of development, nations like the Czech Republic, Poland, and Hungary are making larger investments in AI R&D, particularly applications related to Causal AI. Growing IT scenes in places like Prague, Budapest, and Warsaw present prospects for Causal AI startups and tech companies.
- China: With large investments in Causal AI research, development, and applications, China is a dominant player in the global AI market. Innovation is being driven by Chinese IT giants and startups in sectors like fintech, e-commerce, healthcare, and smart manufacturing. The development of China’s Causal AI business is aided by government programs like the New Generation AI Development Plan. India: With a burgeoning ecosystem of AI startups, research facilities, and technology suppliers, India is becoming a center for AI innovation. Businesses in India are investigating the potential of Causal AI in fields like retail, healthcare, banking, and agriculture. The National AI Strategy is one of the government’s efforts to hasten India’s adoption and use of AI.
- Latin American nations, such as Brazil, Mexico, and Argentina, are progressively using artificial intelligence (AI) technologies, such as Causal AI, to tackle societal issues and stimulate economic growth. Applications are found in many different industries, including telecommunications, healthcare, agriculture, and finance. Buenos Aires, Mexico City, São Paulo, and other tech hubs are hubs for AI innovation and entrepreneurship.
- Countries in the Gulf Cooperation Council (GCC): As part of their plans for digital transformation, nations including the United Arab Emirates, Saudi Arabia, and Qatar are investing in AI technologies. Applications of causal AI are found in the energy, healthcare, finance, and smart city industries. Talent and capital in AI are drawn to tech hotspots like Dubai and Riyadh. South Africa and Kenya: To solve socioeconomic issues and spur innovation, African nations are investigating AI technologies, such as Causal AI. Applications cover everything from financial inclusion and transportation to healthcare and agriculture. Cities such as Lagos, Nairobi, and Cape Town have developed tech ecosystems that support AI research and business.
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