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. The causal AI market is rapidly growing due to the increasing demand for accurate predictions and decision-making. Traditional machine learning models have limitations in making causal predictions, leading to the need for causal inference models.
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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.
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Key players operating in the Causal AI market 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). These vendors adopt different types of organic and inorganic growth strategies, such as product launches, partnerships and collaborations, and mergers and acquisitions, to expand their offerings in the Causal AI market.
IBM is a global technology and consulting company that provides a wide range of hardware, software, and services to businesses and organizations around the world. It was incorporated in 1911 and is headquartered in Armonk, New York. The company's offerings include cloud computing services, data and analytics solutions, AI and machine learning tools, and blockchain technology, among others. IBM’s product portfolio includes IBM Cloud Pak for Data, IBM Data Science Experience, IBM Cloud Machine Learning, IBM Watson Studio AutoAI, IBM SPSS Modeler, IBM Watson Discovery, IBM Watson Assistant, IBM Watson Natural Language Understanding, IBM Watson Machine Learning, and IBM Watson OpenScale. Within Data & AI, IBM has a strong performance in causal AI offerings. It enables the company to take advantage of AI-powered technologies. The company has its presence in more than 175 countries in North America, Europe, Asia Pacific, the Middle East & Africa, and Latin America. IBM's IBM Causal Inference 360 Toolkit offers range of tools and technologies, including software libraries, development frameworks, and cloud-based services. These tools are designed to help data scientists and other analysts build and deploy causal models quickly and easily, using a variety of ML and statistical techniques.
Microsoft develops software, services, devices, and solutions to compete in the era of intelligent cloud and intelligent edge. With continuous investments in the mix-reality cloud, Microsoft enables customers to digitalize its business processes. Its offerings include cloud-based solutions that provide customers with software, platforms, and content, and deliver solution support and consulting services for its clients. Microsoft develops software, services, devices, and solutions to compete in the era of intelligent cloud and intelligent edge. With continuous investments in the mix-reality cloud, Microsoft enables customers to digitalize its business processes. Its offerings include cloud-based solutions that provide customers with software, platforms, and content, and deliver solution support and consulting services for its clients. Microsoft has several offerings related to Causal AI, including the DoWhy library for Python, which is an open-source software package that provides a range of causal inference methods for researchers and data scientists. Microsoft also offers the Microsoft Causal Inference Platform (MCIP), which provides a suite of tools and algorithms for causal inference, including matching, weighting, and structural equation modeling. MCIP is designed to help researchers and data scientists explore and analyze causal relationships in their data. Additionally, Microsoft has integrated causal inference features into their Azure Machine Learning platform, allowing users to build and deploy causal models in the cloud.
CausaLens is an AI startup that specializes in developing causal AI technology for businesses. The company was founded in 2017, and its headquarters are in London, UK. CausaLens is a rapidly growing company with a presence in several key global markets, including the US, Europe, and Asia. The company's mission is to revolutionize the way businesses approach decision-making by helping them harness the power of causal AI. CausaLens offers a platform that uses advanced machine learning algorithms to extract causal insights from complex data sets. The platform leverages state-of-the-art causal inference techniques to help businesses identify the causal relationships between different variables in their data and make more informed decisions based on this understanding. CausaLens platform is designed to be highly scalable, enabling businesses of all sizes and across a wide range of industries to harness the power of causal AI. CausaLens also offers a range of specialized tools and features that enable businesses to build and deploy machine learning models with causal inference capabilities. These include features such as automated feature engineering, automatic model selection, and real-time monitoring, which can help businesses optimize the performance of their models and ensure that they are delivering the best possible results.
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