Causal AI Market worth $119,500 thousand by 2030, growing at a CAGR of 47.1%: Report by MarketsandMarkets™
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As per the report by MarketsandMarkets, the global Causal AI market size is projected to reach USD 119,500 thousand in 2030, at a CAGR of 47.1% during the forecast period, 2023-2030
Chicago, June 06, 2023 (GLOBE NEWSWIRE) — The Causal AI market is projected 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 inference models are better suited for applications where accurate predictions are crucial. Causal inference models provide a more transparent and interpretable approach to predictions, making them suitable for applications where explanations are necessary. This is important for industries such as healthcare and finance, where the ability to explain predictions is critical.
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158 – Tables
42 – Figures
200 – Pages
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Scope of the Report
| Report Metrics | Details |
| Market size value in 2023 | USD 8010 thousand |
| Revenue forecast for 2030 | USD 119,500 thousand |
| Growth Rate | 47.1% CAGR |
| Market size available for years | 2020–2030 |
| Base year considered | 2023 |
| Forecast period | 2023–2030 |
| Forecast units | USD Thousand |
| Segments covered | Offering, Vertical, and Region |
| Geographies covered | North America, Europe and Rest of World |
| Key Market Drivers |
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| Key Market Opportunities |
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| Companies covered | 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). |
The Causal AI market, by offering, is segmented into platforms and services. The services segment is expected to experience a higher compound annual growth rate (CAGR) during the forecast period. Causal Al services offer professional guidance, consultation, and assistance to organizations seeking to adopt causal inference tools and techniques. These services encompass Consulting Services, Deployment and Integration, Training, support, and maintenance. Causal Al services prove particularly beneficial for organizations lacking internal resources or expertise to independently implement causal inference. By leveraging these services, organizations can effectively identify and comprehend causal relationships within their data, thereby enhancing prediction accuracy and data-driven decision-making. Service providers in this domain may comprise data scientists, statisticians, software developers, and domain experts possessing specialized knowledge in causal inference.
Based on vertical, the Causal AI market is segmented into BFSI, retail & eCommerce, manufacturing, healthcare & life sciences, transportation &logistics, and other verticals (IT/ITeS, Government & Defense and Telecommunication). The healthcare and life sciences industries are having a significant impact on the development and adoption of causal AI. These industries are using causal AI to improve patient outcomes, streamline operations, and drive innovation. One of the primary applications of causal AI in healthcare and life sciences is in drug discovery and development. Causal AI can be used to identify potential drug targets and predict the efficacy and safety of new drugs, which can significantly reduce the time and cost required for drug development. By leveraging causal AI, pharmaceutical companies can accelerate the drug development process and bring new treatments to the market faster.
The Causal AI market has been segmented into three regions: North America, Europe, and Rest of the world. Causal AI and Causal ML is an emerging field of artificial intelligence that focuses on identifying the cause-and-effect relationships between different variables. It involves using statistical and machine learning techniques to infer causality and predict the outcomes of various interventions or actions. The adoption of causal AI varies by region, with North America and Europe being the most advanced in terms of research and development.
North America has a significant presence of tech companies and research institutions focused on causal inference, such as IBM, Microsoft, and Google which has helped to drive innovation in the field. North America is projected to hold the largest market size during the forecast period. The presence of various players and the growing adoption of AI and ML technologies by businesses are some of the key factors favoring the growth of the Causal AI market in North America. Europe is steadily adopting Causal AI solutions within its market. Europe is expected to be second in terms of market size and share during the forecast period. Europe has also made significant investments in AI research and development, with several leading research institutions.
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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).
Frequently Asked Questions (FAQ)
Which countries are considered in the European region?
The report includes an analysis of the UK, Germany and France in the European region.
What is Causal AI?
Causal AI is an emerging form of machine learning that strives to go beyond traditional ML models. While traditional techniques identify the extent to which multiple events are related, causal AI identifies the root cause of events by understanding the effects of any variables that may have led to it, providing a much deeper explanation of their true relationship.
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