Causal AI Market

The global market for Causal AI Market is projected to grow from USD 8,010 thousand in 2023 to USD 119,500 thousand by 2030, at a CAGR of 47.1% during the forecast period. Causal AI is driven by the need to understand and reason about cause-and-effect relationships. It allows the progress of Artificial Intelligence from simply recognizing patterns to finding the underlying reasons for those very patterns. It enables counterfactual reasoning, which is a very fundamental element in some areas like exploring alternative scenarios, particularly important in such areas as health and credit risk assessment.

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Market Dynamics

Driver: The Importance of Causal Inference Models in Various Fields

Causal inference models are better suited for applications where accurate predictions are crucial. They are increasingly being adopted in the healthcare industry for diagnosis, treatment planning, and drug development due to their ability to identify causal relationships between medical conditions and treatments. The finance industry is also driving the growth of the causal AI market, with causal inference models being used for credit risk assessment, fraud detection, and portfolio optimization. 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. In healthcare, causal inference models can identify causal relationships between medical conditions and treatments, leading to more accurate diagnosis, treatment planning, and drug development. In finance, causal inference models are used for credit risk assessment, fraud detection, and portfolio optimization. The ability of causal inference models to identify causal relationships and provide accurate and interpretable predictions is making them increasingly essential for businesses looking to make data-driven decisions.

Restraint: Acquiring and preparing high-quality data

Causal AI models need a huge amount of high-quality data to be trained on, which is impractical to acquire in several domains. In some cases, the data may not exist or are hard to get, while in other cases, the data could be incomplete, noisy, or biased, which leads to inaccurate or unreliable models. Besides the restraint of acquiring high-quality data, there are also challenges associated with the preparation of data for use in causal AI and causal ML models. Causal AI models often require the data to be structured in a very specific way so that the relationships demonstrate a clear cause-effect relationship between the different variables. This requires immense effort and expertise, especially in domains that are complex, with many interacting factors and variables. Researchers and practitioners will especially need to study ways in which data can be acquired and prepared in such a manner that could enable the development of high-quality data to empower Causal Models.

Opportunity: Refined causal inference techniques propel AI advancements

Causal AI offers a significant opportunity to build better AI technologies and algorithms through new approaches to causal inference. This pushes the boundary of artificial intelligence toward higher dimensions, thereby allowing for the discovery of more complex models beyond pattern recognition to understand the real cause of any observed phenomenon. Integrated with techniques for causal inference, an AI system would gain deeper insight and higher accuracy of predictions, thus inspiring innovation across a variety of applications. The more casual AI techniques will get refined, the more they will contribute towards enhancement in capabilities of AI systems for further intelligent and adaptive systems with capability to solve complicated problems with a lot of more precision and effectiveness. It is this transformative potential that places causal AI at the very frontier of next-generation AI technologies, promising significant value in how we understand and apply artificial intelligence.

Challenge: Causal Inference from Complex Data Sets

One of the major challenges of Causal AI is establishing causality from big data sets. While the datasets are larger and more complex, they can hardly establish any kind of causal relationship. It is also true that traditional statistical models lack efficiency in dealing with such complications; therefore, more advanced methods and tools must be designed and developed. Also, these casual relationships might not be immediately apparent and may require extensive analysis to uncover. This challenge is further fueled by noise, incomplete data, and interdependencies between variables. Real data also often arise from dynamic and evolving contexts, thus facing additional challenges to keeping the accuracy of the causal inferences over time. This level of complexity remains one of the big challenges to causal AI, which has to move toward the accuracy of inference across industries and size, in addition to the complexity of large, multifaceted data sets.

Causal AI Market Ecosystem

By deployment, cloud to account for the largest market size during the forecast period

The cloud-based deployment model allows organizations flexibility in accessing the most powerful tools of causal inference with scalability and affordability. Real-time data processing integrated with other advanced technologies, such as machine learning and big data analytics, adds more popularity to the cloud for applications based on causal AI. Organizations working with Causal AI go all out in adopting the cloud-based solution for seamless access, collaboration, and scalability. This trend is highly visible in verticals such as healthcare, finance, and retail, where the ability to scale faster in deploying AI-driven insights becomes critical to competitiveness. Therefore, the cloud segment will dominate the market for casual AI due to the robust infrastructure it provides for further growth and innovation in AI technologies.

By offering, platform segment to account for the largest market size during the forecast period

The intensive adoption of causal AI platforms is due to the high demand for end-to-end solutions to build, implement, and manage applications based on causal AI technologies. These platforms ensure that all the services that are required to carry out causal analysis, such as data pre-processing, model building, visualization, and assistance in decision-making, are all provided from one location. The platform interface abstracts away the difficult task of dealing with numerous tools and technologies required for developing ML-based models. Platform solutions enable companies to extract maximum value from their available data by leveraging complex algorithms provided by vendors or other users on their proprietary or open-source platforms. Moreover, industries including healthcare, finance, and manufacturing require accurate identification of cause-and-effect relationships for efficient decision-making and operations.

North America to account for the largest market size during the forecast period

North America is one of the most important regions in the development and enhancement of Causal AI. Causal AI is gaining momentum since more businesses and organizations have a dire need for higher levels of analytics to gain deep insights and make better decisions. The region is home to leading AI technology companies, research institutions, and innovative start-ups that drive new developments and adoptions of causal AI. The governments of the US and Canada have already started development and deployment-related initiatives to do with AI. The US has developed the National Institute of Standards and Technology for developing standards and guidelines related to the use of AI in various industries, healthcare, and finance.

Key Market Players

The Causal AI vendors have implemented various types of organic and inorganic growth strategies, such as new product launches, product upgrades, partnerships and agreements, business expansions, and mergers and acquisitions to strengthen their offerings in the market. The major vendors in the global market for Causal AI 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).

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Scope of the Report

This research report categorizes the Causal AI market based on Offering, Vertical, and Region.

By Offering:

• Platform
  • By Deployment:
    • Cloud
    • On-premises
• Services
  • Consulting Services
  • Deployment & Integration
  • Training, Support, and Maintenance

By Vertical:

• Healthcare & Lifesciences
• BFSI
• Retail & eCommerce
• Tansportation & Logistics
• Manufacturing
• Other Verticals

By Region:

• North America
  • US
  • Canada
• Europe
  • UK
  • Germany
  • France
  • Rest of Europe
• Rest of World
  • Israel
  • China
  • Japan
  • Rest of the RoW

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 support organizations in making 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 announced the collaboration with AWS to develop a new home for DoWhy on GitHub, which will not only increase the library's availability but also give Microsoft an edge over competitors in the causal machine learning space.
• In, September 2021, IBM introduced Causal Inference 360 Toolkit. The toolkit offers powerful tools and algorithms to conduct Causal Inference with unique scalability on a raft of applications for businesses and researcher teams seeking to learn such knowledge from the system at play.

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