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The causal AI market size is forecast to increase by USD 110.9 million at a CAGR of 39.7% between 2023 and 2028.
The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.
In the realm of Artificial Intelligence (AI), on-premises deployment continues to be a preferred choice for businesses seeking data privacy and regulatory compliance. However, the advantages of cloud deployment, including scalability, accessibility, and cost-effectiveness, have made it an increasingly popular option for AI projects. Causal AI, a subsegment of advanced data analytics, is particularly well-suited to cloud deployment due to its variable usage requirements. Virtual assistants, such as Siri, Google Assistant, and Alexa, are prime examples of causal AI applications that benefit from cloud deployment. These AI models require significant computing power and storage capacity to process user queries and provide accurate responses.
Moreover, cloud providers offer AI-as-a-service (AIaaS) solutions, enabling developers to easily integrate pre-built AI models and services into their applications. Maintenance services are another crucial aspect of AI initiatives, ensuring that models remain up-to-date and perform optimally. Cloud providers offer these services, allowing businesses to focus on their core competencies while leaving the technical complexities to the experts. Data privacy and security are paramount, and cloud providers offer strong security measures to protect sensitive data. In conclusion, the importance of inference in causal AI applications, coupled with the benefits of cloud deployment, make it an attractive choice for businesses seeking to operationalize their AI initiatives.
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Our region market researchers analyzed the data with 2023 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.
Growing innovation of causal AI in healthcare and finance is the key driver of the market.
Integration of causal inference into mainstream AI solutions is the upcoming trend in the market.
Causal inference from complex data sets is a key challenge affecting market growth.
The market forecasting report includes the adoption lifecycle of the market, covering from the innovator's stage to the laggard's stage. It focuses on adoption rates in different regions based on penetration. Furthermore, the market report also includes key purchase criteria and drivers of price sensitivity to help companies evaluate and develop their market growth analysis strategies.
Customer Landscape
Companies are implementing various strategies, such as strategic alliances, market forecast, partnerships, mergers and acquisitions, geographical expansion, and product/service launches, to enhance their presence in the market.
The market research and growth report includes detailed analyses of the competitive landscape of the market and information about key companies, including:
Qualitative and quantitative analysis of companies has been conducted to help clients understand the wider business environment as well as the strengths and weaknesses of key market players. Data is qualitatively analyzed to categorize companies as pure play, category-focused, industry-focused, and diversified; it is quantitatively analyzed to categorize companies as dominant, leading, strong, tentative, and weak.
The market is witnessing significant growth due to the increasing demand for interpretable AI models in various industries. The availability of vast amounts of data and advancements in computing power have made it possible to identify complex causal relationships between various factors. This is particularly important in fields such as healthcare, where understanding the root causes of diseases and developing personalized treatment plans are key priorities. However, there are several challenges in implementing causal AI, including regulatory compliance, data privacy, and the lack of standardization. Additionally, the high computing resources required for causal inference can make it cost-effective only for large organizations or those with significant investments from venture capital firms.
Despite these challenges, the potential benefits of causal AI are immense. In drug discovery, for instance, causal AI can help identify genetic, environmental, and lifestyle factors that contribute to diseases, enabling the development of targeted therapeutic compounds. In finance, causal AI can be used for portfolio optimization and fraud detection, while in healthcare, it can aid in clinical decision-making and patient diagnosis. The market for causal AI is expected to grow further due to the increasing adoption of cloud-based solutions and the importance of scalability and flexibility. Technological advancements such as generative models, federated learning, and explainable AI are also driving growth in the market. However, the lack of interpretability and customization in some AI models may hinder market adoption, especially in regulated industries. Overall, the market presents significant opportunities for innovation and growth in various industries, from healthcare and finance to academia and research and development. With continued investments and acquisitions in this space, we can expect to see further advancements and applications of causal AI in the future.
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Market Scope |
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Report Coverage |
Details |
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Page number |
152 |
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Base year |
2023 |
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Historic period |
2018-2022 |
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Forecast period |
2024-2028 |
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Growth momentum & CAGR |
Accelerate at a CAGR of 39.7% |
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Market growth 2024-2028 |
USD 110.9 million |
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Market structure |
Fragmented |
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YoY growth 2023-2024(%) |
37.59 |
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Key countries |
US, China, Germany, UK, and France |
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Competitive landscape |
Leading Companies, Market Positioning of Companies, Competitive Strategies, and Industry Risks |
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1 Executive Summary
2 Market Landscape
3 Market Sizing
4 Five Forces Analysis
5 Market Segmentation by Deployment
6 Market Segmentation by End-user
7 Customer Landscape
8 Geographic Landscape
9 Drivers, Challenges, and Trends
10 Vendor Landscape
11 Vendor Analysis
12 Appendix
Research Framework
Technavio presents a detailed picture of the market by way of study, synthesis, and summation of data from multiple sources. The analysts have presented the various facets of the market with a particular focus on identifying the key industry influencers. The data thus presented is comprehensive, reliable, and the result of extensive research, both primary and secondary.
INFORMATION SOURCES
Primary sources
Secondary sources
DATA ANALYSIS
Data Synthesis
Data Validation
REPORT WRITING
Qualitative
Quantitative
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