Emphasis on Offering (Platform and Service); End-User (BFSI, Healthcare and Life Sciences, Retail and E-commerce, Transportation and Logistics, Manufacturing, and Others); and Region/Country
The Global Causal AI Market was valued at USD 18.74 billion in 2023 and is expected to grow at a strong CAGR of around 42.6% during the forecast period (2024-2032) owing to Governments and regulatory bodies pushing for explainable and fair AI systems, making causal AI essential for compliance.
The worldwide Causal AI market deals with the construction and utilization of artificial intelligence frameworks that are concentrated on causal, rather than correlative, results. Causal AI can improve the decision-making process when used by identifying factors that contributed to the result, making great improvements in areas such as medical, financial, and promotional. Drivers for the growth of this market are a higher need to gain better prediction systems, new trends toward customized solutions, and the necessity for better critical risk management. As companies seek better ways of having improved information for their operations, Causal AI renews itself as a disruptive technology enhancing decision-making and operations across the world.
This section discusses the key market trends that are influencing the various segments of the global Causal AI market, as identified by our team of research experts.
Service Segment Transforming Industry
The service category is essential to the advancement of the Causal AI market because it provides the specific solutions that organizations need to realize their use of Causal AI, including consulting, integration, and maintenance services. Such services assist organizations in further improving the link between the highly sophisticated AI solutions and their real-world usage, finding efficient ways of leveraging AI to extract patterns and draw cause-and-effect conclusions. The need for services like the creation of a Causal AI model, training, and support is on the rise, especially as organizations look to enhance decision-making in complex conditions, operating in uncertain contexts, thus expanding the market. Moreover, such services make the process of transitioning to Causal AI less painful, which contributes to the development of a large consumer base and the application of the technology across various sectors.
North America is Expected to Grow at the fastest CAGR During Forecast Period.
The market for Causal AI in North America is rapidly developing due to the demand for deeper artificial intelligence capabilities that are not simply capable of showing correlation and causation. Healthcare systems, financial and banking services, and retail and sales services among others are some of the major fields that are using Causal AI to help in decision-making, to improve operations, and to serve customers. The region is characterized by a well-developed technology sector, which includes key AI companies and research centers, thus encouraging further advancements and progress. Furthermore, increasing R&D on artificial intelligence and the increasing number of data-oriented business plans are expanding the use of Causal AI even in North America as well.
The global Causal AI market is competitive, with several global and international market players. The key players are adopting different growth strategies to enhance their market presence, such as partnerships, agreements, collaborations, new product launches, geographical expansions, and mergers and acquisitions. Some of the major players operating in the market are IBM; Scalnyx; Causality Link LLC; causaLens; Omnics Data Automation (data, Inc.); Dynatrace LLC; Xplain Data GmbH; American Software, Inc. (Logility); Aitia; Geminos Software
The global causal AI market can further be customized as per the requirement or any other market segment. Besides this, UMI understands that you may have your own business needs, hence feel free to contact us to get a report that completely suits your requirements.
1. Market Introduction
2. Research Methodology or Assumption
3. Executive Summary
4. Market Dynamics
5. Pricing Analysis
6. Global Causal AI Market Revenue (USD BN), 2022-2032F
7. Market Insights by Offering
8. Market Insights by End-User
9. Market Insights by Region
10. Value Chain Analysis
11. Competitive Landscape
12. Company Profiled
13. Acronyms & Assumption
14. Annexure
Analyzing the historical market, estimating the current market, and forecasting the future market of the global Causal AI market were the three major steps undertaken to create and analyze the adoption of global Causal AI in major regions. Exhaustive secondary research was conducted to collect the historical market numbers and estimate the current market size. Secondly, to validate these insights, numerous findings and assumptions were considered. Moreover, exhaustive primary interviews were conducted with industry experts across the value chain of the global Causal AI market. For the assumption and validation of market numbers through primary interviews, we employed a top-down/bottom-up approach to forecasting the complete market size. Thereafter, market breakdown and data triangulation methods were adopted to estimate and analyze the market size of segments and sub-segments of the industry pertains to. Detailed methodology is explained below:
Step 1: In-Depth Study of Secondary Sources:
A detailed secondary study was conducted to obtain the historical market size of the global Causal AI market through company internal sources such as annual reports & financial statements, performance presentations, press releases, etc., and external sources including journals, news & articles, government publications, competitor publications, sector reports, third-party database, and other credible publications.
Step 2: Market Segmentation:
After obtaining the historical market size of the global Causal AI market, we conducted a detailed secondary analysis to gather historical market insights and share for different segments & sub-segments for major regions. Major segments are included in the report as offerings, end-user, and regions. Further country-level analyses were conducted to evaluate the overall adoption of testing models in that region.
Step 3: Factor Analysis:
After acquiring the historical market size of different segments and sub-segments, we conducted a detailed factor analysis to estimate the current market size of the global Causal AI market. Further, we conducted factor analysis using dependent and independent variables such as offering, end-user, and global Causal AI market regions. A thorough analysis of demand and supply-side scenarios was conducted considering top partnerships, mergers and acquisitions, business expansion, and product launches in the global Causal AI market.
Current Market Sizing: Based on actionable insights from the above 3 steps, we arrived at the current market size, key players in the global Causal AI market, and market shares of the segments. All the required percentage shares split and market breakdowns were determined using the above-mentioned secondary approach and were verified through primary interviews.
Estimation & Forecasting: For market estimation and forecast, weights were assigned to several factors including drivers & trends, restraints, and opportunities available for the stakeholders. After analyzing these factors, relevant forecasting techniques i.e., the top-down/bottom-up approach were applied to arrive at the market forecast 2032 for different segments and sub-segments across the major markets globally. The research methodology adopted to estimate the market size encompasses:
Primary Research: In-depth interviews were conducted with the Key Opinion Leaders (KOLs) including Top Level Executives (CXO/VPs, Sales Head, Marketing Head, Operational Head, Regional Head, Country Head, etc.) across major regions. Primary research findings were then summarized, and statistical analysis was performed to prove the stated hypothesis. Inputs from primary research were consolidated with secondary findings, hence turning information into actionable insights.
Market Engineering
The data triangulation technique was employed to complete the overall market estimation and to arrive at precise statistical numbers for each segment and sub-segment of the global Causal AI market. Data was split into several segments and sub-segments after studying various parameters and trends in the global Causal AI market’s offering, end-users, and regions.
The current & future market trends of the global Causal AI market were pinpointed in the study. Investors can gain strategic insights to base their discretion for investments on the qualitative and quantitative analysis performed in the study. Current and future market trends determined the market’s overall attractiveness at a regional level, providing a platform for the industrial participant to exploit the untapped market to benefit from a first-mover advantage. Other quantitative goals of the studies include:
Q1: What is the global Causal AI market’s current market size and growth potential?
Q2: What are the driving factors for the growth of the global Causal AI market?
Q3: Which segment has the largest global Causal AI market share by offering category?
Q4: What are the emerging technologies and trends in the global Causal AI market?
Q5: Which region will dominate the global Causal AI market?
Customers who bought this item also bought
Emotion Detection and Recognition Market: Current Analysis and Forecast (2021-2027)
Emphasis on Technology (Feature Extraction & 3D Modeling, Biosensors, Natural Learning Processing (NLP), and Others); Software Tools (Facial Expression & Emotion Recognition, Gesture & Posture Recognition, Voice Recognition); Application Area (Marketing & Advertising, Law Enforcement, Surveillance & Monitoring, Entertainment & Consumer Electronics, Others); Region/Country.
November 15, 2022
Generative AI Cybersecurity Market: Current Analysis and Forecast (2024-2032)
Emphasis on Deployment (On-Premise, Cloud-Based, Hybrid); Technology (Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Reinforcement Learning (RL), Deep Neural Networks (DNNs), Natural Language Processing (NLP), Others); Application (Network Security, Endpoint Security, Cloud Security, Application Security, Others); End-User (Banking, Financial Services, And Insurance (BFSI), Healthcare, Retail, IT & Telecom, Government and Defense, Energy and Utilities, and Others); and Region/Country
November 4, 2024
