Global AI in Industrial Automation Market Size is valued at USD 20.2 Bn in 2024 and is predicted to reach USD 111.8 Bn by the year 2034 at a 18.8% CAGR during the forecast period for 2025-2034.
AI in industrial automation is transforming manufacturing and other industries by optimizing processes, improving efficiency, and reducing human error. AI-driven systems enable smart factories with predictive maintenance, real-time data analysis, and autonomous decision-making. AI in industrial automation is driving significant advancements across various industries by integrating intelligent technologies into manufacturing, logistics, and supply chain management. These AI-driven systems allow industries to automate complex tasks, make data-driven decisions in real-time, and achieve greater levels of efficiency, flexibility, and productivity.
The growing adoption of AI in industries owing to its real-time decision-making by industrial procedures has required intelligent systems, which is a factor expected to drive the growth of global AI in the Industrial Automation market. Demand for accurate and quality products, cost savings, and operational efficiency applications in various industry sectors are some of the other factors likely to augment the target market growth. The increasing adoption of autonomous systems and AI robotics to provide safety and mitigate the risk factors to human life globally is expected to boost the market expansion in the coming years.
The AI in the industrial automation market is segmented on the basis of Type, Application, and industrial verticals. By Type, the segmentation includes Machine Learning, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Deep Learning, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Generative Adversarial Networks (GANs), Natural Language Processing, Sentiment Analysis, Language Translation, Speech Recognition, Computer Vision, Object Detection, Image Classification, and Video Analytics. The application segment includes predictive maintenance, quality control and inspection, supply chain optimization, industrial robotics, process optimization, and safety and security. As per the Industry Vertical, the market is divided into manufacturing, automotive, energy, utilities, healthcare, retail, aerospace and defence, and other industries (agriculture, transportation, logistics, finance, etc.).
The manufacturing segment is projected to grow rapidly in the global AI in Industrial Automation market owing to the rising adoption of industrial AI. Hence, with the growing popularity of industrial AI supports manufacturing in predictive maintenance to predict remaining usages of equipment, robotic procedures of automation, an inspection of manufactured products, upgradation of supply chain efficiency by demand forecasting, and warehouse automation, there is an increase in demand for AI in Industrial Automation in the industrial vertical.
The North American AI in Industrial Automation market is expected to record the largest market revenue share in the near future. This can be attributed to the strong focus on technology in the region, with the increasing adoption of AI in Industrial Automation in different industries, including telecom, IT, manufacturing, automobile, and healthcare. In addition, the manufacturing industry in the region is focusing on the production of AI in Industrial Automation to upgrade the technology. Growing demand for AI in industrial automation across industries and widespread adoption of AI in Industrial Automation in the production of intermediate industries in the region are factors increasing the growth of the target market in the region. In addition, Asia Pacific is likely to grow at rapidly in the global AI in Industrial Automation market due to increasing demand for improved solutions, rapid industrialization, government initiatives, and increasing funding in various industries.
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Report Attribute |
Specifications |
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Market Size Value In 2024 |
USD 20.2 Bn |
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Revenue Forecast In 2034 |
USD 111.8 Bn |
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Growth Rate CAGR |
CAGR of 18.8% from 2025 to 2034 |
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Quantitative Units |
Representation of revenue in US$ Bn and CAGR from 2025 to 2034 |
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Historic Year |
2021 to 2024 |
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Forecast Year |
2025-2034 |
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Report Coverage |
The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
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Segments Covered |
By Type, Application, Industry Vertical |
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Regional Scope |
North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
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Country Scope |
U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea |
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Competitive Landscape |
Siemens AG, ABB Ltd., General Electric Company, Mitsubishi Electrical Corporation, Schneider Electric SE, Rockwell Automation, Inc., IBM Corporation, Honeywell International Inc., Fanuc Corporation, Bosch Rexroth AG, Cognex Corporation, and Kuka AG. |
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Customization Scope |
Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
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Pricing And Available Payment Methods |
Explore pricing alternatives that are customized to your particular study requirements. |
By Type:
By Application:
By Industry Vertical:
By Region-
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
InsightAce Analytic follows a standard and comprehensive market research methodology focused on offering the most accurate and precise market insights. The methods followed for all our market research studies include three significant steps – primary research, secondary research, and data modeling and analysis - to derive the current market size and forecast it over the forecast period. In this study, these three steps were used iteratively to generate valid data points (minimum deviation), which were cross-validated through multiple approaches mentioned below in the data modeling section.
Through secondary research methods, information on the market under study, its peer, and the parent market was collected. This information was then entered into data models. The resulted data points and insights were then validated by primary participants.
Based on additional insights from these primary participants, more directional efforts were put into doing secondary research and optimize data models. This process was repeated till all data models used in the study produced similar results (with minimum deviation). This way, this iterative process was able to generate the most accurate market numbers and qualitative insights.
Secondary research
The secondary research sources that are typically mentioned to include, but are not limited to:
The paid sources for secondary research like Factiva, OneSource, Hoovers, and Statista
Primary Research:
Primary research involves telephonic interviews, e-mail interactions, as well as face-to-face interviews for each market, category, segment, and subsegment across geographies
The contributors who typically take part in such a course include, but are not limited to:
Data Modeling and Analysis:
In the iterative process (mentioned above), data models received inputs from primary as well as secondary sources. But analysts working on these models were the key. They used their extensive knowledge and experience about industry and topic to make changes and fine-tuning these models as per the product/service under study.
The standard data models used while studying this market were the top-down and bottom-up approaches and the company shares analysis model. However, other methods were also used along with these – which were specific to the industry and product/service under study.
