AI in Industrial Automation Market Current Trends Analysis 2026 to 2035
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AI in Industrial Automation Market Size is valued at USD 23.76 Bn in 2025 and is predicted to reach USD 131.62 Bn by the year 2035 at a 18.8% CAGR during the forecast period for 2026 to 2035.
AI in Industrial Automation Market Size, Share & Trends Analysis Report By Type (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, Video Analytics)), By Application, By Industry Vertical, By Region, And By Segment Forecasts, 2026 to 2035
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.
| Report Attribute | Specifications |
| Market Size Value In 2025 | USD 23.76 Bn |
| Revenue Forecast In 2035 | USD 131.62 Bn |
| Growth Rate CAGR | CAGR of 18.8% from 2026 to 2035 |
| Quantitative Units | Representation of revenue in US$ Bn and CAGR from 2026 to 2035 |
| Historic Year | 2022 to 2025 |
| Forecast Year | 2026-2035 |
| Report Coverage | The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
| Segments Covered | By Type, Application, Industry Vertical |
| Regional Scope | North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
| Country Scope | U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea |
| 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. |
| Customization Scope | Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
| Pricing And Available Payment Methods | Explore pricing alternatives that are customized to your particular study requirements. |
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By Application:
By Industry Vertical:
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This study employed a multi-step, mixed-method research approach that integrates:
This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.
Secondary research for this study involved the collection, review, and analysis of publicly available and paid data sources to build the initial fact base, understand historical market behaviour, identify data gaps, and refine the hypotheses for primary research.
Secondary data for the market study was gathered from multiple credible sources, including:
These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.
Primary research was conducted to validate secondary data, understand real-time market dynamics, capture price points and adoption trends, and verify the assumptions used in the market modelling.
Primary interviews for this study involved:
Interviews were conducted via:
Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.
All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.
The data validation process included:
This ensured that the dataset used for modelling was clean, robust, and reliable.
The bottom-up approach involved aggregating segment-level data, such as:
This method was primarily used when detailed micro-level market data were available.
The top-down approach used macro-level indicators:
This approach was used for segments where granular data were limited or inconsistent.
To ensure accuracy, a triangulated hybrid model was used. This included:
This multi-angle validation yielded the final market size.
Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.
Given inherent uncertainties, three scenarios were constructed:
Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.