Edge Ai For Industrial Automation Market
PUBLISHED: 2025 ID: SMRC31733
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Edge Ai For Industrial Automation Market

Edge AI for Industrial Automation Market Forecasts to 2032 – Global Analysis By Component (Edge AI Hardware, Edge AI Software and Edge AI Services), Deployment Model, Application, End User and By Geography

4.5 (64 reviews)
4.5 (64 reviews)
Published: 2025 ID: SMRC31733

Due to ongoing shifts in global trade and tariffs, the market outlook will be refreshed before delivery, including updated forecasts and quantified impact analysis. Recommendations and Conclusions will also be revised to offer strategic guidance for navigating the evolving international landscape.
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According to Stratistics MRC, the Global Edge AI for Industrial Automation Market is accounted for $3.04 billion in 2025 and is expected to reach $13.78 billion by 2032 growing at a CAGR of 24.1% during the forecast period. Industrial automation is being transformed by Edge AI, which processes artificial intelligence directly at the machine or device level on production floors. Unlike conventional cloud AI, Edge AI allows instant data analysis, enabling quicker decision-making for essential operations. It improves predictive maintenance, ensures higher quality control, and boosts overall efficiency while decreasing machine downtime. Local data processing also enhances security and privacy by keeping critical operational data on-site instead of sending it to remote servers. Furthermore, Edge AI allows flexible, scalable deployment across various industrial environments, helping manufacturers cut costs, optimize productivity, and swiftly respond to evolving production requirements.

According to Tata Consultancy Services (TCS), data from high-tech manufacturing operations reveals that Edge AI systems can reduce cloud transmission volume by 40–60%, while improving uptime and product quality through localized, intelligent decision-making.

Market Dynamics:

Driver:

Enhanced operational efficiency

Operational efficiency in industrial settings is significantly improved through Edge AI, which optimizes workflows, automates repetitive tasks, and enables predictive maintenance. Intelligent monitoring and data-driven insights reduce errors and minimize resource wastage. Automated decisions facilitate rapid adjustments on production lines, limiting human intervention and enhancing overall performance. This leads to cost reductions, increased throughput, and the ability to meet growing demand without sacrificing quality. By leveraging Edge AI, manufacturers can refine processes, maximize machinery usage, and improve energy efficiency. The focus on efficiency and cost-effectiveness is a key driver of market growth, helping businesses maintains competitiveness and operational excellence.

Restraint:

High implementation costs

Implementing Edge AI for industrial automation demands substantial initial investments in advanced hardware, software, and infrastructure. This financial burden can be challenging for small and medium enterprises, hindering widespread adoption. Integrating Edge AI with legacy equipment often requires costly customization and expert personnel. Ongoing maintenance, software upgrades, and security measures further increase expenditure. Even though Edge AI improves operational efficiency, the high implementation cost remains a key barrier. Organizations with restricted budgets may continue relying on conventional automation techniques, making financial constraints a significant restraint on the global expansion of Edge AI in industrial environments.

Opportunity:

Adoption of smart manufacturing

Edge AI creates opportunities in smart manufacturing by providing real-time monitoring, predictive maintenance, and automated decision-making. Production facilities can optimize workflows, minimize downtime, and enhance product quality using on-site AI processing. Coupled with IoT devices, Edge AI enables seamless data collection and intelligent automation. As the demand for Industry 4.0 solutions grows, Edge AI emerges as a crucial driver of digital transformation. Implementing Edge AI allows manufacturers to increase efficiency, reduce costs, and quickly adapt to evolving production needs. This growing trend offers substantial market potential for Edge AI technologies in global industrial automation sectors.

Threat:

Rapid technological changes

Edge AI adoption faces threats from the rapid evolution of AI, IoT, and industrial automation technologies. New innovations and frequent system updates can quickly make current Edge AI solutions outdated. Companies may incur high costs and face operational challenges to update infrastructure in line with technological advancements. Such rapid changes create uncertainty for investors and manufacturers, potentially delaying implementation decisions. Organizations that cannot continuously upgrade risk lagging behind competitors while slower adopters may struggle to maintain operational efficiency. The fast pace of technological progress and the possibility of obsolescence pose a significant threat to the growth of the Edge AI market in industrial automation.

Covid-19 Impact:

The COVID-19 pandemic had a notable effect on the Edge AI for Industrial Automation Market, disrupting supply chains and postponing industrial initiatives. Workforce limitations and lockdown measures compelled manufacturers to implement automation technologies to sustain operations with fewer on-site employees. Edge AI became valuable for enabling remote monitoring, predictive maintenance, and operational control, minimizing reliance on human intervention. Despite its benefits, economic uncertainties and limited budgets slowed significant investments in advanced AI solutions. As industries recover, there is an accelerated push toward digital transformation, with Edge AI adoption increasing to improve efficiency, resilience, and workplace safety, presenting both market challenges and opportunities globally.

The edge AI hardware segment is expected to be the largest during the forecast period

The edge AI hardware segment is expected to account for the largest market share during the forecast period due to its essential function in enabling real-time data analysis and on-site decision-making. Industrial operations depend on processors, sensors, and dedicated computing equipment to implement Edge AI solutions successfully. Hardware serves as the backbone for supporting software and service applications while integrating with existing industrial systems and IoT devices. The growing need for dependable, high-performance, and energy-efficient hardware strengthens its market dominance. With industries increasingly focusing on automation, predictive maintenance, and process optimization, investments in Edge AI hardware remain substantial, making it the largest and most influential segment in the industrial Edge AI market.

The electronics & semiconductors segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the electronics & semiconductors segment is predicted to witness the highest growth rate, driven by the need for high precision, speed, and automation in manufacturing. Real-time monitoring, predictive maintenance, and defect detection are crucial for maximizing yields and reducing downtime in semiconductor and electronics production. Edge AI enables on-site data processing and analytics, enhancing operational efficiency and accuracy. Increasing adoption of smart factory technologies, Industry 4.0 practices, and automation-based quality control supports the rapid expansion of this segment. Continuous innovation in electronics manufacturing further positions this sector as a major contributor to the accelerated growth of the Edge AI market.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share due to rapid industrial digital transformation, significant R&D spending, and a well-established manufacturing infrastructure. The region hosts major AI and industrial automation companies, facilitating early and widespread deployment of Edge AI solutions. Key industries, including automotive, electronics, and semiconductors, leverage real-time analytics, predictive maintenance, and smart factory practices. Favorable government regulations and growing demand for Industry 4.0 technologies further support its market leadership. Technological innovation, an experienced workforce, and a strong industrial ecosystem enable North America to maintain the largest market share, making it the primary hub for global Edge AI adoption in industrial automation.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, driven by rapid industrial development and increasing adoption of AI-enabled smart manufacturing. Key economies, including China, Japan, South Korea, and India, are implementing Industry 4.0 strategies, real-time monitoring, and predictive maintenance across automotive, electronics, and heavy equipment sectors. Strong government support for industrial modernization and a growing focus on operational efficiency further accelerate growth. The region’s combination of emerging markets, technological progress, and expanding industrial infrastructure positions Asia-Pacific as the region with the highest growth rate, making it a major hub for Edge AI adoption in global industrial automation.

Key players in the market

Some of the key players in Edge AI for Industrial Automation Market include Siemens, Rockwell Automation, ABB, Schneider Electric, Honeywell, Emerson Electric, Mitsubishi Electric, Advantech, Dell Technologies, NVIDIA, Intel, Arm, Cyient, MosChip Technologies and Barbara Tech.

Key Developments:

In October 2025, Siemens Mobility has signed a major contract with Trivia Trens S.A. to modernise three of São Paulo’s commuter rail lines using Automatic Train Operation (ATO) over ETCS Level 2 – the most extensive deployment of this technology in Latin America. The project, covering 140 kilometres of track and 46 stations across lines 11-Coral, 12-Sapphire, and 13-Jade, will deliver a fully digital signalling and control system designed to increase capacity, safety, and efficiency across one of the busiest rail networks in the region.

In October 2025, ABB has signed a term sheet agreement with SwitcH2 to engineer and supply automation and electrification solutions for SwitcH2’s floating production, storage and offloading (FPSO) unit dedicated to producing green ammonia from green hydrogen, to support future demand for low-carbon marine fuels.

In April 2023, Rockwell Automation, Inc signed a Memorandum of Understanding to form a partnership with leading global robot manufacturer Doosan Robotics and its parent company Doosan Corporation, both Seoul-based entities and members of the historic summit in Washington, D.C., commemorating the 70th anniversary of the U.S. – South Korea alliance.

Components Covered:
• Edge AI Hardware
• Edge AI Software
• Edge AI Services

Deployment Models Covered:
• On-Premise Edge
• Hybrid Edge-Cloud
• Federated Learning Models

Applications Covered:
• Predictive Maintenance
• Quality Inspection
• Process Optimization
• Human-Machine Interface (HMI)
• Safety & Compliance Monitoring

End Users Covered:
• Automotive Manufacturing
• Electronics & Semiconductors
• Food & Beverage Processing
• Chemical & Pharmaceutical Plants
• Heavy Machinery & Equipment

Regions Covered:
• North America
o US
o Canada
o Mexico
• Europe
o Germany
o UK
o Italy
o France
o Spain
o Rest of Europe
• Asia Pacific
o Japan       
o China       
o India       
o Australia 
o New Zealand
o South Korea
o Rest of Asia Pacific   
• South America
o Argentina
o Brazil
o Chile
o Rest of South America
• Middle East & Africa
o Saudi Arabia
o UAE
o Qatar
o South Africa
o Rest of Middle East & Africa

What our report offers:
- Market share assessments for the regional and country-level segments
- Strategic recommendations for the new entrants
- Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
- Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
- Strategic recommendations in key business segments based on the market estimations
- Competitive landscaping mapping the key common trends
- Company profiling with detailed strategies, financials, and recent developments
- Supply chain trends mapping the latest technological advancements

Free Customization Offerings:
All the customers of this report will be entitled to receive one of the following free customization options:
• Company Profiling
o Comprehensive profiling of additional market players (up to 3)
o SWOT Analysis of key players (up to 3)
• Regional Segmentation
o Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
o Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary         
         
2 Preface         
2.1 Abstract        
2.2 Stake Holders        
2.3 Research Scope        
2.4 Research Methodology        
  2.4.1 Data Mining       
  2.4.2 Data Analysis       
  2.4.3 Data Validation       
  2.4.4 Research Approach       
2.5 Research Sources        
  2.5.1 Primary Research Sources       
  2.5.2 Secondary Research Sources       
  2.5.3 Assumptions       
         
3 Market Trend Analysis         
3.1 Introduction        
3.2 Drivers        
3.3 Restraints        
3.4 Opportunities        
3.5 Threats        
3.6 Application Analysis        
3.7 End User Analysis        
3.8 Emerging Markets        
3.9 Impact of Covid-19        
         
4 Porters Five Force Analysis         
4.1 Bargaining power of suppliers        
4.2 Bargaining power of buyers        
4.3 Threat of substitutes        
4.4 Threat of new entrants        
4.5 Competitive rivalry        
         
5 Global Edge AI for Industrial Automation Market, By Component         
5.1 Introduction        
5.2 Edge AI Hardware        
5.3 Edge AI Software        
5.4 Edge AI Services        
         
6 Global Edge AI for Industrial Automation Market, By Deployment Model         
6.1 Introduction        
6.2 On-Premise Edge        
6.3 Hybrid Edge-Cloud        
6.4 Federated Learning Models        
         
7 Global Edge AI for Industrial Automation Market, By Application         
7.1 Introduction        
7.2 Predictive Maintenance        
7.3 Quality Inspection        
7.4 Process Optimization        
7.5 Human-Machine Interface (HMI)        
7.6 Safety & Compliance Monitoring        
         
8 Global Edge AI for Industrial Automation Market, By End User         
8.1 Introduction        
8.2 Automotive Manufacturing        
8.3 Electronics & Semiconductors        
8.4 Food & Beverage Processing        
8.5 Chemical & Pharmaceutical Plants        
8.6 Heavy Machinery & Equipment        
         
9 Global Edge AI for Industrial Automation Market, By Geography         
9.1 Introduction        
9.2 North America        
  9.2.1 US       
  9.2.2 Canada       
  9.2.3 Mexico       
9.3 Europe        
  9.3.1 Germany       
  9.3.2 UK       
  9.3.3 Italy       
  9.3.4 France       
  9.3.5 Spain       
  9.3.6 Rest of Europe       
9.4 Asia Pacific        
  9.4.1 Japan       
  9.4.2 China       
  9.4.3 India       
  9.4.4 Australia       
  9.4.5 New Zealand       
  9.4.6 South Korea       
  9.4.7 Rest of Asia Pacific       
9.5 South America        
  9.5.1 Argentina       
  9.5.2 Brazil       
  9.5.3 Chile       
  9.5.4 Rest of South America       
9.6 Middle East & Africa        
  9.6.1 Saudi Arabia       
  9.6.2 UAE       
  9.6.3 Qatar       
  9.6.4 South Africa       
  9.6.5 Rest of Middle East & Africa       
         
10 Key Developments         
10.1 Agreements, Partnerships, Collaborations and Joint Ventures        
10.2 Acquisitions & Mergers        
10.3 New Product Launch        
10.4 Expansions        
10.5 Other Key Strategies        
         
11 Company Profiling         
11.1 Siemens        
11.2 Rockwell Automation        
11.3 ABB        
11.4 Schneider Electric        
11.5 Honeywell        
11.6 Emerson Electric        
11.7 Mitsubishi Electric        
11.8 Advantech        
11.9 Dell Technologies        
11.10 NVIDIA        
11.11 Intel        
11.12 Arm        
11.13 Cyient        
11.14 MosChip Technologies        
11.15 Barbara Tech        
         
List of Tables          
1 Global Edge AI for Industrial Automation Market Outlook, By Region (2024-2032) ($MN)         
2 Global Edge AI for Industrial Automation Market Outlook, By Component (2024-2032) ($MN)         
3 Global Edge AI for Industrial Automation Market Outlook, By Edge AI Hardware (2024-2032) ($MN)         
4 Global Edge AI for Industrial Automation Market Outlook, By Edge AI Software (2024-2032) ($MN)         
5 Global Edge AI for Industrial Automation Market Outlook, By Edge AI Services (2024-2032) ($MN)         
6 Global Edge AI for Industrial Automation Market Outlook, By Deployment Model (2024-2032) ($MN)         
7 Global Edge AI for Industrial Automation Market Outlook, By On-Premise Edge (2024-2032) ($MN)         
8 Global Edge AI for Industrial Automation Market Outlook, By Hybrid Edge-Cloud (2024-2032) ($MN)         
9 Global Edge AI for Industrial Automation Market Outlook, By Federated Learning Models (2024-2032) ($MN)         
10 Global Edge AI for Industrial Automation Market Outlook, By Application (2024-2032) ($MN)         
11 Global Edge AI for Industrial Automation Market Outlook, By Predictive Maintenance (2024-2032) ($MN)         
12 Global Edge AI for Industrial Automation Market Outlook, By Quality Inspection (2024-2032) ($MN)         
13 Global Edge AI for Industrial Automation Market Outlook, By Process Optimization (2024-2032) ($MN)         
14 Global Edge AI for Industrial Automation Market Outlook, By Human-Machine Interface (HMI) (2024-2032) ($MN)         
15 Global Edge AI for Industrial Automation Market Outlook, By Safety & Compliance Monitoring (2024-2032) ($MN)         
16 Global Edge AI for Industrial Automation Market Outlook, By End User (2024-2032) ($MN)         
17 Global Edge AI for Industrial Automation Market Outlook, By Automotive Manufacturing (2024-2032) ($MN)         
18 Global Edge AI for Industrial Automation Market Outlook, By Electronics & Semiconductors (2024-2032) ($MN)         
19 Global Edge AI for Industrial Automation Market Outlook, By Food & Beverage Processing (2024-2032) ($MN)         
20 Global Edge AI for Industrial Automation Market Outlook, By Chemical & Pharmaceutical Plants (2024-2032) ($MN)         
21 Global Edge AI for Industrial Automation Market Outlook, By Heavy Machinery & Equipment (2024-2032) ($MN)         
         
Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.           

List of Figures

RESEARCH METHODOLOGY


Research Methodology

We at Stratistics opt for an extensive research approach which involves data mining, data validation, and data analysis. The various research sources include in-house repository, secondary research, competitor’s sources, social media research, client internal data, and primary research.

Our team of analysts prefers the most reliable and authenticated data sources in order to perform the comprehensive literature search. With access to most of the authenticated data bases our team highly considers the best mix of information through various sources to obtain extensive and accurate analysis.

Each report takes an average time of a month and a team of 4 industry analysts. The time may vary depending on the scope and data availability of the desired market report. The various parameters used in the market assessment are standardized in order to enhance the data accuracy.

Data Mining

The data is collected from several authenticated, reliable, paid and unpaid sources and is filtered depending on the scope & objective of the research. Our reports repository acts as an added advantage in this procedure. Data gathering from the raw material suppliers, distributors and the manufacturers is performed on a regular basis, this helps in the comprehensive understanding of the products value chain. Apart from the above mentioned sources the data is also collected from the industry consultants to ensure the objective of the study is in the right direction.

Market trends such as technological advancements, regulatory affairs, market dynamics (Drivers, Restraints, Opportunities and Challenges) are obtained from scientific journals, market related national & international associations and organizations.

Data Analysis

From the data that is collected depending on the scope & objective of the research the data is subjected for the analysis. The critical steps that we follow for the data analysis include:

  • Product Lifecycle Analysis
  • Competitor analysis
  • Risk analysis
  • Porters Analysis
  • PESTEL Analysis
  • SWOT Analysis

The data engineering is performed by the core industry experts considering both the Marketing Mix Modeling and the Demand Forecasting. The marketing mix modeling makes use of multiple-regression techniques to predict the optimal mix of marketing variables. Regression factor is based on a number of variables and how they relate to an outcome such as sales or profits.


Data Validation

The data validation is performed by the exhaustive primary research from the expert interviews. This includes telephonic interviews, focus groups, face to face interviews, and questionnaires to validate our research from all aspects. The industry experts we approach come from the leading firms, involved in the supply chain ranging from the suppliers, distributors to the manufacturers and consumers so as to ensure an unbiased analysis.

We are in touch with more than 15,000 industry experts with the right mix of consultants, CEO's, presidents, vice presidents, managers, experts from both supply side and demand side, executives and so on.

The data validation involves the primary research from the industry experts belonging to:

  • Leading Companies
  • Suppliers & Distributors
  • Manufacturers
  • Consumers
  • Industry/Strategic Consultants

Apart from the data validation the primary research also helps in performing the fill gap research, i.e. providing solutions for the unmet needs of the research which helps in enhancing the reports quality.


For more details about research methodology, kindly write to us at info@strategymrc.com

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