Artificial Intelligence in Industrial Automation Market Size, Share, Trends & Competitive Analysis By Type (Machine Learning, Deep Learning, Natural Language Processing Computer Vision) By Application (Predictive Maintenance, Quality Control and Inspection, Supply Chain Optimization, Industrial Robotics, Process Optimization, Safety and Security) By Industry Vertical; By Regions, and Industry Forecast, Global Report 2023-2030

  • Report ID: FDS348
  • Forecast Period: 2023-2030
  • No. of Pages: 150+
  • Industry: Industry Services

The global Artificial Intelligence in Industrial Automation Market size was valued at USD 12.18 billion in 2022 and is projected to expand at a compound annual growth rate (CAGR) of 20.2% during the forecast period, reaching a value of USD 55.60 billion by 2030.

Artificial Intelligence in Industrial Automation Market research report by Future Data Stats, offers a comprehensive view of the market's historical data from 2017 to 2021, capturing trends, growth patterns, and key drivers. It establishes 2021 as the base year, analyzing the market landscape, consumer behavior, competition, and regulations. Additionally, the report presents a well-researched forecast period from 2022 to 2030, leveraging data analysis techniques to project the market's growth trajectory, emerging opportunities, and anticipated challenges.


Artificial Intelligence (AI) in Industrial Automation refers to the integration of AI technologies and techniques into industrial processes and systems to enhance automation, decision-making, and overall operational efficiency. It involves the use of machine learning, deep learning, natural language processing, and computer vision to enable machines and systems to learn, adapt, and perform tasks that traditionally required human intervention. AI in industrial automation aims to optimize manufacturing processes, improve product quality, reduce downtime, enhance safety, and streamline supply chain management.

By leveraging AI, industrial automation systems can analyze vast amounts of data, identify patterns, make predictions, and continuously learn from their interactions with the environment. This allows for proactive maintenance, predictive analytics, real-time monitoring, and autonomous decision-making, leading to improved productivity, cost savings, and resource optimization. AI in industrial automation has applications across various industries, including manufacturing, automotive, energy, healthcare, and retail, where it enables advanced robotics, quality control, supply chain optimization, and process efficiency.


One of the primary drivers is the increasing demand for enhanced operational efficiency and productivity in industrial processes. AI-powered automation systems enable faster and more accurate decision-making, predictive maintenance, and optimization of manufacturing processes, leading to improved overall efficiency. Additionally, the need for cost reduction, improved product quality, and increased safety standards in industries fuels the adoption of AI in industrial automation.

However, there are also certain restraints and challenges that need to be considered. One of the major restraints is the high implementation and infrastructure costs associated with AI technologies. The integration of AI into existing industrial systems often requires significant investments in hardware, software, and skilled personnel. Moreover, concerns regarding data privacy, security, and the potential displacement of human workers by automated systems can pose challenges to the widespread adoption of AI in industrial automation.

Nevertheless, the market presents numerous opportunities for growth and innovation. The advancements in AI technologies, such as machine learning and computer vision, provide opportunities for developing more sophisticated and intelligent automation systems. The increasing availability of big data and the development of advanced analytics platforms enable better utilization of data for optimization and decision-making.



Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision are the key types of AI technologies driving this growth. Machine Learning enables machines to learn from data and improve their performance over time. Deep Learning, a subset of Machine Learning, mimics the human brain's neural networks to solve complex problems. Natural Language Processing allows machines to understand and process human language, enabling them to communicate effectively. Computer Vision enables machines to perceive and interpret visual information, facilitating tasks such as object recognition and image analysis.


Predictive Maintenance utilizes AI algorithms to identify potential equipment failures and optimize maintenance schedules, reducing downtime and improving efficiency. Quality Control and Inspection leverage AI technologies to enhance product quality and ensure compliance with industry standards. Supply Chain Optimization employs AI to streamline logistics, inventory management, and demand forecasting, resulting in cost savings and improved customer satisfaction. Industrial Robotics benefits from AI integration by enabling advanced automation and intelligent decision-making, enhancing productivity and precision. Process Optimization utilizes AI algorithms to analyze and optimize industrial processes, reducing waste and increasing operational efficiency. Safety and Security in industrial automation are strengthened by AI-powered systems that detect anomalies, monitor environments, and enhance worker safety.


Manufacturing industries are leveraging AI to enhance production processes, optimize supply chains, and improve overall efficiency. The automotive sector is utilizing AI for autonomous driving, predictive maintenance, and intelligent manufacturing. Energy and utilities companies are deploying AI to optimize energy consumption, monitor infrastructure, and enable predictive maintenance of power grids. In healthcare, AI is being applied for medical imaging analysis, drug discovery, and patient data management. The retail industry benefits from AI-powered recommendation systems, demand forecasting, and inventory management. The aerospace and defense sector employs AI for autonomous drones, predictive maintenance of aircraft, and cybersecurity. Additionally, AI is being adopted in various other industries for diverse applications such as finance, telecommunications, and agriculture.


North America remains a dominant player, driven by technological advancements, strong R&D capabilities, and a supportive business environment. Europe showcases significant growth with a focus on industrial automation and digital transformation initiatives. Asia Pacific exhibits rapid expansion, fueled by emerging economies, increased industrialization, and government initiatives promoting AI adoption. Latin America is witnessing steady growth, driven by industries such as manufacturing and automotive. The Middle East and Africa region are also witnessing a surge in AI adoption in industries like energy and utilities, healthcare, and manufacturing.


The COVID-19 pandemic has had a significant impact on the Artificial Intelligence in Industrial Automation market. While the initial outbreak caused disruptions and uncertainties across industries, it also accelerated the adoption of AI technologies in industrial automation. With the need to maintain social distancing and minimize human contact, companies turned to AI-driven solutions to ensure operational continuity. AI-powered robots and autonomous systems were deployed for tasks like warehouse management, quality control, and production line operations. The pandemic highlighted the importance of predictive maintenance and remote monitoring capabilities offered by AI, enabling proactive maintenance and minimizing equipment downtime. Additionally, AI-based analytics and data-driven insights played a crucial role in optimizing supply chain management and inventory forecasting during the crisis.


Mergers & Acquisitions:

  • In 2022, Rockwell Automation acquired Knowledge Lens, a company that develops artificial intelligence (AI) software for industrial automation.
  • In 2023, Siemens acquired Innoteck Automation, a company that provides AI-powered solutions for the manufacturing industry.
  • In 2023, Schneider Electric acquired EcoStruxure Machine Expert AI, a platform that uses AI to optimize industrial machinery.

Product Launches:

  • In 2022, ABB launched its Ability Genix AI Suite, a suite of AI-powered solutions for industrial automation.
  • In 2023, Emerson Electric launched its Ovation IQ platform, a platform that uses AI to improve the performance of industrial assets.
  • In 2023, Honeywell launched its Connected Factory Suite, a suite of AI-powered solutions for the manufacturing industry.


  • IBM Corporation
  • Microsoft Corporation
  • Google LLC
  • Amazon Web Services, Inc.
  • Siemens AG
  • General Electric Company
  • ABB Ltd.
  • Rockwell Automation, Inc.
  • Schneider Electric SE
  • Mitsubishi Electric Corporation
  • Honeywell International Inc.
  • Intel Corporation
  • NVIDIA Corporation
  • Fanuc Corporation
  • Cisco Systems, Inc.
  • Bosch Rexroth AG
  • Cognex Corporation
  • Omron Corporation
  • Yokogawa Electric Corporation
  • SAP SE
  • Oracle Corporation
  • Hitachi, Ltd.
  • Accenture plc
  • PTC Inc.
  • Others

Table of Contents

1.1. Overview
1.2. Key Benefits of AI in Industrial Automation

Market Overview
2.1. Market Definition
2.2. Market Segmentation
2.3. Market Dynamics
2.3.1. Drivers
2.3.2. Restraints
2.3.3. Opportunities
2.4. Industry Trends and Emerging Technologies

Market Segmentation by Type
3.1. Machine Learning
3.1.1. Supervised Learning
3.1.2. Unsupervised Learning
3.1.3. Reinforcement Learning
3.2. Deep Learning
3.2.1. Convolutional Neural Networks (CNN)
3.2.2. Recurrent Neural Networks (RNN)
3.2.3. Generative Adversarial Networks (GANs)
3.3. Natural Language Processing
3.3.1. Sentiment Analysis
3.3.2. Language Translation
3.3.3. Speech Recognition
3.4. Computer Vision
3.4.1. Object Detection
3.4.2. Image Classification
3.4.3. Video Analytics

Market Segmentation by Application
4.1. Predictive Maintenance
4.2. Quality Control and Inspection
4.3. Supply Chain Optimization
4.4. Industrial Robotics
4.5. Process Optimization
4.6. Safety and Security

Market Segmentation by Industry Vertical
5.1. Manufacturing
5.2. Automotive
5.3. Energy and Utilities
5.4. Healthcare
5.5. Retail
5.6. Aerospace and Defense
5.7. Other Industries

Market Segmentation by Region
6.1. North America
6.2. Europe
6.3. Asia Pacific
6.4. Latin America
6.5. Middle East and Africa

Competitive Landscape
7.1. Key Players Overview
7.2. Company Profiles
7.2.1. Company 1
7.2.2. Company 2
7.2.3. Company 3
7.3. Competitive Strategies

Market Opportunities and Future Trends


Artificial Intelligence in Industrial Automation Market Segmentation

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:

  • Predictive Maintenance
  • Quality Control and Inspection
  • Supply Chain Optimization
  • Industrial Robotics
  • Process Optimization
  • Safety and Security

By Industry Vertical:

  • Manufacturing
  • Automotive
  • Energy and Utilities
  • Healthcare
  • Retail
  • Aerospace and Defense
  • Other industries (agriculture, transportation, logistics, finance, etc.)


By Geography:

  • North America (USA, Canada, Mexico)
  • Europe (Germany, UK, France, Russia, Italy, Rest of Europe)
  • Asia-Pacific (China, Japan, South Korea, India, Southeast Asia, Rest of Asia-Pacific)
  • South America (Brazil, Argentina, Columbia, Rest of South America)
  • Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria, South Africa, Rest of MEA)

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Primary Research:

Primary Research


Desk Research


Company Analysis


•       Identify key opinion leaders

•       Questionnaire design

•       In-depth Interviews

•       Coverage across the value chain


•       Company Website

•       Company Annual Reports

•       Paid Databases

•       Financial Reports


•       Market Participants

•       Key Strengths

•       Product Portfolio

•       Mapping as per Value Chain

•       Key focus segment


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These participants include, but are not limited to:

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Secondary Research:

A broad array of industry sources for the secondary research typically includes, but is not limited to:

  • Company SEC filings, annual reports, company websites, broker & financial reports, and investor  presentations for a competitive scenario and shape of the industry
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  • Regional government and statistical databases for macro analysis
  • Authentic news articles, web-casts, and other related releases to evaluate the market
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•       Top executives of end-use industries

•       C-level executives of the leading Parenteral Nutrition companies

•       Sales manager and regional sales manager of the Parenteral Nutrition companies

•       Industry Consultants

•       Distributors/Suppliers


•       Annual Reports

•       Presentations

•       Company Websites

•       Press Releases

•       News Articles

•       Government Agencies’ Publications

•       Industry Publications

•       Paid Databases


Analyst Tools and Models:



·         Arriving at
Global Market Size

·         Arriving at
Market Size

·         Market Share
of Key Players

·         Key Market Players

·         Key Market Players

·         Market Share
of Key Players

·         Arriving at
Market Size

·         Arriving at
Global Market Size


Artificial Intelligence in Industrial Automation Market Dynamic Factors


  • Increasing demand for automation and optimization of industrial processes
  • Growing need for predictive maintenance and reduced downtime
  • Advancements in AI technologies such as machine learning, deep learning, and computer vision
  • Rising focus on improving operational efficiency and productivity
  • Government initiatives promoting digital transformation and AI adoption


  • High initial investment and implementation costs
  • Concerns regarding data privacy and security
  • Lack of skilled workforce and expertise in AI technologies
  • Integration challenges with existing systems and infrastructure
  • Potential job displacement and workforce transition challenges


  • Emergence of edge computing and IoT for real-time data processing
  • Integration of AI with robotics and industrial IoT for enhanced automation
  • Expansion of AI applications in different industry verticals
  • Increasing adoption of cloud-based AI solutions
  • Collaborations and partnerships among AI technology providers and industrial automation companies


  • Ethical considerations and transparency in AI decision-making
  • Complexity in data collection, cleansing, and processing for AI algorithms
  • Ensuring interoperability and compatibility of AI systems
  • Regulatory and legal challenges in AI adoption
  • Overcoming resistance to change and cultural barriers within organizations

Frequently Asked Questions

The global Artificial Intelligence in Industrial Automation Market size was valued at USD 12.18 billion in 2022 and is projected to expand at a compound annual growth rate (CAGR) of 20.2% during the forecast period, reaching a value of USD 55.60 billion by 2030.

The rising demand for automation and optimization of industrial processes, the need for predictive maintenance and reduced downtime, advancements in AI technologies such as machine learning and computer vision, and government initiatives promoting digital transformation and AI adoption.

The integration of AI with robotics and industrial IoT, the emergence of edge computing for real-time data processing, the expansion of AI applications across various industry verticals, and the increasing adoption of cloud-based AI solutions.

Regions such as North America, Europe, and Asia Pacific are expected to dominate the Artificial Intelligence in Industrial Automation market. These regions have strong technological capabilities, a favorable business environment, and a high concentration of industrial automation industries.

The high initial investment costs, concerns regarding data privacy and security, lack of skilled workforce and expertise in AI technologies, and integration challenges with existing systems. However, there are significant opportunities in terms of the emergence of edge computing and IoT, expanding AI applications, and collaborations among AI technology providers and industrial automation companies.
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