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Industrial AI Market By Technology (Computer Vision, Deep Learning, Context Awareness, Natural Language Processing (NLP)), By Offering (Software, Hardware), By Application (Production Planning, Field Services, Quality Control, Predictive Maintenance and Machinery Inspection), By Industry (Energy & Power, Semiconductors & Electronics, Heavy Metals and Machine Manufacturing, Food & Beverages, Pharmaceuticals), and By Region; Global Insights & Forecast (2024 – 2030)

Published: December, 2024  
|   Report ID: TMT3320  
|   Technology, Media, and Telecommunications

As per Intent Market Research, the Industrial AI Market was valued at USD 3.1 billion in 2023-e and will surpass USD 41.7 billion by 2030; growing at a CAGR of 45.3% during 2024 - 2030.

The Industrial AI market is poised for significant growth as industries increasingly embrace artificial intelligence technologies to enhance productivity, streamline operations, and foster innovation. With the global shift toward digital transformation, organizations are leveraging AI to analyze vast datasets, optimize manufacturing processes, and improve decision-making capabilities. The demand for predictive maintenance, smart manufacturing, and supply chain optimization is driving the adoption of AI technologies across various sectors, including manufacturing, energy, logistics, and construction.

Manufacturing Segment is Largest Owing to Operational Efficiency

The manufacturing segment dominates the Industrial AI market, largely due to the sector’s need for operational efficiency and automation. With the increasing complexity of manufacturing processes, industries are seeking advanced AI solutions to enhance productivity and reduce operational costs. AI-powered tools such as predictive analytics, machine learning, and robotics are being integrated into production lines to facilitate real-time monitoring, quality control, and supply chain management. This segment's growth is further bolstered by the Industry 4.0 revolution, which emphasizes smart factories and interconnected systems, leading to higher efficiency and lower downtime.

Moreover, the rise of smart manufacturing solutions has led to the adoption of AI for predictive maintenance. Manufacturers can now utilize AI algorithms to analyze equipment performance and anticipate failures before they occur, thereby minimizing unplanned downtimes and extending asset life. This trend is increasingly becoming a priority for manufacturing companies aiming to achieve operational excellence. As such, the manufacturing segment is expected to continue leading the Industrial AI market, driven by advancements in AI technologies and the urgent need for manufacturers to remain competitive in a rapidly evolving marketplace.

Energy Segment is Fastest Growing Owing to Renewable Integration

The energy sector is witnessing the fastest growth in the Industrial AI market, primarily driven by the integration of renewable energy sources and the increasing need for efficient energy management. As energy companies strive to enhance their operational capabilities and meet sustainability goals, AI technologies are playing a pivotal role in optimizing energy production and consumption. With the growing complexity of energy grids and the rise of decentralized energy systems, AI is being employed to analyze data from various sources, facilitating better decision-making and operational efficiency.

AI applications in the energy segment include demand forecasting, grid management, and predictive maintenance for energy assets. Companies are utilizing machine learning algorithms to predict energy demand patterns and optimize energy distribution, thereby minimizing waste and maximizing efficiency. Furthermore, the increasing focus on reducing carbon footprints and embracing renewable energy sources such as wind and solar is further driving the adoption of AI solutions. As energy companies increasingly seek innovative approaches to meet regulatory requirements and enhance operational efficiency, the energy segment is expected to witness remarkable growth in the Industrial AI market.

Logistics Segment is Largest Owing to Enhanced Supply Chain Management

The logistics segment emerges as a key player in the Industrial AI market, driven by its critical role in enhancing supply chain management. Logistics companies are increasingly adopting AI technologies to optimize routing, improve inventory management, and streamline operations. With the rise of e-commerce and the need for faster delivery times, AI solutions are becoming indispensable for logistics providers looking to improve efficiency and reduce operational costs. By leveraging AI-driven insights, organizations can enhance their logistics processes, ensuring timely deliveries and better customer satisfaction.

Moreover, AI is revolutionizing warehouse operations through automation and robotics. Advanced AI algorithms enable logistics companies to optimize space utilization, manage stock levels efficiently, and reduce the risk of errors in order fulfillment. This trend is increasingly important as businesses seek to respond swiftly to market demands and improve overall operational resilience. The logistics segment is expected to remain a significant contributor to the Industrial AI market, as companies continue to seek innovative solutions to meet the challenges of an evolving global supply chain landscape.

Construction Segment is Fastest Growing Owing to Project Efficiency

In the construction industry, the adoption of AI technologies is rapidly increasing, marking it as one of the fastest-growing segments within the Industrial AI market. Construction companies are leveraging AI to enhance project efficiency, improve safety, and reduce costs. The integration of AI in construction processes facilitates better project management, allowing stakeholders to analyze real-time data, assess project timelines, and optimize resource allocation. As the industry faces challenges such as labor shortages and rising material costs, AI-driven solutions are becoming essential for maintaining competitiveness and operational effectiveness.

AI applications in construction include predictive analytics for project planning, risk management, and safety monitoring. By utilizing machine learning algorithms, companies can identify potential risks before they escalate, thereby mitigating delays and reducing costs. The rise of smart construction technologies, including drones and IoT devices, further supports the integration of AI into construction processes. With the increasing emphasis on smart city initiatives and sustainable construction practices, the construction segment is set for remarkable growth in the Industrial AI market, driven by the need for efficiency and innovation.

Healthcare Segment is Largest Owing to Diagnostic Accuracy

The healthcare segment stands out as the largest within the Industrial AI market, primarily due to the growing demand for enhanced diagnostic accuracy and patient care. AI technologies are increasingly being integrated into healthcare systems to analyze medical data, support clinical decision-making, and improve patient outcomes. Hospitals and healthcare providers are leveraging AI for applications such as medical imaging analysis, predictive analytics for patient care, and personalized medicine. The ability of AI algorithms to process vast amounts of data quickly and accurately is revolutionizing the way healthcare providers deliver services.

Furthermore, AI is playing a crucial role in managing healthcare costs and improving operational efficiency. By automating administrative tasks and optimizing resource allocation, healthcare organizations can enhance productivity and focus more on patient care. The ongoing advancements in AI technologies, coupled with the rising adoption of telemedicine and digital health solutions, position the healthcare segment for sustained growth in the Industrial AI market. As the industry continues to evolve, AI is expected to play a pivotal role in shaping the future of healthcare delivery and improving overall patient experiences.

Leading Region in Industrial AI Market: North America

North America emerges as the leading region in the Industrial AI market, driven by its technological advancements and robust industrial base. The region is home to many pioneering companies and research institutions that are at the forefront of AI innovation. The United States, in particular, is witnessing significant investments in AI technologies across various sectors, including manufacturing, healthcare, and energy. Government initiatives aimed at promoting AI research and development further bolster the region’s growth in the Industrial AI market.

Moreover, the presence of key industry players and a well-established technological infrastructure facilitate the rapid adoption of AI solutions in North America. Companies are leveraging AI to enhance operational efficiency, improve decision-making, and drive innovation. The increasing focus on digital transformation and automation across industries in North America is expected to sustain the region's dominance in the Industrial AI market. As organizations continue to seek advanced AI solutions to meet their operational challenges, North America is well-positioned for sustained growth in the coming years.

Competitive Landscape of the Industrial AI Market

The competitive landscape of the Industrial AI market is characterized by the presence of several leading companies that are driving innovation and advancements in AI technologies. Among the top players are:

  1. IBM Corporation: A pioneer in AI and cloud computing, IBM offers a range of AI solutions tailored for industrial applications, including predictive maintenance and data analytics.
  2. Siemens AG: Siemens is at the forefront of digitalization in industries, providing AI-driven solutions for manufacturing, energy, and transportation sectors.
  3. General Electric (GE): GE integrates AI into its industrial Internet of Things (IIoT) solutions, enabling predictive maintenance and operational optimization.
  4. Microsoft Corporation: Microsoft leverages its Azure cloud platform to deliver AI tools and services for various industrial applications, fostering innovation across sectors.
  5. Amazon Web Services (AWS): AWS offers a wide range of AI and machine learning services that support industries in automating processes and improving decision-making.
  6. NVIDIA Corporation: Known for its advanced GPU technologies, NVIDIA is instrumental in powering AI applications across various industries, including manufacturing and healthcare.
  7. Rockwell Automation: Rockwell focuses on industrial automation and digital transformation, integrating AI into its products to enhance operational efficiency.
  8. Honeywell International Inc.: Honeywell provides AI-powered solutions for industries, emphasizing safety, efficiency, and productivity in operations.
  9. SAP SE: SAP offers enterprise software solutions that incorporate AI capabilities to enhance business processes and operational insights.
  10. Intel Corporation: Intel focuses on providing hardware and software solutions that support AI applications across various sectors, enhancing processing capabilities.

The competitive landscape is marked by continuous innovation, strategic partnerships, and investments in research and development. Companies are increasingly collaborating with technology startups and research institutions to enhance their AI capabilities and address the evolving needs of their clients. As the Industrial AI market continues to expand, the competitive dynamics will likely shift, with emerging players also carving out a significant share of the market.

Report Objectives:

The report will help you answer some of the most critical questions in the Industrial AI Market. A few of them are as follows:

  1. What are the key drivers, restraints, opportunities, and challenges influencing the market growth?
  2. What are the prevailing technology trends in the Industrial AI Market?
  3. What is the size of the Industrial AI Market based on segments, sub-segments, and regions?
  4. What is the size of different market segments across key regions: North America, Europe, Asia Pacific, Middle East, and Rest of the World?
  5. What are the market opportunities for stakeholders after analyzing key market trends?
  6. Who are the leading market players and what are their market share and core competencies?
  7. What is the degree of competition in the market and what are the key growth strategies adopted by leading players?
  8. What is the competitive landscape of the market, including market share analysis, revenue analysis, and a ranking of key players?

Report Scope:

Report Features

Description

Market Size (2023-e)

USD 3.1 billion

Forecasted Value (2030)

USD 41.7 billion

CAGR (2024-2030)

45.3%

Base Year for Estimation

2023-e

Historic Year

2022

Forecast Period

2024-2030

Report Coverage

Market Forecast, Market Dynamics, Competitive Landscape, Recent Developments

Segments Covered

Industrial AI Market By Technology (Computer Vision, Deep Learning, Context Awareness, Natural Language Processing (NLP)), By Offering (Software, Hardware), By Application (Production Planning, Field Services, Quality Control, Predictive Maintenance and Machinery Inspection), By Industry (Energy & Power, Semiconductors & Electronics, Heavy Metals and Machine Manufacturing, Food & Beverages, Pharmaceuticals)

Regional Analysis

North America (US, Canada), Europe (Germany, France, UK, Spain, Italy & Rest of Europe), Asia Pacific (China, Japan, South Korea, India, and rest of Asia Pacific), Latin America (Brazil, Mexico, Argentina, & Rest of Latin America), Middle East & Africa (Saudi Arabia, South Africa, Turkey, United Arab Emirates, & Rest of MEA)

Customization Scope

Customization for segments, region/country-level will be provided. Moreover, additional customization can be done based on the requirements

1.Introduction

1.1.Market Definition

1.2.Scope of the Study

1.3.Research Assumptions

1.4.Study Limitations

2.Research Methodology

2.1.Research Approach

2.1.1.Top-Down Method

2.1.2.Bottom-Up Method

2.1.3.Factor Impact Analysis

2.2.Insights & Data Collection Process

2.2.1.Secondary Research

2.2.2.Primary Research

2.3.Data Mining Process

2.3.1.Data Analysis

2.3.2.Data Validation and Revalidation

2.3.3.Data Triangulation

3.Executive Summary

3.1.Major Markets & Segments

3.2.Highest Growing Regions and Respective Countries

3.3.Impact of Growth Drivers & Inhibitors

3.4.Regulatory Overview by Country

4.Industrial AI Market, by Offering (Market Size & Forecast: USD Billion, 2024 – 2030)

4.1.Hardware

4.2.Software

5.Industrial AI Market, by Technology (Market Size & Forecast: USD Billion, 2024 – 2030)

5.1.Computer Vision

5.2.Natural Language Processing (NLP)

5.3.Deep Learning

5.4.Context Awareness

6.Industrial AI Market, by Application (Market Size & Forecast: USD Billion, 2024 – 2030)

6.1.Production Planning

6.2.Field Services

6.3.Quality Control

6.4.Material Movement

6.5.Predictive Maintenance and Machinery Inspection

6.6.Reclamation  

           7.Industrial AI Market, by Industry (Market Size & Forecast: USD Billion, 2024 – 2030)

7.1.Energy & Power

7.2.Automobile

7.3.Pharmaceuticals

7.4.Semiconductors & Electronics

7.5.Food & Beverages

7.6.Energy & Utilities

7.7.Heavy Metals and Machine Manufacturing

7.8.Others

8.Regional Analysis (Market Size & Forecast: USD Billion, 2024 – 2030)

8.1.Regional Overview

8.2.North America

8.2.1.Regional Trends & Growth Drivers

8.2.2.Barriers & Challenges

8.2.3.Opportunities

8.2.4.Factor Impact Analysis

8.2.5.Technology Trends

8.2.6.North America Industrial AI Market, by Offering

8.2.7.North America Industrial AI Market, by Technology

8.2.8.North America Industrial AI Market, by Application

8.2.9.North America Industrial AI Market, by Industry

              *Similar segmentation will be provided at each regional level

8.3.By Country

8.3.1.US

8.3.1.1.US Industrial AI Market, by Offering

8.3.1.2.US Industrial AI Market, by Technology

8.3.1.3.US Industrial AI Market, by Application

8.3.1.4.US Industrial AI Market, by Industry

8.3.2.Canada

                        *Similar segmentation will be provided at each country level

8.4.Europe

8.5.APAC

8.6.Latin America

8.7.Middle East & Africa

9.Competitive Landscape

9.1.Overview of the Key Players

9.2.Competitive Ecosystem

9.2.1.Platform Manufacturers

9.2.2.Subsystem Manufacturers

9.2.3.Service Providers

9.2.4.Software Providers

9.3.Company Share Analysis

9.4.Company Benchmarking Matrix

9.4.1.Strategic Overview

9.4.2.Product Innovations

9.5.Start-up Ecosystem

9.6.Strategic Competitive Insights/ Customer Imperatives

9.7.ESG Matrix/ Sustainability Matrix

9.8.Manufacturing Network

9.8.1.Locations

9.8.2.Supply Chain and Logistics

9.8.3.Product Flexibility/Customization

9.8.4.Digital Transformation and Connectivity

9.8.5.Environmental and Regulatory Compliance

9.9.Technology Readiness Level Matrix

9.10.Technology Maturity Curve

9.11.Buying Criteria

10.Company Profiles

10.1.Siemens

10.1.1.Company Overview

10.1.2.Company Financials

10.1.3.Product/Service Portfolio

10.1.4.Recent Developments

10.1.5.IMR Analysis

*Similar information will be provided for other companies 

10.2.Intel

10.3.Alphabet

10.4.IBM

10.5.Nvidia

10.6.General Electric

10.7.Sight Machine

10.8.Mitsubishi Electric

10.9.Oracle

10.10.Microsoft

11.Appendix

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A comprehensive market research approach was employed to gather and analyze data on the Industrial AI Market. In the process, the analysis was also done to estimate the parent market and relevant adjacencies to major the impact of them on the Industrial AI Market. The research methodology encompassed both secondary and primary research techniques, ensuring the accuracy and credibility of the findings.

Research Approach - Industrial AI Market

Secondary Research

Secondary research involved a thorough review of pertinent industry reports, journals, articles, and publications. Additionally, annual reports, press releases, and investor presentations of industry players were scrutinized to gain insights into their market positioning and strategies.

Primary Research

Primary research involved conducting in-depth interviews with industry experts, stakeholders, and market participants across the Industrial AI ecosystem. The primary research objectives included:

  • Validating findings and assumptions derived from secondary research
  • Gathering qualitative and quantitative data on market trends, drivers, and challenges
  • Understanding the demand-side dynamics, encompassing end-users, component manufacturers, facility providers, and service providers
  • Assessing the supply-side landscape, including technological advancements and recent developments

Market Size Estimation

A combination of top-down and bottom-up approaches was utilized to estimate the overall size of the Industrial AI Market. These methods were also employed to estimate the size of various sub-segments within the market. The market size estimation methodology encompassed the following steps:

  1. Identification of key industry players and relevant revenues through extensive secondary research
  2. Determination of the industry's supply chain and market size, in terms of value, through primary and secondary research processes
  3. Calculation of percentage shares, splits, and breakdowns using secondary sources and verification through primary sources

Bottom Up and Top Down - Industrial AI Market

Data Triangulation

To ensure the accuracy and reliability of the market size estimates, data triangulation was implemented. This involved cross-referencing data from various sources, including demand and supply side factors, market trends, and expert opinions. Additionally, top-down and bottom-up approaches were employed to validate the market size estimates.

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