AI Quality Inspection Market Size, Share, Opportunities, And Trends By Type (Pre-trained, Deep Learning), By End-Users (Semiconductor, Pharmaceutical, Automotive, Textile, Others), And By Geography - Forecasts From 2023 To 2028

  • Published : Apr 2023
  • Report Code : KSI061614653
  • Pages : 140

AI quality inspection process refers to the application of artificial intelligence software and computer vision technology to process and detect any anomalies in the goods and products manufactured by the companies. Owing to its accuracy and time-saving characteristics, AI-based quality inspection applications have been increasingly adopted in the semiconductor, pharmaceutical, textile, automotive, and other manufacturing industries. The AI quality inspection software can be manufactured either based on the machine learning model or as a pre-trained software service. The relative advantage of precision offered by AI-powered quality control techniques as compared to manual quality control is making it a preferable choice opted for by leading manufacturing companies across the world. Therefore, considering the increasing demand for AI-based products and other factors influencing the consumption of AI quality inspection software, it can be expected that the artificial intelligence-based quality control market shall expand to reach a larger market size in the forecast period.

The increasing adoption of AI-based quality control software in the manufacturing sector can be attributed to the increase in operating costs for manufacturing companies as a result of the production of poor-quality products. For instance, Toyota Company incurred a recent loss of $1.3 billion as a result of manufacturing defects. Often, when a damaged component goes undetected, it is used in the process of manufacturing the final product. This results in a rise in the operating expenses for the manufacturing company and leads to defective goods being not sold in the market. Such cases are prevalent in companies that engage themselves in the mass production of goods in batches. The manual quality control offered by the human eye can sometimes fail to detect such failures in such large batches. To overcome this limitation, leading manufacturing companies of all countries are actively investing in AI-based quality inspection software to identify defective goods at an earlier stage and prevent the incurring of additional expenses. For instance, a Taiwanese AI startup company named Profet AI helps companies in electronics, chemical, semiconductor, and other manufacturing sectors to detect damaged goods in the manufacturing process as soon as such damage occurs.

Additionally, the waste and scrap generated by the production of such defective goods can also be reduced through the application of AI-based quality inspection and control in the manufacturing process. The increasing adoption of AI quality inspection products in manufacturing companies as a result of the reduction in operating costs and waste generation is a key factor driving the demand for the AI quality inspection market.

The quality inspection offered by AI-based quality inspection software is limited to visual inspection which could slow down the speed of its market growth.

The AI-based quality inspection applications use visual input, computer vision AI, and machine learning methods to detect faulty goods using the visuals of the goods. Therefore, they are not capable of detecting defective goods in other quality measures such as taste and smell. A large number of factories in the manufacturing sector are involved in the production of edible and fragrance products such as food and beverage manufacture and perfume manufacture. The AI-based quality control software can be useful in detecting the quality of the outer packaging of the product however, it cannot be used to detect the quality of the original product inside the packaging since it is incapable of detecting smell and taste variations. This could limit the expansion of the AI quality inspection market

Key Developments

  • In February 2023, Kruger Packaging, a Canadian company specializing in manufacturing furniture products using recycled materials invested around US$22 million to adopt new technological changes including an AI-assisted quality control and emission control technology.
  • In August 2021, a startup based in Seattle, Loopr, introduced its AI-assisted quality control tool which can discover damaged goods and components.
  • In June 2021, Google Cloud, a software application by Google LLC, introduced AutoML, an AI-based quality inspection application that incorporates AL software, ML methods, and analytical tools to enhance its accuracy with increased exposure to the products being manufactured.

North America holds notable potential in the AI quality inspection market and is expected to grow in the forecast period.

North America, being a strong technological evolution force in the international artificial intelligence market has been actively investing in expanding the scope and applications of AI software, including AI quality control and inspection. The top companies in the software sector are working on developing and competing with other companies to enhance their AI products and services portfolio. For instance, Microsoft has introduced its virtual AI quality inspection product, Spyglass Visual Inspection which integrates technological services to identify any product defects. In addition to this, IBM Company has introduced its latest AI quality inspection product which implements a federated learning model. Apart from these established companies, there are several startups in the USA that are dedicating their product line to innovating novel models and methods to improve AI-assisted quality inspection. For instance, the AI-based quality control application of Neurala Inc., a Boston startup, has been incorporated by one of the leading manufacturers in the world, IHI Corporation. Therefore, considering the present trends in the AI market and the recent developments in AI quality inspection products in the USA, it can be anticipated that the North American AI quality inspection market is likely to witness an expansion over the forecast period.

AI Quality Inspection Market Scope:


Report Metric Details
Growth Rate CAGR during the forecast period
Base Year 2021
Forecast Period 2023 – 2028
Forecast Unit (Value) USD Billion
Segments Covered Type, End-Users, and Geography
Regions Covered North America, South America, Europe, Middle East and Africa, Asia Pacific
Companies Covered Intel Corp, Kitov Systems, Mitutoyo America Corporation, Landing AI, NEC Corporation, elunic AG, Robert Bosch GmbH, deevio GmbH, craftworks GmbH, Pleora Technologies Inc
Customization Scope Free report customization with purchase


Key Market Segments:

  • By Type
    • Pre-trained
    • Deep learning
  • By End-Users
    • Semiconductor
    • Pharmaceutical
    • Automotive
    • Textile
    • Others
  • By Geography
    • North America
      • USA
      • Canada
      • Mexico
    • South America
      • Brazil
      • Argentina
      • Others
    • Europe
      • United Kingdom
      • Germany
      • France
      • Italy
      • Spain
      • Others
    • Middle East and Africa
      • Saudi Arabia
      • UAE
      • Others
    • Asia Pacific
      • China
      • Japan
      • India
      • South Korea
      • Australia
      • Singapore
      • Indonesia
      • Others

Frequently Asked Questions (FAQs)

2021 has been taken as the base year in the AI quality inspection market.
Prominent key market players in the AI quality inspection market include Intel Corp, Kitov Systems, Mitutoyo America Corporation, Landing AI, and NEC Corporation, among others.
The global AI quality inspection market has been segmented by type, end-users, and geography.
The increasing adoption of AI quality inspection products in manufacturing companies as a result of the reduction in operating costs and waste generation is a key factor driving the demand for the AI quality inspection market.
North America accounted for major shares of the AI quality inspection market and is expected to grow in the forecast period.


1.1. Market Overview

1.2. Market Definition

1.3. Scope of the Study

1.4. Market Segmentation

1.5. Currency

1.6. Assumptions

1.7. Base, and Forecast Years Timeline


2.1. Research Data

2.2. Assumptions


3.1. Research Highlights


4.1. Market Drivers

4.2. Market Restraints

4.3. Market Opportunities

4.4. Porter’s Five Force Analysis

4.4.1. Bargaining Power of Suppliers

4.4.2. Bargaining Power of Buyers

4.4.3. Threat of New Entrants

4.4.4. Threat of Substitutes

4.4.5. Competitive Rivalry in the Industry

4.5. Industry Value Chain Analysis


5.1. Introduction

5.2. Pre-trained

5.3. Deep learning 


6.1. Introduction

6.2. Semiconductor 

6.3. Pharmaceutical 

6.4. Automotive 

6.5. Textile 

6.6. Others 


7.1. Introduction

7.2. North America 

7.2.1. USA

7.2.2. Canada

7.2.3. Mexico

7.3. South America 

7.3.1. Brazil

7.3.2. Argentina

7.3.3. Others

7.4. Europe 

7.4.1. UK

7.4.2. Germany

7.4.3. France

7.4.4. Italy

7.4.5. Spain 

7.4.6. Others

7.5. Middle East and Africa 

7.5.1. Saudi Arabia

7.5.2. UAE

7.5.3. Others

7.6. Asia Pacific 

7.6.1. China

7.6.2. Japan

7.6.3. India

7.6.4. South Korea

7.6.5. Australia 

7.6.6. Singapore 

7.6.7. Indonesia 

7.6.8. Others


8.1. Major Players and Strategy Analysis

8.2. Emerging Players and Market Lucrativeness

8.3. Mergers, Acquisitions, Agreements, and Collaborations

8.4. Vendor Competitiveness Matrix


9.1. Intel Corp

9.2. Kitov Systems

9.3. Mitutoyo America Corporation

9.4. Landing AI

9.5. NEC Corporation

9.6. elunic AG

9.7. Robert Bosch GmbH

9.8. deevio GmbH

9.9. craftworks GmbH

9.10. Pleora Technologies Inc

Intel Corp

Kitov Systems

Mitutoyo America Corporation

Landing AI

NEC Corporation

elunic AG

Robert Bosch GmbH

deevio GmbH

craftworks GmbH

Pleora Technologies Inc