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 2024 To 2029

The AI Quality Inspection market is estimated to grow at a CAGR of 28.40%, attaining US$438.705 billion by 2029 from US$179.806 billion in 2024.

When it comes to using software-driven artificial intelligence and vision technologies, AI quality inspection helps detect and process inconsistencies in products, including semiconductors, pharmaceuticals, textiles, and automotive manufacturing. Hence, AI-owned applications that make quality checks are becoming more common in the semiconductor industry as well as in medicine, clothing production, car-making industries, and others because of their precision and ability to save time.

The AI quality inspection software can be manufactured either based on the machine learning model or as a pre-trained software service. The precision offered by AI-powered quality control techniques is a significant advantage over manual quality control, making it the preferred choice for leading manufacturing companies worldwide.

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 AI-based quality control market will reach a larger market size in the forecast period.

What are the drivers of the AI quality inspection market?

• Increasing adoption of AI-based quality control software in the manufacturing sector.

The growth can be attributed to increased operating costs for manufacturing companies due to the production of poor-quality products. For instance, Toyota Company incurred a 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 not being sold in the market. Such cases are prevalent in companies that engage in mass production of goods in batches.

The manual quality control offered by the human eye can sometimes fail to detect such failures in large batches. To overcome this limitation, leading manufacturing companies worldwide are actively investing in AI-based quality inspection software to identify defective goods earlier and prevent additional expenses.

• Increasing use of deep learning models.

Deep learning models are a subfield within artificial intelligence crafted to behave like the complex neural networks found in the human brain. These models can identify intricate patterns and features within images since they have been trained heavily upon large datasets. In visual inspection systems, deep learning models are utilized to accurately detect abnormalities, defects, or specific features in images or videos.

On the other hand, pre-trained models are AI models that have been trained before on general tasks such as object recognition or image identification from sizeable datasets. These models are then adapted or changed to meet the dire needs of applications involving visual inspection at the finest level imaginable. By applying knowledge gained during earlier learning periods, pre-trained models enable fast implementation as well as reduced training times.

Major challenges hindering the AI quality inspection market:

• High initial investment.

AI visual inspection systems require a lot of money at once for hardware, software, and training. This makes it hard for small and medium enterprises (SMEs) or other organizations with small budgets. Due to their substantial cost, the early introduction of high-powered cameras, magnetic field detectors, and other processing devices used to control stage performances is crucial.

Moreover, purchasing and customizing software to meet unique inspection requirements increases the cost. Training staff members to properly operate and maintain these complex systems requires specialized knowledge and resources, increasing costs. The cost of these investments may be too high for SMEs and other businesses with limited resources, discouraging them from utilizing AI visual inspection technologies in the first place.

Geographical outlook of the AI quality inspection market

• North America is forecasted to hold a major share of the AI Quality Inspection Market.

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 has introduced its latest AI quality inspection product, which implements a federated learning model. Apart from these established companies, several startups in the USA are dedicating their product line to innovate 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, the North American AI quality inspection market will likely expand over the forecast period.

Major players and products in the AI quality inspection market:

• Mitutoyo America Corporation is an American-technology-based company that offers solutions for multiple industries, like aerospace, defense, automotive, energy, and general manufacturing. The company provides a wide range of products and solutions, including coordinate measuring machines, form measurement, linear encoder & DRO systems, sensor systems, test equipment, and custom solutions, among others. In the global AI quality inspection market, the company offers AI Inspect, which offers multiple features like defect marking, training setup, result & threshold, and classification of images.
• KITOV Systems Ltd. is among the leading fully automated visual inspection solution providers, offering products and solutions for multiple markets, like aerospace, automotive, medical, and electronics, among many other markets. The company’s visual inspection products include Core Plus and K-Box. Core Plus is a type of ready-to-use visual inspection solution that integrates automated lighting control, deep learning, 2D/3D imaging, and intelligent robotic planning with the traditional machine vision technique. Similarly, K-Box is also a ready-t-use visual inspection controller, which can easily be connected to any robot virtually.

Key developments in the AI quality inspection market:

• In January 2024, many new features were added to the latest version of Visual Applets to enable the best FPGA graphical programming for frame grabbers such as CoaXPress and Camera Link. The integrated development environment for real-time FPGA image processing applications is called Visual Applets. It makes it possible to use data flow models on a graphical user interface to program FPGAs
• 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 AI-assisted quality control and emission control technology.

AI Quality Inspection Market Scope:

Report Metric	Details
AI Quality Inspection Market Size in 2024	US$179.806 billion
AI Quality Inspection Market Size in 2029	US$438.705 billion
Growth Rate	CAGR of 28.40%
Study Period	2019 to 2029
Historical Data	2019 to 2022
Base Year	2024
Forecast Period	2024 – 2029
Forecast Unit (Value)	USD Billion
Segmentation	Type End-Users Geography
Geographical Segmentation	North America, South America, Europe, Middle East and Africa, Asia Pacific
List of Major Companies in AI Quality Inspection Market	Intel Corp Kitov Systems Mitutoyo America Corporation Landing AI NEC Corporation
Customization Scope	Free report customization with purchase

 

AI Quality Inspection Market is analyzed into the following 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

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1. INTRODUCTION

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

1.8. Key benefits for the stakeholders

2. RESEARCH METHODOLOGY  

2.1. Research Design

2.2. Research Process

3. EXECUTIVE SUMMARY

3.1. Key Findings

4. MARKET DYNAMICS

4.1. Market Drivers

4.2. Market Restraints

4.3. Porter’s Five Forces Analysis

4.3.1. Bargaining Power of Suppliers

4.3.2. Bargaining Power of Buyers

4.3.3. The Threat of New Entrants

4.3.4. Threat of Substitutes

4.3.5. Competitive Rivalry in the Industry

4.4. Industry Value Chain Analysis

4.5. Analyst View

5. AI QUALITY INSPECTION MARKET BY TYPE

5.1. Introduction

5.2. Pre-trained

5.3. Deep learning

6. AI QUALITY INSPECTION MARKET BY END-USER

6.1. Introduction

6.2. Semiconductor

6.3. Pharmaceutical

6.4. Automotive

6.5. Textile

6.6. Others

7. AI QUALITY INSPECTION MARKET BY GEOGRAPHY

7.1. Introduction

7.1. North America

7.1.1. By Type

7.1.2. By End-User

7.1.3. By Country

7.1.3.1. United States

7.1.3.2. Canada

7.1.3.3. Others

7.2. South America

7.2.1. By Type

7.2.2. By End-User

7.2.3. By Country

7.2.3.1. Brazil

7.2.3.2. Argentina

7.2.3.3. Others

7.3. Europe

7.3.1. By Type

7.3.2. By End-User

7.3.3. By Country

7.3.3.1. United Kingdom

7.3.3.2. Germany

7.3.3.3. France

7.3.3.4. Italy

7.3.3.5. Spain

7.3.3.6. Others

7.4. Middle East and Africa

7.4.1. By Type

7.4.2. By End-User

7.4.3. By Country

7.4.3.1. Saudi Arabia

7.4.3.2. UAE

7.4.3.3. Israel

7.4.3.4. Others

7.5. Asia Pacific

7.5.1. By Type

7.5.2. By End-User

7.5.3. By Country

7.5.3.1. China

7.5.3.2. Japan

7.5.3.3. India

7.5.3.4. South Korea

7.5.3.5. Australia

7.5.3.6. Singapore

7.5.3.7. Indonesia

7.5.3.8. Others

8. COMPETITIVE ENVIRONMENT AND ANALYSIS

8.1. Major Players and Strategy Analysis

8.2. Market Share Analysis

8.3. Mergers, Acquisitions, Agreements, and Collaborations

8.4. Competitive Dashboard

9. COMPANY PROFILES

9.1. Intel Corp

9.2. Kitov Systems

9.3. Mitutoyo America Corporation

9.4. Landing AI

9.5. NEC Corporation

9.6. Robert Bosch GmbH

9.7. Wenglor Deevio GmbH

9.8. Craftworks GmbH

9.9. Pleora Technologies Inc

9.10. IBM Corporation

9.11. Qualitas Technologies

9.12. Lincode

9.13. Crayon AS

Intel Corp 

Kitov Systems 

Mitutoyo America Corporation 

Landing AI 

NEC Corporation 

Robert Bosch GmbH 

Wenglor Deevio GmbH 

Craftworks GmbH 

Pleora Technologies Inc 

IBM Corporation 

Qualitas Technologies 

Lincode 

Crayon AS