AI Quality Inspection Market Size, Share, Opportunities, And Trends By Type (Pre-trained, Deep Learning), By Deployment (On-Premises, Cloud-Based, Hybrid), By Component (Hardware, Software, Services), By End-Users (Semiconductor, Pharmaceutical, Automotive, Textile, Others), And By Geography - Forecasts From 2025 To 2030

AI Quality Inspection Market Size:

The AI Quality Inspection Market, valued at US$490.485 million in 2030 from US$231.586 million in 2025, is projected to grow at a CAGR of 16.19%. 

When 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, due to their precision and time-saving capabilities, AI-powered applications that make quality checks are becoming more common in the semiconductor industry, as well as in medicine, clothing production, car-making industries, and other sectors.

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, the AI-based quality control market is expected to reach a larger market size in the forecast period.

AI Quality Inspection Market Overview & Scope:

The AI Quality Inspection Market is segmented by:

• Type: 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 critical requirements of applications involving visual inspection at the highest level of precision.
• Deployment: Cloud-based AI Quality Inspection services have been growing significantly as the deployment is cheaper and needs less maintenance for small and medium enterprises.
• Component: Software AI Quality Inspection services have been showing notable growth as manual quality control offered by the human eye can sometimes fail to detect defects 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.
• End-User: The expansion of the automotive sector has been significant in the last few years. This growth is due to the increasing demand for electric vehicles. Further, the semiconductor and electronics industry is propelled by the increased demand for electronic gadgets worldwide. 
• Region: By geography, the AI quality inspection market is segmented into the Americas, Europe, the Middle East and Africa, and Asia Pacific. 

Top Trends Shaping the AI Quality Inspection Market:

AI Vision

The benefit of AI vision in quality inspection is that it provides benefits similar to rules-based machine vision systems, and it can also be iterated over time to improve performance with human supervision.

AI Quality Inspection Market: Growth Drivers vs. Challenges:

Drivers:

• 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 due to manufacturing defects. When a damaged component goes undetected, it is often used in 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.
• Growing 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.

Challenges:

• 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 organizations with small budgets. Due to their substantial cost, the early introduction of high-powered cameras, magnetic field detectors, and other processing devices used in quality inspection systems is crucial.

AI Quality Inspection Market Regional Analysis:

North America: 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.

AI Quality Inspection Market: Competitive Landscape:

The market is fragmented, with many notable players, including 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, and Crayon AS, among others:

A few strategic developments related to the market:

• New Technology: In January 2024, many new features were added to the latest version of Visual Applets to enable optimal 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.

AI Quality Inspection Market Scope:

Report Metric	Details
AI Quality Inspection Market Size in 2025	US$231.586 million
AI Quality Inspection Market Size in 2030	US$490.485 million
Growth Rate	CAGR of 16.19%
Study Period	2020 to 2030
Historical Data	2020 to 2023
Base Year	2024
Forecast Period	2025 – 2030
Forecast Unit (Value)	USD Million
Segmentation	Type Deployment Component 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

 

The AI Quality Inspection Market is analyzed into the following segments:

• By Type
  • Pre-trained
  • Deep Learning
• By Deployment
  • On-Premises
  • Cloud-Based
  • Hybrid
• By Component
  • Hardware
  • Software
  • Services
• By End-Users
  • Semiconductor
  • Pharmaceutical
  • Automotive
  • Textile
  • Others
• By Region
  • 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. EXECUTIVE SUMMARY 

2. MARKET SNAPSHOT

2.1. Market Overview

2.2. Market Definition

2.3. Scope of the Study

2.4. Market Segmentation

3. BUSINESS LANDSCAPE 

3.1. Market Drivers

3.2. Market Restraints

3.3. Market Opportunities 

3.4. Porter’s Five Forces Analysis

3.5. Industry Value Chain Analysis

3.6. Policies and Regulations 

3.7. Strategic Recommendations 

4. TECHNOLOGICAL OUTLOOK 

5. AI QUALITY INSPECTION MARKET BY TYPE

5.1. Introduction

5.2. Pre-trained

5.3. Deep learning

6. AI QUALITY INSPECTION MARKET BY DEPLOYMENT

6.1. Introduction

6.2. On-Premises

6.3. Cloud-Based

6.4. Hybrid

7. AI QUALITY INSPECTION MARKET BY COMPONENT

7.1. Introduction

7.2. Hardware

7.3. Software

7.4. Services

8. AI QUALITY INSPECTION MARKET BY END-USERS

8.1. Introduction

8.2. Semiconductor

8.3. Pharmaceutical

8.4. Automotive

8.5. Textile

8.6. Others

9. AI QUALITY INSPECTION MARKET BY GEOGRAPHY

9.1. Introduction

9.2. North America

9.2.1. By Type

9.2.2. By Deployment

9.2.3. By Component

9.2.4. By End-Users

9.2.5. By Country

9.2.5.1. USA

9.2.5.2. Canada

9.2.5.3. Mexico

9.3. South America

9.3.1. By Type

9.3.2. By Deployment

9.3.3. By Component

9.3.4. By End-Users

9.3.5. By Country

9.3.5.1. Brazil

9.3.5.2. Argentina

9.3.5.3. Others

9.4. Europe

9.4.1. By Type

9.4.2. By Deployment

9.4.3. By Component

9.4.4. By End-Users

9.4.5. By Country

9.4.5.1. United Kingdom

9.4.5.2. Germany

9.4.5.3. France

9.4.5.4. Italy

9.4.5.5. Spain

9.4.5.6. Others

9.5. Middle East and Africa

9.5.1. By Type

9.5.2. By Deployment

9.5.3. By Component

9.5.4. By End-Users

9.5.5. By Country

9.5.5.1. Saudi Arabia

9.5.5.2. UAE

9.5.5.3. Others

9.6. Asia Pacific

9.6.1. By Type

9.6.2. By Deployment

9.6.3. By Component

9.6.4. By End-Users

9.6.5. By Country

9.6.5.1. China

9.6.5.2. Japan

9.6.5.3. India

9.6.5.4. South Korea

9.6.5.5. Australia

9.6.5.6. Singapore

9.6.5.7. Indonesia

9.6.5.8. Others

10. COMPETITIVE ENVIRONMENT AND ANALYSIS

10.1. Major Players and Strategy Analysis

10.2. Market Share Analysis

10.3. Mergers, Acquisitions, Agreements, and Collaborations

10.4. Competitive Dashboard

11. COMPANY PROFILES

11.1. Intel Corp. 

11.2. Kitov Systems 

11.3. Mitutoyo America Corporation 

11.4. Landing AI 

11.5. NEC Corporation 

11.6. Robert Bosch GmbH 

11.7. Wenglor Deevio GmbH 

11.8. Craftworks GmbH 

11.9. Pleora Technologies Inc. 

11.10. IBM Corporation 

11.11. Qualitas Technologies 

11.12. Lincode 

11.13. Crayon AS 

12. APPENDIX

12.1. Currency 

12.2. Assumptions

12.3. Base and Forecast Years Timeline

12.4. Key benefits for the stakeholders

12.5. Research Methodology 

12.6. Abbreviations 

LIST OF FIGURES

LIST OF TABLES

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