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AI Quality Inspection Market - Forecasts from 2025 to 2030

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    Report

  • 140 Pages
  • March 2025
  • Region: Global
  • Knowledge Sourcing Intelligence LLP
  • ID: 6014312
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.

Market Trends:

  • Rising Use of AI-Based Quality Control Software in Manufacturing: The surge in adoption stems from escalating operating costs for manufacturers caused by substandard product quality. For example, Toyota faced a $1.3 billion loss due to production flaws. When defective parts go unnoticed, they are often incorporated into final products, inflating operational expenses and resulting in unsellable goods. This issue is especially common among firms mass-producing items in batches.
  • AI Vision: AI vision enhances quality inspections by offering capabilities akin to rules-based machine vision systems while allowing for iterative improvements over time with human oversight, boosting its effectiveness.
  • North America: As a leader in the global artificial intelligence landscape, North America is heavily investing in broadening the reach and functionality of AI software, including applications in quality control and inspection. Leading software firms in the region are focused on advancing their AI offerings, competing to strengthen their product and service portfolios.
Some of the major players covered in this report include 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:

Key Benefits of this Report:

  • Insightful Analysis: Gain detailed market insights covering major as well as emerging geographical regions, focusing on customer segments, government policies and socio-economic factors, consumer preferences, industry verticals, and other sub-segments.
  • Competitive Landscape: Understand the strategic maneuvers employed by key players globally to understand possible market penetration with the correct strategy.
  • Market Drivers & Future Trends: Explore the dynamic factors and pivotal market trends and how they will shape future market developments.
  • Actionable Recommendations: Utilize the insights to exercise strategic decisions to uncover new business streams and revenues in a dynamic environment.
  • Caters to a Wide Audience: Beneficial and cost-effective for startups, research institutions, consultants, SMEs, and large enterprises.

What can businesses use this report for?

Industry and Market Insights, Opportunity Assessment, Product Demand Forecasting, Market Entry Strategy, Geographical Expansion, Capital Investment Decisions, Regulatory Framework & Implications, New Product Development, Competitive Intelligence.

Report Coverage:

  • Historical data from 2022 to 2024 & forecast data from 2025 to 2030
  • Growth Opportunities, Challenges, Supply Chain Outlook, Regulatory Framework, and Trend Analysis
  • Competitive Positioning, Strategies, and Market Share Analysis
  • Revenue Growth and Forecast Assessment of segments and regions including countries
  • Company Profiling (Strategies, Products, Financial Information, and Key Developments among others)

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

Table of Contents

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

Companies Mentioned

  • 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

Methodology

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Table Information