AI Quality Inspection Market - Forecasts from 2024 to 2029

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

AI quality inspection market 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 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.

AI quality inspection market geographical outlook

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.

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Report Coverage:

• Historical data & forecasts from 2022 to 2029
• Growth Opportunities, Challenges, Supply Chain Outlook, Regulatory Framework, Customer Behaviour, and Trend Analysis
• Competitive Positioning, Strategies, and Market Share Analysis
• Revenue Growth and Forecast Assessment of segments and regions including countries
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AI Quality Inspection Market is segmented and analyzed as follows:

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