Impact of AI Assisted Quality Inspection on Manufacturing: A Socio-Economic Analysis

In the manufacturing industry, the average tenure of factory workers is decreasing, and tribal knowledge of machine-method interaction is diminishing, leading to more errors and affecting overall product quality. Traditionally, quality checks depend on manual visual inspections by operators or inspectors. While manual inspections are valuable, they can be time-consuming, error-prone due to operator variation, labor-intensive, vulnerable to human fatigue, inconsistent training, and subjective judgment. These issues can allow defects to slip into later production stages or reach customers, causing scrap, rework, product returns, downtime, and loss of customer trust. Overall, this process can be costly and time-consuming. Therefore, AI-assisted quality inspection is increasingly adopted in manufacturing to improve process efficiency by enhancing defect detection, analysis, and prevention before products reach customers. AI-assisted inspection systems address these challenges by utilizing computer vision, machine learning, sensors, and data analytics to detect defects more quickly and, often, to inspect 100% of products. Additionally, these AI-based methods raise important social issues, such as workforce development and job security. While AI can aid inspection, it may also lead to concerns among workers about displacement or reduced control over quality decisions. Therefore, implementing AI inspection systems requires training, reskilling, and clear communication about how humans and AI share responsibility. This work presents a systematic literature review to identify existing findings, AI implementation challenges, and benefits. This paper examines the economic and social effects of AI-enabled inspection in manufacturing facilities. This study combines published case studies with new survey data collected from manufacturers worldwide to evaluate both financial and social effects. The outcomes of the work include the impact of AI-assisted inspection on costs, productivity, scrap reduction, implementation costs, job security concerns, etc. More importantly, it explores how AI inspection impacts production costs, quality, labor needs, operator responsibilities, job security, and responsibility transfer within modern manufacturing systems.