Computer vision (CV) systems driven by artificial intelligence (AI) are gradually replacing manual, real-time, and data-driven processes in industrial quality inspection, enabling automated decision-making based on visual data. Conventional inspection procedures are usually limited in terms of scalability, human-related errors, and high operational costs, which drives the increasing reliance on smart vision-based technologies. A practical, practice-oriented review of AI-based computer vision systems for industrial quality control is provided in this paper, with emphasis on real-world deployment issues and performance aspects. Two representative industrial case studies are examined. The first investigates the use of real-time extrusion monitoring in robotic building construction, where geometric deviations, surface defects, and process inconsistencies are detected during material deposition using deep learning-based vision models. The second case study focuses on automated inspection of bolts and screws in manufacturing lines, addressing presence detection, orientation recognition, and defect classification under high-speed production conditions. In both cases, widely adopted AI techniques, including convolutional neural networks, image processing pipelines, and edge-computing hardware, are discussed and compared. The analysis shows that AI-enabled computer vision systems significantly outperform traditional rule-based or manual solutions in terms of inspection accuracy, consistency, and throughput. Nevertheless, challenges related to dataset quality, model generalization, lighting variability, and real-time computational constraints remain critical in industrial environments. In conclusion, AI-based computer vision plays a central enabling role in intelligent quality inspection within the context of Industry 5.0. Future research should focus on adaptive model capabilities, tighter integration with cyber-physical systems, and scalable deployment strategies to achieve reliable and autonomous inspection across diverse industrial sectors.