How AI is reinventing inspection

AI-powered quality control combines high-resolution cameras, sensors and deep learning algorithms to detect product defects in real time. These systems can outperform traditional inspection methods, spotting flaws invisible to the human eye and learning over time to improve their accuracy.

The availability of well-structured data sets within the quality function generally means quality control in manufacturing is an optimal application for training AI models. The leap in speed, consistency and traceability these systems provide is unprecedented, enabling smarter decisions and fewer quality failures.

Within the automotive sector, Audi is renowned for its AI-powered quality control system, which uses high-resolution cameras and machine learning to inspect cars for the tiniest flaws, while BMW uses AI and computer vision to automate real-time fault detection and visual inspection of components, reducing inspection times by two-thirds through the use of synthetic data and no-code AI tools.

Smart quality control is deployed elsewhere too, of course: in the food and beverage industry, it can be used to check consistency, size, and colour of goods, scanning thousands of items per minute, identifying even the tiniest imperfections that might compromise quality. In fruit sorting, algorithms analyse images of produce on conveyor belts, assessing size, colour and defects in real time. The result is faster sorting and greater consistency, ensuring only top-quality food reaches the consumer.

Adaptability of AI in quality control

Beyond speed and accuracy, one of the key advantages of AI and machine learning in quality control is adaptability. Legacy rule-based systems require extensive reprogramming to handle new products or changes in specification, but  AI can be trained on new data, allowing it to adapt to evolving manufacturing requirements with minimal downtime.

Machine learning models improve as they are exposed to more data, meaning that inspection systems become more precise with use, reducing false positives and catching subtle, previously undetectable issues. These systems can also uncover patterns in production errors—pinpointing recurring problems at specific stages of the manufacturing process and enabling proactive interventions before larger faults arise.

AI-powered quality control also supports compliance by creating a rich digital record of inspections, complete with annotated images and real-time reports. This traceability is especially valuable in highly-regulated sectors like medical devices, aerospace and pharmaceuticals, where rigorous documentation is essential.

Consequences for hiring

The rise of smart quality control systems have resulted in a shift in demand for skills.  Manual inspection roles are being replaced by technical ones, with a sharp rise in demand for professionals who can train machine learning models, calibrate optical systems and interpret the data outputs. We're seeing a rise in demand for machine vision engineers, manufacturing data analysts, process engineers familiar with automation and maintenance technicians trained on AI-enabled equipment.

At the same time, traditional quality managers are now expected to be digitally fluent, collaborating with IT, data teams and engineers to drive process improvements.

Automated quality control offers significant benefits for manufacturers from improved consistency to reduced wastage and scrap rates and stronger traceability. But it’s not without its challenges. Many SMEs still find it difficult to justify the cost and complexity of AI adoption—especially where tech talent is in short supply.

And for those that do invest, success still depends on people. OEMs today are a collaboration between AI and people, creating a massive requirement for employees who can work with intelligent systems, and for hybrid roles that blend digital, mechanical and data skills. In most cases, AI won’t replace people, but will empower people to shift from detection to prevention, and from repetitive tasks to strategic problem-solving.

Computer vision and AI are transforming the world of quality control from static checkpoints to dynamic, data-driven processes. We believe there’s exciting potential for talent. Individuals who understand engineering fundamentals, automation systems and data analytics will be at the forefront of this shift. And as more manufacturers adopt these technologies, it’s clear the future of quality won’t just be smart, it will be human, too.

What caught our eye this month

Epoch Biodesign, a London-based start-up born from a senior school project, has secured $18.3 million in funding to scale its AI-engineered enzymes that can break down synthetic fabrics in hours. The company is set to process 150 tonnes of textile waste per year at its new UK facility.

BYD has unveiled a breakthrough in EV tech: its new superfast charging technology that adds up to 400km of range in five minutes.  

Toyota plans to build battery electric vehicles in the UK and maintain all eight of its European factories as part of its gradual transition to EVs, with two new electric models announced and three more promised by 2026.

Toyota has confirmed plans to build battery electric vehicles in the UK while retaining all eight of its European factories, signalling a steady shift towards electrification.