Manufacturers Are All In on AI – But What Role in Quality?
The global manufacturing industry is undergoing a profound transformation. Confronted with persistent labor shortages, complex supply chains and rising tariffs, manufacturers are turning to artificial intelligence (AI) and automation not just as tools of convenience, but as essential ingredients for future success.
In fact, according to the 2025 ETQ Pulse of Quality in Manufacturing survey – which polled 752 quality leaders and project managers across manufacturing firms in the U.K., U.S. and Germany – an overwhelming 99 percent of respondents have either already adopted AI or plan to do so. Specifically, 33 percent are using AI today, 49 percent plan to deploy it within the next two years, and another 17 percent expect to adopt it further down the line.
This surge in AI adoption spans nearly every manufacturing sector – from electronics and medical devices to chemicals, automotive and aerospace/defense – ushering in a new era of smart manufacturing. By embedding AI throughout their operations, from supplier management and internal inspections to predictive analytics and final product validation, manufacturers are gaining real-time quality insights, reducing variability and ensuring quality is built into every stage of production. These AI-driven systems are replacing error-prone manual processes with data-driven automation, enabling faster, more consistent decisions and elevating quality outcomes at every step of the production lifecycle.
AI in Action
A leading Tier-1 transmission supplier reduced warranty costs by 30% by applying AI-driven predictive analytics to production and end-of-line (EOL) test data. Each EOL test involved more than 100 steps covering various performance-based quality assessments. Previously, the supplier’s Statistical Process Control (SPC) system analyzed just ten percent of this data, requiring engineers to manually review signals when potential failures were detected – an inefficient and error-prone process. By using machine learning to highlight anomalies, the team reduced the number of signals requiring manual root-cause analysis by 99.8 percent – enabling earlier intervention and preventing defective units from reaching customers.
Product defect detection is just one example of how manufacturers are applying AI to improve quality. According to the ETQ Pulse of Quality in Manufacturing survey, manufacturers are also using, or planning to use, AI in the following ways:
Automate Core Processes and Document: Nearly half of respondents indicated they are either already using AI, or plan to use it, to automate core quality processes (47%) and document-related tasks (46%). From streamlining compliance reporting to managing supplier audits, AI is helping reduce manual workload and enabling faster, more consistent decision-making across the quality function.
Spot Defects on the Factory Floor: One of the most widely adopted use cases, cited by 45% of respondents, involves using AI-powered machine vision systems to spot defects on the factory floor. These systems operate continuously, identify issues too subtle for the human eye, and adapt over time to new defect types. As a result, manufacturers are reducing scrap, rework and costly product recalls.
Predict Quality Trends and Prevent Issues Before they Occur. Thirty-eight percent of respondents said they are using or plan to use AI to analyze real-time production data and identify early warning signs of potential quality problems—such as process drift or equipment degradation. This predictive capability allows teams to intervene sooner, shifting from reactive quality control to proactive quality assurance.
AI continues to evolve rapidly, but several foundational technologies are already driving meaningful improvements in manufacturing quality. Below are five core AI approaches that manufacturers are leveraging to modernize their operations and elevate quality performance.
Machine Vision and Deep Learning
AI-powered machine vision systems use deep learning algorithms to perform real-time visual inspections—identifying issues such as micro-cracks in metal parts or misaligned components on an assembly line. These systems improve over time, learning from each inspection and continuously refining their accuracy to reduce defects and enhance inspection consistency.
Natural Language Processing (NLP)
A branch of AI focused on understanding human language, NLP is being used to automate the processing of quality documentation, supplier audits and compliance reports. By extracting insights from large volumes of unstructured text, NLP-powered tools reduce the administrative burden on quality teams and help ensure consistent reporting and ultimately compliance.
Predictive Analytics and Machine Learning
Machine learning–based predictive models analyze historical and real-time data to forecast quality issues before they occur. From anticipating equipment failures to detecting process drift and product deviations, these models help manufacturers take preventive action—reducing downtime, avoiding costly defects and improving overall process reliability.
Generative AI
Powered by large language models (LLMs), generative AI is gaining traction in manufacturing for tasks such as drafting standard operating procedures (SOPs) or developing training materials. These capabilities help accelerate documentation and improve knowledge sharing. By standardizing how quality information is created and communicated, generative AI helps drive consistency in execution and shortens the time it takes to train teams or respond to change. According to the ETQ Pulse of Quality Manufacturing survey, 38 percent of respondents said they plan on using generative AI to improve their quality operations over the next three years.
Digital Twins and Simulation
According to BCC Research, the market for digital twins is expected to grow from $18.2 billion in 2024 to $119.3 billion by the end of 2029. AI-powered digital twins, virtual replicas of physical systems, are enabling manufacturers to simulate processes and predict how changes will impact quality before they are made in the real world. This allows for safer experimentation and rapid iteration.
Powering a Quality Management Rebirth Enterprise-Wide
While AI is transforming manufacturing quality, it’s not a cure-all, nor a replacement for human expertise. Its true value lies in augmenting human decision-making, not displacing it. To unlock AI’s full potential, manufacturers must invest in workforce training and change management, ensuring that teams are equipped to collaborate effectively with AI systems.
Equally important is building a culture where quality is everyone’s responsibility. Technology may provide the tools, but it’s people who will always make decisions and drive improvement.
From reducing product recalls and ensuring better document management, to enabling a safer plant floor, AI will continue to grow and evolve and most certainly help to improve quality operations in manufacturing. Manufacturers have spoken and the data supports them. AI is fast becoming a fact of life, empowering not only a smarter factory, but a quality-driven organization – across all facets.
Author: Diptesh Shah is Executive Director of Product Management for AI/ML at ETQ. He leads the strategy and vision for embedding AI, machine learning and advanced analytics into the company’s quality management platform and product portfolio to deliver intelligent capabilities that help manufacturing organizations worldwide improve quality and compliance outcomes.
For more information: www.etq.com
