AI in Quality Management: Separating Hype from Reality

Artificial intelligence (AI) is making headlines across industries, from retail to healthcare to financial services. In manufacturing, the excitement is just as strong. Research from consulting firm McKinsey shows that manufacturing lighthouses, which are the standard-bearers of manufacturing and supply chains, are betting big on AI, with nearly 60% of top use cases among digital innovators relying on AI.

The results, such as reducing defects, increasing productivity, and streamlining operations at a scale that was previously out of reach, are already here. Or are they?

AI is driving the next wave of industrial transformation for some manufacturers. McKinsey’s research indicates that AI leaders are achieving impressive results, such as a 300% increase in productivity and a 99% reduction in defects. These aren’t futuristic projections; they’re happening today for manufacturers that have invested in connecting their people, processes, and data.

For example, Ford is using AI to reduce engineering cycle times by accelerating tasks like 3D modeling and stress predictions, while GM is using AI to streamline plant floor workflows. GE Aerospace is leveraging AI tools to assist employees in finding the information they need, including solutions to quality issues. Schaeffler Group’s Hamburg plant has deployed an AI assistant to help track ball bearing defects and uncover potential root causes based on production data.

Despite these success stories, the reality is that most manufacturers are still at the starting line with AI. McKinsey’s 2025 State of AI report notes that only 5% of manufacturing functions had adopted AI as of 2024. Boston Consulting Group also reports that nearly three in four companies are struggling to achieve and scale value from AI initiatives.

AI vision detection systems are the most developed use case in manufacturing quality, primarily used for surface inspection and defect detection. Other manufacturers are beginning to use AI to analyze large datasets, hoping to leverage insights from the thousands of variables that can exist within a given product. However, many are still struggling to make that data actionable.

For example, if you have 7,000 data points on a product, how can you relate data point #453 to #2671? Is it truly a causal relationship, or are they simply correlated? Connecting the dots between variables and assessing root causes still represents a big gap for manufacturers, requiring significant process knowledge and context to fill.

The key insight is that AI can’t work its magic without the right inputs and context. That’s why foundational readiness is so critical. If your processes are inconsistent or undocumented, AI won’t save you. Imagine a vision detection system that catches surface weld defects but can’t detect subsurface fusion issues caused by inconsistent operator technique. Without process verification, AI alone will fail to adequately detect quality risks.

Before relying on AI, manufacturers need to standardize and document key processes, verify that those processes are followed through routine checks, and make institutional knowledge accessible and sharable.

AI works best when built on a strong digital foundation, but many manufacturers still face a major gap: the digital divide between systems, information, and people. Operators often rely on tribal or tacit knowledge, siloed paper-based processes, or verbal instructions, while leadership lacks visibility into what’s actually happening on the plant floor.

To bridge that divide, manufacturers need foundational tools that connect people to the right information at the right time, including connected worker tools that deliver real-time guidance and capture tribal or tacit knowledge, ongoing process checks such as layered process audits to verify critical-to-quality steps, on-the-job training workflows that ensure operators understand and apply new standards, and digitized data collection that makes insights accessible across shifts, teams, and locations.

Without this groundwork, AI efforts risk becoming expensive science projects — impressive in theory, underwhelming in execution.

There’s no question that the rise of AI marks the most significant inflection point in the Industry 4.0 revolution to date. But the road to real impact starts with process discipline, cultural alignment, and connected operations. The manufacturers best positioned for success are those that have established a solid foundation for AI to build upon.