AI in Quality Management: Why Most Manufacturers Are Still at the Starting Line

AI in Quality Management: Hype vs. Reality

Artificial intelligence (AI) is grabbing attention across industries like retail, healthcare, and financial services. Manufacturing shares this enthusiasm. Research by McKinsey highlights that manufacturing leaders—often called “lighthouses”—are investing heavily in AI, with nearly 60% of their top use cases relying on it. The promised benefits include fewer defects, higher productivity, and streamlined operations at scales previously unreachable. But are these benefits real for most manufacturers?

AI is Driving Industrial Transformation—For Some

McKinsey’s data shows that AI leaders are achieving remarkable results: up to 300% productivity increases and 99% defect reductions. These are not just projections; companies like Ford, GM, GE Aerospace, and Schaeffler Group are already reaping these rewards.

• Ford speeds up engineering by using AI for 3D modeling and stress prediction.
• GM streamlines workflows on the plant floor with AI.
• GE Aerospace uses AI to help employees quickly find information and solve quality issues.
• Schaeffler’s Hamburg plant employs an AI assistant to track bearing defects and find root causes using production data.

What’s Holding Others Back?

Despite these success stories, most manufacturers are just getting started. McKinsey’s 2025 State of AI report reveals only 5% of manufacturing functions had adopted AI by 2024. Moreover, many companies struggle to get value from their AI investments. A Boston Consulting Group survey found nearly 75% of firms face challenges scaling AI benefits.

Currently, AI vision detection systems are the most common quality management tool, mainly used for surface inspection and defect detection. Some are experimenting with AI to analyze vast datasets to uncover insights from thousands of product variables. Yet, many still find it difficult to make that data actionable. For example, connecting data points meaningfully to identify true root causes remains a challenge. AI requires accurate inputs and deep context to deliver value—something many manufacturers still lack.

Why AI Can’t Fix Broken Processes

Here’s a critical point: AI cannot fix inconsistent or undocumented processes. It might only speed up poor decisions. Take a vision detection system that identifies surface weld defects but misses subsurface fusion problems caused by inconsistent operator techniques. Without verifying critical process steps, AI won’t fully detect quality risks.

Before adopting AI, manufacturers need to:

• Standardize and document key processes
• Verify process adherence through routine checks
• Make institutional knowledge accessible and shareable

Digital Foundation First, AI Second

AI delivers its best results when built on a solid digital foundation. Yet, many manufacturers face a digital divide—systems, information, and people remain disconnected. Operators often depend on tribal knowledge, paper-based processes, or verbal instructions, while leadership lacks clear visibility into plant floor realities.

To bridge this gap, foundational tools are essential. These include:

• Connected worker tools offering real-time guidance and capturing tacit knowledge
• Layered process audits (LPAs) to verify critical quality steps
• On-the-job training workflows to ensure understanding and application of standards
• Digitized data collection that makes insights accessible across teams and shifts

Without this groundwork, AI projects risk becoming costly experiments that perform well in theory but disappoint in practice.

AI’s Promise is Real, But Only for the Ready

AI marks a significant moment for Industry 4.0. However, the path to meaningful impact begins with disciplined processes, cultural alignment, and connected operations. AI doesn’t replace solid processes—it enhances them. Manufacturers best positioned to succeed are those who have built a firm foundation for AI adoption.

For managers looking to deepen their AI knowledge and practical skills in manufacturing, exploring targeted AI training can be a smart step. Resources like Complete AI Training’s latest AI courses offer accessible options to build relevant expertise.