In industrial manufacturing, quality failures carry consequences far beyond the factory floor. A single defect can trigger warranty claims, production downtime for customers, costly recalls, and long-term damage to brand reputation. Yet despite significant investments in quality programs, many manufacturers continue to operate reactively, identifying problems only after products have reached production or, worse, the field. According to PTC’s latest e-book, Reduce Costs and Improve Quality with AI, artificial intelligence is changing that equation by enabling manufacturers to prevent quality issues before they occur.

The challenge is significant and persistent. For many industrial organizations, the cost of poor quality represents between 15% and 20% of total sales revenue. Much of that cost can be traced back to decisions made early in product development. In fact, more than 60% of quality issues originate during the engineering and design phase, where seemingly minor choices can have far-reaching effects on manufacturing, serviceability, compliance, and product performance.

Poor quality remains one of manufacturers’ biggest business challenges because its root causes are often embedded upstream in design decisions, distributed across disconnected teams and systems, and discovered too late to resolve cost-effectively. By the time defects surface in production or in the field, organizations may already be facing rework, delayed launches, warranty exposure, and customer disruption.

Traditionally, engineering, manufacturing, and service teams have worked in separate systems with limited visibility into one another’s processes. This fragmentation often creates gaps in traceability, slows decision-making, and makes it difficult to identify the downstream effects of engineering changes. The result is a cycle of rework, delays, and defects that could have been avoided.

AI offers a new approach. Rather than functioning as a stand-alone technology, AI is being embedded directly into product life cycle management (PLM) systems and engineering workflows. When connected to manufacturing, supply chain, and service data, AI can transform large volumes of information into actionable insights that help teams make better decisions earlier in the development process.

AI enables this shift by continuously analyzing connected engineering, manufacturing, supplier, and service data to identify patterns that would be difficult for teams to detect manually. Instead of waiting for defects to appear during production or after deployment, manufacturers can use AI to anticipate where quality risks are likely to emerge, prioritize the most critical issues, and take corrective action earlier in the product life cycle.

PTC has identified three key ways AI is reshaping quality management. First, AI can advise users by providing instant access to relevant product knowledge and recommending design decisions based on factors such as supplier availability, component life cycle status, cost requirements, and compliance considerations. Second, AI can assist teams by automating portions of complex workflows, such as verifying regulatory compliance or checking components against bills of materials during design reviews. Finally, AI can automate entire processes, including engineering change management, helping organizations assess impacts, notify stakeholders, update documentation, and maintain traceability with minimal manual intervention.

These capabilities support a broader industry shift toward preventive quality management. By leveraging AI-driven simulation, predictive analytics, and generative design technologies, manufacturers can validate products virtually before they enter production. Identifying and resolving issues during design is significantly less expensive than correcting them later on the factory floor or after deployment.

However, successfully implementing AI requires more than deploying new software. AI needs structured and trusted data in order to deliver valuable insights. Although most companies have much of their data organized, the data tend to be stored in siloed systems and databases. When data are not linked or contextualized, the value AI delivers is significantly reduced.

To realize the full value of AI within existing systems, data need to be connected throughout the product life cycle, from requirements to architecture (“building blocks”) to actual product designs to manufacturing procedures and through to test cases. With this context, issues can be tracked back to earlier design or manufacturing decisions, and AI will be able to provide meaningful input for future design decisions.

To unlock these insights, manufacturers should take several practical steps.

Build a strong data foundation by establishing reliable, connected product data and a single source of truth across engineering, manufacturing, supply chain, and service.

Use the digital thread to give AI context by linking product requirements, designs, bills of materials, suppliers, production processes, quality records, and service feedback.

Select AI solutions designed for industrial environments with transparency, scalability, security, and intellectual property protection.

Embed AI into existing workflows rather than introducing disconnected applications that create additional complexity for users.

Strengthen collaboration across engineering, quality, manufacturing, and service teams so AI-driven insights can be acted on earlier and more consistently.

Taken together, these steps help manufacturers move beyond isolated AI experiments and build a scalable foundation for predictive quality management. By connecting product data through the digital thread, embedding AI into everyday workflows, and aligning teams around shared insights, manufacturers can make quality decisions earlier, reduce avoidable costs, and improve performance across the full product life cycle.

The downside of overlooking these fundamentals is that AI-supported analysis and reasoning cannot become a driver of measurable quality improvement. When teams don’t trust the recommendations or insights aren’t valuable for decision-making, adoption will stall. That’s why the most successful manufacturers treat AI implementation as both a technology program and a change management effort, with clear ownership, phased deployment, measurable business goals, and close alignment between engineering, quality, manufacturing, service, and IT.

The business effects can be substantial when organizations build on the measurable improvements that PLM enables through earlier quality intervention, including reduced costs associated with poor quality, fewer nonquality deliverables, improved first-pass approvals, and lower rework rates. These outcomes demonstrate how AI can help organizations move from inspection and correction to prediction and prevention.

With data foundations in place, Volvo CE achieved up to a 30% reduction in the cost of poor quality, while NIDEC reported a 40% decrease in costs for nonquality deliverables. Vaillant Group saw a 53% improvement in first-pass sample approval and a 16% reduction in rework.

As industrial products become more sophisticated and global supply chains grow more complex, manufacturers face mounting pressure to deliver high-quality products faster and more efficiently. AI is emerging as a powerful enabler of that goal—not by replacing human expertise, but by augmenting it with better visibility, stronger collaboration, and faster access to critical insights.

For manufacturers looking to improve quality while controlling costs, the path forward is to harness connected product data, identify risks earlier, and build intelligence directly into the product life cycle. By applying AI across the digital thread, organizations can move from reactive quality management to a more predictive, preventive approach that reduces rework, protects customer trust, and turns quality into a strategic driver of efficiency, resilience, and competitive advantage.

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Manufacturers should begin by assessing how well their product data are connected across the digital thread and where quality decisions are still being made reactively. From there, they can prioritize targeted AI use cases, embed intelligence into existing PLM and engineering workflows, and scale proven approaches throughout manufacturing, supply chain, and service operations.