It’s not a matter of if, it’s a matter of when. While the buzz around AI continues to grow, many quality professionals wonder about AI use cases that they can leverage today.  

Quality professionals often approach change with skepticism. When it comes to artificial intelligence, many of them still see AI as tomorrow’s tech, while others treat it like a buzzword used to create marketing hype. The hesitation is somewhat understandable since we’re navigating the AI revolution with questions about its practical applications, genuine utility, and implementation. However, this cautious stance risks missing the transformative impact AI is already having across the quality landscape. 

Forward-thinking organizations are already deploying AI. We’re witnessing initial applications that streamline quality operations, including conversational interfaces that allow operators to chat with their quality management system (QMS) using AI. And this is just the beginning. The impact of AI will continue to expand, transforming from helpful digital assistant to a sophisticated system capable of guiding more complex decision-making processes.  

Using AI is incredibly exciting as we unlock new possibilities and dramatically improve efficiency in ways that were previously not possible. It’s important to remember that to get the most value from AI, you don’t need to reinvent the wheel. Often the best approach is to adapt existing solutions to your specific needs. 

From hype to high impact 

About 25-35% of companies are already using AI, mainly for text generation. For quality professionals, text generation might be less relevant. Still the underlying technology highlights major opportunities for other technical applications that could amplify capabilities and turn reactive quality management into proactive. Think about it. How proactive would your quality management be if you’d be able to use AI predictive analytics and identify early signs of process drift before they lead to failures? Or if you could optimize your risk-based prioritization to automatically focus resources on what is most critical? Or even if you could use AI to determine optimal sample sizes and inspection frequencies in order to reduce over-inspection? The value AI can bring is considerable even if most organizations have yet to discover it.  

The gap between early adopters and those waiting for “proven” applications is widening rapidly, creating a competitive divide that will become increasingly difficult to bridge as AI systems become more sophisticated and trained on industry-specific quality data. And then, the question isn’t whether AI will transform quality management, but how quickly you’ll harness its potential to create competitive advantage while others are still sorting through the hype.  

Use cases and lessons learned 

Beyond the popularity of conversational AI tools like ChatGPT, the greater potential lies in AI’s application for data processing, advanced analytics, and seamless instrument integration within quality systems. As mentioned before, it’s not about reinventing the wheel but giving the bot a problem and allowing it to solve this for you rather than putting time into finding the solution yourself.  

For instance, AI’s ability to extract meaningful information from unstructured content without predefined mapping allows exported analyzer data - in formats like CSV, JSON, or XML - to be automatically mapped to the correct data model in your QMS, eliminating countless hours previously spent on setup and integration tasks.  

Another promising use case is AI for Certificates of Analysis (CoA). More specifically, AI can be used to automatically scan incoming quality certificates, extract relevant quality data and trigger alerts when specifications are not met. This real-time monitoring and proactive approach can significantly help teams improve quality assurance. 

AI can also be of great help when it comes to Excel sheets. Most quality professionals drown in routine administrative tasks that are both mundane and time-consuming. With AI however, they could quickly extract the information that they need from Excel sheets, but even screenshots, handwritten notes and whiteboard diagrams.  

Chatting with your quality management system is also something that could be done with AI. Since it’s a tool that can be context aware, users can simply ask questions about quality data rather than navigating complex databases. This intuitive interaction with your QMS not only accelerates access to critical information but also democratizes data analysis across the organization, allowing even non-technical team members to extract meaningful insights without specialized training.  

All these use cases support teams and redefine the current way of working. They nurture and improve an organization’s Culture of Quality by driving awareness, involvement and ownership. This, in turn, can have a significant impact on the Cost of Quality. The more efficient way of working using AI can reduce failures, while optimizing prevention and appraisal activities.  

There are of course many more lessons to be learned. When experimenting with AI, it’s important to start with the low-hanging fruit. In quality management, which would be leveraging AI’s ability to read and extract unstructured data from documents and thus eliminating the burden of manual work.  

Trust and other legitimate concerns 

As AI adoption accelerates, the setup, implementation, and maintenance of QMS systems will become easier and faster. But that doesn’t mean that it’ll only be smooth sailing. Using AI comes with several challenges. On the one hand, it’s the issue of trust and accuracy. All the information and data need to be verified. AI will need to be trained to provide the best results and only over time you can build confidence in the results it’s providing. On the other hand, training AI requires careful legal and privacy considerations – ownership, GDPR, compliance and more.  

Conclusion 

AI in quality management isn’t about flashy demos or sweeping disruption. It’s about simplifying workflows, improving data quality, and freeing up time for more strategic work. By embedding AI into your QMS platform, you’re empowering your team with real-time insights, smarter tools, and more time for what really matters: quality improvement. Not experimenting will have companies and quality professionals miss out on an opportunity to add real value, and help teams work smarter - with AI as an assistant and not a disruptor.  

As mentioned above, there are many ways in which AI could be leveraged. Here are a few use cases that can already be helpful today: 

• Let AI extract data from your documents 
• Suggest improvements in your audit planning 
• Assist with basic reporting and root cause analysis 
• Schedule recurring events 
• Use AI to visualize your data in charts and graphs 

Yes, with these you are just dipping your toes in an ocean of possibilities. Yet, in the beginning, it’s important to track what works and learn from what doesn’t. It’s important to adapt existing solutions to your organization’s needs rather than getting stuck finding new ways or overcomplicating things. 

Experimenting with AI and finding the ways it can support your workflows will also build confidence. Over time, you’ll move from automation to augmentation and eventually to strategic enhancement. 

The time of smart quality management is here, and AI can already act as an assistant, not a disruptor—helping teams work more effectively while maintaining ownership and control.