By Barbora Milena Matulová, Project Manager and Advanced Surgery AI Champion
When people talk about artificial intelligence in manufacturing, the conversation often focuses on future possibilities. I had the opportunity to see something different: how AI can solve a practical problem today.
Our team was facing a quality challenge related to product labeling. A series of customer complaints revealed that small manual data-entry mistakes were creating non-conformities. In one case, a mistyped lot number became part of a larger corrective and preventive action process.
We knew the process needed to change, but the challenge was timing. A long-term systems solution was already planned, but implementation was months away. The business needed an effective answer much sooner.
Looking at the Problem Differently
My manager, Petr Šimek, Team-Lead, Automation & Project Management, recognized that the underlying issue was manual retyping.
Critical information already existed in a barcode. Employees were simply re-entering parts of that information into separate fields. Every manual touchpoint created an opportunity for error.
The question became straightforward: How could we remove the typing altogether? The answer was not to replace people. It was to remove a repetitive task that added no value.
Using Claude, I built a lightweight application that reads information from an existing barcode, separates the data into individual elements, and generates barcode outputs that operators can scan directly into the appropriate fields.
From Idea to Reality in Days
The most surprising part of the project was the speed. After discussing the concept on a Friday, I had a working version available by Monday morning. The application was immediately tested and adopted on the shop floor.
That speed challenged some common assumptions about technology development. Traditionally, solving this type of problem might require extensive programming, new software development, IT resources, or system integrations. AI enabled us to quickly prototype, test, and refine a practical solution while staying focused on the user experience.
The Human Side of AI
What I find most exciting about AI is that it lowers the barrier between an idea and a solution.
I am not a software engineer. My background is not in programming. Like many professionals, I started by learning the basics and experimenting with smaller projects. The more I learned, the more I realized that successful AI adoption is often less about technical expertise and more about understanding problems clearly.
If you can describe a process, identify a pain point, and think critically about outcomes, you can begin building solutions.
Innovation Starts with Curiosity
The project has already sparked new ideas from the people using it every day. As soon as operators saw the application working, they started suggesting additional opportunities for automation and improvement.
That reaction reinforces an important lesson. Innovation rarely starts with technology alone. It starts when people become curious about a problem and are willing to explore a different approach.
For our team, a customer challenge became an opportunity to collaborate and create something useful. The result was more than a new tool. It was proof that AI can help teams move faster, solve practical problems, and deliver measurable business value when innovation and operational knowledge come together.