When people talk about artificial intelligence in industrial vision, the discussion often becomes too generic. In practice, the real value of AI does not come from following a trend, but from knowing exactly where it makes sense and where it does not.

This is the approach that I bring to the development of vision solutions at Dimac. My work starts from the inspection problem itself: the defect to detect, the variability of the part, the production speed, the required reliability, and the real operating conditions on the sorting machine. 

Only after that comes the choice of technology. In many cases, traditional machine vision remains the best solution. In others, AI becomes essential, especially when defects are too variable, too subtle or too dependent on appearance to be described by fixed rules alone. 

For this reason, the approach is not tied to a single tool or model. Depending on the application, I may use structured vision pipelines or advanced neural‑network‑based methods for classification and analysis, always selecting the most effective strategy for the specific inspection challenge.

What matters is not the label of the technology, but the ability to turn it into a robust industrial solution.