If you’ve ever been responsible for data quality, you know that internal lab QC is where digestion issues, interference problems, calibration drift, and contamination quietly reveal themselves.

It’s tempting to stop at the assay numbers and call it a success. But real understanding takes more than that. It’s about turning AI into a useful partner that answers a specific question.

As AI technologies become increasingly prevalent, it's crucial for companies to approach their adoption with a critical eye.