Why AI Inspection Accuracy Is Not Enough To Earn Factory Trust

Rajesh Iyengar, Founder & CEO of Lincode Labs and author of Teach the Factory to See.

​When manufacturing leaders evaluate AI’s inspection capabilities, the conversation often begins with a percentage: How accurate is the model? It is a reasonable question. But in my experience developing and deploying visual inspection systems, it is rarely the most useful starting point.

I prefer to ask a different question: What happens when the inspection decision is wrong?

A defective component may move into another operation, accumulating material and labor costs before anyone discovers the problem. An acceptable component rejected unnecessarily may trigger reinspection, delay production or consume an engineer’s attention. Both errors matter, but their consequences differ.

Factory trust begins when leaders understand those consequences and design the inspection system around them.

A strong accuracy score can hide a weak inspection process.

Consider a hypothetical evaluation containing 990 acceptable parts and 10 defective ones. A system that passes every part achieves 99% overall accuracy while missing every defect.

The arithmetic is correct, but the inspection is useless.

Overall accuracy can mislead when one class is rare or different mistakes carry different costs. In Chapter 13 of my book, Teach the Factory to See, I explore this distinction, but my main takeaway is as follows: A reassuring score can conceal an error profile the factory cannot afford.

Leaders should ask what sits behind the number. Which defects were tested? Did the evaluation include borderline cases and acceptable variation? Were reference decisions agreed upon by qualified quality personnel?

Track missed defects and false rejections separately, using clear denominators. Examine performance by defect type and severity. Connect those measures to the operating problem the project is supposed to solve.

An impressive average should never conceal poor performance on the defect that justified the investment.