Manufacturing software should make its evidence visible before it offers an intelligent interpretation. A production total, recorded duration, source identifier, or comparison group can be inspected. An explanation is a claim built from those facts.
That boundary matters because a signal is not a diagnosis. An unusual machine pattern may deserve attention, but it does not by itself explain the cause. The same is true of an observation: describing what changed is different from establishing why it changed.
Interfaces and data models should preserve these distinctions. Measured facts should remain traceable to their source, analytical observations should state their comparison basis, and interpretations should be presented with the uncertainty they carry. Combining all three into one authoritative-looking result makes review harder and confidence less meaningful.
Trustworthy AI begins with trustworthy evidence. Intelligence is more useful when an engineer can inspect the facts beneath it and determine whether the conclusion is warranted.