For decades, manufacturers have relied on traditional analytics—correlations, trendlines, dashboards—to make operational decisions. But there's a limit:

Correlation ≠ Causation

Just because two variables move together doesn’t mean one causes the other. 

This blind spot can lead to poor decisions and surface-level fixes that don’t solve the real issue.

For example, a machine’s temperature spikes often coincide with defects. Traditional analytics might alert you when it happens—but not why. Is it the temperature? A faulty sensor? Operator error?

Causal Inference flips the script. Instead of just observing data patterns, it asks:

“What actually caused this outcome?”

I recently sat down with Daniele Gamba, CEO of AISent Srl to learn more about building industrial intelligence solutions with Caussal AI.