Pattern vs. Purpose: Why Causal AI Is the End of the Guessing Game

Most AI systems live in the past.

By Mark Stouse

Most AI systems live in the past.

They’re built to recognize patterns—repeating relationships, familiar inputs, and the curve-fitted comfort of statistical symmetry. But in the real world, and especially in business, pattern recognition without causal understanding is like a ship steering by the wake.

Here’s a simple truth:

Patterning tells you what was. It’s correlation, and that’s it.

Causality tells you what matters. The “why.”