For the past decade, the enterprise AI narrative has been dominated by deep learning and pattern matching. These models are incredibly powerful at answering “What is happening?” but fundamentally incapable of answering “Why is it happening?”
In this episode, Ari Joury (PhD, particle physics; Founder & CEO of Wangari Global) explores the “correlation trap” and why it is failing enterprise decision-makers. He introduces the concept of Causal AI — a paradigm shift from pattern matching to systemic understanding — and explains why the ability to answer “what if” questions is the prerequisite for robust governance, auditability, and true autonomous action in the enterprise. He also explains why banning ice cream will not stop the murder rate, and why your predictive maintenance model is basically a very anxious psychic.
Topics covered: Causal AI, causal inference, deep learning limitations, correlation vs causation, enterprise decision-making, predictive maintenance, AI governance, Judea Pearl, GenAI Academy.
Wangari is the newsletter and podcast for practitioners and leaders navigating the real work of enterprise AI. New episodes every Friday.
