We live in the "data economy," with dashboards, models, and petabytes to give us insights. And yet data is dumb. Left to its own devices, raw data can't tell you why anything happens. It can tabulate, correlate, and predict (sometimes) with accuracy, but data does not understand causes and effects like humans do.
We've already talked about ETAs in What's the ETA?. Another classical question for product and engineering teams is:
"Is this feature available?"
"Do we already have XYZ?"
"How much is built vs. how much is aspirational?"
They're valid questions.
A recent WSJ op-ed reflects exactly what we see from Causify's customers.
For nearly a century, business leaders have relied on a simple rule: scale drives efficiency. In 1936, aeronautical engineer Theodore Wright showed that costs fall predictably every time production doubles. Experience compounds through repetition — build more, learn more, spend less. That logic built the modern world.
Imagine you see people carrying umbrellas, and it often rains on those days.
Any human understands perfectly that umbrellas don't cause rain, while
traditional AI might think umbrellas cause rain, even if it is not true. This is
the difference between correlation and causation.
For decades, the digital world has been on a foundation of pattern recognition. We've celebrated systems that could tell us what happened, predict what might happen next, and optimize based on historical correlations. These systems have been revolutionary, powering search engines, recommendation systems, and countless applications that define modern life. But pattern recognition has reached its ceiling in the environments that matter most. Enterprise decisions. The future belongs to systems that understand cause and effect.