If machine learning and data tell you one story, but the real world tells you another, which one should you trust? Let me share a story of where machine learning failed at a leading coffee brand.

It started with an ML engineer analyzing sales data, who confidently concluded that coffee demand drops sharply just before 6:00 PM, as people are starting to go home. This insight was shared with the business leaders, who thought the logical decision was to start closing the coffee shop at 6:00 PM.

However, something didn’t add up. The senior leadership team brought in a behavioral scientist to observe the coffee shop firsthand. What he discovered was completely different: the staff wanted to leave early, so they began cleaning around 5:30 PM. They would take out the mop, put it by the door, and flip chairs onto tables. That small signal—chairs upside down on tables—subconsciously told customers, “We’re closing. Don’t come in.” Naturally, sales began to drop around 5:00 PM.

In this case, relying solely on data would have led to a serious mistake. Collecting data is essential, but understanding what’s actually happening on the ground is even more critical before making key decisions.