A woman orders one meal at a McDonald's drive-through. By the time she reaches the window, the staff hands her nine orders of chicken nuggets. The system kept adding items from the background conversation in her car
The tech worked, but the product failed & McDonald's pulled the system.
Here is the thing though. That story is not really about AI going wrong. It is about a team that shipped without ever asking what it would feel like to actually use the thing. And once you see that pattern, you start seeing it everywhere.
We sat down with Sai Kishore, Head of Product at Zoho Tables, who has spent over a decade in the thick of this. And the first thing he said was that most teams are solving for the wrong thing from the very beginning.
They look at where the money is flowing, pick a space that seems hot, and start building. They run surveys. They get back polite, encouraging answers. They call it validation and move on. But a survey response is just someone being nice. It tells you nothing about whether the problem is real enough for someone to actually change their behaviour over.
The real test is simple, would someone pay for this today? Not eventually. Today. That one question cuts through more noise than any survey ever will.
The contrast he kept coming back to was builders who live inside the problem. Someone in Kerala, which produces around 26% of India's coconuts, built a bot to climb trees and identify which ones to harvest because finding skilled climbers was becoming harder every season and he felt that pressure daily.
He did not need to validate the idea. He was the user. That kind of closeness to the problem is almost impossible to fake, and teams that try to shortcut it with research almost always end up building something slightly off.
And even when you do build something real and people start using it, the next mistake is reading early momentum as product-market fit.
Clubhouse grew fast, attracted massive names, and felt unstoppable during the pandemic. When lockdowns ended, 80% of users left. The product had not changed. The circumstances had. Real fit is a different feeling entirely, it is when the old way of doing something stops being an option in people's minds. Nobody had to remind you to use WhatsApp. You just could not imagine going back.
AI makes all of this harder to catch because it fails differently from traditional software. When an API breaks, you get an error. You see it, fix it, move on. When AI drifts, it keeps giving you answers. Confident ones. Well-worded ones. Ones that look right until someone actually acts on them. Waymo had a vehicle block a presidential motorcade because it read a police officer as an obstacle. Baidu's fleet in Wuhan stopped mid-trip and locked passengers inside for over an hour.
These were not bugs in the code. They were gaps that only real-world use could expose.
By the time your users notice something is wrong, the trust is already gone. The only way to stay ahead of it is to keep checking before they have to tell you.
So here is what that actually looks like in practice. Pick 20 real examples of what your AI needs to handle. Write down what a correct response looks like. Check it regularly as your data shifts and your users push into edges you never designed for.
That habit is the difference between a product that earns trust over time and one that quietly erodes it.
Most teams skip this because it feels small. But the gap between "this works in testing" and "this works in real-world conditions" is exactly where products go to die. The ones that survive are the ones where someone decided to keep looking even after launch
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