A hospital hired me to fix their valet chaos.
That wasn't the real problem.
THE OBVIOUS MESS
Multiple entries. Multiple exits. Keys everywhere. Cars misplaced. Pure operational nightmare.
They knew this was broken. They hired me to fix it.
I fixed it. Standard valet logic—tracked vehicles, managed flow, assigned pickups. Done in weeks.
BUT THEN I WALKED THE FLOOR.
And I saw something they didn't even mention.
THE INVISIBLE PROBLEM
Validation and discounts.
Senior rate. Employee rate. Visitor. Emergency. Each one different pricing. Each verified differently.
Every operator had to:
- Look at the validation sticker
- Remember which discount it represents
- Calculate the correct rate
- Hope they got it right
Every mistake = lost revenue or argument with a family leaving the ER.
They'd accepted this as "just how it works."
Nobody asked me to fix it.
SO I BUILT VATS.
Validation Tracking System.
Scrambled barcode stickers. Scan once. System automatically applies the correct discount.
No memorization. No calculation. No mistakes.
The operator's job changed from "figure out the discount" to "scan the sticker."
3 months later, customer calls:
"The validation system already paid for the entire project."
20 years later: Still running. Zero upgrades. Outlasted three CEOs.
THE PATTERN AI COMPANIES KEEP MISSING
They solve the problem in the spec. Build what the client asked for.
Then wonder why adoption stalls.
Because the client doesn't know what's actually killing them until someone is on the ground watching operators work.
Today I'm watching AI companies make the same mistake. They build the inventory system the warehouse asked for. They never see the operator in gloves who can't use the touchscreen. They don't ask about WiFi dead zones, or the fact that workers are moving fast and won't stop to type.
The methodology isn't complicated. Get on the field. Watch what breaks. Ask why they do it that way. Build what they actually need.
But that requires someone willing to spend weeks in warehouses, hospitals, hotels—watching operators work in the cold, the heat, the chaos.
AI companies won't do this. That's why adoption fails.
Not because they lack smart people. Because nobody goes to the field.
Can you hear me?
If you're building AI tools, that's the only question that matters.
If you're an operator watching AI miss the point, you're not alone.