The Accuracy Threshold Problem: When Your AI Feature Is Too Good to Ignore and Too Bad to Trust
McDonald's deployed its AI voice ordering system to over 100 locations. In testing, it hit accuracy numbers that seemed workable — low-to-mid 80s percent. Customers started posting videos of the system adding nine sweet teas to their order unprompted, placing bacon on ice cream, and confidently mishearing simple requests. Within two years, the partnership was dissolved and the technology removed from every location. The lab accuracy was real. The real-world distribution was not what the lab tested.
This is the accuracy threshold problem. There is a zone — roughly 70 to 85 percent accuracy — where an AI feature is precise enough to look like it works, but not reliable enough to actually work without continuous human intervention. Teams ship into this zone because the numbers feel close enough. Users get confused because the feature is just good enough to lure them into reliance and just bad enough to fail when it matters.
