The forward-deployed model: putting engineers on the factory floor
The fastest way to deploy useful AI in a factory is not a better model. It is an engineer standing next to the machine, watching how the work actually happens.
Remote AI projects optimise the wrong thing. They tune accuracy on a dataset that has already lost the context that matters: the lighting on the line, the way an operator rotates a part, the shift where the numbers always look strange.
Sit where the work happens
A forward-deployed engineer spends the first week not coding but watching. Which exceptions do operators quietly fix by hand. Which report gets rebuilt every month because no one trusts the export. That is where the value is, and none of it shows up in a requirements document.
Proximity also changes trust. When the people on the floor see the engineer fix a real annoyance in days, they start surfacing the problems worth solving. AI lands not as a threat but as something that takes the tedious part off their hands.
The model still matters. But the order is the point: understand the work first, deploy next to it, and let the floor tell you what to build. Everything ships faster when the engineer is in the room.