Models are not the scarce resource
Industrial companies now have more AI options than they can sensibly test. The constraint is rarely access to a model. It is the work required to connect a useful capability to a real process, a trusted data source and a decision owner.
A model can recognise patterns in maintenance data or support quality checks, but it only becomes valuable when someone knows what to do with the result. Workflow design turns a prediction into an action.
Make the decision path explicit
Teams need to define who receives a signal, what evidence accompanies it and what happens when the signal is uncertain. That may sound procedural, but it is what lets people use automation without losing judgement.
The most successful projects tend to begin with a narrow process that already has an owner. They improve one decision, learn from the exceptions and expand only when the operating model is ready.
The practical advantage
Companies that treat AI as a workflow question can move beyond pilots more calmly. They are not searching for a dramatic use case. They are building reliable ways to make everyday work better.