The distance between a model and a decision.
Useful AI needs more than a prediction. It needs a place in the work.
Dr. Benjamin HarveyBegin with the workflow
The practical question for an AI system is often simple: what can a person do differently because this exists? A useful starting point is the workflow around a decision—the information available, the people responsible, the time constraints, and the consequences of being wrong.
Design the handoff
A model output is one step in a longer process. Someone needs to interpret it, decide whether to trust it, and take an action. The product should make uncertainty visible, explain what the output represents, and provide a route to the underlying evidence. These are product decisions as much as technical ones.
Measure the work that changes
Accuracy and latency matter. So do adoption, the quality of the decisions people make, and whether a system reduces friction in a real process. A pilot can be useful precisely because it reveals where a technically promising system does not yet fit the work.
Keep learning after deployment
Shipping opens another phase of research. Inputs change, people discover unexpected uses, and failure modes become clearer. Treating deployment as a continuing learning process creates room for better systems—and for people to remain responsible for their use.