A model demo asks, “Can it generate an answer?” A product asks a harder question: “How does a person know whether this output is usable, what can they do when it is wrong, and where does responsibility sit?”
For consequential or business-critical workflows, quality is not only a model property. It is also a workflow property: the hand-off, review surface, correction path, and record of what happened.
A useful AI workflow makes five things clear:
LingoA explores OCR-enabled document translation. Its proposed flow keeps the source and translation reviewable side by side, with a person approving the localized document before it is treated as final. LittleReels explores AI-assisted content for toddlers, but deliberately avoids optimizing the product for endless autoplay. These are self-directed concepts, not shipped products or measured outcomes.
The product judgment is in the boundary: automate repetitive work where useful, but do not quietly transfer high-stakes decisions to an opaque system.
For an AI-assisted workflow, I would start with measures such as time to an approved result, correction rate by task type, escalation rate, and whether users can explain when they trust or override the output. These are proposed measures—not results from the concepts above.
If the human review step feels like friction, improve the review experience. Do not remove the control until evidence shows it is unnecessary and the risk is understood.
The model may be the engine. The product is the whole journey around it.