Automation is most useful when the problem is well bounded. Before selecting a model, define the decision, identify the evidence needed, and understand the consequences of getting it wrong.
Define the decision before choosing the model
Write the decision as a choice between feasible responses. A late shipment might require a hold, a revised route or an escalation. Identify the constraints that could rule an option out before deciding whether a rule, an analytic method or a model would help compare the remaining choices.
Keep source context visible
Show the records and timestamps behind an output. A receiving window from yesterday and an arrival estimate from a few minutes ago may describe different realities. Keep observation, inference and uncertainty distinguishable so the operator knows what still needs confirmation.
Design escalation alongside automation
Specify when the workflow must stop for review: missing evidence, conflicting constraints, uncertain output or an unavailable target system. Separate a recommendation from approval and approval from acknowledgement. The fallback should tell a person what needs attention, not merely return an error code.
