Optimize for the workflow
An AI capability earns its place when it shortens or improves a real decision. Start with the job, the evidence available at that moment, and the consequence of a wrong answer. The model comes after the workflow contract.
This changes the design conversation: response quality still matters, but so do latency, escalation, citations, permissions, and the handoff between human judgment and machine assistance.
Design for learning after launch
Production AI needs observable failure modes and a review loop. Teams should know which questions fail, which sources are weak, what the system costs, and who can change the behavior.
A demo proves possibility. An enterprise system creates a controlled way to learn repeatedly.
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