● AI
The real work begins after the AI prototype.
A convincing demo proves possibility. A useful AI capability proves reliability, ownership, and economic value under real operating conditions.
The prototype answered one question: can a model produce a plausible result? Production asks harder questions. Can it do so repeatedly, with the right data, inside a real workflow, at an acceptable cost—and can someone explain what happens when it fails?
The prototype hides the expensive questions
A hand-picked prompt, clean document set, and expert operator remove most of the uncertainty. Real use restores it: incomplete inputs, ambiguous intent, policy exceptions, latency spikes, and people who need to know whether to trust the answer. The gap is not polish. It is the difference between a model response and an accountable business process.
Evaluation is a product feature
A useful evaluation set represents the work users actually do, including edge cases and costly mistakes. Measure task completion, evidence quality, refusal behavior, latency, and the rate at which humans must correct the system. A single “accuracy” score compresses decisions that deserve separate owners.
of the original workflow was unnecessary.
Illustrative example: mapping the decision before automating it often removes duplicate review, copy-paste handoffs, and approvals that exist only because the old system lacked context.
Design the failure path before the happy path
A confident wrong answer is not the only failure. Silence, delay, stale evidence, and unnecessary escalation all carry cost. Define what the system may decide, what requires confirmation, and what must move to a person. Then make that boundary visible in the interface and observable in operations.
The 60-second version
- 01
Start with the business decision, not the model.
- 02
Build evaluation from real work and costly edge cases.
- 03
Make human oversight explicit in product and operations.
- 04
Own quality, cost, latency, and improvement after launch.
Illustrative metrics showing how change could be measured—not claimed Azsura client results.
Want to discuss this with someone who actually builds it?
Renata SilvaDirector of Product & AI · 3 ideasRenata helps teams turn uncertain product and AI opportunities into decisions, systems, and measurable operating changes.
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