Find out what the pilot has proved
A demo may show that a model can produce a useful answer under selected conditions. It may tell you little about permissions, unusual inputs, response times or what happens when a dependency fails.
An FDE reviews the evidence with the customer and identifies what still needs testing. That gives the team a practical plan for the next sprint instead of an assumption that the pilot is almost ready to launch.
Design around the workflow
Map the task the user needs to complete. Identify the systems that supply data, the decisions that require human review and the action to take when the AI is wrong or unavailable.
This work often changes the design. A system may need an approval step, a fallback process or a clearer interface before a better model would make any difference.
Test the conditions for production
Use the risks in the workflow to set priorities. A production-readiness plan should cover the following:
- Evaluate representative tasks, edge cases and known failure scenarios.
- Check identity, permissions, privacy and data access boundaries.
- Test integrations with live applications and approval workflows.
- Measure reliability, response times and the team’s ability to detect failures.
- Estimate cost at expected usage and set practical spending controls.
- Rehearse runbooks, incident response and handover with the operating team.
Make an evidence-based launch decision
Testing may support a launch, reveal a dependency to resolve or show that the use case needs a different approach. Sometimes the expected benefit does not justify further investment.
The FDE’s job is to make that decision clearer. Where a launch is justified, the customer should know what is ready, what risks remain, who owns the system and what the next release needs to address.
