AI Outcomes Interview

A regional operations team asked for a dashboard. I interviewed six coordinators and learned the bottleneck was inconsistent handoff data, not visibility. We standardized the intake first, piloted it with two teams, and only then added a small exception view.

I tracked complete handoffs, exception rework, and time-to-owner rather than dashboard visits. The pilot improved completeness from 62% to 88%, but the sample was only two teams, so I reported it as pilot evidence rather than a company-wide result.

I removed automated assignment from the first release because ownership policy was unresolved. We kept a human dispatch step and logged the override reason. That gave policy owners evidence without silently automating a disputed decision.

Finance wanted a hard validation gate while field teams needed an emergency path. I proposed a visible exception with a named approver and a 24-hour reconciliation task. Both groups accepted it because the control and the recovery path were explicit.

I would test the intake language with new users earlier. Three labels were clear to the pilot team but confusing to people outside it. I would also define the expansion threshold before the pilot so the go/no-go decision is less subjective.