A fast draft increases the full task’s rework
A fictional business team pilots a generative assistant for preparing customer explanations. Draft production time falls, but reviewers find unsupported statements and spend additional time checking sources and correcting errors. The supplied pilot also uses a restricted approved dataset and has not been tested for every customer group. A colleague proposes reporting the drafting-time reduction as the complete return on investment and scaling immediately. Another distinguishes output speed from total workflow quality, review cost, data permission and supported scope. This written exercise invokes no model and sends no customer material. The learner must identify what benefit is actually observed, what wider result is unproven and how a measured adoption decision should account for the full task.
1. What does the pilot show?
Read the worked answer
It shows faster drafting under the supplied conditions, not necessarily lower total task cost, acceptable quality or safe applicability to every customer group.
2. What should the next review measure?
Read the worked answer
Compare end-to-end time including verification/rework, representative quality/error consequences, access/privacy constraints, actual costs and accountable safeguards before an authorized scaling decision.