Run an AI-Augmentation Pilot

Outcome

A small, reversible pilot returns comparable evidence about workflow and beneficiary outcomes.

Activity, speed, and polished output do not prove improvement. A pilot must test a frozen expectation.

Use only after the workflow boundary, model strategy, baseline, owner, and stop thresholds exist.

Bring representative cases, baseline measures, evaluation rubric, permissions, trained operators, incident path, budget, and review date.

Freeze prediction, baseline, sample, and acceptance standard.
Rehearse the stop and escalation paths.
Run the smallest representative sample with human review.
Record inputs, versions, outputs, corrections, time, cost, incidents, and outcome signals.
End at the declared review point.

AI performs only the authorised contribution. A human approves consequential output and can halt the pilot.

Return the run log, comparison data, incidents, operator feedback, beneficiary observations, and no recommendation disguised as evidence.

Separate mechanism, adoption, and beneficiary outcome. Stop immediately at a threshold; do not expand scope to rescue a weak result.