Evidence note: AI in grant administration

Last checked: 19 August 2026. Version française.

This note bounds the synthetic A2 grant-dossier case. It separates administrative handling from a decision that affects access to funding.

Useful anchors

• In Candid's 2025 survey, 1% of 529 responding foundations reported using generative AI to screen applications or assist decisions. Summaries and administrative tasks were described as more acceptable uses. This measures reported practice, not productivity.
• Degrees of Change uses a central system to handle more than 1,000 applications and support 150 volunteer reviewers. Staff review extracted information and matching recommendations. The supplier case gives a useful workflow analogue, not a causal time estimate.
• The National Zakat Foundation case reports an 80% reduction in disbursement delay after several data, automation, and agent changes. Treat it as a high scenario, not a forecast.
• Swiss FDPIC guidance explains the information and human-review rights that can apply to fully automated individual decisions.

The worked case therefore lets the agent check a published checklist, prepare a packet, and perform writes after approval. It does not score merit, vulnerability, or success probability. A high productivity scenario can still be explored for a standard digital sub-flow, while offline, ambiguous, sensitive, and new-program cases stay visible in the whole-workload denominator.

For a real pilot, measure active human time, corrections, escalation, critical errors, complaints, appeals, reversed decisions, access by channel and group, and the downstream outcome. A nominal human review is not enough unless that person has the evidence, time, competence, and authority to disagree.