The question this answers
Where exactly do humans decide, and where does AI assist in your grant program?
What the problem looks like without Human/AI workflow maps
AI gets introduced into your grant program without anyone mapping where it actually sits in the process. Assessors aren’t sure if they’re supposed to use the AI summary or ignore it. Panel chairs don’t know if AI-generated scores are recommendations or starting points. No one has documented what happens when a human disagrees with an AI output.
When something goes wrong, you discover there was no shared understanding of the workflow. Just assumptions.
What I deliver
A visual process map covering the entire grant lifecycle, from application intake through to acquittal and evaluation. For each stage, the map shows:
- What AI does (if anything)
- What humans do
- Where AI outputs feed into human decisions
- Where AI can handle the work outright (no human review needed)
- Where humans can override, escalate, or reject AI outputs
- Where human oversight is non-negotiable (high-risk decisions, sensitive cohorts, significant funding)
- What gets documented at each handoff
It’s not a technology diagram. It’s an accountability map. It shows who is responsible for what, where the human must remain in control, and where AI can genuinely take work off the pile.
What good looks like vs what bad looks like
Bad: A flowchart that says “AI assists assessment” with no further detail.
Good:
| Stage | AI role | Human role | Human oversight | Scales? | Override rule | Documentation |
|---|---|---|---|---|---|---|
| Triage (incomplete) | Rejects applications missing required fields | None needed if rules are black and white | Not required | Yes | N/A | Rejection log |
| Triage (eligibility flags) | Flags potentially ineligible applications | Staff review flags before rejection | Required before any rejection | Partial – humans only review flagged items | Staff can reinstate any flagged application | Flag log with reinstatement reasons |
| Assessment | Generates draft summary of application | Assessor reads full application, uses summary as reference only | Non-negotiable: assessor must read original | No – still requires full read | Assessor can disregard summary entirely | Summary attached to record; assessor notes if not used |
| Scoring | None | Assessor assigns score | N/A | N/A | N/A | Score and rationale recorded |
| Moderation | Identifies outlier scores | Panel reviews outliers | Required for high-value grants | Partial – humans only review outliers | Panel decision is final | Outlier report with panel notes |
| Funding decision | None | Decision-maker approves | Non-negotiable | N/A | N/A | Decision recorded with reasons |
| Reporting | Aggregates data across funded projects | None needed if data is structured | Not required | Yes | N/A | Report retained |
This level of detail means your team knows exactly what’s expected, where human oversight cannot be skipped, and you can explain the process to anyone who asks.
Why it matters
“Human oversight” is easy to say. It’s harder to operationalise. And if you require human oversight on everything, AI doesn’t save you time. It just adds a step.
A workflow map forces you to answer the practical questions: Where is human judgment non-negotiable? Where can AI handle the work outright? Where do humans only need to review exceptions?
For a 1000-application round, this is the difference between AI that helps and AI that creates more work. The goal is humans on the decisions that matter, AI on the mechanical work that doesn’t.
Without this, you’re relying on individual judgment in the moment. With it, you’ve got a defensible process you can train to, audit against, and explain under scrutiny.
Other AI‑Augmented Grantmaking Deliverables
Where Can AI Be Used in Grant Programs? → An AI governance architecture that defines where AI strengthens decision quality and where it introduces risk. Every AI touchpoint has a defined purpose, boundary, and accountability structure. The program knows exactly what AI does, what it doesn’t, and who is responsible at each point.
Is your AI use in your grant program audit-ready? → An explainability and audit architecture built into every AI-influenced step. Every decision that AI touches can be reconstructed, explained, and defended under audit, FOI, parliamentary scrutiny, or public challenge. Accountability is designed in, not documented after the fact.
What makes grant program data AI-ready? → Grant program architecture designed to produce the structured, consistent data that AI requires. Guidelines, forms, and reporting rebuilt so AI operates on reliable inputs rather than inheriting the ambiguity and inconsistency of legacy design.
