How to use AI for grant reporting without inventing numbers

Why reporting is a good place to start

Most funder reports follow the same pattern. Someone gathers figures from spreadsheets and a case management system, pulls a few stories from case notes, fits it all into the funder's template, then checks it against last year's report. The work is repetitive, it happens on a schedule, and the inputs already exist. That makes it a good candidate for an AI agent.

It also has a built-in safety net that many other tasks don't: a draft report is reviewed by staff before it goes anywhere. The goal is to make that review fast and trustworthy, not to remove it.

The real risk: confident numbers with no source

Language models are good at producing fluent text. Left to themselves, they will also produce plausible numbers to fill a gap: a participant count that is close to last year's, a percentage that sounds right. In a funder report that is worse than a blank, because it looks finished.

So the design question isn't how well it writes. It's how you make sure every figure in the draft came from your records, and that you can see where.

Four rules that keep a reporting agent honest

1. 01Every figure points to a source row. The agent can only put a number in the draft if it can link it to the spreadsheet row, database record or note it came from. No source, no number.
2. 02It lists what it couldn't map. If the funder asks for something your data doesn't cover, the draft says so in a list for staff to decide on, instead of guessing or quietly dropping it.
3. 03Checks run before a person reads it. Automated checks confirm that each figure matches its source, every citation resolves, totals add up, and no client names or identifying details have slipped into the text.
4. 04A named person signs off. The program manager or whoever owns the funder relationship reviews, edits and approves the report before export. The agent prepares; people decide.

Those rules are what make the time saving real. A reviewer who can click from any number to its source can check a draft in a fraction of the time it takes to build one.

See an illustrated grant reporting workflow

What you need before you start

• The funder's template or outcome framework, whether that's their own form or a standard such as IRIS+ or GRI.
• Access to the records the numbers come from: program spreadsheets, attendance logs, a case management export.
• One past report you were happy with. It becomes the first test: can the agent reproduce last year's figures from last year's data?
• Clear rules about client information: what the agent may read, what may appear in a report, and where data is stored.

Start with one funder and one cycle

Pick the report your team finds most tedious, not the most important one. Build the agent for that one funder, run it alongside your normal process for a cycle, and compare the two. If the agent's draft matches your team's figures and the review is quicker, extend it to the next funder. If it doesn't, you've learned where your data needs tidying, which is useful either way.

What about stories and quotes?

Funders often want a participant story alongside the numbers. An agent can suggest passages from case notes that fit an outcome, but this is where consent matters most. Only draw on notes where the person agreed to their story being shared, remove identifying details, and have staff choose and edit the final text. If in doubt, leave it out.

The short version: let the agent gather, map and draft. Make every figure traceable, check it automatically, and keep a person's name on the sign-off.