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AI for non-profits. The reporting burden is the target.

Non-profits run on small teams doing several jobs each, and a disproportionate share of their capacity goes to funding administration rather than to the work itself. That is the load worth attacking — not because the reporting is unimportant, but because it is the least mission-critical use of the few hours available.

Grant applications and reporting

Most organisations apply to multiple funders, each with different forms asking overlapping questions in incompatible formats. And each successful grant brings its own reporting obligations on its own schedule.

The honest framing: this does not make a weak application strong. It removes the assembly work so the person who understands the programme spends their time on the parts that persuade.

Volunteer and community coordination

Donor and supporter communication

Small organisations often have supporter lists they cannot work — people who gave once, attended once, volunteered once. Personalised acknowledgement and updates at a scale a two-person team cannot reach manually is a genuine capacity increase, not a marketing trick.

Where to be careful

For organisations working in te reo Māori, Pacific languages or other community languages, general models handle them considerably worse than English and will produce fluent errors without signalling uncertainty. Verification by fluent speakers is not optional.

Common questions

Can AI write grant applications?

It can assemble a strong draft from your own existing material — past applications, programme descriptions, outcome data — into a new funder's format. It cannot supply the insight into your programme that makes an application compelling, and a funder reading generic text notices. Use it for assembly; keep the argument yours.

Is it appropriate for a charity to use AI?

It is a capacity question. If administrative load is consuming hours that could go to the mission, reducing it serves the mission. The care needed is around voice, cultural material and beneficiary data — not around the principle.

What about data on the people we serve?

Information about beneficiaries, particularly vulnerable people, carries the strongest protections and the strongest ethical obligations. Keep that processing in-house rather than sending it to third-party services, and treat it with the same care as health information.

We have almost no budget. Is this realistic?

Open-source models running on hardware you already own cost nothing per use, which suits organisations with more time pressure than money. Start with the single heaviest administrative burden — usually funder reporting — rather than attempting a broad rollout.

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