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Getting started with AI for nonprofits

What these tools do, what they don't, and how to get something useful out of them this week.

We work with nonprofit teams who are somewhere between curious and skeptical about AI, and the skepticism is usually well-earned. The pitch most organizations have heard is that AI will transform everything, which is not a claim anyone can act on, and it tends to arrive from people who have never written a grant report at eleven at night.

Here's a more useful starting point. These tools are good at a specific and fairly narrow set of things, and knowing which things is most of the skill.

What they're genuinely good at

First drafts of things you already know how to write.

A thank-you letter, a program summary, a job description. You know what good looks like, so you can judge the output quickly and fix what’s wrong.

Reformatting and reshaping.

Turning a long report into a one-page board summary, or a program update into six social posts. The content already exists and the work is translation.

Getting unstuck.

When the blank page is the problem, a mediocre draft you can react to is worth more than the empty document you’ve been avoiding.

Reading long documents against a question.

Handing over an RFP and asking which requirements your program doesn’t meet is faster than reading it four times.

What they're bad at

Facts about your organization.

They don’t know your numbers unless you provide them, and they will produce plausible-sounding figures if you let them. Never let a number reach a funder without checking it yourself.

Anything requiring current information.

Deadlines, funder priorities, who runs a foundation now. Verify anything time-sensitive.

Your specific voice, at first.

The default register is a bit corporate and a bit eager. It takes correction to sound like your organization, and that correction is worth doing once properly rather than fighting every time.

Judgment about people.

Which donor to approach, whether a staff member is struggling, how a board member will take news. These stay yours.

The one habit that changes your results

Specificity.

"Write a donor thank-you letter" produces something generic because the request was generic. "Write a thank-you letter to a donor who gave $500 for the first time after attending our fall open house, warm but not effusive, four sentences" produces something you can use.

The pattern underneath: give it the situation, the audience, the constraints, and the length. Most disappointing output traces back to a request that left the tool guessing.

Where the real objections live

Three concerns come up in almost every conversation we have, and all three are reasonable.

Donor data and privacy.

Don’t paste donor records, financial detail tied to names, or anything from a client file into a general AI tool. Ask about data handling before you put anything sensitive into any tool, ours included. (Our backend holds to GDPR-level standards and doesn’t train on your data, and we’ll sign a DPA on request.)

Staff worry about being replaced.

In the organizations we’ve worked with, the outcome has been fewer hours on the parts of the job nobody enjoys rather than fewer people. That’s worth saying out loud to your team early, because the fear doesn’t go away by itself.

Mission drift.

If the writing stops sounding like you, the tool has taken over more than it should. The correction is to use it for drafting and reshaping while keeping the judgment and the voice with the people who have them.

A reasonable first week

Pick one task you do regularly and dislike. Run it through a tool three times, correcting as you go, and see whether the third attempt is better than what you'd have written alone. That's a small enough experiment to run between other things, and it tells you more than any demo.

If it works, do the same with a second task the following week. Adoption that sticks tends to look like this rather than like a launch.

TO ADD

Add a specific example here from a client — one task, what changed, roughly how much time.

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