10 Things Nonprofit Leaders Should Expect From AI

You have a donor meeting this afternoon and a board update due tomorrow. An AI tool has drafted the email, summarized the fundraising numbers, and suggested which supporters deserve attention first.

It reads well. You can already see how much time it might save.

Then you pause. Where did that number come from? Does the donor actually care about the program mentioned in the email? Is the recommendation based on this month's information or last year's?

Those are reasonable questions. You should be able to ask them without becoming an AI expert.

For a nonprofit, trust has practical consequences. A confident mistake can become a misleading board update, an awkward donor conversation, or a decision made with part of the picture missing. Useful AI should make those moments easier to handle.

Whether you are evaluating a new tool or using one already in your donor system, here are ten things worth expecting.

1. It should understand what the numbers mean in nonprofit work.

A pledge and a payment can tell different stories. So can a restricted gift and money available for general operations. Counting them together without explanation might produce an encouraging total that answers the wrong question.

AI built for nonprofit work should recognize these distinctions, or make clear when the available information cannot support them. The same applies to relationships: someone who gives every December is not necessarily overdue in October.

Try a question your team already knows the answer to. Ask what has been received, what is still promised, and what period the answer covers. Notice whether the explanation respects the differences your finance and development teams depend on.

2. You should be able to check where an answer came from.

Imagine putting a fundraising figure into your board packet. When someone asks how it was calculated, you need more to say than, “The AI gave it to me.”

A useful answer should make its basis available: the records or report used, the dates included, and the way the figure was calculated. A link to a source is a start; the source also needs to support what the answer says.

You do not need to inspect every record every day. You do need a dependable way to check an important claim before relying on it.

3. It should tell you what it does not know.

“There is no recent conversation recorded” is different from “Nobody has spoken with this donor.” A board member may have called yesterday and not entered a note.

Missing information should remain visible. If a system has not updated, a record is incomplete, or two sources disagree, the answer should say so. Filling that gap with a polished explanation can leave the team more confident and less informed.

An honest limit can still be useful: “The giving history suggests a change, but check with the relationship owner before reaching out.” That gives someone a sensible next step.

4. Its recommendations should come with understandable reasons.

A list of donors ranked from one to ten tells you where a system wants your attention. It does not tell you why.

A better explanation might point to a donor's usual giving date, a change in gift size, or a recent response to a program update. Each is something your team can examine and put in context.

You should be able to ask why a recommendation appeared and receive an explanation in ordinary language. You should also be able to disagree. A useful suggestion leaves room for what your staff knows about the relationship.

5. It should treat donor relationships with care.

A thank-you draft can save time, especially when it includes a verified detail about the donor's support. But warmth does not excuse an invented memory, an assumed personal circumstance, or a claim about impact that your organization cannot substantiate.

Someone who has supported your work for years should hear from you as a person. They should not feel that every expression of appreciation has become another opportunity to ask.

Read a sample message aloud. Would you send it under your own name? Can you stand behind every personal detail? If you would feel uncomfortable explaining how the message was prepared, revise it before it leaves.

6. You should get clear answers about your information.

Donor records and program information belong to real people who trusted your organization. Before sharing those records with an AI service, you should understand what it needs and how it will use them.

Ask which information is accessed, who can see it, whether it is used to train AI models, how long it is kept, and what happens when you disconnect. Your team should receive understandable answers it can review in writing.

A reassuring label such as “secure AI” is not enough on its own. The explanation should be specific enough for the person responsible for your organization's information to evaluate.

7. Your team should control what happens next.

There is a meaningful difference between preparing a donor email and sending it. Reading a financial record is different from changing it. A suggestion to contact someone is different from permission to do so.

These boundaries should be clear before work begins. Staff should know what the tool may prepare, what needs review, and how to pause or stop an action.

During a demonstration, follow one task all the way through. Who approves the message? Where does it go? What happens if the recommendation is wrong? Seeing those steps is more useful than a broad promise that people stay in control.

8. It should help you finish a useful piece of work.

“Donor retention needs attention” may be true. It can also leave a busy development director with the entire job still ahead.

Useful assistance goes further: a short list worth reviewing, the reasons those relationships stand out, relevant background, and a starting point for the response. Your team can then choose an appropriate call, note, or other next step.

The right amount of help depends on the task. A meeting summary should make the agreed next steps easy to find. A board update should help explain what changed. Look for something your team can use that day.

9. It should fit a busy nonprofit workday.

A tool can look impressive in a demonstration and still require more preparation than a small team can sustain.

If someone must rebuild a spreadsheet, paste the same information into several places, and rewrite the result every week, include that effort when judging the benefit. Some setup may be worthwhile. The ongoing work should be clear.

Test the ordinary version of the task: a staff member has twenty minutes, the information is imperfect, and another priority is waiting. Does the assistance still help? Can the person who actually owns the work use it comfortably?

10. Its value should show up in something you can observe.

A completed draft is evidence that a draft was created. It is not yet evidence of a stronger donor relationship or more revenue.

Start with a result close enough to see: less preparation time for a meeting, fewer corrections in the board update, or follow-up completed when the team intended. Compare the work before and after, including time spent checking the AI's output.

Fundraising results matter, too, but many things affect whether someone gives. Be careful about crediting the tool for a gift just because it helped prepare a message. Confidence grows when the benefit is real and the claim stays proportionate to the evidence.

A practical way to begin

Choose one recurring task your team knows well. Keep a recent example, then use it to evaluate the AI's work. Check the facts, read the explanation, follow the approval steps, and see whether the result makes the task easier.

This is the standard we believe Donorlytics should be judged against, too. Donorlytics is an independent intelligence layer for nonprofit leadership: it interprets information from approved connected systems, brings important changes to attention, and supports the next useful action. Important figures are calculated through structured logic, with AI used to explain the supported results.

The scope of the connections and available features should be clear for your organization. No software is error-proof, and your team's knowledge still matters.

AI earns a place in nonprofit work when you can understand its contribution, check its claims, and use the help with confidence. You should leave the work better prepared to make a decision—and still able to explain it to the people who trust you.

Further reading: NIST's AI Risk Management Framework offers a broader approach to evaluating trustworthy AI.

Explore Donorlytics
Talk to our team →

Donorlytics helps nonprofits bring their data together, understand what is changing, and move on what matters. Because too much important work still depends on piecing it together by hand.

‍