Free course: AI for nonprofit fundraising

Most nonprofit fundraising and development teams are stretched thin: one or two people covering appeals, grants, gift processing, and donor stewardship at once. AI will not close that gap by itself, but it can take a real bite out of the work that eats your week: the thank-you letters that pile up, the funder research nobody has time to finish, the report that never quite gets written.

This course is about fundraising and grants specifically. It covers what AI is genuinely good at in development work, how to get output you can actually send or submit, which tools are worth knowing, what can go wrong (donor trust and financial compliance both raise the stakes here), and a concrete plan to get started. It takes about 20 minutes and you do not need to buy anything to follow along.

*️⃣ Note
If you want the general foundations first, how AI actually works, the broad opportunities and risks across an organization, start with our free course on AI for nonprofit organizations. This course assumes you just want to get to work on fundraising, so it skips the general theory.

What can AI do for nonprofit fundraising and grants?

The fastest way to find your own uses is to walk your real fundraising pipeline (prospect research, cultivation, the ask, gift processing, stewardship, reporting) and your grants pipeline (discovery, eligibility, writing, submission, reporting) and notice where AI fits. Here is the landscape, grouped by the work you already do. If you want to browse more widely afterwards, our database of the most common AI use cases in nonprofits covers every department.

AI for drafting donor communications and grant materials

This is where most fundraising teams start, and where the time savings are most immediate.

Personalized thank-you and acknowledgment letters, drafted from the gift amount, the fund, and a recent result, then checked against the legal language your receipts must include.
Segment-tailored appeal and email copy, with different versions for lapsed, first-time, monthly, and major donors instead of one message to your whole list.
Grant budget narrative drafts, explaining standard cost types and flagging where the budget narrative does not match the program narrative. Finance still owns the actual figures.
Event and gala planning timelines, turned into a week-by-week task list from a short prompt about the event’s size, date, and staff capacity.
Annual and impact reports, built from your program data into something donors will actually read.
Corporate partnership pitch angles, matched to a company’s stated priorities, beyond the default of logo placement.

AI for reshaping and summarizing fundraising content

The most underrated category, because the raw material (a funder notice, a CRM export, a call recording) is already sitting in your files.

Grant eligibility screening and deadline extraction: paste a funding announcement and pull out the funder, amount, deadline, and eligibility rules.
Dense federal or foundation funding notices turned into a working requirements checklist, so nothing gets missed before submission.
Board and major-donor briefing documents, summarizing a prospect’s public philanthropy record into a one-page briefing before an ask.
Donor-call and meeting notes, transcribed and summarized into action items logged to the CRM contact record.
A CRM export turned into a plain-language impact report, instead of a spreadsheet nobody outside development can read.

AI for funder and prospect research

Useful, but this is also where AI most often invents things, so verification is not optional.

Grant-opportunity matching: score how well a funder’s priorities and past grantees fit your mission before you invest in a full application.
Board-network mining for prospect lists: cross-reference board members’ professional networks against public affinity indicators to build a briefed list they can personally ask.
Matching-gift discovery: check whether a donor’s employer offers a matching-gift program, so revenue that would otherwise go unclaimed gets captured.
Donor-advised fund and estate-gift signals: use public tax and philanthropic filings to spot patterns that suggest planned-giving potential, informing outreach, not confirming intent.
First-pass funder or sector landscape research, using a tool that cites its sources so you can check them before a proposal goes out.

AI for analyzing donor data

Fundraising leans on this category harder than most departments, because so much of the work runs through your CRM and donor pipeline. That means it deserves more care, not less.

Donor segmentation, moving past broad recency-and-value buckets into finer groups you can message differently.
Next-gift and lifetime-value forecasting, scoring who is likely to give again, how much, and when.
Churn and lapse-risk scoring, flagging donors showing early signs of disengagement before they fully lapse.
Legacy and planned-giving prospect identification, surfacing likely bequest conversations from age, tenure, and engagement signals, not certainty.
Campaign-level revenue forecasting, projecting an upcoming campaign from past performance and seasonal patterns, most useful with several years of clean data.

AI for accessibility: translation and plain language

Easy to skip for lack of time, but often high-value for reaching more of your donor base.

Translations of appeals, receipts, and impact reports into the languages your donors and the people you serve actually speak.
Plain-language rewrites of dense proposals and reports, to a target reading level, so more of your supporters can actually follow what you did with their gift.

New fundraising projects AI makes possible for small teams

Everything above is work you already do. The bigger opportunity is work you skip today for lack of time or budget.

Thank every donor personally within 48 hours, not only your major donors.
Run a real legacy and planned-giving program instead of handling bequest conversations only when they come up.
Publish a full impact report every year instead of every other year.
Automatically sync every gift to your CRM with an instant thank-you, instead of batch-entering gifts once a week.
Apply for grants you currently skip because no one has time to read the guidelines.

⬆️ Important
The best first AI project is usually boring: repetitive, low-stakes, and based on non-sensitive information. Ideas that touch donor data directly, speak to donors with no human in the loop, or generate media of real people are worth noting, but save them until you have some experience and the guardrails from the risks section below in place.

AI agents for nonprofit fundraising

Everything above treats AI as an assistant: you ask, it answers, you decide what to keep. Agentic AI goes a step further. You give an agent a goal, and it plans and carries out the steps itself across your files, your apps, and the web, pausing for your approval on the actions that matter. Instead of prompting turn by turn, you delegate a whole task and review the result.

That opens work that used to be too slow or fiddly to attempt: multi-step jobs that meant a dozen separate chats, constant copy-pasting between tools, or an afternoon of manual clicking through funder portals. Part of what makes agents more capable is that they can connect to your other tools (through connectors, the emerging MCP standard, or APIs), create and edit real files, and run saved routines called skills. The most capable general-purpose options right now are knowledge-work agents like Claude Cowork or ChatGPT Work. Most major AI vendors are shipping one, so check what your current tools already include before paying for a new seat.

This is a fast-moving space, so treat the list below as a few examples, not the full set. Some of what agents make newly practical for a fundraising or grants team:

Assemble a first-draft grant package as one delegated project. Hand an agent a funder’s call for proposals, your notes, and a folder of past proposals, and get back a complete first-draft application with every claim traced to your source documents. A human grant writer still reviews and rewrites; the agent removes the assembly work, not the judgment.
Get a recurring prospect-research or funder-deadline brief. On a schedule, an agent checks your tracked funders’ pages and sector news and delivers one short brief with any deadline or guideline change, no prompting required each time.
Connect an agent to your CRM to draft donor updates. Through a connector or API, an agent can read last month’s giving activity and draft a stewardship email or a board update, so you get a finished draft rather than a blank page.
Build a case-for-support style guide from your archive. Point an agent at a folder of funded proposals and successful appeals and get back a written guide to your winning patterns (structure, evidence, phrasing), with examples cited from the source files.
Hand off portal and directory chores to a browser agent (e.g. Claude for Chrome, or ChatGPT Work’s built-in browser): updating your organization’s profile on grant directories, or pulling opportunity details from a funder portal with no export button.

⚠️ Warning
Because an agent acts on its own, it carries risks a chatbot does not. The main ones to know, among others:
It acts, so mistakes ship. A wrong edit, proposal, or send can go out with no person in the loop, and in fundraising that can mean a factual misstatement to a funder or a donor. Keep an approval gate on anything that submits, sends, or pays, and start any agent in read-only mode.
Hidden instructions can hijack it (prompt injection). A funder portal or document the agent reads can contain instructions it may follow without you realizing.
Connected tools and add-ons widen what can go wrong. Every tool, connector, or skill you grant an agent is more it can reach and more code you are trusting, so connect only what a task needs and install skills or plugins only from sources you trust.
Wider access means wider data exposure. An agent reaching into your CRM, drive, or inbox can touch far more donor data than a single pasted prompt, so your data rule matters more here, not less. Donor gift histories, wealth indicators, and financial records are exactly the kind of data an agent should never see without a clear reason and a safe setup.

Our fuller guide to AI agents for nonprofits covers what they can do today and the guardrails to set, and agent skills, plugins, and connectors explains how to extend one safely.

How to write better AI prompts for nonprofit fundraising

This is the part most people skip, and it is the difference between “AI writes generic junk” and “AI saves me half a day”. These habits matter far more than which tool you pick.

Build a reusable context pack

The single biggest quality jump comes from telling the AI who you are, who you are asking, and what you are trying to achieve. Write this once and reuse it forever. A good context pack includes:

Your mission and what your organization actually does, in plain language.
Your donor segments, and roughly what each one cares about.
Your voice, described concretely (warm, specific, never guilt-tripping).
Things you never say or imply, including claiming a personal relationship a staff member does not actually have.
Two or three examples of your best past appeals or thank-you letters.

Prompting techniques that actually improve output

Show, do not describe. Pasting one or two of your strongest past thank-you letters teaches the AI your voice better than any adjective. If you only do one thing from this course, do this.
Ask for options, not an answer. “Give me two versions, one more emotional and one more straightforward” beats “write a thank-you” every time.
Say what you do not want. Constraints improve output sharply: no invented figures, under 150 words, keep the required receipt language exactly as given.
Give it a role and an audience. “You are a busy prospective donor who has never given to us” produces a sharper reaction to a draft appeal than an unframed “review this”.
Iterate in the same chat (“warmer”, “shorter”, “lead with the impact”), rather than starting a new prompt from scratch each time.
Handle a long grant proposal one section at a time. Quality drops when you ask for the whole narrative at once.
Save what works. A prompt that produced a strong appeal is an asset. Keep a document of working prompts so your team stops reinventing them.

If you want to go further on this, our guide to prompt and context engineering covers the techniques above in much more depth.

✅ Example

A reusable prompt skeleton you can adapt for almost any fundraising drafting task:

# CONTEXT
> Our organization: [NAME], working on [CAUSE].
> Our voice: [e.g. warm, specific, grateful. Never guilt-tripping].
> Donor segment: [e.g. first-time online donors, monthly donors, lapsed donors].
> Examples of our style: [paste 2 short excerpts of your best past work].

# SOURCE MATERIAL
> [the gift amount, fund, and any facts. Nothing confidential.]

# REQUEST
[e.g. Draft a thank-you letter under 150 words, and a shorter version for a
text message. Give me two tone options.]

# CONSTRAINTS
Use only facts I gave you. Do not invent a giving history, a statistic, or a
personal relationship. Keep any required legal language exactly as given.
Flag anything you were unsure about at the end.

The best AI tools for nonprofit fundraising (and how to get them cheaper)

You do not need to buy anything to start. A free general assistant covers most of what is above, and a surprising amount of fundraising AI is already sitting inside tools you own. Every tool below is an example within its category, not a recommendation to adopt: the field moves fast, and the practice matters more than the vendor.

General assistants for drafting, summarizing, and research: ChatGPT, Claude, or Gemini. Start here, and check whether your CRM already has one built in (donor databases such as Bloomerang or Salesforce Nonprofit Cloud now ship AI writing features at no extra cost).
Grant discovery and tracking: Instrumentl, GrantWatch, or GrantKit for a lighter, paste-and-extract tracker.
Grant writing assistance: Grantable or Granted AI, which draft in your organization’s voice from your own past proposals.
Wealth screening and prospect research: DonorSearch or iWave for major-gift capacity signals. Treat this as a later step, not a day-one purchase, once you have donor history to work from.
Predictive donor analytics: Dataro or Virtuous Insights for segmentation, churn scoring, and revenue forecasting layered on top of your CRM.
Matching-gift automation: Double the Donation, to catch matching-gift revenue that otherwise goes unclaimed.
Transcription and call notes: Otter or Fireflies for donor-call and meeting summaries.
Connecting tools together: Zapier, Make, or n8n, for example so a new donation automatically creates a CRM record and fires a thank-you.

For a fuller, regularly reviewed list, see our AI tools for fundraising and development guide, or the broader best AI tools for nonprofit organizations.

*️⃣ Note
Before you pay for anything, check the nonprofit programs. TechSoup offers discounted and donated software to eligible organizations, and Google for Nonprofits includes Google Workspace with a free Gemini app. Many AI vendors also have nonprofit pricing that is not advertised on their pricing page (Claude for Nonprofits and OpenAI for nonprofits both offer discounted plans), so it is worth asking, and worth checking before you pay full price for a specialist fundraising tool.

AI risks for nonprofits: what can go wrong and how to prevent it

None of this is a reason to avoid AI. It is a reason to use it with a few habits in place, and fundraising raises the stakes on two fronts at once: donor trust and financial compliance. Our database of key AI risks for nonprofits covers each of these in more detail, with mitigation strategies.

Invented facts, figures, and sources. AI states false things with complete confidence, including grant statistics and citations that do not exist. In a proposal or an appeal this is not a typo, it is a factual misstatement to a funder or a donor. Verify every fact, figure, and funder detail against a primary source before it ships.
Donor data leaving your control. Many consumer tools may use what you type to improve their models unless you change the settings. Never paste donor personal data, gift histories, wealth-screening data, or financial figures into a consumer AI tool.
Compliance language in receipts. US tax law requires specific wording in gift-acknowledgment letters for gifts of $250 or more, and the IRS can disallow a donor’s deduction over incomplete language. Never let AI freely rewrite that wording.
Bias in donor and wealth scoring. Predictive models can over-value donors who match a “traditional” major-donor profile. Treat a score as one input, not a verdict, and keep a baseline stewardship touch for every donor.
AI images of the people you serve or your donors. A generated “beneficiary” or “donor” is not a real person, and presenting one as real damages trust.
Disclosure and donor trust. Nearly 80% of nonprofits use AI in some way, but only about 9% feel ready to use it responsibly, and donors notice: 76% say disclosure matters, while only about 26% of professionals say their own organization discloses AI use today (AFP Global, “The New Currency of Fundraising”). A supporter who discovers an undisclosed AI-written “personal” appeal may not give again.
Funder-specific AI rules. Major funders now set their own policies. NIH’s NOT-OD-25-132 states that applications “substantially developed by AI” will not be treated as the applicant’s original work. Check each funder’s current policy before you submit.
Automation without a human. Any automation that sends a receipt or an acknowledgment should still get a human spot-check on the compliance wording, not run fully unattended.

⚠️ Warning
One of the most useful things you can do early is write a short no-go list: the handful of things your organization decides in advance never to do with AI. Deciding now removes the pressure to cut a corner later, when a grant deadline is looming. A reasonable starting list for a fundraising team:
Never submit an AI-drafted grant proposal without a human fact, voice, and compliance check.
Never let AI generate program outcome data, donor testimonials, or impact statistics from scratch.
Never paste donor financial data, personal data, or wealth-screening data into a consumer AI tool.
Never send an AI-drafted tax-acknowledgment letter without verifying the required legal language is intact.
Never rely on an AI wealth or churn score as the sole basis for deciding who gets stewarded.

Next steps: how to start using AI in your fundraising team

Reading about AI changes nothing on its own. Here is a realistic order of work, from this week to the next few months.

Pick one repetitive, low-stakes task and try it this week. Drafting acknowledgment letters from gift data, or building an event planning timeline from a single prompt, are usually the best starting points.
Build your context pack (mission, donor segments, voice, things you never say, two examples of your best work). Save it somewhere shared. Every future prompt gets better from this one document.
Write your data rule: the short list of what nobody pastes into a consumer AI tool. Donor personal data, gift histories, wealth-screening data, and unreleased financial figures are the usual entries.
Agree a short no-go list with whoever owns fundraising decisions, using the list above as a starting point.
Name who reviews AI output before anything reaches a donor or a funder, with a simple check for facts, required legal language, voice, and disclosure. This one decision is what turns “we should check it” into something that actually happens.
Run a short pilot on that first task. Write down what “working” means before you start (a real baseline number, less editing time than writing from scratch) and when you would stop (for example, it invents a figure twice, or fixing output takes longer than writing it yourself). Then keep it, adjust it, or drop it honestly. Our checklist for new AI pilots and projects walks through what to decide before you start.
In your first month, expand to a handful of uses rather than everything at once, and keep a shared document of the prompts that worked. Most of the compounding value comes from reuse, not from new tools.
In months two and three, write a one-page AI policy covering your data rule, your no-go list, who reviews what, and when you disclose AI use to donors and funders. One page that people read beats ten pages that nobody opens. Our AI policy template gives you a ready-to-adapt starting point.
Bring your colleagues and board in. Show two or three concrete before-and-after examples from your own pilot. That persuades far better than an article about AI, and it surfaces the objections you need to answer, especially around donor trust.
Set a review date every few months. Tools, prices, and funder policies change quickly, so a use that failed may be worth revisiting, and your policy will need updating.
Go deeper when you are ready to do this properly. Our full AI course for nonprofit fundraising covers the complete use catalog, tool selection and budgeting, the full risk and disclosure framework, team roles and buy-in, and how to measure whether a pilot actually worked. We also have equivalent courses for communications, operations, programs, HR, and leadership.
Get help if you would rather not do it alone. We offer AI consulting for nonprofits, including a free first consultation to talk through your situation and where AI would genuinely help.

Whatever you do next, the first step matters more than the plan. Pick one task, try it, and decide with evidence rather than opinion.