AI for Nonprofits 101: A Beginner’s Guide to Using AI Tools Safely & Effectively

AI is everywhere right now, to the point where it’s frankly exhausting. It’s the focus of conference sessions, software demos, the webinar invite that hit your inbox this morning, and that one board member who finds a way to bring it up whether or not it’s on the agenda. Half of what you’re hearing says AI is going to save the sector. The other half says it’s going to quietly wreck the human relationships the sector runs on (and/or kill us all, which would also wreck said relationships).

Both of those outcomes are possible, and we’ll tell you right now that we are on team “nonprofits should be using AI.” We’re on that side for exactly one reason, though: Because we think that AI tools can give your staff more time—and better information—to put toward real conversations with real supporters. Being “left behind” is not a good reason to adopt it. Your board member (ugh, John) is a flat-out bad reason.

Sure, you’ve got concerns. So do we, but we also know that the nonprofit sector needs help. Between 2023 and 2025, the average small nonprofit lost 8.12% of its revenue and 5.5% of its donor database. And we think that, when used strategically, judiciously, and ethically, AI can provide some of the support that you need.

This article is a beginner’s guide to nonprofit AI, and we wrote it to help small nonprofits like yours get their digital sea legs under them. We cover what AI actually is, where you’ll run into it, what to try first, how to keep your donor data out of the news, what to put in writing before anyone touches a tool, and how to get your team comfortable using it. 

That’s a lot of ground to cover, so let’s get moving!

AI for Nonprofits: FAQs

If you don’t have the time to read everything in this post, don’t worry. We’ve rounded up all the most common questions about AI—all of which we touch on in the article itself—and gathered them here with straightforward answers that are as simple as we could manage. 

Level With Me: Should Our Nonprofit Be Using AI?

Yes, you should. Because AI, when used well and used safely, can deliver better outcomes like increased fundraising and more impactful programs. You’re going to hear a whole lot of hype about this being “the future of everything” and fearmongering about being “left behind” if you don’t adopt it, but you should ignore all that.

Better outcomes are the only reason to start using AI at your nonprofit. Anything else is just adopting AI for its own sake, and that’s guaranteed to lead you nowhere productive. 

Look at the transition from horse and buggy to car. The purpose of getting a car wasn’t to own a machine instead of owning an animal. The point was to go faster and farther. AI is much the same.   

In fact, if you start to think about the environmental concerns surrounding AI data centers and compare them to the very real environmental impact that moving from horses to cars has had over the past century-and-change … AI is really the same.

Which, now that we mention it…

I Have So Many Concerns About AI; Is it Really a Good Idea?  

Again, we think that it is, and we think that you should use it at your nonprofit. But we say that, knowing that there are a bunch of issues with AI, both as a technology and as a business, that are a) genuinely concerning, b) totally valid, and c) not at all answerable right now. 

Your AI Worries Are Legit

We brought up those parallels between cars and AI in the previous section for a reason. We have zero interest in being the kind of wild-eyed AI boosters that have all of us reaching for the “unfollow” button. No one here’s looking to end up on the r/LinkedinLunatics.

The environmental concerns about data centers are very real. Concerns around the economic model of some of the large AI labs (that the cost of running their models is too high, that their financing is distressingly circular) are also very real.

We Don’t Have a Crystal Ball

AI very much looks like the future right now; but what the future looks like from the present and what the future actually turns out to be aren’t always the same.

Data center backlash could be the death knell for AI in the United States… or advances in cooling technology and more generous local tax regimes could make it a non–issue. Likewise, improvements in training could reduce the cost of running the major LLMs and resolve all the outstanding financial concerns.

On the other hand, a swarm of thousands and thousands of AI agents trying to find the most efficient way to maximize paperclip production could break into the nuclear arsenal and wipe humanity off the map, freeing up a lot of resources to spend on making new paperclips.

We really don’t know! 

All of that is to say… adopting AI to support your mission is the best advice we can give you right now; but if AI goes away or something better shows up to replace it, our advice will change. 

Nonprofits Need Help; AI Can Deliver It 

If you work at a nonprofit, any nonprofit, then you know that the state of the sector right now is … not something that we can accurately describe without going full NSFW. 

Donor Databases are Shrinking for Nonprofits Everywhere

The view from 5,000 feet is that overall giving, the total number of donors, and donor retention—the big three—are not trending in the right direction. Not to mention all the uncertainty around federal grant funding. 

We have some actual data from Neon One’s own research that backs this up. 

According to our Recurring Donor Report, which analyzed transaction data from 4,107 nonprofits between 2023 and 2025, the average small nonprofit saw total revenue drop 8.12%, as one-time donor retention fell from 34.81% to 31.21%. The average nonprofit’s donor databases shrunk by 5.5%

Even the Q1 2026 FEP Report, which saw some “bright spots” like donation revenue increasing YoY and donor retention rates holding steady instead of dropping, seemed to capture more of a momentary lift within a large decline.

Recurring Giving, However, Is On the Rise

But if you dig deeper, there is one area where the forecast is legitimately sunnier: recurring donors. According to that same report, recurring donor bases grew 31.24% over the same window, and recurring donation revenue rose 35.79%. 

So while the kinds of casual supporter relationships that many donors had with nonprofits are continuing to fray, the orgs that are investing in building deeper, more personal relationships with their donors—the kind of relationships that lead to and sustain recurring gifts—are seeing their revenue grow. 

Recurring gifts aren’t the only area where this trend shows up. Neon One’s Generosity Report from 2025 found donors who gave consistently for five years contributed $3,034 on average, against just $187 for one-year donors—a 1,519% increase. 

The biggest driver of sustained nonprofit growth is sustained supporter relationships. And this might seem a little counterintuitive at first, but that’s exactly what AI can help you with. 

Use AI at Your Nonprofit to Support Relationships, Not Replace Them

In the meantime, the best reason to adopt it is this: 

AI can take care of the many (many!) important but tedious and time-consuming tasks that can suck up your staff’s entire day, leaving you with more time—and better insights—to put towards the real, personal interactions with your supporters that foster sustained growth.

Alternately, you should not be adopting AI just because one of your board members won’t stop talking about it. (Shut up, John.)

How Can My Nonprofit Implement AI Safely? 

The short answer is by being deliberate in what you’re using it for, being careful in how you implement it, and measuring your results.  

AI is safe for nonprofits to use, but that doesn’t mean it’s without risks. Data security, bad publicity, strained donor relationships, impaired programs, and just straight-up lack of ROI are all things that can happen if your nonprofit implements AI tools poorly.

But if you’re taking care to think through why you are using AI, where you’re going to implement it in your work, and what the results will be once you do, you’re going to be fine. And fine might even mean “we can’t use AI here” or even “we can’t use AI at all.”

Here are two great stories of nonprofits that implemented AI in their work.

At Chayn, Chatbots Weren’t Right for Sensitive Conversations

Chayn is a nonprofit that works with survivors of gender-based violence, pulled its resource chatbot in 2020, and published three words of advice for anyone planning the same thing: Don’t do it. 

Even when they clearly labelled it as a bot, not a human, they found that survivors were using it as a crisis line. They weren’t typing things like “I am facing domestic abuse;” they were typing “Why does my husband hurt me?” And the chatbot could not ethically have that conversation.

But their experimentation with AI didn’t end there. In 2025, Chayn released Survivor AI, which writes takedown letters for image-based abuse. This was a task for which the AI was much better suited, helping empower survivors to more easily and quickly remove these images. 

AI Helped Counselors at Empower Work Reduce Their Admin Load

Empower Work got there from the other side. Its AI assistant drafts session summaries and suggests responses for peer counselors, who rewrite everything in their own voice.

As a result of these tools, counselors were producing summaries 60% faster. When donors offered to fund a help-seeker-facing chatbot, Empower Work turned the money down because the help needs to be provided by counselors, not bots.

In both these cases, the nonprofits ended up at a place where it was people working with people, with AI providing an assist to help them to do it better and faster. 

This brings us to a topic you might not have even considered when considering AI: When you say “AI,” what exactly are you talking about? 

Here Are the Different Kinds of AI & AI Tools. 

When people talk about AI nowadays, what they’re talking about is usually chatbots powered by Large Language Models (LLMs). We’ll get into them below, but LLMs are not the only kind of AI out there. There’s another kind of AI, machine learning, that can also be really useful.

Here’s a bit more about each of them…

Generative AI (LLMs)

LLMs are the technology behind tools like ChatGPT and Claude. These are basically pattern machines. Since they’re trained on enormous amounts of text, they’ve learned what word tends to follow what word, and they use that to generate a plausible next sentence. 

Generative AI is the umbrella term for LLMs (and similar models for images or audio) that actually produce new content on request rather than looking up something that already exists.

Predictive AI (machine learning)

Machine learning is the older, quieter cousin of Generative AI. It’s also a pattern machine, but it’s built for analysis rather than creation. Instead of generating new content, it finds patterns in data you already have and uses them to predict something specific. 

In a nonprofit’s fundraising platform, the patterns that machine learning identifies might be “which donors are likely to lapse this quarter,” or “what ask amount a given supporter will probably respond to.” That’s pretty useful!

Predictive AI is less “write me something” and more “tell me what’s likely to happen,” and it’s been running quietly inside a lot of different software for years, well before that one board member (ugh, John) started bringing up “AI” every meeting.

Next, let’s talk about the different kinds of AI tools your nonprofit will be working with.

General Purpose LLMs

These are the ones you know by name—ChatGPT, Claude, Gemini, etc. They are general-purpose chat interfaces sitting on top of an LLM. In fact, when people refer to “LLMs,” they are usually referring to one of these.

They work like this: You log in to your account, then ask a question or make a request, and the tool starts “chatting” with you, trying to help you solve whatever issue you’ve handed to them.

One of the many impressive things about these tools is how much they know about, well, pretty much everything under the sun, but it’s important to remember that they were not designed with nonprofits in mind.

So if you’re interested in getting something that will give you a good answer instead of an answer that just sounds good, you’ll need to look elsewhere. These tools can help you a ton, but that lack of specific focus means they can only help you so much. 

Single-Use AI Tools

Unlike general-purpose LLMs, which can solve (or at least appear to solve) any question you put in front of it, these tools are designed to do only a few things and to do them very well.

Some of these tools were designed for nonprofit-specific tasks, while others are still general-use for all kinds of business and personal reasons. 

Here are a couple of prime examples of single-use AI Tools:

• Grammarly: You’ve probably heard of this one! It’s a tool that marks up anything you’re writing as you go, noting misspellings, grammatical errors, and so on. It’s available in both free and paid versions.
• Grantable: Here’s a tool designed to help nonprofits write better grant applications. It combines your own data and documents with grant writing best practices to quickly produce a great first draft that you can then edit, improve, and generally de-AI (this is true of all AI writing tools).
• Otterly.ai: This is a general-use transcription tool for live video calls and video recordings that also lets you go beyond simple transcription (though, again, talk about saving time on a necessary but tedious task) and create “smart” transcriptions with action plans and next steps based on what was discussed. 
• Dataro.ai: Another tool built for nonprofits, Dataro uses your org’s own donor data and its predictive AI algorithms to produce actionable, “next best step” items that your staff can take to build on the trends and behavior it has identified from your dataset. 

AI Features in Nonprofit Platforms

The final category of AI tools for nonprofits is the AI features you’ll find in nonprofit software platforms like Neon CRM. The great thing about these is that they can leverage both machine learning and LLM capabilities while combining nonprofit best practices with your nonprofit’s own data.

In terms of outcomes, tools like these are going to deliver the best results. The downside being that they generally cost a lot more, and that switching, say, from one nonprofit CRM to another, while easier than you might think, is still a bigger hassle than switching from ChatGPT to Claude. 

Still, it’s a little hard to overstate the importance of being able to use these AI features without any of your existing data having to leave your system. Data security is a huge concern with all these AI tools. As a general rule, the fewer database exports you need to make, the better.

Which AI Tools Should I Start With? 

If your nonprofit’s just getting started with AI, the order to think about these is roughly the reverse of how confusing they sound to you. 

We’re guessing that you’ve played around a bit with ChatGPT or Claude. So keep doing that, but be more intentional. Ask it to build you a communication plan for a specific donor segment. And once it gives you one, actually review it and ask the tool to make improvements. As it improves its outputs, you’ll be improving your inputs—aka, you’ll get better at prompting it. 

While you’re playing around with LLMs, you should also check what AI features are already inside nonprofit tools you’re paying for; you’ll be working with a system you already understand (and have already vetted), and the risk to your donor data is basically zero. 

From there, look for areas where you can use some additional help and shop around for single-use tools to fill those gaps. Some guides would tell you that you can just vibecode your own tools, but if you’re an AI beginner, that would be, in point of fact, the worst thing you could do.

That brings us to an important point: using AI isn’t going to do anything to help your nonprofit unless you use it well. But what exactly does that mean?

What Does “Using AI Well” Actually Look Like?

Using AI well at your nonprofit means increasing the time and the attention you can devote to personally interacting with your supporters. 

There are a number of individual jobs that AI can perform and a whole host of individual tasks it can check off any number of to-do lists, but everything it does needs to ladder up to helping you spend more time with your supporters and being able to use that time more effectively. 

Saving time by automating (or at least greatly speeding up) tedious admin tasks? That’s more time for your supporters. 

Instant summaries of a donor’s history with your nonprofit? That’s a better conversation you’ll have with them. 

Asking your CRM for a custom report in plain language and having it instantly generate one? That’s more time, better insights, and just a general improvement in your mood. Every little bit helps.

We actually have a perfect story that explains these principles in action, but, in a fun little twist, it’s not a story from one of our nonprofit customers; it’s actually one from Neon One itself!

How AI Helped Neon One Deliver Free Live Chat and Phone Support for All Our Customers

In 2026, we overhauled—that is, we vastly improved—our customer support. The core of that overhaul was bringing on a whole new team of representatives so that we could offer in-platform live chat and live phone support to all of our customers, totally free of charge.

We Used AI to Help Us Add More People

But one of the ways that we made that plan a reality was by also layering in ACE, an AI agent that’s trained on all of our support materials, to handle all the kinds of easy-to-answer queries that don’t really require a human touch but can eat up a lot of time when humans are the ones handling them. 

By using ACE to handle all these queries via live chat, our flesh-and-blood team members were freed up to handle the thornier issues that require the insight, empathy, and creativity that only real people can provide. 

As a bonus, ACE doesn’t need to sleep, so it’s available to answer questions 24/7.  

Our Customers Loved The Results

As a result of these changes, average response times dropped 80%, and customer satisfaction scores jumped 30 points, up into the mid-90s. In other words, it worked like gangbusters!

Remember, the most important part of this story wasn’t the AI; it was the dedication to spending more time with our customers and having better conversations when we did. The AI was just a helpful tool to help us get there.

We’re also sharing this story to make it clear that this isn’t just nice-sounding advice. When it comes to AI, putting people first is one of Neon One’s bedrock principles. When we advocate for it, we do it because we know, firsthand, that it works. 

If you’d like to know more about that story, check out this article from Neon One’s Vice President of Customer Support & Enablement, Meaghan Misener.

The Golden Rule of AI for Nonprofits

Any time your nonprofit is using AI, the goal should always be to help your staff build stronger, more personal connections with supporters.

Put this rule in place and let it be your north star, guiding every AI decision your nonprofit makes. Any use of AI that swaps personal interactions with fully robotic interactions is a nonstarter. You’re not using AI to replace people, after all. You’re using it to help them.

Later in this piece, we’ll talk about various AI policies and scorecards you can use to improve and guardrail the ways your nonprofit uses AI, but those are all downstream of this. 

If using a potential AI tool or process means that you’ll be spending less time with your supporters, then don’t do it. If it means you’ll be spending more time with them, proceed.

How Do I Know If My Nonprofit is Actually Ready for AI?

Before you start officially using AI at your nonprofit, run a quick gut check on where your organization actually stands. 

This short self-assessment of AI readiness will surface the gaps in people, data, process, or leadership buy-in before you invest further. It even provides you with a customized scorecard that tells you where you need to focus—and what you need to do—to get your org ready.

What Should I Try to Do First with AI?

The easiest ways to start using AI at your nonprofit are for basic, generalized tasks like fundraising campaign planning; drafting communications like emails, texts, and social posts; wrangling internal communications; and organizing or analyzing data that currently sits across many different tools (or spreadsheets).

Here’s where we repeat our warning about free and personal AI accounts: If you are putting confidential donor data like names, email addresses, transactions, etc. into any kind of AI tool, you need to be using a business or enterprise account. This way, all that data will be protected. 

Moving on: If you’re using an LLM like, say, Claude, here are some ways you can get started:

Campaign Planning

You can give Claude some basic details about your nonprofit, your campaign strategy, and your goals and ask it to generate a calendar for every touchpoint. From there, you can either refine it by hand or chat with the system to improve it. 

The more specifics you give it about your mission, your programs, your community, and your past successes, the more carefully it can move from “one-size-fits-all” ideas to a campaign plan that fits your needs specifically. 

Organizing & Analyzing Data

If you don’t have an all-in-one platform like Neon One, where every donor touchpoint sits in the same profile, AI can let you take data from many different systems, combine them together, and analyze them for trends, opportunities, and weak spots. 

Lagging volunteering, for instance, could be a sign someone’s about to lapse even though their recurring gift is still going strong. You can then know to reach out to them before they lapse and rebuild their connection to your org.

Internal Communications

Think about the amount of time and energy that your team spends taking careful notes during a meeting, copying those notes into their records, translating them into to-do lists, and then still having to untangle all the (metaphorical) crossed wires when others don’t remember things the way they do.

AI can fix all of that. Use it to record and transcribe your meetings, then create action-item-based summaries and next steps that are instantaneously shared with the whole team. You’ll get used to saying things like “I am going to say this carefully so that Google Meet gets it all” and you won’t even be annoyed. You’ll be too grateful for all the time you have back. 

Drafting Emails & Social Posts

Use previous communications and any brand voice or style guides you have on hand for your org, and attach them to your chat. Then ask it to draft an email to group X in style Y that you want to accomplish outcome Z. 

You can also ask the chat to quiz you more deeply on your goals for this message to achieve a more precise output. But no matter what you do, you should always thoroughly edit and vet the results yourself before you even think about sending it.   

And to help you do just that, we built a handy dandy AI checklist that comes with, among other things, a list of common AI writing ticks to watch out for in your messages. Any time you use AI to draft anything public-facing—an appeal, a thank-you, a report—run it against this simple checklist before it goes out. 

When You’re Ready to Start, Run an AI Sprint

Of course, adopting AI is just going to be one of the 10,000 things your nonprofit’s staff are trying to juggle. That’s why, when your team is ready (or close enough), you should run a focused “AI sprint” over 4–6 weeks to get you started on the right foot. 

Here’s how these sprints work in just four simple steps:

• Weeks 1–2: Audit your systems for friction. Where are the areas where your team spends the most time on work that feels repetitive, manual, or low-judgment? List them out. That list dictates your AI use cases, which also dictates your options for potential AI tools. 
• Week 3: Research and select tools. Don’t try to evaluate every piece of software on the market. Take the top two or three friction points from your audit and find tools that address those specifically.
• Weeks 4–5: Pilot one use case. Run a single workflow through an AI tool. Measure whether it saves time, whether the output needs heavy editing, and whether it changes the donor’s experience. Adjust based on what you learn.
• Week 6: Document and expand. Write down what worked. If you want to move forward with this AI tool, train your whole team so they know how to use it. Then lather, rinse, and repeat with a new friction, a new use case, and (possibly) a new tool.

The worst way to implement AI at your nonprofit is to start willy-nilly by asking Claude to just spin up emails, web pages, and chatbots or by impulsively dumping .csv file after .csv file filled with highly confidential donor data in your ChatGPT window and asking it for miracles.  

Running an AI Sprint instead will help you be calm, deliberate, and focused on the areas where AI can address a real need. Once you’re ready to start, it’s definitely the approach we recommend.   

What AI Guardrails Should My Nonprofit Put in Place?

AI guardrails at your nonprofit should be primarily focused on protecting two things: your data and your reputation. Leaking confidential data about either your supporters or your beneficiaries could put you in real legal jeopardy, while a burst of bad AI-driven PR could set you way back—and both, at their worst, could leave you closed for good. 

That’s why your nonprofit must have an AI policy, no matter how small you are. The policy doesn’t need to be long, but it does need to answer three questions: 

1. What tools are approved for use with donor and/or beneficiary data? Name them specifically. If a tool is not on the list, then the data stays out.
2. What data can never go into any AI tool? Any Personally Identifiable Information (PII), financial, or other high-risk data you have (like medical records) should either be explicitly excluded or require explicit, high-level authorization. 
3. Who’s responsible for reviewing AI-generated content before it reaches a donor? Every AI-touched communication needs to be reviewed by a real human. And the review can’t be a quick scan on their way out the door. It needs to be considered and rewritten in a way that sounds warm, engaging, and personable.

Having an AI policy in place will help your nonprofit maintain trust with your community. If you lose that trust, no amount of AI-aided time savings will help you.

As you start using AI more and more, that simple policy will need to grow to match it. Our free Organizational AI Governance Policy Template gives you a great starting point. Just make sure that you still review the whole document with both your team and your own lawyers to make sure it fits your org.

Staying Comfortable With AI as a Team

How Do I Train My Team to Actually Use AI?

Start small, start hands-on, and lean on the free training that already exists. Most AI adoption doesn’t fail because of the technology itself. Instead, it fails when people are poorly trained on how to use it.

The best-case scenario when giving an untrained team AI tools is that nobody actually uses them. The worst case is that they use them wrong and make a mess that takes a long time to clean up.

The good news is that training your team doesn’t require a whole program. Instead, you need to cover three things: what a tool is for, what it’s explicitly not for, and how to review its output before it goes anywhere near a donor.

And you don’t have to build any of it yourself.

Resources That Are Free and Good Enough to Start Today

• Microsoft Digital Skills Center (via TechSoup): Free, on-demand video modules covering the fundamentals, including how to write a decent prompt. Over 70,000 people have enrolled, which should tell you something about how low the barrier to entry is. Start here if you’re starting anywhere.
• Data.org’s Nonprofit AI Impact Hub: Also free, also plain-language, and available in both English and French. Built for the people on your team who are still at the “okay, but what is it, though” stage. (No shame in that. Two years ago, that was all of us.)

Resources That Are Worth Paying For

• NTEN’s AI for Nonprofits Professional Certificate: Thirteen self-paced courses you can mix and match, from an intro to large language models, to streamlining grants management with AI, to racial equity in tech planning. It runs $500 for NTEN members and $1,000 for everyone else, with a limited number of scholarships available.
• Nonprofit Tech for Good: A cheaper, webinar-based certificate series aimed squarely at marketing and development staff. The focus is tactical, aka what to actually do with these tools when you’re starting on a mountain of tasks on a random Tuesday afternoon.

Resources for Your Executive Director and Your Board

• Kellogg’s Leading in the Age of AI & MIT Sloan’s equivalent programs: Executive education built around governance, change management, and telling a real capability apart from a sales deck. These cost real money, so treat them as a leadership investment rather than a staff perk. What your ED needs to know about vendor contracts and liability isn’t what your development coordinator needs to know about donor data. (And sure, maybe this is also where John goes. Then maybe he suffers a terrible… accident… on his way back. Everybody wins)

Whatever you pick, make the training hands-on. Skip the dry overview session and run a workshop where your team does a real task—like drafting a donor segment, building a report, de-AI-ing an appeal—with someone walking them through it live.

For the ongoing training, keep it light. AI capabilities move fast enough to date a training session within six months, so a shared Slack channel where somebody posts what changed will serve you better than a quarterly review meeting. 

If you want that conversation happening outside your org too, Fundraising.AI runs an open community and an annual summit, and they’re the people behind the Responsible AI for Fundraising framework.

Your staff doesn’t need to become AI experts. They just need to know what each of your tools is for, what it isn’t for, and who to ask when something looks off. Or, even better, to feel confident enough in their own skills that when something looks off, they know how to fix it.

How Neon One is Building AI into Our Platform

Handing donor data to an AI tool feels like a risk because it often is one. 

Gen, the AI assistant built into Neon CRM, reduces that risk by limiting itself to the existing data in your system and by not retaining anything beyond what it looks up. 

Think of it like an open-book exam: Gen pulls the relevant records from your secure database, answers your question, and closes the book. Nothing gets memorized, and Neon One only works with AI providers under a zero-retention guarantee (this metaphor is really dunking on the very idea of open-book exams, but that’s beside the point).

That same setup solves hallucination too. Since Gen is looking at your actual data instead of guessing, it doesn’t invent numbers. It can’t invent a donor’s giving history any more than a filing cabinet can. 

If you’re a Neon One customer, you’ve probably already seen our AI tools in action. 

• Our Generosity Indicator uses machine learning to flag which donors are ready to give more or drifting away. 
• Ask Gen answers plain-language questions about your supporters based on the data in their profile. 
• Intelligent Donation Amounts personalize the suggestions on your donation forms based on a donor’s own history. 

These are a few examples, and there’ll be more. What actually matters is that your data stays yours and, while Gen informs your judgment, it never replaces it.

Take the Next Step With Our AI Xchange Hub

AI for nonprofits is (we think) neither the crisis some fear nor the silver bullet others are selling. 

Used well, it buys back staff time for the relationship work that drives retention. Used poorly, it quietly undermines that all-too-crucial and all-too-fleeting trust that makes fundraising possible in the first place.

Adopting AI should never be the goal. Furthering your mission—and centering the people who carry that mission out, not the bots that support them—is what makes the difference.

If you’d like to learn more about how you can use AI effectively at your nonprofit, check out our AI Xchange Hub, which is chock full of templates, prompts, skills, videos, and articles to help you on your way.