What AI Is, What It Isn’t, and What Your Nonprofit Can Actually Do With It
A primer for nonprofits new to AI
The social sector is still early in its adoption of AI. That is not a criticism. Nonprofits and social impact organizations have good reasons to move carefully. Limited staff, sensitive data, communities who deserve more than a rushed experiment.
What strikes me, though, is how lopsided that adoption is. Some organizations have gone all in: AI embedded in operations, staff trained, workflows rebuilt around it. Others are still trying to figure out where to start, or have tried a few tools and can’t quite make them work. Most are somewhere in the middle: using it inconsistently, without much of a shared understanding of what it is or what it is for. That gap is widening, and it is worth paying attention to.
But early does not mean untouched. Your organization is probably already encountering AI, even if you have not made any deliberate decisions about it. Maybe someone on your team has been drafting with ChatGPT on their own. Maybe your email platform is ranking messages with it. Maybe your browser is serving you AI-generated answers to your search queries. Maybe a funder portal you log into every quarter has it built into the interface.
The question was never really whether to use AI. The question is whether you are using it intentionally.
Here is what I can offer: a plain-language account of what AI actually is, what it is not, and how to use it more deliberately, in a way that serves your mission without creating new problems in the process.
A quick note on terminology
These terms get used interchangeably, and they shouldn’t. Machine learning is the broader category: algorithms that find patterns in data and improve over time. It has been embedded in software nonprofits use for years: the algorithm that ranks your email, the tool that flags duplicate donor records, the platform that recommends content. That is one conversation about AI, and it is worth having separately.
Generative AI is a more recent layer: systems that produce content (text, images, audio) in response to prompts. Large language models, or LLMs, are the specific technology behind the tools most people are now talking about: ChatGPT, Claude, Gemini. They were trained on enormous amounts of text and generate responses by predicting what words should come next, in sequence, based on patterns in that training data.
When people in the social sector talk about AI right now, they usually mean generative AI, and specifically LLMs. That is what this post is about.
What AI actually is
Here is the short version of how it works: the model was trained on an enormous amount of text. It learned patterns in that text: which words tend to follow which other words, in which contexts. When you ask it a question or give it a task, it generates a response by predicting what words should come next, based on everything it has seen.
That is most of it.
It is not thinking. It is not reasoning in the way you reason. It is very sophisticated pattern matching, operating at a scale that can feel like understanding.
Sometimes it is genuinely useful. Sometimes it produces confident nonsense. Knowing which is which is your job, not the tool’s.
What it is not
It does not know your community. It does not know your history, your relationships, your context, or what your participants actually need. It has never been to your city. It cannot read the room.
It is not neutral. It was trained on text that exists on the internet, which reflects existing power structures, dominant voices, and historical exclusions. It will reproduce those patterns unless you actively push back.
It makes things up. This is not a bug that will eventually be fixed. It is a feature of how the technology works. The model generates plausible-sounding text. Plausible and accurate are not the same thing. It will cite sources that do not exist. It will state incorrect statistics with complete confidence. Every output needs a human review.
It is not faster by default. Drafting with AI, then reviewing, correcting, and rewriting to make it sound like you, often takes about as long as writing a decent first draft yourself. The time savings are real in some situations and illusory in others.
Where it can actually help
Drafting. Grant narratives, donor emails, job postings, board updates. Feed it context about your program, your population, and what you are trying to say, and it will get words on the page. A program director I know used it to turn her messy bullet points into a first draft of a foundation narrative in twenty minutes. She spent another hour making it sound like her organization. That is still faster than starting from scratch.
Summarizing. Long reports, meeting transcripts, policy documents. Paste in the text, ask for a summary, check it against the original. If your team produces a lot of documentation that nobody has time to read, this is where AI earns its keep. A thirty-page evaluation report becomes a two-page brief. A two-hour meeting transcript becomes a list of decisions and next steps.
Generating first-pass questions. Interview guides, survey drafts, focus group prompts. Tell it who you are talking to and what you are trying to learn, and it will give you a starting set of questions in minutes. You will still need to adapt them for your specific community and context, but you are editing rather than staring at a blank document.
Brainstorming. Program names, communications angles, ways to frame a problem. Treat the output as raw material, not finished thinking. It is useful for getting unstuck, less useful for getting to something genuinely yours.
One capability that tends to surprise people: AI can do things that are simply not feasible for humans at scale. The clearest example I have seen is qualitative analysis of open-ended survey responses. Hundreds of respondents, open-ended questions. A human team would need weeks to code and synthesize that data, and most small nonprofits would never attempt it. AI can surface themes, patterns, and outliers in that dataset in a fraction of the time, with a human reviewing and interpreting the output. That is not AI replacing judgment. That is AI making a certain kind of analysis possible that was not practical before.
Tools worth knowing about
Claude (Anthropic), ChatGPT (OpenAI), and Gemini (Google) are the frontier models, the underlying systems that most other AI tools are built on. They all have free tiers that are sufficient for most of the tasks above. If your team already lives in Google Workspace, Gemini is built into Docs and Gmail and has a low-friction on-ramp. Airtable has AI features built into its interface that can help with data organization and summarization if you are already using it for program tracking.
A lot of the AI products marketed to nonprofits (writing assistants, grant tools, donor engagement platforms) are wrappers around these frontier models. They add a specialized interface, prompts tuned for specific tasks, and sometimes workflow integrations. That can be genuinely useful. But it is worth knowing that the underlying model is usually one of these three. When you are evaluating whether to pay for a specialized tool, part of the question is whether the wrapper adds enough value over using the frontier model directly.
You do not need to pay for a premium tier to get started. Start with the free version of a frontier model and see whether it actually fits into your workflow before spending money on a specialized product.
What to protect
Before you put anything into an AI tool, know what tier you are on and what the terms actually say.
This is not just a free-account problem. Free accounts on most major platforms default to using your inputs to improve their models, but so do many paid individual plans. ChatGPT Plus and Claude Pro, for example, carry the same default data policies as their free tiers unless you actively opt out in your account settings. Opting out is usually available and does not affect your experience, but you have to do it deliberately.
The real protection comes at the business or enterprise tier, where providers offer contractual commitments about how your data is handled, no training on your inputs by default, and in some cases the ability to enter into a business associate agreement for sensitive data. If your organization is working with client information, health data, or anything that carries legal or ethical obligations around privacy, that is the tier worth looking at — and the cost is worth comparing against the risk.
The question to ask before using any tool with sensitive data is not “is this AI?” It is “what does our agreement with this vendor actually say, and does it meet our obligations to the people we serve?”
That is a governance question, not a technology question. And it is one your organization should answer deliberately, not by default.
Your judgment does not get delegated
The tool produces output. You decide what to do with it. If something the tool generates feels off, it probably is. Trust that.
AI can help you work faster on certain tasks. It cannot tell you what your community needs, what your values require, or whether a decision is the right one. That knowledge lives with you and the people you work with. No tool has access to it.
Using it more intentionally
You do not have to have a position on AI to use it carefully. You do not have to be an early adopter or a skeptic. Being early in adoption is actually an advantage. You have room to make deliberate decisions before habits form and tools accumulate.
If your use of AI right now is ad hoc (someone drafts with it occasionally, someone else avoids it, no one has talked about the data question) that is worth changing. Not because AI requires a grand strategy, but because mission-driven organizations have specific obligations: to the people they serve, to the data they hold, and to the values that brought them to this work in the first place.
Start with one conversation. What are we using, who is using it, and what are we putting into it? Then try one low-stakes task deliberately. Notice where it helps and where it misses.
That is enough for now.
Anthralytic is a strategy and evaluation studio helping mission-driven teams clarify and amplify their impact. If someone in your network makes decisions about resources in the social sector, this newsletter is for them.
