Think about the donor we know best. The one we could describe to a colleague over lunch without looking anything up. We wouldn’t recite a giving total. We’d say something like, “She’s given three times this year, each gift a little larger, and she’s really passionate about the mobile pantry program.” Or, “He’s given every year for six years, and his family went through a rough patch and our organization kept them fed. That’s why he gives.”
Notice what we’re doing when we describe these donors. We’re weaving together three very different kinds of knowledge, and most donor databases capture only two of them.
An AI-powered relationship-building system can draft donor communications that draw on all three dimensions, but only when the fundraiser has put the third one into the record. The “why” behind a donor’s giving is something only a human can discover, through conversations, observations, and the kind of attentive listening that no algorithm can replicate. When that knowledge makes it into the file, every AI-drafted communication shifts from generic to personal.
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In Holding Fire: A Skeptic’s Framework for the Fundraiser with AI at Your Side, Stephen Christopher Nill, JD, lays out the Human-Centered AI Framework—his five-principles approach to keeping humans at the center of every donor interaction, even as AI quietly does the work no one fundraiser could ever do alone.
This book was written by a skeptic, for skeptics.
Most Databases Capture Two Dimensions and Miss the Third
The first kind of knowledge is facts. Name, address, email, phone number, employer, board membership. Every database captures this well. It changes slowly, it’s easy to verify, and it’s necessary for basic operations. Knowing that a donor lives on Oak Street and prefers email is useful, but it doesn’t tell us how to deepen the relationship.
The second kind is behavior. Giving history, event attendance, email engagement, volunteer participation. Behavior reveals patterns that facts alone cannot. Three gifts in one year, each larger than the last, tells a story of accelerating commitment. Six consecutive years of giving signals loyalty that a modest gift amount might obscure. A lapsed donor who stopped writing checks but still attends events and opens every newsletter is telling us the relationship isn’t over, even if the giving paused.
Most organizations have at least some behavioral data, though it’s often scattered across multiple systems: gift records in the database, event attendance in a spreadsheet, email metrics in the email platform, social media activity untracked entirely. Consolidating all of it into a single donor record is a significant upgrade over how most shops operate, and it’s one of the first things a good relationship-building system does.
But the third dimension holds the real insight, and it’s the one almost nobody tracks systematically. Values and motivation, the why behind the giving.
Why the “Why” Changes Everything
When we sit down with a six-year donor and learn that the family went through a difficult stretch years ago, that a job loss left them struggling, and that our organization kept them fed during the hardest months, that piece of information reshapes our understanding of who this person is. The motivation is the memory of what it felt like to need help, combined with the pride of staying connected to the organization that provided it. That knowledge changes how we thank this donor, what stories we share, and what we eventually invite this person to do beyond writing an annual check.
When a physician tells us about seeing food insecurity in patients every day, children with diet-related health issues that are entirely preventable, and explains that nutrition education is the intervention that changes outcomes, we’ve just received the key to that donor’s philanthropic identity. The gift designation to a nutrition program is a deeply personal commitment rooted in professional experience.
This kind of knowledge has always been the most valuable thing a fundraiser possesses. The problem is that in most organizations, values and motivation data exists in the fundraiser’s memory, in handwritten notes from a lunch meeting that never got entered into the database, in the back of a notebook from a gala conversation three years ago. It’s fragile, it’s incomplete, and it walks out the door when the fundraiser takes a new job.
What Changes When the “Why” Is in the System
The practical difference is significant. Without values and motivation data, an AI drafting a thank-you note for a $300 annual gift produces something generic. “Thank you for your generous gift of $300. We are grateful for your support and the difference it helps make.” It’s adequate and forgettable, the kind of note that makes a donor feel like a row in a spreadsheet.
With values and motivation data in the record, the same AI produces something entirely different: a note that references the family’s personal connection to the mission, that acknowledges the consistency of six years of giving, that connects the gift to a specific outcome the donor cares about. The donor and the gift amount are the same, but the relationship is completely different.
We’d still edit that draft before sending it. We’d tighten it, add a detail from last week’s program update, maybe adjust the tone. The AI gives us something specific and personal to work with, and that’s enormously different from starting with a blank page or a mail-merged form letter. But the quality of what the AI produces depends entirely on what’s in the donor’s file. When the file is thin, the draft is generic. When the file is rich with context about who this person is and why this person gives, the draft speaks to the whole human being.
The virtuous data cycle, one of the five elements of the Human-Centered AI Framework, becomes visible at this point. Every conversation note we log, every giving motivation we record, every personal detail we capture after an event or a phone call becomes raw material for more thoughtful communications down the road. A donor record that gets a little richer each month produces better AI output by the end of the year than it did at the beginning. The system compounds, turning each small investment of attention into permanently better donor communications.
A fundraiser who has logged good notes becomes irreplaceable. I’ve seen it happen in real time: the AI reviews two donor records with similar gift histories and similar engagement patterns. One record includes detailed notes from three conversations about why this donor gives, which programs they care about most, and what role philanthropy plays in this person’s family. The other record has gift dates and amounts, and nothing else. The drafts for those two donors aren’t even in the same league, and what separates them is what the fundraiser put in the file.
How the Enrichment Actually Works
The enrichment happens inside the work we’re already doing.
During the Monday morning review, a weekly 20-minute check of each active donor’s record, the AI flags a donor whose giving has increased three years running. We recognize the name and remember a conversation about why this person cares so deeply about a particular program. We add that context to the donor’s record before editing the draft and sending it. That took 30 seconds. Next week, when the AI reviews that donor again, the draft is better because it draws on what we added. The virtuous data cycle in action. Every small investment of context produces permanently better output. The file gets richer through the natural rhythm of paying attention to the donors who need it most, not through a separate enrichment project.
The same thing happens after a phone call, a gala conversation, a volunteer event where someone shares a personal story. We learn something new about why a donor gives, and we enter it. It takes a moment. Over weeks and months, those moments accumulate into a donor record that captures the whole person, and every piece of communication the AI drafts reflects that depth.
Try this experiment this week. Pick five donors whose stories you could tell over coffee. Open each record and ask whether the database captures what you know about why each one gives. If the answer is no, add it. A sentence or two is enough: the giving motivation, the personal connection, the conversation that revealed what truly drives this person. That’s all the AI needs to shift from generic to personal.
The database doesn’t need to be fully enriched before the system starts delivering value. Start with the donors the AI is already flagging during Monday morning reviews. Add context when we have it. The system improves incrementally, and the improvement compounds.
In my book, Holding Fire, I walk through the complete analytical framework: the three-category donor profile, the qualification lenses that reveal hidden potential across the entire donor base, and the data architecture that makes individualized fundraising intelligent rather than arbitrary. I also show what happens when every donor in the system, from the $50 first-time giver to the $50,000 prospect, gets the same quality of strategic attention.
Next week, I’ll make the case that the annual fund is not a mailing. It’s a campaign, and when AI handles the analysis and drafting while we focus on strategic decisions and personal relationships, the results change dramatically.
Stephen Christopher Nill, JD, founded CharityChannel in 1992, building it into one of the largest online communities of nonprofit-sector practitioners in the world. He has edited, designed, and published dozens of books for the nonprofit sector through CharityChannel Press. Over four decades, Stephen has built, administered, and advised fundraising programs in the United States and internationally that have raised billions of dollars. He has served as senior vice president of development at a hospital system, chief advancement officer at a West Coast university, director of development at a parochial school he helped to found, and founder and executive director of a charity that feeds the homeless in Southern California.
The Grant Professionals Association presented him with its first President’s Award. The Grant Professionals Certification Institute later honored him with the Pauline G. Annarino Award for foundational contributions to its existence. Stephen has been a frequent commentator on charitable giving for CNN, Fox News, MSNBC (now MS NOW), and other news shows.
He has provided training to attorneys on advanced charitable gift planning techniques on behalf of the State Bar of California’s continuing legal education program. As a lawyer and consultant, he advised hundreds of nonprofit organizations and educational institutions.
Stephen’s engagement with artificial intelligence reaches back to the 1970s, decades before the current wave. Holding Fire: A Skeptic’s Framework for the Fundraiser with AI at Your Side draws on that long history and on his career inside development offices.
He writes weekly to development professionals at charitychannel.com. These days he’s busy writing his first novel and composing symphonic music in his music studio.