By Will Schrepferman - CEO, DonorAtlas
What would your development team do differently if every donor profile came with the full story, not just a score?
Not just an estimated net worth or a capacity rating, but the actual narrative of who someone is: how they built their wealth, what causes they've supported and in what amounts, which boards they serve on, what connects them to people in your existing network, and whether any of it suggests they'd care about your mission. Most teams already know that this is the information that matters. The problem is that assembling it has always taken so long that only your highest-priority prospects ever get the full treatment. Everyone else gets a score and a best guess.
That dynamic is starting to change, though, in the age of AI. AI models can now do (a lot of) the research. They can go out to the open web, find and read information across dozens of public sources, synthesize information into a coherent profile, and cite every data point back to where it came from. The result is a different category of output altogether, one where profiles are intelligent, contextual, and verifiable rather than opaque, static, and built on data you can't trace.
To be clear: AI does not replace the essential role of the prospect researcher. It replaces the tedium that keeps prospect researchers from doing their actual job. The judgment, the analysis, the ability to look at a profile and know whether someone is worth pursuing and how to approach them, that's human work. No AI is going to do it well. But what AI can do is make sure that when a researcher sits down to evaluate a prospect, the information is already assembled, already cited, and already current, so the time goes toward thinking instead of gathering.
This is the move from scores to stories. And development teams that have made it are finding donors that others miss.
When Research Gets Faster, Everything Downstream Changes
Speed sounds like an incremental benefit, the kind of thing you'd see in a product demo and think "nice, saves a few minutes." But in practice, faster research changes what kinds of projects a team can take on and what they're able to see.
OutVote, a civic engagement organization, saw research time per donor drop by 87% after adopting AI-powered research tools (they tracked it; before, it took their team thirty minutes per prospect to generate a profile, and after it took under a minute). That alone would be useful. But what mattered more was what the faster research uncovered. The platform revealed that a supporter who had been giving $2,500 actually had the capacity for a five-figure contribution. With verified, cited data on their giving history and wealth indicators, OutVote could approach that next conversation with confidence instead of guesswork. The donor's capacity was always there. It just wasn't visible at the speed the team was working.
Aspire Research Group, a prospect research firm, experienced the speed shift at a different scale. Jennifer Filla's team needed to build a curated prospect list for a client focused on economic justice, but the organization had no mailing list, no board connections, and no existing donor network. A completely blank slate. Using AI-powered research, Aspire generated a targeted prospect pool and then used cited bios and contextual summaries to evaluate whether each prospect's philanthropic priorities and policy positions aligned with the client's mission. The disqualification turned out to be just as valuable as the discovery: when a prospect's giving history or public positions didn't match, the team could rule them out in minutes rather than spending days on manual review only to reach the same conclusion. They researched over 550 donors and delivered more than 400 fully vetted, high-quality prospects to their client. The platform helped her team determine alignment and capacity at scale, most importantly by eliminating unnecessary work on low-fit prospects.
Both of these stories illustrate the same principle. When the research is fast enough to be comprehensive at scale, you stop triaging which prospects deserve your attention and start evaluating all of them. You catch the $2,500 donor who should be a $25,000 donor. You build a list of 400 vetted prospects from scratch instead of settling for the 50 your team had time to research manually. The depth of your intelligence stops being limited by the hours in the day.
Relationship Intelligence Changes the Workflow Entirely
The other dimension of this shift that doesn't get enough attention is relationship mapping, because it changes not just the quality of individual profiles but the entire way teams build prospect pipelines.
Rodman for Kids is a nonprofit serving over 100,000 children annually with a team of eight. Jessica Feenan, their Director of Development, was spending up to ten hours a week manually tracing connections and mapping networks. The question she was trying to answer is one every development shop deals with: which of our existing supporters, board members, and volunteers are connected to the people we're trying to reach, and how? Who went to the same school? Who served on a board together? Whose kids are in the same community? These are the connections that lead to warm introductions, and warm introductions are how major gifts actually happen.
The problem was that none of Rodman's existing tools could surface this information. They could generate basic donor profiles, but they couldn't show the connective tissue between people. So Jessica's team did it manually, spending hours each week piecing together who might know whom.
When the team adopted AI-powered relationship mapping, the platform automatically surfaced over 3,000 connections across their network that they had no efficient way of seeing before. The team raised more than $2 million in the year that followed, but the bigger change was operational. Instead of spending hours figuring out relationship pathways, they could start every prospect conversation with that context already in hand and focus their energy on building the relationships that mattered most.
The Work the Tools Should Have Always Done
The pattern across all of these organizations is the same. When AI handles the assembly, the nature of prospect research changes. Researchers spend less time gathering information and more time evaluating it. Gift officers walk into meetings better prepared. Pipelines get built proactively instead of reactively. And the intelligence itself is better, because a cited, current profile built from the open web is a fundamentally stronger foundation than a black-box score from a third party database.
Development teams have always needed stories about their prospects, not just scores. The difference is that AI can now build the first draft of those stories at a speed and depth that was never possible before, pulling from the full breadth of publicly available information and citing its sources so that the humans in the loop can do what they've always done best: evaluate, strategize, and build relationships.
Hundreds of organizations are already working this way. If you haven't looked at what's possible yet, it's worth seeing for yourself.
Will Schrepferman is the co-founder and CEO of DonorAtlas, the donor research platform built from the ground up with AI. Will raised his first dollar at age 13, studied government and data science at Harvard, and has spent his career helping mission-driven organizations find and connect with the people who can support them. Last year, DonorAtlas became the first donor research tool to release fully embedded relationship mapping, helping nonprofits see not just who can give, but who knows whom.
