Episode 219: Screening in Philanthropy: Yesterday, Today and Tomorrow, And the Need for Embracing AI
Welcome to another edition of Around with Randall, your weekly podcast for making your nonprofit more effective for your community. And here is your host, the CEO and founder of Hallett Philanthropy, Randall Hallett.
It's always a privilege to have you join me, Randall, on this edition or any edition of Around with Randall. We hop into an area of growth, investigation, inquiry, and uncertainty. And that's the idea of screening. I've got a number of clients that are going through a process and trying to figure out where the future of screening work is headed.
Now, most of us probably immediately put a word in front of screening, and that's the word wealth—wealth screening. I want to pull it back a little bit because, really, what we need to be thinking about isn’t so much wealth but connection. However, the history of utilizing data from various sources is deeply tied to the idea of wealth.
So today, I want to talk a little bit about where we’ve been, where we are, and where we're going. And of course, the tactical—what can you do and be thinking about when it comes to figuring out how to use data more effectively? If we look back, we’ve always tried to figure out who are the best people for us to talk with.
Let’s go back to the pre-internet days. Maybe some of you listening are young enough that you’re not even aware there was a pre-internet era, or you’ve only read about it in books or heard your parents talk about it. But some of us who are a little older, with a little more gray hair on top, know that this existed because we lived in it. When I started my career, data gathering for identifying the best donor pipeline or campaign prospects was a manual screening process.
Even today, I still advise utilizing this method when necessary because it can be effective on a more detailed level. But originally, it was a manual effort involving a group of volunteers—often a board, campaign cabinet, or committee—reviewing a prospect list. Who do they know? What do they know about them? Do they have money, or would they be interested? Then, you might try to match that manually with a printout or report of donors to the organization.
Everything was done manually through list reviews. In my earliest days in philanthropy, which actually began in education rather than healthcare, we printed out alumni lists and had class captains review them to provide insight. We would then input that information into a more advanced Excel spreadsheet—our CRM at the time—and use it to determine whom to contact and who could provide an introduction.
Some key factors made this process effective. We needed reliable people in our group who had accurate information—or any information at all. Public records were sometimes used, but they were paper-based and inefficient. I knew people who would manually retrieve publicly available data from wills and estates filed in court or business transaction records. It was a highly inefficient method, but when the information was accurate, it could be a goldmine. However, these were more one-off successes rather than scalable processes.
The late 1990s and early 2000s marked not just an evolutionary change but a revolutionary one. As we entered the internet age, everything changed. Suddenly, we could perform online searches—what we now casually refer to as Googling—to gather data much faster. In the early days, this information was often more accurate because it came from government databases or news sources.
This period saw the rise of prospect research. We realized that donor profiles, especially when preparing for a solicitation, helped paint a broader picture of a prospect’s potential. Wealth registries and public disclosures became key resources. SEC filings revealed stock ownership, jet and yacht ownership became publicly available, and real estate records in certain states provided insights into multiple-property ownership.
This shift dramatically improved our data-gathering abilities, but the process remained cumbersome. Initially, all of these methods still centered around a common theme—wealth. That’s why we still use the term wealth screening today. But if you’ve spent any time with me, attended my trainings, or been to any of my educational sessions at national and international conferences, you know that I always emphasize this:
Eighty percent of what we need to know when identifying prospects for pipeline development and engagement is not about wealth—it’s about inclination and action.
As we moved forward from the early internet era, new companies emerged that began aggregating this data. WealthEngine and DonorSearch are two major examples—ironically both developed by the same individual, Bill Tedesco, who doesn’t get the credit he deserves but probably doesn’t seek it either. These companies changed the game by consolidating scattered data into unified profiles. Instead of manually compiling lists, we could now generate a single report with a prospect’s name, address, phone number, giving history, and other data points like political donations, SEC filings, and real estate holdings.
This aggregation allowed us to run large sets of names through a process that produced predictive analytics—helping us compare a prospect’s potential capacity with their current giving levels. This shift enabled more strategic prospect identification.
But what comes next? The dawn of artificial intelligence is going to push us even further, shifting the focus away from wealth alone and more toward what truly matters: inclination and connection.
We are on the first stages of an equivalency of changing from the manual to the internet. We're going from data aggregation, internet grabbing of data companies that provide it all to actually, actually artificially driven, artificial intelligence driven predictive modeling that leads us to inclination. The limitation on wealth screening, air quotes, is, as it talked about what people have, where artificial intelligence is going to change is what their behaviors are.
And like anything else, change is hard. You can go back and listen to a couple of my podcasts on change. Nobody wants this. The two people I know the most that do not want change in the world are myself and my son. If you read my blog, they wrote about that. It took my wife and I 60 days to get him to change over his room, to move to a little bit more of a room that's not a little kid, because he's not a little kid. He's 11. Amazing 11-year-old. But it was a plan because one picture moved. It was, oh, don't like change. Gift officers are no different. Prospect researchers are no different.
I was recently with a client, hopefully A to B client, struggling with this. This is a larger place. So they've got prospect management, data integrity. They have their own HR individual that they used to maximize gift officer turnover. So this is a highly sophisticated place. We were into the weeds of artificial intelligence, and they've got talented, smart people. Impressive, man. They are hesitant. We're going to be giving up our data to someone else's opinions. And they're actually interested in the conversation. Gift officers are recalcitrant. "I know best. I know my portfolio. I know my people."
What we know is that artificial intelligence can take thousands of pieces of data about one person and begin a process of figuring out who you should be talking to. Think about this: we, in an aggregation of wealth engine, AI Wave, my choice donor search of just pure data—what's publicly available—adding to that anything you can figure out about the person and what they do with your organization. From the basics of giving. In health care, we are using HIPAA-compliant data for the number of visits, the number of doctors they are seeing. Then on top of that, it's coming down the road.
And we'll talk about this here in a moment about what's next, which is going to talk about behavioral information. In education, it's about the clubs they were involved in, how long they were at the school, what fraternity or sorority, what groups they did, what they participated in. In social services, do they volunteer in other places? All of these pieces of data.
But here's the crazy thing. Artificial intelligence isn't a stagnant thing when done correctly. True artificial intelligence, machine learning, is about re-applying a mathematical equation and changing the equation to make it better. So every time you put more data in, as people do things, the better the data analysis gets. The mathematics has the ability to look at millions of records in moments and give you 200 names, 400 names that are your best prospects.
And what I find more often than not is when we do this, two things are true. The ability of artificial intelligence doesn't mean we're getting rid of the database team or research. Actually, I would advocate we need more people, but doing different things, which I'll talk about here in a moment. The key here is that it's producing better options, but there's a recalcitrance to accept them.
Without that, they haven't given enough money. "I'm a major gift officer. I only deal at $25,000 and above. This person has only given $5,000. Why would I be involved with that? Why would I call them? I only want to deal with the rich people." But the option, if we built a better relationship, could be much greater with these people who may not be giving us $1 million today but could be our million-dollar donors in 2 or 3 years if we built a stronger connection.
Remember, wealth screening is about how much they might be able to give. Predictive AI predicting connection is how likely they are to give. And this then gets us into conversations of estate giving, where all the assets are. Merrill Lynch is telling us $85 to $86 trillion, depending on the market, will transfer in the next 20-25 years, and most of it—85 plus percent—isn't tagged to charities. But we know people are thinking about that.
All this is to say that there are all these pushes and pulls. I think the other thing, just for a moment before I get into the tactical, is to talk about what's coming next or what I think might be coming next in the screening identification process. The modeling will get better in overlaying wealth with the connections.
I do this when I kind of have a presentation that I work with, where I literally say, we're getting to the point where if we have ten donors, we'll be able to overlay connection number one and then re-sort it by wealth. So we're not only replacing the highest options of connection, but we're also then stratifying it by wealth.
One of which is this strong innovation. The second thing is personalization at scale. There are experts—I think about my friend Nathan Chapelle, my friend Scott Rosenkranz—who have kind of led these conversations in the nonprofit industry. If you're not following them, you should. Two dear friends that I trust, not only professionally but personally. They're going to start predicting what are the next steps for gift officers to take with individual donors.
What's the donor journey? How do you tailor individual messaging to individual donors? How do you take strategy and make it dynamic? That might change depending on the time of the year, when their last gift was. We're going to get to the point where artificial intelligence is driving conversations about gift officer choices and activity. And what I anticipate is that will not go over well for many.
"Well, I know best." We're going to talk here at the very end about AI not being a replacement. So hang on. If you're like, "Oh my gosh, I don’t like Randall telling me how to do my job." Actually, no, I think you're going to be better at it.
I think the other thing is that we need to consider ethical considerations. Again, Nathan Chapelle and Scott Rosenkranz have led the conversation about ethics in AI, particularly in the nonprofit space, lecturing, teaching, and talking about it all over the world.
This is going to get into things like, can we grab social media posts so we hear and know in real-time what people are saying on LinkedIn, Facebook, X, Instagram, whatever, and tie that into behavioral modeling of exactly what somebody is thinking and doing? Now there's an argument: say it's all public information. They put it out there. But there are ethical concerns here. Is it being used for good? There could be negative ways to use this information.
All this is to say that this is the starting point of AI, of the modeling towards the inclination with an overlay of wealth.
What we know is that we're going to run into things we haven't thought about yet. So what's the tactical takeaway in my last few minutes? What are you supposed to do with all of this? This isn't just a history lesson, although I appreciate you listening and letting me teach a little bit. There are five things that I think are really important, and I don't have all the answers, but I know what I should be doing.
That's what I want to share with you now. Number one is you need to engage in the conversation. If you're burying your head in the sand and you're not willing to think about this, read about it, or follow it, you're going to get left behind. The Association for Health Care Philanthropy is hosting a conference in the summer of 2025 focused entirely on AI because health care likely has more real-time data than any other sector of the nonprofit world. How are we using it? What's appropriate? How do we manage it? Health care is probably leading this discussion due to the volume of HIPAA-compliant health data available. You have to be willing to learn and accept that it may look different. Follow experts, read about it, attend conferences, take webinars. I'm not asking you to become an expert, but if you don't adjust, AI will either run you over or pass you by.
Number two is you're going to have to fight for investment. Invest in your database, your data, your analysis, and your modeling. I have a friend I advise informally, and one of the issues their organization faces is a reluctance to spend money. I keep telling their leadership that every day they delay investing in this, they are leaving money on the table. If you don't invest in this, your fundraising ability diminishes. Some may argue about the costs and ROI, but organizations that are succeeding philanthropically are investing in this.
There has to be a long, hard conversation about these costs. The good news is that this isn't a million-dollar investment. We’re talking about tens of thousands of dollars per year. If you spend, say, $35,000 on predictive modeling and it results in $240,000 in new gifts, who would argue against that kind of 6:1 or 7:1 ROI? But it’s hard to secure those budgets. You have to fight for them, show results, bring in experts, work with your CFO, work with your board, and advocate for this investment because it will pay off. Expense can't be an excuse. This is what we need to be more successful.
Number three is that this data can't just sit on the sidelines. It has to be integrated into your CRM. Most of the work being done can be uploaded regularly. I won’t get into details today about shadow databases, where data is held until it becomes more effective, but one key point is that CRMs often charge based on the number of records. The bottom line is this: if you collect data, use it.
Number four is influencing and mandating appropriate use—for gift officers, for reporting, for metrics, and for the infrastructure team. If we don’t use this data, it’s wasted. There will be pushback from some, but at the end of the day, philanthropy needs this tool. It's not a replacement—I'll touch on that in a moment—but it makes us more effective. Gift officers will need to adapt, working with smaller portfolios and focusing more on the right people. This doesn’t reduce the need for prospect management or research; it actually increases it. In the future, prospect researchers and database professionals won’t just maintain records—they will be actively identifying who to prioritize. Leadership will need to enforce this shift.
Number five is about how to use AI strategically. Predictive modeling should be used to mitigate costs, particularly with the rising expense of direct mail for annual giving. Are there 30-50% of donors you should stop mailing because the data suggests they won’t give? Just because someone gave five years ago doesn’t mean you should keep mailing them. Predictive modeling can also improve major gift fundraising by identifying who should be prioritized in your pipeline.
It can also help increase giving. If someone has the capacity to give significantly more but is only giving a small amount, wouldn’t you want to know why? Predictive modeling can identify 25 or 50 potential donors worth having a conversation with. It can also support campaign feasibility studies by helping determine who should be interviewed. Long-time donors may be expected to participate, but how do you expand your base? AI can help identify new prospects.
All of these tactical applications show the power of AI, but my final thought is this: AI is not a replacement. I recently heard someone say that AI could eventually replace parts of nonprofit work, but I’m not there yet. Relationship-building is still the most important part of philanthropy. Trust and personal connections drive giving.
Prospect management will evolve, but AI isn’t a substitute—it’s an improvement. And innovation is the mother of invention. If you’re unwilling to think about these changes—not necessarily making them today, but at least being open to them—you risk being left behind as a leader, gift officer, database professional, infrastructure expert, board member, or CEO. This is where we are headed, and accepting it will be crucial. AI is a part of the nonprofit sector’s future.
The world is changing, much like when my parents in the 1960s watched the space race unfold. But philanthropy has remained a constant force for good throughout history.
Remember my favorite saying: some people make things happen, some people watch things happen, and some people wonder what happened. Don’t be the one wondering what happened when it comes to AI and the evolution of fundraising. Be the one who makes things happen for the people and communities that need it most.
What you do—whether as a board member, major gift officer, infrastructure professional, or leader—is valuable. The nonprofit world needs you. Philanthropy needs you because people in need rely on philanthropy.
Looking forward to seeing you next time on the next edition of Around with Randall. Until then, make it a great day.