How Non-Profits Use AI for Impact (Scott Rosenkrans)

Large corporations and startups are dominating the AI conversation. But how do non-profits adopt AI without compromising trust?

In this episode of "What’s the BUZZ?," host Andreas Welsch welcomes Scott Rosenkrans, VP of AI Innovation at DonorSearch, to the show to discuss how non-profits use AI to make a measurable impact.

Catch the BUZZ:
• Why does “just because you can” not always mean you should?
• How can predictive and generative AI work together to support human fundraisers?
• Why must donor relationships come before fundraising transactions?
• How does culture readiness in addition to tech readiness drive AI success?
• Which success metrics in fundraising go beyond total dollars raised?

If you’re a non-profit leader or change manager tasked with delivering results from AI, this conversation will help you set yourself up for success.

Transcript

Andreas Welsch: 0:30

Today we'll talk about how AI innovation helps nonprofits pursue their mission, and who better to talk about it than someone who's actively working on that. Scott Rosenkranz. Hey, Scott. Thank you so much for joining.

Scott Rosenkrans: 0:42

Hey Andreas, thanks for having me. I'm very excited to be here.

Andreas Welsch: 0:44

Hey, wonderful. Why don't you tell our audience a little bit about yourself, who you are and what you do.

Scott Rosenkrans: 0:49

Sounds great. I'm Scott Rosencrans. I'm VP of AI Innovation at Donor Search. I've been in the nonprofit sector my entire career. I actually went to grad school to be a therapist to help people solve problems and realized in about six to nine months that I was gonna burn out too quickly and found myself in the nonprofit sector. So I've been in there for 15 years now working with nonprofits both. In a nonprofit doing a lot of charitable fundraising, working behind the scenes on identifying who we should be asking for gifts and so on. And then also working as a consultant to help nonprofits do more with the limited resources that they have. But for the past eight years, I've been prioritizing and focused on predictive machine learning models. So building custom machine learning models for nonprofits to predict who's likely to make a gift next 12 months, who's likely to make their first gift and help. Now help organizations really utilize, again, those limited resources. They're usually working over budget. They have limited staff, people wearing a lot of hats, so been helping them just make the most outta what they have. And then also doing a lot of consulting about how to adopt AI. We know that a lot of work you talk about too, just because they have tools doesn't mean that they're seeing the su the success with it. So how do we get them to just move a little bit further in their AI journey?

Andreas Welsch: 2:10

We met in Phoenix at Machine Learning Week a couple weeks ago, and said, we should definitely record an episode together. But you also mentioned that you're working on a book, right? Or you actually have a book out?

Scott Rosenkrans: 2:19

Yeah we, have a book out Nonprofit AI. It came out about two months ago. Comprehensive Guide to implementing Artificial Intelligence for Social Good. So my colleague and I, my, my friend, my mentor, Nathan Chappelle, we've been in this space together for the past eight years, worked with hundreds of nonprofits and only a few of them have really seen the success that we would expect them to see with these predictive models. And then it even expanded, as you would expect with generative AI, right? Everybody wants to jump on the bandwagon. They talk, they hear about how the ROI is limitless, right? And it's just such an easy thing to adopt, but nobody's really seeing that come out. So this book is really to help anyone in the nonprofit space. Move a little bit further along, identifying the challenges that are getting in the way, whether they're cultural, technical. Spoiler alert, they're usually cultural, right? And then giving you some examples of how to actually implement, whether it's predictive, generative, or automation. And then once and you're seeing success, go back to the book, read a little bit to how to get further along the journey and then see what's coming down the pike. And AI is constantly evolving. New technologies are coming out, so this isn't a read it once and you're done. It's a, guide to help you along the way.

Andreas Welsch: 3:30

That sounds great. And, by the way, overall, I love the mission of helping others help others as well, that you shared as working with nonprofits. So as I said, super excited to have you on. So now Scott, should we play a little game to kick things off?

Scott Rosenkrans: 3:45

Love it. Absolutely.

Andreas Welsch: 3:46

So let's do this in good old fashion. So this game when I hit the buzzer, you'll see a sentence and I'd like for you to answer with the first thing that comes to mind and why, in your own words. And to make it a little more interesting, you'll only have 60 seconds for your answer. Okay? Scott, are you ready for, what's the buzz?

Scott Rosenkrans: 4:07

As ready as it'll ever be. Yes.

Andreas Welsch: 4:10

Alright, so here we go. If AI were a vehicle, what would it be? Vehicle. Okay. 60 seconds on the clock. Go.

Scott Rosenkrans: 4:21

First thing this, I'm not sure if this answer applies, but we're gonna go with it anyway. I'm gonna say a transformer. It could be a lot of things all at once. It's whatever it needs to be in the moment, whether it's a semi or a bot. Whatever it is, it's, there to get you a little bit further and help you along and can change and evolve and adapt to the surrounding circumstances.

Andreas Welsch: 4:47

Perfect. Sounds like a optimal prime answer. Fantastic. Thank you so much for doing this on on the fly. I really enjoy seeing how my different guests answer these, questions and what comes to mind. Yeah. Great analogies. Yeah. We're here to talk more about nonprofits. And one of the first questions that comes to mind for, me is I've spent 25 years working in, corporate, in for-profit a lot of time on on, AI projects, AI programs, strategy enablement and so on. But I'm wondering what does it actually look like when you work for a nonprofit? How, is it different in that environment from traditional corporate for profit?

Scott Rosenkrans: 5:33

Yeah, the most obvious thing is that our goals are not profit based, right? It's not just increasing revenue, having better more returns for stakeholders and shareholders and so on. It's doing good. It's completing your mission. The goal of most nonprofits is to put themselves out of business because they've solved the problem, right? And when I, as a consumer give money to a for-profit company, I get something in return, right? I get a iPad, an iPhone. I get I access to a social media platform or whatever it is. I get something in, in return to evaluate. I. Whether I thought my investment was worth it. For a nonprofit, all we get to give in return is trust. If you give money to a nonprofit, you are trusting that nonprofit is doing what they said they would do with it and doing it efficiently. Trust is very easy to break. And so there's a classic example of a nonprofit that, like many others was under-resourced and so they got rid of, this was a, for an eating disorder. National Eating Disorder, nonprofit. They got rid of their call center and replaced it with an AI chat bot to save resources and see all the impact that AI has and so on. And within three or four days that chat bot was giving harmful advice, that would be fine for everyone else, but the audience in terms of if you wanna lose weight, just check the scale or eat less calories. It's not helpful advice. It's not beneficial for those suffering from an eating, eating disorder, right? And so that model may have passed ethical guidelines, responsible guidelines, but it wasn't beneficial, right? And as a result, that nonprofit likely suffered in terms of their fundraising performance. And other nonprofits similar to them probably also suffered. If I'm reading that in a newspaper, I might not pay attention to who it is, but I know it's a nonprofit. And then even more so in, in today's political climate, there's a lot of a lot of. Information coming out about how nonprofits have been misusing funds in certain circumstances, right? Or a lot of allegations along those lines. So there's a lot of trust mistrust there. And so we need to make sure that what we're doing with nonprofits with artificial intelligence, that we're always putting trust first and we relationships first. And we're not just going for what's a quick win and what will prioritize transactions over relationships. So another thing is. Nonprofits are very overburdened. There's a study that came out recently in our space that 75% of nonprofit employees are looking to leave their job in the next 12 months. 60% of them are considering not coming back to the nonprofit sector overall, and the overwhelming majority by 60% is they're overworked and under-resourced. And then again, you have AI that says it'll give you 98% of your time back, right? It's$20 a month, like all these things, but it's just not connecting. And so sometimes people just jump in that AI pool without thinking what? How should we be employing this? How should we be using this? And not just oh, here's a cool use case. Let's try it and see what happens, and we'll figure out the rest afterwards. I think that's the first super important distinction that you're making, right? It's about what's the mission? How do we bring good to the cause that, we're advocating for, that we're trying to, solve. And it's interesting to, to hear how that changes, how you approach innovation, how you approach things like AI and, what really needs to be at the forefront. Yeah, and we've been working a lot of colleagues and I have been working on this program called Fundraising AI, right? We know that many, there's no governmental regulation on AI, right? There's the NIST framework. EU has the EU AI Act, but in the US we don't really have any specific guidelines or frameworks on how to employ this. So we built fundraising AI, which is the first of its kind. It's a responsible, beneficial framework for nonprofit artificial intelligence, and gives those organizations governance template, a tool to say, this is how we should be looking at it, how should we should be using it, and how we should be evaluating it. Constantly being adapted and evolved as technology moves on so that we're providing that resource and saying, you can't just go with what Microsoft says is, responsible or meta is responsible, right? We need to build this for our own and have our own construction around it.

Andreas Welsch: 10:06

Now, Earlier you mentioned it's not just about quick wins and showing some value, but it's in ensuring that you do that, right? That you do it in the right spaces, that you connect it deeply to the nonprofit's mission. So I'm, curious, what does AI innovation look like in this space and how do you even prioritize what should pursue when, especially you need to check it for additional dimensions?

Scott Rosenkrans: 10:31

Yeah. Great question. So it, there's a lot of that just because we should. Just because we can, should we, right? Chatbots are coming out virtual assistants are coming out. There's all these technologies and all these use cases in the for-profit world that we see you turn on your TV and there's 40 new applications of artificial intelligence, right? You, can't separate yourself from it. So an example that's coming out more recently in the nonprofit sector is an autonomous fundraiser, right? Fundraising is. Should be about human to human relationships, right? It should be that I'm seeing you as a potential supporter of organization. I'm trying to find out what makes you tick. How would you want to support this, and how can we establish a strong relationship between the two of us to further this mission and help you feel like you're expressing your generosity in a positive way. If I say, you know what, I'm not gonna do that and I'm just gonna replace myself in this with a autonomous bot that can work all the time and knows exactly what to say. It also is manipulative by nature and is more successful at being manipulative and has to be goal oriented. And that goal is raising dollars like. I'm not treating you as an individual that I want to establish a relationship with, right? I'm not, putting trust in this relationship between us and I am in some senses misrepresenting the organization because I'm saying what matters more is transactions and getting dollars in the door than an actual human to human in relationship. So where we say AI should be used is more informational. Use it to save time and those things that you don't want to do that are menial tasks. That are manual tasks, like putting together a report, right? Or processing a gift. But when it comes to relational, that's where AI should stay off to the side. Keep that for the humans to, continue on and do what we do best.

Andreas Welsch: 12:48

I think there's this debate on the corporate and on the for-profit side, what does AI do, what do people do? To me, the way that you are articulated that just now makes it very, clear, right? It's, a relationship based, it's people based business in that sense or relationship. We need to have this transaction between people or not even the transaction, but the relationship between people and AI can support us on everything else that we do to, run our nonprofit.

Scott Rosenkrans: 13:17

Exactly. And the nonprofit sector is again, like I mentioned, under attack, but it's also been on a downward trend for a long time, right? 20 years ago, two thirds of people, if you ask them, they said that they're making gifts to nonprofits. Now it's less than half, right? And if this trend continues in 40, 50 years, there's gonna be no individuals giving to nonprofits. The problem is there's a lot of problems, but part of the problem is that we've been using the wrong tools to identify who we're gonna ask. We prioritize wealth over relationships. We prioritize transactions over relationships. And so if you throw in AI to a broken system, it's just gonna make that broken system. More broken and faster, right? It's not gonna make it stronger and it's not gonna fix it. So you need to reevaluate what do we want to prioritize and how do we use the AI to do that as opposed to just come in and, do things quicker and move us faster towards that, brick wall that we're already gonna hit.

Andreas Welsch: 14:15

Again I see so many parallels here. On the for-profit side, we talk about don't just automate a process. First of all, look at first do you need it? Do you need all the steps? Can you make it leaner? And then once it's optimized, yeah, automated. So some of that is, is what I'm hearing from you as well in the nonprofit space. So it doesn't just help to throw more technology at it. It might just give you worse outcomes faster.

Scott Rosenkrans: 14:44

Exactly, Nobody wants worse outcomes faster.

Andreas Welsch: 14:45

No, please, no. But then I'm curious on in, in the organization that you work for at, donor search, what are some of the other organizational capabilities do to even bring new AI features, models and initiatives to market? What are the skills? What does the culture look like? What does the data infrastructure look like to bring AI for nonprofits out in the world? How does it change?

Scott Rosenkrans: 15:13

Yeah, I, again, that's something that's always changing, right? It's not it's not like the internet. The internet came out however many years ago, and people are like what is this thing? But then once you learn it, you learn it, right? Like it doesn't change all that much. Maybe there's new, like social media is a new thing, but it's still based on the internet, right? Electricity hasn't changed much since it's came out. So it's, not like it's, you learn it and then you're done and you're caught up. You never have to think about it again. AI is constantly evolving and. AI is now building new forms of AI, right? Like it's, this exponential technology that we've never really seen before. So you always have to stay up to date and always be, treat it as like an iterative process, right? Not just in terms of the tech, but also the culture. We know that we. A number that we keep hearing is that 70% of successful AI adoption for-profit nonprofit doesn't matter is based on culture, not data or models, right? So it doesn't matter what tool you have, if your organization is not set up in a way to really change their processes. And this is one thing we talk about often, like predictive AI, which is again where, we spend most of our time and what we do at donor search is it makes the decisions for you, right? A lot of people have valued themselves as being able to make decisions with a lot of information, but if you don't have to do that anymore, where do you put your value? And so people wanna hold onto that and keep AI like at arm's length because it's threatening to their own view of self. But instead, if you say, okay, now if someone else is making decisions for me, I get to free up my time to do other things that I can do better, right? And leverage other skill sets that I haven't been able to. To flex as much because I've spent so much of my time making decisions in this sea of data, right? And oftentimes decisions were wrong anyway, right? Or at least wrong to the extent that they could be now with, artificial intelligence. And so it's constantly going back to say how can we treat this as iterative process? How can we focus on, sometimes not focus on the outcome, but the process itself. To see how we can make those tweaks like you're talking about, right? Oh, there's new ways to do things. There's new technologies that, that you can apply. In terms of what we're producing here, we're we have, again, these predictive models custom for each organization.'cause we know no two organizations are alike. But now knowing that generative and agent are coming into play as well, how do we combine the two? We, see most value, most organizations. Really move the needle when they're using predictive AI, right? Generative is incredibly valuable and efficient, but it's not gonna turn a million dollar organization to a billion dollar organization, right? Am Apple, Amazon, Netflix, they're not. Billion, trillion dollar companies because of generative AI, they're there because of predictive AI, right? Generative just allows you to, speak more individually to each person and so on, and treat them as an n of one as opposed to a cohort of individuals. So like combining the two so that nonprofits can really dig in and hone their human skill sets, right? And offload anything that could be. Offloaded elsewhere to something that's more efficient in doing so.

Andreas Welsch: 18:32

That sounds great. The part especially around how you do that in a nonprofit I think many parallels. Again, you still need the data. You still need the skills and the resources. It's interesting to, to hear you talk more about predictive analytics or predictive AI, but I'm assuming if you work with numbers and you want to make predictions about how do people likely behave and what are the, dollar amounts that they might be likely to donate and to gift and, give. genuinely, I really doesn't help as much. It's not the computation. It's maybe the email or the report or something around it that articulates when or puts these numbers in context.

Scott Rosenkrans: 19:17

Yeah it's funny, we again, we've been building these models for eight years now, right? So before November, 2022, right? When ChatGPT came out, and at that time we used to have to in our sales pitch, we would've to say AI is not this scary, like science fiction thing. You're already using it. When you use Google Maps or Apple or Netflix or Amazon, you're using it. So it's here ready. It's just now we can use it for our own purposes, november, 2022 came around and now we said no. AI is not just Chacha. Bt there's two different things. There's generative predictive. And so and everyone now thinks that they have a different concept and a grasp on what this thing is, but it muddies the water even more because they are two very distinct tools with two very distinct outcomes and purposes. And it's about knowing what's important for what. And so that's, that was a big part of. When we wrote nonprofit AI, because you'll see a lot of if you were to Google, how do nonprofits use AI? 99 times out of a hundred, it's gonna be a blog that has no real examples. And it's saying a list of generative AI use cases, but not an organization that's actually done it. So we wanted to put emphasis on predictive, but also not forget that generative exists and then also. Automation, right? Automation plays a big part with AI and leveraging that type of technology. So make it more holistic and more of a, broader view on all the different capabilities that it can do, right? And not just hone in on this is the end all, be all, it's ChatGPT and that's all you need to know.

Andreas Welsch: 20:48

So how do you then in this environment measure the impact and for. It's not, or it's maybe not customer tickets solved or call volume reduced or, something else. What's the impact that you measure and for.

Scott Rosenkrans: 21:06

Yeah. Yeah. So most of my work in nonprofit space is more on the fundraising side, right? The, again, the charitable giving. So how do we identify the people to make the gifts, right? And obviously a, very clear metric would be dollars raised. But my, my friend often quotes Charlie Munger, show me thy thy. And I'll show you the incentive or show me the incentive and I'll show you the outcome, right? If you're prioritizing dollars raised, then you're just gonna look to try to find dollars, right? And you're gonna speed over any hurdles, speed over any relationships to really get there. So a lot of what we're trying to do, not a result of AI, but in conjunction with employing AI is. To change the metrics that are being prioritized from, again, transactions to relationships. So instead of looking at how much did you raise this year, what's your three year rolling average, right? So that way you could see if it's increasing and you, could prioritize more long-term relationships, more sustainable relationships. What's your retention rate? What's your acquisition rate? So bringing new people in, but then keeping them along right, is gonna be, is gonna help nonprofits stay and stay stronger and be able to commit to their mission better than just saying, I raised X this year and now I'm gonna try to raise X more because that's you're, not. You're either going back to the same wealthy people and then they get tired of giving to you, or you're not looking to bring in someone that's maybe a smaller level now, but could grow to be a stronger donor, because that's not what your incentive is. Your incentive is just dollars now.

Andreas Welsch: 22:44

So it's almost like a customer lifetime value.

Scott Rosenkrans: 22:49

Exactly. So that's another model that we build long term. RFM is a pretty traditional model, right? Recency, frequency, monetary, but it keeps bringing the same names in terms of a, fundraising perspective. It keeps bringing the same names over. Over and over again, right? So you keep going back to same people. So we put that into kind of an AI environment to say who's most likely to be at the higher end? Just hasn't given enough runway to get there yet. And so it helps people prioritize those that are flying under the radar that are a little bit newer in their relationship, but showing up really strong and wouldn't be prioritized in other methods with FM or anything like that. So we're helping to lead the way with that customer value, right? Customer lifetime value and, get there through the technology that we can offer.

Andreas Welsch: 23:39

Awesome. That sounds really great and I think puts the technologies, the models the, kind of techniques that you can apply, in a really good context to show how that is connected and again, drives the outcomes that you actually want to achieve. Now, Scott, we're getting close to the end of the show and I was wondering if you can summarize the key three takeaways for our audience today.

Scott Rosenkrans: 24:02

Yeah. Key three takeaways. Nonprofits prioritize trust. They're built on trust, and so if trust is our currency, we need to go in with a different approach to how we're treating AI. Especially again, in terms of what's responsible, what's beneficial than those that are presented in the for-profit space. That would be one. Two is to. Prioritize relationships over transactions, right? So whenever you're using AI, how can you do it in a way that keeps the human in the center right? And isn't just saying, how do we get more money quicker? And then three is just because we can, does it mean that we should. So identifying those ways that you want to grow and want to build a program that's sustainable. And again, isn't heading towards this brick wall just on a, faster moving train, right? But something that allows you to get over the hurdles of what we refer to as the generosity crisis. And move in a way that, that. Changes that trajectory of decreasing dollars, decreasing donors year after year, and helps you establish relationships with people who wanna maintain and watch you grow.

Andreas Welsch: 25:16

Wonderful. Thank you so much for painting this broad picture for us. What doing AI in a nonprofit looks like and doing it for nonprofits. Looks like I certainly learned a lot. I think there are a lot of parallels that I could see between corporate, for-profit and nonprofit, how you apply them, but how you apply them in the context of this particular domain, and especially the part about fostering the human relationships and putting emphasis on them. When technology seems to make everything go faster and cheaper and more efficiently, it's actually still the human to human relationship that matters at the end of the day. I love coming back to.

Scott Rosenkrans: 25:55

Yeah. Absolutely. Thank you Andreas. I really appreciate the time and it was great speaking with you, and talking to you, and your community. I love what you built here. Thank you so much.