AI and the Future of Fundraising

DonorSearch has been our go to as far as the standard for ADA pen services primarily in the wealth screening space. And for many, many years, our clients have relied on that partnership to be able to bring in wealth

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DonorSearch has been our go to as far as the standard for ADA pen services primarily in the wealth screening space. And for many, many years, our clients have relied on that partnership to be able to bring in wealth

so that they can screen their donors and figure out who they should be communicating with. As far as high capacity and really a good return on the investment DonorPerfect is what we consider a fundraising CRM. So it’s very purpose built for fundraising. It includes all of the fields that you would need in terms of tracking all of your fundraising activities where your donations are coming from grants and information of that sort. So you, you can get really great reporting, as well as great history in terms of where to drive additional fundraising in the future by work with many of the other wealth screening solutions that are out there. Donor search has by far been the easiest to work with as far as a willingness from an integration standpoint, from a commercial standpoint, to be able to bring new products to market. Their data has always been top notch. They’ve communicated to me what their value proposition is in terms of what differentiates them.

And that has just made the difference in the world as far as being a good partner to us. We’ve just heard the stories of of nonprofits that have been able to build new buildings. Just using this recipe of how to fundraise. It’s it’s the ideal situation for a major gift stamp on it.

Good afternoon, everyone. My name is Amanda Padron skin. I’m a senior DonorPerfect training specialist. And I’m here to welcome you guys to Scott Rosencrantz session AI and the future of fundraising. But first, I’d like to tell you a little bit about Scott. He’s the associate vice president of donor search AI and Scott leads a data science team dedicated to developing and implementing custom machine learning or L ml models that enhanced fundraising efforts for a diverse range of nonprofit organizations. With a deep understanding of both AI technology and the unique challenges faced by nonprofits. His work has not only improved fundraising outcomes, but it’s also set a standard for the integration of AI and philanthropic strategies is dedications of beneficial and responsible AI practices. And his passion for making a meaningful impact has been recognized by his peers in the industry at large, as evidenced by accolades such as the world changing ideas award from the Fast Company. Scott’s epic that academic background, which includes a master’s degree in psychology from Loyola University, complements his technical acumen, providing him with a unique perspective on the human elements of technology and data. As a founding member of fundraising AI, he continues to contribute his valuable insights and experience to the nonprofit sector, driving innovation and positive change through the power of beneficial and responsible AI.

Before I hand the session over to Scott, I have a few housekeeping items. All presentations are attached to the session and can be downloaded for your review. And please be sure to add your questions to the q&a tab so that we can see and get those answered for you. All sessions will be recorded and will be found all the DonorPerfect website post conference. So let’s give a warm welcome to Scott. And Scott. I’m gonna let you take it away.

Wonderful. Thank you so much, Amanda. I’m very excited to be here today, here in my home office of outside of New York, but here virtually with a DonorPerfect team for this conference. as Amanda mentioned, I’m Scott Rosencrantz, and associate vice president of donor search AI. I’ve been in the nonprofit sector for my entire career. I started as a prospect researcher, I’ve been building predictive models for 10 years, overseeing machine learning models for the past six years, and just solely dedicated to this sector. My work in fundraising AI with Mallory, Erickson, who you all know, and Macon Chapelle, who you probably know, we’re really driving that conversation of beneficial responsible AI. And I love the work that I’ve been able to been honored to do with donor search for the past three years of building learning models for organizations of all sizes, as well as introducing things such as MLR, which we’ll talk about in a little bit. So I’m going to talk to you today about AI and the future of fundraising. This is a topic that’s obviously on the top of everyone’s mind. I’m sure this is probably not the only a confidence you’ve heard today or this month or this year. There’s there’s plenty of them. Obviously the conversation that everyone wants to have, and really just want to talk about what does it mean what does it mean?

for you, and how’s it going to change our sector as a whole in the years coming. So I’ll have the chat up, please any questions you want to put in there, I’ll get to them, I have a number of screens. So bear with me as I as I go through this. But happy to again, be here and talk to you about this today.

So things have changed. Things are not the way that they used to be. Ferris Bueller said it famously, Life moves pretty fast. If you don’t stop and take a look around once in a while, you just might miss it. That rings true for us personally, but also the sector at large. The way things were is not how things are going to be in the future. And we need to be addressing this pivotal point right now. And make sure making sure that we’re setting ourselves up for as much success as possible, both as individual organizations which each of you represent. But as a sector as a whole, that often falls behind larger sectors that have resources and things that we might not have as readily available. We need to put ourselves front and center in addressing everything that’s changing with AI being introduced into the world as it has globally a sense, you know, had been going on for a long time now, but primarily since November of 2022, when Chad GPT was introduced and really changed the game for everybody.

So it is not a matter of if AI is going to change your roles, your tasks, your day to day job, your organization, your personal life, your health, whatever it is fill in the blank as you see fit. It’s a matter of how and when we are experiencing unprecedented change. Yes, we’ve seen technological advancements make significant changes in impact in our work, the internet for that computers, before that calculators, but nothing like this. This is exponential growth and exponential change and advancement in what AI can do and the capabilities.

Those that are running open AI, and obviously chatty BT in their recent conversations, they’ve been saying this for the past few months. Now, the models that we have access to are laughable compared to what they’re testing, and what’s going to be released probably later on this year. So what we think is a significant advancement, a huge overload, or offload and being able to address new challenges, or being able to review your own materials, or this level up your own skill set. And what you can produce are laughable, they’re almost embarrassed by the tools that we’re using now. So it moves so quickly and move so fast that what we’re using today is often referred to as the worst ai do you ever use, and it’s gonna seem like parlor tricks when we see what comes out in later November and the years to come. But the problem is, while this tool and AI is accessible for everyone, a not everyone is using it and be there 2.6 billion people in the world that don’t have access to internet, right, they don’t have access to these types of technologies that we’re we’re taking advantage of or trying to take advantage of as often as possible. So it really is making sure that if these tools are available, you know how to use them, you know what they’re doing, you know what the value is, and that you stay up to speed on all different types of applications that can benefit you and your work, but not just benefit, you benefit your organization and your mission. So the digital divide is widening very quickly, there are going to be winners and losers. There’s a study that came out, or a report that came out a couple of years ago, kind of early on in this AI process. Of those that are late to adopt AI and this is before generative AI and large language models, those that are late to adopt may never be able to catch up. Right, we’re seeing you know, there are reports coming out all the time, Microsoft just announced their work Index report saying that 85% of people that are using or everyone that’s using AI 85% are seeing significant improvements of the quality of work, and the speed at which with which they’re able to produce the same type of information. So there are individuals using it, but we want to be able to make sure that everyone knows how to use it, and that you can apply it to what you’re doing on on your regular day to day. Now while this is beneficial, there is significant negative impact as well. Right? It’s not that AI is good or is bad. As as Mallory Erickson says it’s not binary like that AI is just a tool. But it does come with results that lead to increasing inequality. Right. Some people are saying it’s going to make the rich richer, and the poor poor. So this is a significant opportunity for us in the nonprofit world because these are challenges that we essentially only we can address. Right? This this inequality is something that we’re going to be able to use the AI to

help us further our mission better, but there’s going to be more challenges coming up that we need to use AI to further the mission to solve. So it’s gonna become this kind of cycle that we obviously we want to ultimately put ourselves out of business by solving all the challenges of the world. But we’re going to be seeing a lot of new challenges that need to be addressed with artificial intelligence, and as things become more accessible to Thai people across the world.

So in 2010, the average number of algorithmic interactions per person per day worldwide was 298 2010. Seems like a lifetime ago, right? There was no Alexa, there was no Siri, there was no Spotify, I think Pandora might have just come out. So it was a long time ago, your smartphones could not do what they’re able to do now. But when we look at what this number is, in today’s world, that’s 4909. So that is a significant increase. But what does that mean for you personally? Right? How often? Are you looking at your phone? Or how often is data being tracked on you, or you’re being fed advertisements? What does this mean for you, we’re also what does this mean for your donors and your prospects, because they’re in the same position, they’re constantly being bombarded with new information with new materials.

Looking at some of these stats, the average person receives over 333 emails a day, I was just out of the country for two weeks at the AI for good conference in Geneva. And I was afraid of what my inbox was going to look like when I returned. So don’t tell my wife. But I snuck away every couple of days just to clear out some of the nonsense that was in there to make it a little bit more manageable. But the fact that that has to be done otherwise, it just becomes an overwhelming burden to come back to 1000s of emails that I need to sift through, that’s two days worth of work just to kind of make sense of everyone, the average person looks at their phone 344 times per day, scrolls, 300 feet per day has 80 apps on their phone 12 subscriptions like it just never ends. And this number is not stagnant. If we were to reassess these numbers in six months a year, they just continually grow and grow and grow. 1.7 megabytes of data is created every second for every person on earth. And as we move into the age of AI, more data is going to be tracked more data is going to be used, more data is going to be applied, to provide information to create content to give kind of bombard each individual with more and more and more and more, but more is not always good. Right? So what is our limit to consume all this information as it becomes a constant flood?

Well,

average attention span of a human 25 years ago, was 12 seconds. That’s decreased 25%. Due to all this information. It’s a constant bombardment and constant over stimulation, stimulation of information that our brains just were not made to process, right? Think about our brains back in caveman days, which is what it evolved from, there was not this overstimulation, right, there might have been some animals that were maybe trying to attack us that we were pray for. But other than that, you don’t really need to worry about checking your phone first thing in the morning to see what messages you might have missed through the eight hours that you were trying to get some good night rest. So this, this stimulus and this constant need to be connected, is causing significant impact and what we humans can manage, right. So where there’s more information for us to be looking at taking into account, there’s less ability for us to do so because it just constantly takes advantage of that of that system. So I’m not sure if any of you have watched it, if you haven’t, it’s a wonderful little bit eye opening, a lot of eye opening a little bit terrifying documentary on Netflix called the social dilemma. But the creator that that documentary, Tristan Harris said, that isn’t no longer a race to the bottom of the brainstem. It’s no longer a race for these larger organizations, primarily social media, to just get your attention. They want it to be intimate, they want to have a relationship with you as an individual. When we’re at this conference, there was a study that was being reported on saying that 57% of Gen Z would rather be friendless than be without a phone. That is a level of intimacy. So if this is what the race has been, they’ve succeeded, that they would rather be without human contact and be without their phone, which is just insane to think about, but you probably can see examples of that in your day to day with individuals, you know, in that in that age group. So

These organizations, the for profit organizations have really been delving into our space, right? Intimacy, relationships connection, this is something that the nonprofit sector has done so well for so long. But now, we’re trying to go against large organizations that we just don’t have the skills to be able to do so in the normal day to day. And

I do need to kind of convey this that things aren’t going back to the way that they were, right, this is not going to be like, alright, well, we’ll just self correct and everything will be fine. You can’t put this genie back in the bottle. Ai cannot be contained. We can’t reverse go back and say, All right, well, let’s just maybe not throw it out the way that we did in chat to PT and make it accessible to everybody. Let’s kind of reverse the social dilemma and the harm that AI is done to two different populations, these things can’t be reversed, but they do need to be addressed. And then you need to be prioritized in terms of how to resolve them moving forward, it cannot be contained, the best we can do is create guardrails around what we’re doing now, and again, when we say for the nonprofit sector, it’s not just about responsible AI, it’s about beneficial and responsible AI, we need to marry the two and it really is the conversation that we have at fundraising AI. So please join that. But also what we’re building at donor search models that we built for our clients, we built for DonorPerfect. These are responsible beneficial models to make sure that we’re developing these types of products to continue, continue to provide sustainability for the nonprofit sector.

Where we are right now we’re in what we’re referring to as an A new era of competition. This is the generosity crisis, which you probably heard of, but we are competing, we nonprofits are competing against the largest organizations, ever that the world has ever seen. Right? And have almost unlimited capacity and resources. Right? Think back to that that slide that we had about the number of emails and number of images being delivered per day.

Those are coming from for profit organizations constantly bombarding your donors, your constituents. So where do your messages fit in? Right out of the 333 emails? How much do you think your email is standing out? Are your images, how much 5007 1000 images? How much do you think your images your content is standing out? Now that I’m recommending to do more, but we need to make sure that we’re making space for ourselves to continue the relationship and connection that we have, with our constituents, with our donors with our prospects for those people who are closest to us. So we don’t competing for dollars anymore. There’s 1.8 million nonprofits, those aren’t our competitors, right? It’s the for profit organizations that are taking a page from our playbook, and building intimate relationships with their individuals. So that’s who we’re trying to compete against, or competing against them for a connection for who we want to establish that relationship with. And the need has never been greater. We are currently losing this game. Right? If this is a game for connection, we are losing it. But it’s not that we’re just losing it, as my colleague, Nathan says.

It’s like nonprofit sector is showing up at halftime of the football game, but they’re in the parking lot, and they have a pair of ice skates. They’re not even playing the same game. Right? There are for profits that are trillions of dollars in terms of size, right? Every single for profit company that is a billion or larger, is dependent on AI and either using it building it or strongly investing in it. Ai plays an integral role in how these organizations are driving their work, because they know that their donors, their constituents, their customers, are not all the same person, just like your donors are not all the same person, you don’t have just one type of donor, you have multiple donors. They’re being able, they’re able to leverage the information that they have the data, the tools, and really drive connections and intimate relationships with each one of those individuals independently, as opposed to what we’ve been able to do so far. So we need to change we need to flip the script and get back to where we’re supposed to be of having those strong relationships with those individuals. Because currently,

if we continue on this trend

in 49 years, we only have 1% of households giving charity to organizations right now donors are the minority. And I’m sure that you see this in your day to day. I’m sure that it’s harder for you to raise more dollars year after year from fewer individuals, right? Retention is abysmal, right? Organizations are having a hard time acquiring and

visuals, what is it point 8% is the standard for acquisition, like, these numbers are not working in our favor. And these numbers are decreasing year after year after year. So I know that this is not the most exciting conversation so far, and probably not the most uplifting. But I assure you that it does turn around, so just bear with me.

So what we need to do as a sector is we need to change, right? We can’t just do the same things over and over, we can’t just say, All right, well, you know, this is just the way that it goes. And we’re just going to have to suck it up and push through it. And somehow it’ll eventually change, maybe we’ll get a gift from MacKenzie Scott or Melinda French gates. But that’s not how it’s going to work. We need to change what we’re doing, we can’t just assume that our standard way of processing is going to ultimately produce different results, because it’s not. So we need to do something different to get those different results.

AI is the only scalable solution. Right, again, for profits have been relying on this for years for decades. AI is what drives the Netflix recommendations. You use that sometimes Amazon, they have me so well, even though I hate to admit it, I’ll just go on Amazon to say, what do you think I should buy? Like, what are things that you know me so well, Amazon, because you know what I watch? You know what I purchase? You know what I don’t purchase? What do you think I would like you’re probably right, and I’m I’m such a sucker for Amazon. But I’m hoping I’m not the only person. They know it so well, because they have all of that data, they have all that information. And they’re leveraging it on a regular basis. So we are also able to do that we just haven’t been doing it. Because we’re sometimes risk averse, we sometimes don’t have the budget, there’s no r&d budget for many nonprofits. That’s what we’re doing a donor search by being able to make this legible and make this accessible to every single organization. And that’s our work with DonorPerfect as well to make sure that you do have access to this type of information.

There is an upside,

because of AI, right? It’s not just down, down down. It’s not just negative information. But because of AI, we know more about the motivations of giving than any other time in history. Right? Big Data started maybe 1015 years ago, as I started in the nonprofit sector. predictive models still weren’t even the buzzword, then it was it was all big data. But that big data has now built kind of into this snowball effect that we can take advantage of that. And we can identify what are these motivations, knowing that motivations are not all the same. You, me and 10. Other people, you know, might give to the same nonprofit. And we even give that same amount. But that doesn’t mean that we all give for the same reason. And that doesn’t mean that our likelihood to make our next gift will be the same likelihood, we have our own motivation, we have our own interest in that organization, other organizations we support. So we need to be able to delve into this and not treat every single individual as just one. You’re a donor or you’re not a donor, but treat them as unique individuals. And that’s where this information comes into play. So by doing this, we can build those unique relationships with our donors with our prospects to see better results to establish stronger relationships, build that stronger connection, and get to that level of intimacy that these four prophets have, again, been taking our page from and doing so well.

This is what we call precision philanthropy. Now, if any of you are in the health sector, you probably know precision medicine, right? Precision Medicine has been doing this and using the same tools. It’s all about identifying that

lung cancer is not the same disease for every single person. Lung cancer shows up differently, breast cancer shows up differently. These diseases show up differently. So it’s not just you have this diagnosis, that means your outcome, your treatment, it’s all going to be the same. It’s a playbook that we follow. Precision Medicine is getting into that unique individual what is their specific DNA about their specific disease so that they can have a customized, personalized and precise treatment and therefore outcome. That’s what we could do with precision philanthropy. It’s not just about hiring more or better gift officers. It’s not just about getting better information on who’s wealthier, who is in it’s about finding who has a relationship with you finding out who’s engaged with you, and one of those markers of engagement or disengagement that allow you to prioritize the people that you shouldn’t be seeing, or allow you to capture people before they’re on their way out. Losing a donor is an expensive thing to lose, right? It’s much harder to recapture them down the road or find a new donor to take their place of their lifetime giving. So if we can identify who’s not likely to make it Yeah, if there’s not likely to retain and retention is abysmally low. We’re seeing these numbers decrease year after year. We can identify that early on with AI and with big data and predictive models. They

And we can be more efficient, we can establish that relationship, we can strengthen that relationship and continue them working with us and supporting our mission in the years moving forward. So precision philanthropy is what we’re doing with artificial intelligence. And, you know, we’ve probably heard AI again, many, many times today, this month, this year, AI, it’s kind of this this behemoth of a concept, right, so I’m not going to break down a concept. But AI becomes a catch all the term becomes a catch all for a lot of different things that are not the same. AI is not one thing, there are multiple tools in this toolbox, right? This image comes from Ai, but this image is not the same AI, that’s going to be predicting who’s going to make the next $25,000 gift to you, right, or who’s likely to make four gifts in a year to you. There are different types of models of outputs of targets, but they’re all under this field of artificial intelligence. So not everything is going to be applicable. Right. Not everything is going to be applicable to you and your role in fundraising. But just to kind of demystify, there are many different things, which makes it hard to kind of wrap your arms around, right. And, as has been a term for 50 plus years now, which means that what AI defines has evolved as well, which also makes it hard to kind of wrap your mind around as well. Right. But AI was defining 50 years ago was a really great checkers model. Who could play checkers the best right? Now we can play Go, we can play chess, we have models that can navigate the English language, expertly. This is not the same type of AI, but it all falls into the same bucket. So and no, it is it is confusing, and it gets confusing more and more every day, just because there’s so much coming out. But it is we’re gonna be talking about two specific things here. Predictive and generative AI, they are two completely unique types of AI, and do two different purposes. Right, so you’re not going to use one for the other. It’s a matter of knowing what when you’re using AI, what is it? What is the outcome? What is the intended outcome? What is the target? And how is it roughly designed? Is it designed to predict? Or is it designed to generate, again, two distinct components?

So when we talk about it, it’s really kind of comes down to precision and personalization. Predictive AI is precise, predictive AI. This is again, what we do a donor search and what we’re doing in partnership with DonorPerfect.

Deductive AI precise AI can help answer yes or no questions, right kind of a quantitative questions. There’s a target that they’re designed to identify the behaviors that lead to that target, and then identify other people that are exhibiting those similar behaviors. So things like Will this individual make their first gift in the next 12 months? Will they make a repeat gift? Which donors have a greater lifetime value? Is this person likely to give four gifts are more? Are they likely to respond to this next appeal that we send them? Those are a yes or no questions. They are binary, right? That’s what predictive AI is incredible at identifying something that has a definite range. Generative AI is the complete opposite. Generative AI is more about personalization. Once you know who is likely to make a gift, then with the information you have on them, you can identify how best to reach out to them, right, personalizing it to that individual, so that you have better success. So while these are both very powerful tools, their best when they are combined, by using precision by using personalization, you identify who you should be engaging with. And then you engage with them in an incredibly appropriate, efficient and accurate ways. So generative AI can do things like introduce unique themes or experiences for next fundraising event, creatively recognized donors in a way that encourage long time long term relationships. We do

on the podcast hosts, we talk about how generative AI can be used for different roles. And one concept I love is the annual report, right? The Annual Report is the same for every single donor, you send it to every single donor and the message is the exact same thing. What did we do as an organization this year because of your collective help. With generative AI, you can make it specific for each person. This is what we did as organization with your specific help because you gave to these three appeals with one gift that was 150. Another gift that was 500. And it’s not creating manually 1000s of different don’t aren’t annual reports, but it’s using generative AI to really target into that individual and say, Thank you, thank you specifically for what you’ve done for us. We wouldn’t be able to be here without you and really just again, strengthen that relationship with that person. They’re more likely to continue to do that. If they know that they’re being appreciated, and they’re not just thrown into this

large bucket of, oh, I gave $5,000. And it’s treated basically the same thing as someone with your 500, because you have too many donors, and it’s hard to manage them all. So by using precision personalization, you’re able to dig down into why someone is doing something, how they’re going to do it in the future, and how to best engage with them.

So this leads us to what we’re doing with DonorPerfect, or we’re building predictive AI models, we have six models that are going to be that you’re going to be able to leverage within your CRM, they all have different targets. So it’s a matter of knowing what each target is for.

The first one is MLR. And I think there was a comment about MLR, in the very beginning of this chat, ml R stands for most likely to respond. MLR is an AI version of RFM. So RFM, I’m feel like I’m just throwing random letters at you. So again, bear with me. But for those of you that are familiar enough, RFM is a very quick and dirty way of analyzing all of your donors and prioritizing based on recency, frequency, and monetary value. So how recently have they given how many gifts have they made? And how much have they given? The problem with RFM is even though it’s quick and dirty, it’s reprioritized as the same individuals over and over and over again. So it’s gonna be very hard for anybody new to pop up in a high RFM score, because they’re competing against people that have given to you for so long, right. MLR is predicting who will ultimately be there by how they’re engaging with you currently. So I have just as good a strong enough chance of getting to a high MLR score, if I made my first or second gift to you than someone who’s been giving to you for the past three decades, because it’s all about how I’m engaging with you. Currently, I’ve seen clients, I’ve seen a lot of success with this, really love this model. It’s kind of

just very proud of of what this is. But that’s what it’s targeted, most likely to respond who’s engaging with you who’s more likely to engage with you. And in the coming future. Retention is saying, Who’s most likely to make a gift in the next 12 months and the following 12 months? It’s about getting someone identifying all of your donors, anyone who made a gift in the last four years? Where do they fall on continuing this relationship with us? If nothing else has changed? Are they likely to continue this relationship with us? Or if they have a low score? Do we need to reengage them? Do we need to intervene to find out? Hey, is there something going on? Are you not likely to continue this relationship? Is it something that we can do or have your priorities just changed? Even if they say you know our priorities have changed, and we’re not likely to continue. There’s value in that because at least you know, and you won’t be continuing to chase them. Right. And my one of my consulting friends refers to this as blessing release, the sooner you know that the better. But if you just keep chasing people that aren’t likely to engage with you aren’t likely to make that gift. And you don’t know that you’re wasting your time you’re wasting your energy, you’re wasting your limited resources. So this model will be able to help you identify where do people fall on that continued level of engagement with us sustainers, predicting who’s likely to make four gifts or more in the next 12 months.

This is anyone who’s looking to build their Sustainer program to enhance their standard program, the target while it’s designed to say four gifts or more in the next 12 months, it’s also very good at identifying more gifts, six gifts, 12 gifts, those people that have very high Sustainer scores are more likely to give more than just four gifts in the next 12 months. So if you’re looking to build your Sustainer program, which all of you should be, if you haven’t already, that’s a great way to establish the foundation of your foundational support,

I would definitely recommend using that model upgrade is another model that we built, it’s identifying the likelihood that each of your donors is going to give they’re

a little bit more more complex to explain. So the likelihood that someone is going to make a larger gift that is 10%

or greater than the larger gifts that they made in the last three years. So if my last three gifts, largest gifts in the last three years are $1,000 each, and I have a high upgrade score at saying I’m likely to make a gift of $1,100 or more in the coming 12 months. So a 10% match above and beyond their largest gift in the last three years. So again, who’s strengthening that relationship with you’re not just maintaining the status quo, but who’s going to get to you at larger amounts. And moving forward lifetime value. This is saying who’s going to be kind of at the end of the day when all the dust is settled? Who’s going to be the top 10% of your lifetime value donors so they’re committed in the long haul. This is great for campaigns for identifying people that

They just have such a strong relationship with you, they’re really going to maintain this for a very long time and identify some people that are maybe flying under the radar. And then acquisition is as as pretty clear who’s likely to make their first gift to you that they haven’t made a gift to you in the past. So all these models are going to be rolling out in your donor, perfect CRM, many different ways to use them, but a lot of different value in putting some of them together identifying Sustainer program for people that are likely to make larger gifts, and they were above you can use these combined and you know, kind of put two plus two equals five, right? Adding some of these models together to really hone in to the people that are more engaged, or that you should be prioritizing above all others that you maybe haven’t kind of flown under the radar for a while. So look forward to those coming out. And just one last line, before we wrap this up, and we move into questions, I just some things to kind of think about as you navigate your own AI journey, either individually or as an organization.

Are you in the game with the right tools to serve the mission? Are you using the right piece of technology that are going to help sustain your organization? What happens if you don’t change? What happens if you just keep doing the way things the way that you have been doing? What will it take to change? And then how do you change in a way that prioritizes responsible and beneficial AI? So again, just conversations to have, you know, everyone probably is gonna be having their own AI governance conversations in the coming months, coming years. So please

use these as guidelines and reach out to us if you have any follow up questions.

Okay, man, does that give us time for questions before we wrap this up? Yeah, I think we’ll be able to do at least one or two. So Natalie G asked, What can we do to convince an executive director or CEO who’s hesitant and doesn’t quite get behind the AI movement?

So that’s a great question. And one thing related to that

70% of the success or failure of AI adoption is not based on data or models, it’s based on people. It’s all based on change management, it’s all based on again, this concept of knowing that you can’t keep doing the same thing over and over again. So it’s really getting that person to see, AI is everywhere. These organizations are using it all the time. They wouldn’t be using it if it wasn’t applicable. And these models are built specific for fundraising with privacy built in with security built in, and responsible and beneficial practices. So if we don’t do this, then what are maybe posted them? What are our other methods? Look at how we’ve been performing over the past few years, again, probably more challenging to raise more dollars year after year to increase retention year after year. What are the alternatives if we don’t use AI moving forward?

salutely. And then our other question is Emily asked, our donor demographics are aged 50? Plus, is there any data that may reflect how older generations will perceive AI in a negative, not genuine or positive way?

I mean, the studies really have shown that AI adoption, or at least from the again, the studies are more on the agenda of AI adoption, but it kind of speaks to how they feel about AI, that it’s consistent kind of across the ages. Right. It’s not just Gen Z that’s jumping into it and using it, they have more opportunities because they’re in school, and they’re kind of tasked with it more. But adoption is pretty consistent across generations. So 50 and above. I mean, I would not expect that there will be any significant changes, you would run into hurdles, regardless of that age, just based on where that person is mentally. But again, that’s a change management issue.

Yeah, I don’t I don’t see

the trends change based on the age. But great question.

All righty. And that’s about the time that we have today. So thank you, Scott for such an informative session. I hope you all had some great takeaways. Our next session is going to begin at 135. And we’re gonna have Lynn Westar with four pillars of donor retention techniques and strategies to retain donors and grow revenue. And Josh Bloomfield from gift cloud talking about how to spark online donor relationships built on trust. No matter what session you choose, you will not miss any content since all sessions are going to be recorded. So we’ll see you guys in a few minutes. Thank you, Scott. Thanks all