Name: 5 Essentials for AI Success: What Today’s Fundraising Teams Need to Know (and Do) to Thrive
Uploaded: 2025-08-14T19:32:32.194Z
Duration: 1 h 3 min 40 s
Description: 5 Essentials for AI Success: What Today’s Fundraising Teams Need to Know (and Do) to Thrive

Transcript for "5 Essentials for AI Success: What Today’s Fundraising Teams Need to Know (and Do) to Thrive": Are getting going today. We are super excited for our webinar. I know we have folks joining from all over, and so as you are joining us today, let us know who you are, let us know where you're joining from. I think we're gonna have a good amount of people on today and super excited to dive in. So as you're joining us, as you're hearing my voice, you're getting logged in, let us know. We have it coming we have people posting here. Honolulu, Hawaii. Alright. Welcome. That is awesome. We have Boise, Idaho, Wakanda, Illinois. Amazing. I'm in Grayslake, Illinois. So right next to you. Shout out to Lake County, Illinois joining here. Yeah. We have so many joining us. I love this. There's not it's going so fast I can't even read it, which I think is a good problem to have. I usually like to call out some of where people are joining from, but we have people from all over today and we are pumped for that. Now we're gonna jump into it. We're gonna have plenty of time, but before we do, I wanna give you a little bit of the lay of the land of our webinar platform here so you know what's going on. You already see the chat moving and grooving there, so let's stay active today. We would love to hear from you. We'd love to hear your comments, your feedback, all of that. So I'll be active in there. We also have Riley, our executive producer behind the scenes. She'll be active in there. I'm sure Nathan will as well as he can even though he's gonna be speaking for most of the time, but let's stay active in the chat, engage with one another. I think it makes it a really helpful experience for all of us. Also, you'll notice right above the chat, you'll see the word chat, messages, docs, q and a. If you click docs, you're going to notice, two things. One, the nonprofit AI field guide that you can click and download as well as the slide deck from today. So feel free to take those with you. We'll have links to get those as well later. And then the q and a tabs, your chance to ask questions throughout the day. So, submit your questions, we're gonna be answering them throughout the next sixty minutes, and you can also actually upvote questions as well. So if you go in there and you're like, hey, I'm actually super interested in that as well, you can upvote the question and that will just let us know, hey, we got a lot of people looking to answer, looking for the answer to this question, and we will jump to it. And then finally, we're gonna have polls as well throughout the day. We wanna know where are you at with AI? Everyone is is probably all over the place. Some are deep in it, some are exploring, some are wondering how do we do this. And so we're gonna be asking polls, and we would love to have you participate in that. We'll actually show the results on the screen and you'll get a good sense of where are where are some of your peers, where are some other people in this process. The other thing I love about this platform too, I haven't seen a whole lot of these yet and sometimes people just need, some permission is you can include an amazing gift to say hello as well. So I just put my favorite webinar one. We have Forrest waving. Riley put hers with the puppy saying hi. So make sure to share that as well. Again, a lot of ways to interact today. So you are at the five essentials for AI success. What today's fundraising teams need to know and do to thrive. Now there's so much to this conversation and, we are really pumped to jump into it today. Now I'm gonna bring on, Nathan Chapelle here, who is gonna be leading us today through this content. Super excited to have him here. So wherever you're at in your, office in wherever, give him a round of applause. People wondering why you're clapping, but, we'll know that it's for Nathan jumping on today. Just give you a quick intro, then I'll toss it to Nathan for his, but my name is Scott Holt House here in the Chicago area. Been at the intersection of non profit and tech for, about the four about the last four years or so and before that worked in local ministry and faith based non profit. So love the conversation of what does technology look like with, the the really impactful mission driven work that you all are doing and especially what does AI look like in that conversation. So really glad to play host today. And, Nathan, I'll toss it to you, for your intro. Yeah. Great, Scott. It's so great to be here. It's great to see how many people came from all over the place to join this conversation. I really view this as as a conversation for you, all of you that are waking up every day doing, you know, really good work, you know, trying to make the best out of the time that you have to spend and looking for ways that AI can help power boost some of your activity. I, just for reference, if you don't know me, I spent twenty years as an accidental fundraiser. Earlier in my, younger self, I was a, a technologist and serving as a a board member at a local Boys and Girls Club when suddenly our director quit. I found myself quickly raising my hand to see how I could help, and I didn't know that meant, basically twenty years of fundraising for really incredible organizations, including the Boys and Girls Club for seven years, UC San Diego, where I served as associate vice chancellor, and City of Hope where I served as senior vice president. And my my entire career took, basically a bookend, where I started out as a technologist. I ended up getting really interested in AI in around 2017. Did a TEDx on AI and the future of generosity in 2018. Written two books, The Generosity Crisis and, most recently, Nonprofit AI, which leads us to today. I'm super excited because I'm one of the newest members of Virtuous. Although, I don't know if I'm that new anymore because we keep on hiring people, but I started a few months ago as a chief AI officer. And then, essentially, what that means is I get to look at, all the different ways that Virtuous is considering and building AI applications into our, CRM to essentially enable, you know, our clients and and our fundraising professionals to work better, faster, and be happier in the end. And in some ways, I get to serve as our AI conscience to make sure that we are, do using AI in ways that are responsible. They're beneficial. They're aligned with our values as an organization, which are aligned with values of the sector as a whole. So today's conversation, let Scott kinda kick this off some more, really comes from, really, eight years' worth of work in using artificial intelligence within a nonprofit. A lot of that is captured in a in my latest book called nonprofit AI, which then I ended up taking in building out this field guide that Scott mentioned. And it's I just reread it yesterday because it was a couple months ago that I wrote it. And I'm like, wow. This is actually pretty good. I mean, this takes actually a lot of the lessons I've learned the hard way over the last eight years and really guides, the reader through, like, if I had to distill all the the key elements of, like, how to be successful with AI, they're in that guide. And then, when Scott and I were, ideating over this presentation, his idea was, like, why don't we take that guide and then bring it home and make it a bit more practical even and distill it down into kind of just the essence, the key essence of, like, the nonnegotiables, things that you need to consider as you're looking at deploying AI responsibly and beneficially through your organization. Yeah. Yeah. And super pumped to dive in. I think it's that's what's helpful. Like, we have the resource for you to take with you, but today, you have the author of that resource. So how often do you get to sit down and and dive deeper, ask questions? And so wanna give you an idea of where we're going. This is today's agenda. Again, talking about why now, AI's inflection point in fundraising. I think we can all agree that we're hearing this talked about often, and so, you know, why why are we diving in? Why are we digging into this? Nathan's gonna lead us through the five essentials framework, share a couple examples of what does this look like in action, and really that's helpful in sparking, some dreaming, right, of wow, how could we how could we, maybe implement these tools in a way in our organization and that sort of thing. And then we'll wrap up q and a as what with q and a. We'll take questions throughout, so please make sure to share those and then we'll catch any at the end that we might have missed. So we wanna dive in here, with part one, talking about why now AI's inflection point in fundraising, and I will toss it to Nathan here to, to get us going. Yeah. You know, thanks, Scott. And, again, this is gonna be a lot of fun. I really encourage people to jot down those questions, put them in the chat, up, you know, upvote them or whatever, save it till the end. I really you know, anytime I I try to, whenever I present, I try to create that space where, you know, there are no dumb questions, that there are people that are doing really good work that don't have all the answers. And I'll tell you a little secret. I don't have all the answers, and anyone who says they do in AI is a liar or selling you something. So we somehow get through this together, and we share, you know, what's worked and what's not worked. This idea of, you know, why now, our inflection point, I don't think can be, overstated. In fact, I remember back in 2017 or 2018, I started using machine learning to predict which, patients in our cancer hospital were likely to make a gift. And I read an article from Harvard Business Review, so this would have been 2018, and they wrote this article that said, basically, like, those who wait may never catch up. Like, this is a formative moment in history. And, again, this is way before AI was cool, before ChatGPT, that that that everything was going to change. And that AI, because it's exponential in nature, which I'll talk a little bit more about, is it moves fast. So if you're feeling like the world is moving fast, it is. It's going to move faster. That's why being agile and and be getting comfortable, you know, being in the gray is gonna be really helpful. But I start the premise of every conversation around AI, not because it's a new tool and because it's gonna change things and because it can, you know, make us do our our work faster and better, but because our sector deserves it and and it it needs it. I look at this from almost a moral obligation to the point several years ago, I hypothesized there'll be a moment where donors will look at your organization. And if you are not deploying AI to amplify your mission, in fact, they probably won't give to you. And if you think that's absurd, all you have to do is, in retrospect, look at how many donors would give to you if you chose not to use the Internet. You were just saying, you know what? The Internet has some bad things in it. It's not foolproof. Like, we're not sure about all these things. We're not gonna use it. You know, it would be absurd for donors to think that you were most effectively serving your mission. The same thing will be true with AI. My first book, The Generosity Crisis, was not a book necessarily about AI, although we talked about a future that AI is the only scalable solution to reversing declines in generosity. Why it's so important now is that I spent twenty years fundraising, and in each year, I raised more money than the last. I broke record fundraising every year. I was really proud of, you know, moving up the ladder and being able to do that. But I did that essentially raise more money from less people year over year over year, and that just becomes really hard over a period of time. So when we talk about why now, it's not just because there's this new technology and it's exciting and it's fun and it's very different than any other type of technology. It's because our sector deserves it. You you deserve it as professionals. Your organizations deserve it. And the best chance that you have to serve your mission, to amplify your mission, and do what ideally most nonprofits seek to do, which is essentially put themselves out of business by curing or solving whatever problem you were created to do in the first place, We're gonna approach this by the idea that the need for innovation is greater than it's ever been. Our sector is facing tremendous headwinds, you know, political pressure and societal pressure and and just these systemic declines and the number of people that give to charity. We cannot continue to do things the same way and expect a different result. And, therefore, we're gonna approach all this conversation, not because it's a new shiny thing. And by the way, the answer isn't about spending more money faster than the other person. It's about changing the way you think and changing the way that you work. Also, this slide deck will be very light in slides because I really wanted this to be, like, from my heart commentary about the things that I've learned and providing that space. So we're talking about this this idea of, you know, why now this inflection point. It is, you know, obviously, there's a change in donor expectations. You are no longer in the business of raising money. You are in the the competition, for connection. And I I talk a lot about this. I do a lot of keynotes on the competition for connection. The average person interacts with around 5,000 algorithms per day. So every donor of yours is completely overwhelmed, not just based on other nonprofits seeking dollars that you think you're competing for, but they're essentially in competition for connection from Nike and Samsung and Google and Instagram and TikTok and whatever it might be that is really consuming that. So while AI is moving really, really fast, the expectation also is that you should know me. In the same way that, just for a fun experiment, you know, you know that you go on Amazon. Amazon is actually pretty good based on your browsing history of predicting things that you may or may not like. And in the end, you know, that becomes our expectation. Just for fun one day, I actually decided to turn off all my, the prediction engine from Amazon because I was just curious, like, what would it do if it didn't know anything? Clear all my browsing history and create a basically new account. And not surprisingly, the only thing that it could recommend for me to buy was was nail clippers. Because it assumed if it didn't know anything about me, it assumed that at some point in the next few weeks or month that I would need nail clippers to trim my nails because, essentially, I'm I'm a human. And so all to say is that our expectations so we don't even realize, like, how much, our consumer expectations have risen because it's like, well, that organization knows me. That one, you know, sent me a birthday thing, or that one, you know, you know, makes recommendations for what I should watch or do. And the same is true in the delta between what your donors are thinking about when they're purchasing something from an organization that they have some sort of relationship with and the nonprofit that in some ways has is at risk for for feeling like feeling like a relic at some point. One of my biggest concerns is that we don't rise to this challenge, and and our sector doesn't learn, the idea of personalization at scale. And so, all to say is that, you know, we have, you know, this huge, huge opportunity. The other, you know, piece of this right now is that while people equate AI to things like the Internet, and it is in the sense that AI is in everything, but AI is very, very different than the Internet in the sense that it dwarfs all their technologies combined. It is, combined. I mean, this is, like, a profound, you know, and not an understatement. I'm not, you know, fear mongering or just casting out these, like, wild ambitions. That AI is unlike any other technology that humans have ever created. It is called an exponential technology because no other technology we've created actually adapts and learns. And and while that requires guardrails and really a lot of thoughtful, process, which I'll go into in some of these essential elements, it also means that it has tremendous power to uplift and to do good and to be, you know, a trust enabler, if you use it in the right way. I shared the story about Harvard Business re review saying that those who wait may never catch up, and it's because of that exponential nature. It's because AI learns and grows. If you're like, you know what? I'm just not ready. I'm tired. I don't wanna learn another thing. I'm gonna wait another year. It's not that another nonprofit will be a year ahead of you. They'll be 365 cycles ahead of you. So AI development is essentially measured in cycles, and these these cycles move very, very quickly. You know, things that we are using today in AI, many people out there are using tools like ChatGPT or Claude or Gemini or even a Google notebook LM, which will take all of your content and turn it into a human sounding podcast. That was a technology that was not even fathomable in two years ago. Like, it literally, we couldn't even we weren't really dreaming that big. And so all to say is that you gotta jump in now, and I'll give you some really practical steps on how to move this this forward. And then the last bullet point, I think, is just so important to hear. I get super excited about this. Obviously, I use a lot of words per minute because I'm just like, I really believe not only in the power of technology to to uplift, but I believe that it's a moral imperative that we do, is that your AI superpower and what I found in the last eight years has very little to do with data models. In fact, 70% of AI transformation has nothing to do with data models. You don't need to be a data scientist. You don't need to go back to school. You don't you know, if you failed stats, I think I got a d in stats. Like, I not my favorite subject. What you need to learn how to do is to be curious, and you need to learn how to connect dots. And I'll talk a little bit more about that, but I just wanted to, like, bring this all the way down so that you can really know that you're in the right place. You know? If you're just, like if you're a little bit curious and you're able to connect some dots, you are going to thrive in the age of AI. Yeah. One of the things that we want to, again, do throughout today is kind of understand, where you all are at. And so we actually are gonna have our first poll. So that just opened up. You'll see poll above the messages with the red dot. You'll see a little, you know, pop up on your screen. Go ahead and open that up, and the question that we wanna know is which describes your organization's current use of AI. So, there is a spectrum here from we haven't even started. Right? We've heard about it. We haven't even jumped in. We're exploring but haven't tried. We've tested. Maybe we are actively using it a bit, or we have a really clear strategy in place. And so I'm actually gonna share these results. They're anonymous to to who voted, but it's gonna show us a little bit of what's going on here. And, Nathan, it's interesting to me so far, so most, it looks like, have have jumped in, are exploring, started a few tools, some working actively. But so far, we don't have anyone oh, we have one who says we have we have a clear AI strategy in place. So it's that it's that middle ground, it seems like, that a lot of people find themselves in. Yeah. You know? And and while, you know, some more votes are coming in, I well, you find that all the time. I do a lot of public speaking. I typically do a raise of hands, you know, how many people are using AI. And even formally and informally, when we do studies, there's about 80% adoption when we think about you know, most people are aware that AI is embedded in even tools like Canva or Microsoft Copilot, Gemini, whatever. So the most people now recognize, hey. I'm using AI. But when I ask that follow-up question, how many of you have a a formalized AI strategy, It's, you know, around 5% of nonprofits that I'm I'm with. And, ultimately, that might serve you well for today. But as your organization matures and takes on more AI applications over time, and you if you don't have a concerted strategy on how those applications talk to each other, how they work together, how they complement each other, and how they align to your values, and that's one of the essential, framework elements I'll talk about in a minute, then you're you're gonna be like a a ship without a compass. Like, you're just gonna be doing all this stuff, and and maybe the ship's gonna go faster, but it's gonna be zigzagging all over the place, without that AI strategy. So it's, it's great. And not great to know, but it's great to be aware that, you know, use is one thing, and it's great that we've got high adoption in our industry now, and a lot of curiosity. But I feel like there's a moment where we can step back and provide some guidance. And, actually, this is kind of the essence of the essentials framework. Yeah. You know, we we got a couple questions in. One of them, I think, is is helpful for us just to start, but, Henry asked, AI is a very broad term. Can we define maybe what we mean by AI? Yeah. Yeah. So it is a very broad term and, you know, and it it's a term now that means something and, you know, nothing. You know, it means everything and nothing at the same time. We're we're gonna talk about AI today. I'm gonna talk about it in the broadest sense. So AI is a toolbox of lots of different tools. And just like the toolbox at your house, there's, you know, tools that you'll use. If you're gonna cut a piece of wood, you're gonna grab a saw. But if you're gonna hammer a nail, you're you're, you know, you're gonna grab a hammer. So the right tool for the right, you know, project, essentially. And so for today's purpose, I'm gonna generalize AI as to be all the tools in the toolbox. But most of the time, what we're categorizing as AI for the average person is probably more generative. It's like ChatGPT and Cloud and Copilot. That's one, but I'm not gonna only relegate it to that. The other, tool that we tend to use a lot is around prediction. So at Virtuous, we've got an entire prediction engine that's gonna be rolling out here very soon. Lots to to pay attention to because it's super cool, and it's like the culmination of of my work and coming into this into the CRM. It's really exciting. And that's about predictive AI. So AI ML machine learning. So it's a lot of times referred to AI ML. That's around taking lots of data and distilling it into, like, smaller granular, like, you know, predictive elements. So today, I'm gonna generalize predictive AI, generative AI holistically, and we'll save robotics and, some other things for another webinar at a future point and, you know, down down the road. Or maybe we'll just have robots do that one. You know? That it's actually not a bad idea. By that time, we're we'll be ready to go. Yeah. At the speed of innovation, who knows? We did get one more. It says, I feel the use of AI gives me a serious ethical concerns. Can you speak to that in regards to the nonprofit field? And and if the answer is, hey. We're about to jump into that, that's great, but certainly want to, you know, address that question as well. Well, you know, I'll commend the question because it we will talk be talking about it. But, you know, I'd I'm more concerned in our sector, a sector that operates in the currency of trust when we're not thinking about ethics. Right? I mean, it you know, if I'm selling tennis shoes or cell phones, like, I probably don't care that much. Like, I'm just like, you know, the stakes are very low, but we operate in a currency of trust. Trust is, you know, hard won and easily lost. Right? We could use AI in in ways that really amplify trust and actually, you know, bring people closer to you. In fact, when we surveyed a thousand and six donors, last year, we just did a repeat of the study a few days or a few weeks ago. They'll be rolling out here very soon. The number one way donors thought AI should be used by nonprofits was to increase trust. And that was essentially by saying increase your ability, to ensure that my data is safe and secure. And and so all to say is, like, the idea of, like, innovation and ethics aren't mutually exclusive. And there but there are some frameworks that we should think about to be able to use AI in ways that will be not just responsible, and that will support your organization in the short term, but also in the long term. So we call that responsible and beneficial. So I appreciate the question to elevate it because, again, I get more concerned about our sector when we're not thinking about what does ethics mean, ethics according to whom, and it brings up lots of other philosophical conversations that I love to wrestle with. And so if whenever we meet in person, first drink is on me. We can talk about all the the philosophical questions that our sector has to face right now. Yeah. I love it. Well, I know we have, some more questions coming in, but we're gonna keep going and we're gonna pause here in a bit to to answer more of those. But I do want us to jump into part two, and this is where we're gonna spend the bulk of our time, is really looking at this five essentials framework. So Nathan would love to toss it to you as we dive, fully into this. Awesome. Yeah. So we're we broke out, obviously, the five essentials into five, you know, five core elements. I would really consider these, like, the nonnegotiable. And, of course, definitely download the the full guide because there's a lot of contextual information in there. Got two to three slides per, per essential, and so we'll just guide guide through this really quickly. So, you know, when I think about the nonnegotiables, like, the things that, you know, over the last eight years that I was like, man, I wish I had known that in the very beginning and not made so many mistakes the hard way, you know, it really boils this down down into a couple things. Like, I started out with this premise that, like, AI could do anything, and it essentially, like, tried to boil the ocean with it. And I, you know, quickly realized when I did that and when others around me did that, they just got lost. They just got lost in the sea that, you know, it was just too much. And so really kind of breaking this down in, like, two key elements. One is to pinpoint real problems that AI can solve. There are lots of ways that you know, lots of things that AI is not best at. This is not a, you know, round peg, square hole kinda thing. Like, find the round hole and you've got the the round peg, and you're like, okay. This is the perfect fit for AI to solve this problem. And I usually, you know, tell people, and I I think this might be in another slide, it's worth reiterating, is that, you know, when you're starting small, you're really looking especially at the beginning, what is, like, an a really annoying problem that's, like, low stakes? So, like, I don't wanna pick, like, annoying problem that's high stakes. Like, if we use AI and it goes wrong, like, board members will be upset. Your stakeholders will be upset. You know, people won't donate to you. But find something in your organization that is, you know, something mundane, something that nobody likes to do. People aren't waking up thinking this is the best day of the year because I get to do this one thing. Like, find those things that are basically, like, high yield but but low risk, and then and pinpoint those. Again, going back to that toolbox analogy, you know, there might be some of these where generative AI, like writing something better or creating images better or personalizing something better, might be the right way to do. In my case, I started out with this the philosophy that using predictive AI, it didn't matter when I asked somebody for money or how I asked or in what way or or, you know, for what purpose if it wasn't the right person to begin with. And for me, using predictive AI, machine learning, AI, ML, to identify who's the most likely to respond first and then figuring out generative you know, using generative AI to, like, how we would approach someone came later. So pick the real problems in your organization. What are the things that are holding you back? Then go deep on that toolbox and figure out, you know, which type of AI is best suited for that problem. So if it's that you have too much data and you need to distill it down to make sense of it, then predictive AI is your thing. Like, that's the thing you're gonna do. And there's, you know, a little bit of work, and I'm here for you to guide you through that. We've you know, our book talks a lot about it as well. There's lots of great use cases. I mean, I've deployed predictive AI at probably 200 of the largest charities in America, so it's not an uncommon thing that no one's, you know, doing right now. But, again, high yield, low risk. The other, you know, part is, like, is not identifying what success looks like. And so one is picking a problem that or just a suite of problems that's too big, and I'm gonna boil the ocean to use AI. I used to get calls not as much anymore. Thank goodness. It'd be like, hey. You know, I heard you know about AI. Like, I need to I need to use AI because my boss said I need to use AI, but could you describe what AI is first? So, like, let's not do that. But also on the other side is, like, what does success look like in fundraising terms? So for your organization, is it about, you know, increasing your conversion? That's how I started in 2017. It was, like, literally, we went from point six percent conversion rate for newly acquired donors. These are patients in a hospital. In our first model, we hit two point five percent. So, like, we had a very clear definition of what success looked like, and the same could be true in using generative AI for donor relations. Like, you know, if you're doing, you know, you know, donor satisfaction surveys, like, could you use generative AI to improve essentially how people are feeling connected to you? And I'll bet you the answer is gonna be yes, but you wouldn't know that unless you take proactive steps to determine, you know, what success looks like. I often get a lot of questions, and so I added a slide because I get questions like, but what are all the ways people are using AI? And so, you know, we look at this in two different lenses. On the private sector, you know, it's a lot of, like, emotional support and planning vacations and doing all these kind of things and financial institutions about, you know, kinda rank ordering, you know, customer profiles and mitigating risk. But in our sector, kind of in general, these are the areas. And I don't think these are necessarily rank ordered, but these are, like, the most common examples of how nonprofits are using AI today. The one is the clear and clear example I just shared with you. Too much data, not enough employees, you know, that, you know, got thousands of prospects, but I only have, you know, one fundraiser or two fundraisers, whatever it might be. Like, how do I make sure that person isn't going to lunch every day, but they're going to lunch every day with the right person? Because that's a really expensive lunch. I used to know in the first, like, four minutes of my meeting if it was gonna be good or bad. Like, I just knew. But what if we could increase the chances of that time spent, you know, or are gonna yield a stronger relationship with a donor who is really interested? And so that's where predictive AI comes in. Of course, content creation editing, I think you're all probably pros at that at now by now. If you're one of, you know, the few that are, like, holding out because AI is not ethical and you're worried about the environment, I I'm actually writing an article about the environment, so just stay tuned for that, and and the implications. But content creation's very obvious use. Like, to frankly, like, ChachiPT, who knows me pretty well by now, because he's read everything that I've written in books. Like, it's basically my voice, you know, amplified, and I can shave off instead of eight hours to write an article. I can do that in two hours now. So that's a great use case. Grant writing, of course. Grants, submissions are, like, a thousand x up right now, so they're also being reviewed by AI. So that creates this whole kind of vicious cycle. Lots to discuss there. But the donor experience and donor relations, one of the areas I get the most excited about. It's actually one of the areas that I found throughout my career. I never had a donor come to me and say, you thanked me too much. Just stop it. It's getting embarrassing now. Like, or you didn't thank me. You know? It's like, you're thanking me too good. I've just never had that in my entire twenty year career. So I'm thinking, like, how do we really lean into using generative AI to, like, delight donors? Like, to really, like, delight them into a point where, like, wow. This is like it feels so different than it has in the past. Of course, prospect research, very obvious use case. You know? Google Gemini is, or ChatGPT four, four point o and and five are really great research engines at this point and can give you the of someone. We've got some generative tools and Virtuous coming out really soon. They're already in beta that basically take our data and the and connect to a generative tool and, like, amplify it in scale. It's, like, amazing. It's just, like, what everyone's expectation will be. One of my favorite use cases right now is role playing. You know, one of the things is, like, sometimes you got one shot to make the ask and you wanna make it well. You know? And, like, any other thing, you like, pull a friend in your office or, you know, it's like, hey. Can you role play this with me? Because I wanna make sure that, you know, I anticipate questions. AI is really, really good with that. One of my good friends, Mallory Erickson, she created a company that does this called Practivated. Practivated, which actually will be in the partner ecosystem of Virtuos soon, is basically a role playing simulation. So instead of, you know, me my one shot of getting in front of a donor is gonna be, like, totally unscripted. It actually you can role play, and it will give you feedback on, like, how well you did and and that kind of thing. But you can do that with GPT four o on your own. Be like, hey. Take the persona of a really cantankerous donor who makes decisions based on the legacy of their grandchildren. Don't have to give it identifiable information. Just give it a persona. Role play. What are the kind of questions you'll ask me? What are the questions that won't likely be asked? And how do I leave this in a good footing? And then number two, communications and persona development, using AI to really look at, you know, at the end of the day, like, understanding your donors at more intimate ways. So taking all of your data, being able to create personas, and then be able to, you know, segment down into and this is the last data point, really understanding your data, getting to a point where you can segment to kind of what we call, like, an n of one. Like, there's no such thing as a donor or a prospect in the future. It's just a person, an n of one, and it it's a it comes from the like, from medicine where clinical trials treat every person as an individual. This idea is that every individual has a varying degree of engagement with you, and that engagement changes over time. So if you can start tracking that and looking at that and, fostering that at scale, it's really exciting. So those are the the most common use cases, but there's a million others. Scott, did we say something? No. No. I I I think those are are great examples. And, again, it's like the, it's like as we're gonna, you know, talk about examples later, it's it's a hopefully, these are sparks for people as well to say Yeah. Here's what could be. Yeah. And you know what? And that's such a good exam like, I love that word could be because I do as you know, I do a lot of keynotes on this idea of a culture of curiosity. I call it the curiosity code or culture of curiosity. And at the end of the day, when people come to me and they're like, hey. What's the one thing that people use for AI? It's actually the the the wrong approach. The approach is, like, start with the abundance mindset, the unlimited mindset. The biggest limitation for us using AI in our sector, in fact, is our imagination. It's the the biggest limitation is going back to the things that you've tried before. Like and it's like, well, we've always done it that way, or we tried that one time and it didn't work. The biggest opportunity in AI is looking at the the that your work and your role and your organization with, like, a blank sheet of paper. And I did this the other morning. I actually just, you know, I oversaw nonprofit organizations. I categorized 18 different kinda job functions within nonprofits that I led, and 11 of them found I found very easy ways, that 11 of the 18 roles that I manage could be power boosted or amplified by AI, not replaced. Wow. Yeah. Amplified by AI. And so the biggest limitation is us not taking the time to think, which is why I really this is not in my script, but I encourage everyone to now in this information overload time that we're in, this in the information abundance world that we live in now, find time to think. Like, I literally dedicate an hour every morning. That seems like extreme, but I dedicate an hour in the morning to really think. I sit with a notebook and a pad of paper, no phone zone. I think if you are a curious generalist and that you are just, like, white, you know, sheet of paper, like, what could I do with my organization? You will be a superhero in your organization. I I kid you not. Like, you'll be the person that is just going to power boost into all the different things. Alright. Thanks for moving me along, Scott. Alright. So so here, we're gonna clip through the the five essentials frame. So this is we're already talking about this. And in the guide, actually, there is a checklist. So this is like I put the screenshot on here so I don't need to read through all of these. But this literally was the the guide was developed as a checklist. Like, you start with, like, the understanding of, like, what does this represent, and then how do I check off these boxes? So in the first essential framework, AI is not the mission. Let's just be very clear about that. It is you know, AI is not the mission. It's the accelerator of your mission. So let's make sure that we don't, like like, pretend that AI is more important than what you set out to do. Not saying that at all, but anchor every initiative to real fundraising outcomes that serve your cause. And so at the end of the day, the same way you hire an employee, the same way you, you know, hire a vendor, the same way you you hire a new technology tool, you wanna make sure that the money that you're spending is helping you achieve your stated outcome, and the same is true with with AI. So the checklist on this is, like, make sure this is not super obvious. Like, make sure it's, you know, aligned with your mission. Don't do things that are, like, aligned, you know, with the private sector, and that's what you see in a lot of the use cases. Identify the use cases. So really spend that time to think, about, like, what are the things that we do? Again, high yield, low risk is why you start. Number three, and I find a lot of organizations failing with this, is they don't gain executive sponsorship. They either not they're not sure where their leaders are and how they're thinking about AI, so they're just kind of afraid about it. But I will tell you what, because I've interviewed a lot of leaders, on our podcast and some other things. And the thing is, like, one, chief development officer who's now become very curious about AI but was very against it about a year ago said it's not that I'm unimaginative or not curious. I'm just putting out fires all day long. And if you can't show me how you know, if you if you come to me and show me how you can help me put out a fire, you'll get my attention. And so getting executive sponsorship is key. Like I said in the previous slide or the one before that, define success metrics. So if you're gonna define what that problem is, also say, look. We wanna reduce the time that we're spending on writing auction descriptions by 50% or 80%. Those are very tangible outcomes that you're gonna get the attention of leader going back to that. And then, you know, of course, this doesn't come for free. I I do get concerned that a lot of people in the nonprofit sector are using free versions of chat, GPT, or Cloud, or Copilot, or Gemini. If there's a truism, not just in AI, but in all technology, if you're not paying for the technology, you are the you are if you're not paying for the product, you are the product. You are. Yeah. And yeah. So that data is going somewhere, and it's not going to your organization. So by essentially doing some resource planning, making sure that you've allocated a budget. In fact, we're we're building out a resource guide right now. This is my, August deliverable to Virtuos. I try to do one a month. It's basically how to assess vendors. And and so it's basically how you're taking your dollars and whether those vendors are actually going to be deploying, you know, AI solutions in a way that that, align with your mission. So stay tuned for that as well. So that's number one. Again, not rocket science, but, like, I think those are the basics where you start from. Yeah. Hey. We're gonna, open up another poll here, as we are continuing. And so we'd love to know what's your biggest concern about using AI and fundraising. So, answer that poll. It just opened up. We do have a question though, that you touched on, Nathan, that has been actually uploaded the most. It says just, I struggle with the environmental impact and ethical challenges. How do you cross that bridge? I know you mentioned writing on this, but would love to kinda hear your thoughts on it. Yeah. It does and and, Scott, I have, I I have a clear sense that you and I will be back on on that topic because it's such a hot topic. And it is for more the nonprofit sector than others because it strikes that core of, like, can we use something that helps us in the short term but does long term harm? That's what it essentially equates to. Right? The truth is it's much more complicated than that. And, actually, while AI does consume lots of energy and has environmental impact, So does you know, if I spend eight hours writing an article, you know, while I'm connected to the Internet and doing lots of searches, and now that eight hours is now one hour, there is an offset of my carbon footprint that's really significant. What most people don't understand, the the impact on, the environment to AI is is not in the individual searches that most people are thinking. It's on the training of the models and the models that cost a billion dollars in compute power. It's a very complicated, and nuanced topic, but it's one that I think we have agency in and one that will change rapidly. When I started working in the Internet 1997, there was no option to buy Internet service from a wind powered Internet provider. But now I own probably, I think, about 80 URLs. Every one of them is is because I picked a provider because I, as a consumer, I have agency. I picked a provider that powers all of my my URLs by wind. And so that is not yet, but it will come very soon. So the environment impact that we see now is not a like, an exponential because AI will also break through new and and very much more efficient ways of producing energy like fusion energy, which Microsoft has already invested in. So it's exciting, important. The ethics part, again, I think I already, touched on that. It starts with having a governance framework, which we're gonna talk about in one of these essentials real quick. Yeah. Yeah. And it speaks to the speed of innovation. Right? It's not just innovation for the end user of what we're experiencing, but it goes to kind of a holistic, you know, sense of of the way that that things are are being innovated and growing in the speed. And so, I do wanna, yeah, make sure we have time for all all five of those essentials. So let's jump back in. We'll keep this poll open for just another minute longer. Keep the questions coming. We're gonna we're gonna get to them. And, yeah. We're we're super excited to to keep going today. Awesome. Yeah. Thanks, Scott. Alright. So here's, you know, one of the fallacies of of people using AI is that, you know, you're ever ready, and you're ever gonna be done. AI is a transformation in in in that. It's a journey, not a destination. And so take a deep breath and recognize, like, you're never ready and you're never done. One of the concerns that I hear a lot is, like, well, our data we don't have good data. You know? And by the way, no nonprofit has good data, and probably most for profit, you know, companies that I know well also don't have great data. The real there's a lot of freedom in understanding you're never ready and you're never done. And so while, while that is true, data is also no longer a luxury. And, you know, most of my career, twenty years, data was a luxury, a fundraiser who put a contact report because they were a good citizen, you know, and be like, well, you know, in case I win the lottery tomorrow and peace out, like, I I wanna have some sort of data so that you can move forward with the relationship with this donor. That was a nice to have. But I realized around, you know, I don't know, probably, 02/2017, that data itself was starting to become much more valuable because especially in predictive AI, that data is actually helping you make decisions on a real time basis. And so this is why, like, Virtuous has now built this whole, you know, basically AI machine learning recommendation engine inside. So it's listening and actually capturing all that data and then making recommendations on who's likely to convert and, you know, make another gift or make their first gift and so on. Those are all, like, really exciting things, but only can do as well as the data that is in the system. So number one is, well, it's not a you know, if I don't have good data, I shouldn't use AI. That's the last thing I want people to think. I I want people to recognize that data is no longer a luxury. It it needs to be a a strategy. So in this, number two and we're gonna go through these because, again, I want you to read the whole guide because it's written in a way that's, like, very approachable. You know, is that, you know, without trustworthy donor data, it's like AI is like building a house in the sand. Beautiful ideas will collapse without integrity of the foundation. You can all of a sudden start making predictions that are completely flawed because you realize that you just never included a certain data segment, into your system or you've, you know, never had or anyone ever file contract reports and so on. I will tell you in the future, you know, what it means is that you should look at every activity that every person in your organization does and think about how can we best capture the data from that person's work. And so if you're in donor relations or you're a prospect research or you're a frontline fundraiser, the data that you have, no matter what your role is in a nonprofit, the data that you touch is important because especially in AI, ML, machine learning, that data starts to drive decision making that happens in real time. That decision making is what will yield your success or failure in the future. So to do this, and the guide really breaks this down for you in this checklist, is to have, you know, a quality audit. It's it, you know, it's it might be uncomfortable, but just have the real conversations, like, where's our holes and where are our gaps? Like, where are the areas that we need to do, you know, some data enrichment or some, you know, some augmentation, some cleanup, and know that that's okay. But don't wait till your data is clean to start using AI. That is literally the thing that would pain me the most if you do that. Like, you're never ready and you're never done, so just start now. Data accessibility, we need to really make sure that data is, accessible, and that it can be seen. We need to look at what is the infrastructure and the readiness of your technical capacity and, software storage and processing power to actually make sense of it? When we I told you I talked about AI or data governance. Do you have governance policies that talk about, like, what do you what do you keep? How do you is it secure? Making sure that's secure. It complies with all, you know, local and and state and federal laws, and any kind of consent requirements. And do you have people around you in your ecosystem, either outside or inside, that can help guide you into this this new world. And so and I think we can go on to the next one. So that one, you know, again, I don't think any of these are like rocket science. I think these are like, oh, yeah. But until you put them all in this list and you're like, okay. Now I have the road map for, like, this checklist of how I work down, and everything's gonna be okay. When we go into this number three, I you know, to be honest, this could probably be number one for me. I think if you don't have a culture of curiosity in your organization, your experience in leveraging AI will be very limited. So, you know, this is rethinking what the org chart looks like. This is really thinking, you know, does not do the data have a seat at the table, but do you have a culture of experimentation? Do you have the ability to, you know, allow people to color outside the lines and try new things, to iterate, to, you know, fail, fail fast? And, I'll share an example at the end about, you know, what this looks like is, you know, some leaders that I've seen who've done this really, really well. So the essential number three is that AI success isn't a software rollout. I think we've already disabused that. It's not about software. It's a cultural shift toward curiosity, experimentation, and collaboration. This is I've become, like, insanely passionate about it because, you know, to be honest, I have implemented so much AI that failed that it's embarrassing. And I have implemented AI that succeeded, and it was rewarding. And the the thing about it is that in both of those scenarios, the organization had the same data and models. It wasn't about the data or models. It was the way they were thinking and and how they were rewarding creativity and curiosity and and that. And so there are some things that you can do to ensure your increase your chance of success here. Number one is to create an AI task force. This should be you know, somebody is in charge of this. You don't have to have a chief AI officer, but you need to have someone who's kind of the champion. Find the person that people respect based on their curiosity or their enthusiasm or their excitement about AI, and let them kinda go with it. You know, of course, provide staff training. This is a great training. We offer lots of it. There's so much training, overwhelming amount of training. If you I don't even I hesitate to think how many YouTube videos there are on how to use generative AI, like gazillions. Yeah. But providing training that's relevant for your mission. Be sure to address the fears. Right now, we are living in an exponential time where change is happening faster than ever. I happen to like change, but I know I'm in a minority of people who like change. If you don't like change, this is an uncomfortable time to be around and feel like, you know, your cheese is moving. I have the slide in one deck that says, you you know, AI moved your cheese, and the next slide, it's never coming back. Your cheese is gonna continue to move, and as soon as you think you got it, you know, it's gonna move, you know, you know, a mile away. So address those fears. Like, just be cognizant of that. Not everybody's on that same path and help people, you know, guide through some of that that change. And that starts by having open dialogue. And, really, at the end, this last checkbox is, you know, is your team encouraged to share new ideas? Are they encouraged to try new things, or are you just telling people, keep your eyes on your paper and, you know, everything will be fine? Because that's how I hired and promoted people for twenty years. For For twenty years, I'd hire a subject matter expert and, like, keep your eyes on your paper. Don't worry about all the other chaos, and you're gonna thrive if you just focus on your one thing. And I will tell you now the fundamental shift that AI has convinced me of is that that model no longer works, That if we incentivize curiosity and in more of a generalist kind of connect the dots kinda way and we power boost our organizations by infusing that culture, what we call an AI first mindset, then your your organization is going to thrive. Yeah. Alright. Power through? Yep. Let's keep going. Number four. Number four, champion responsible AI governance. This speaks to ethics, privacy, security. It speaks to the environment. In fact, this is something I became really concerned about in 2018. I actually, created a group called fundraising dot ai. Our global summit's coming up in a few weeks. Virtuous is the the, presenting sponsor of it. It's we have about 15,000 people from over a 100 countries. You're in really good company. So fundraising.ai, if you can't remember that, then you're not gonna remember anything in this presentation anyway. So fundraising.ai, super easy. We created a big map. Just real quick too. It's in the chat. If people wanna register for that summit, the link's just right in there. So Love it. Yeah. So so we, we became I became concerned in 2018 that AI could be used, you know, for evil and and not good. And so we created fundraising AI not to advocate for AI, but to advocate for the responsible and beneficial use of it. And so things like transparency, bias, and privacy start with having an AI governance policy. We, in fact, have a policy that is totally open source and free. And with a little AI, you know, generative AI magic, you can throw that in there, tell it a little bit about your values of your organization, and you will have an AI governance policy tomorrow. We're actually gonna do a live demo of it this year, so you could build yours on the fly at the summit in September. So, you know, when we talk about this, trust is on the nonprofit sector's currency. I've already mentioned that governance is how we protect it in the AI area era. But it's absolutely imperative that we are, you know, using AI in ways that promote and protect trust. In fact, if I if I could tell you one way to evaluate a good versus bad vendor and or any tool or any any tool that's presented to you today or in the future, all you have to do is ask, does this tool promote and protect trust, or does it take it away? You know, if it automates and and removes the human essence of philanthropy and it it just makes giving more transactional, you should really pause and consider where whether you should use use it or not. The way you address these is by having an AI task force. Oh, I'm sorry. Nope. Wrong one. It and to really having your ethical guidelines, your some oversight roles, some bias checks, data privacy, and transparency communication. These start with one thing called AI governance. That AI governance, again, you you adapt it, make sure it's aligned with your values. At don't at Virtuous, we have our AI governance policy, but we have a second set of guidelines that are called acceptable use guidelines. And those guidelines are the idea that we want everyone to experiment with AI, everybody. No matter your role, we want you to experiment with AI, but we want you to do it in ways that are additive, like and that are secure and that are going to be, supportive not just in the short term but the long term as well. And as we get to our last one here, number five, and I'll just give you a quick example of some, organizations I've seen really thrive in this new world, embrace iteration and continuous learning. I said it before. I'll say it again. AI is a is a it's a journey, not a destination. You're never ready, and you're never done. Start, you know, again, with small pilots, learn how to measure them, and actually, you know, continue to iterate, iterate, iterate, iterate, and never be done. Any of you that have really succeeded with generative AI know what I'm talking about because I'll be like, hey. That's really you know you know, generative AI gives me something that's partially good and partially bad. I'm like, hey. This part's good, but that part's bad. It gives me something else. I'm like, okay. Well, I like that, but what if we tune it to something else? So I'm just like a big fan of this idea of, like, it's very different than, you know, doing a search and having a binary answer. Completely different. Iterate, iterate, iterate is the key to really, you know, really power boosting your your, AI performance at your organization. And and maturity AI maturity is built through cycles of testing, learning, and refining, not, not one time deployments. Again, lean into this idea that it's a journey, not a destination. And, really, you know, hopefully, for your team, they're not like, hey. We need to use AI, and then we'll just be we'll check that box. There's gonna be an AI budget line forever in your in the future of your organization. And if there isn't, I'd be concerned because you should be looking at all the different ways that AI could be deployed by your organization to amplify mission, to get to know donors better, to be able to thank people better, really to encourage and promote trust versus take it away. You know, to do that, you know, of course, we have a couple check boxes in here. You know, again, start with pilot projects, measure your success. Have a plan and make sure that leadership understands, you know, and that leadership is conveying that this is something that we're just on. Like, we're just all in this journey together. And until we achieve what's called an AI first mindset, we have a Virtuous has an article coming out this month on the AI first nonprofit because I just wrote it, and it's going through editing right now. And it it really represents it's actually a guide on how to become an AI first nonprofit. And so that idea until your primary orientation is, can AI help? You know, you if if you have to put a post it note on your screen and say, how can AI help? Until you don't even need that post it note anymore because every time you think of doing something, you're like, how can AI help? That's when you're there. And so stay tuned to that article. Scale your criteria and then just be willing to be on that journey and continue to review. When we talk about the end of this, you know, what this looks like in the future there we go. What this looks like in action, not in the future. It it's like I'll give you two examples. One is I can't give you one, one case study because it will like, you'll be like, well, that's not my case study. Nonprofit AI, the book has lots of case studies. But I will tell you, there's an organization called Furniture Bank in Toronto. They're small and they're scrappy, but they're super curious. They don't have egos. They're just, like, willing to try new things. They have taken their small, little, scrappy organization, and they have amplified their mission. Dan Kershaw, who's the, executive director there, is, like, one of the most curious people I know. They represent themselves as an organization that raises a 100,000,000 a year now. It is amazing what they can do, and they've done it in a completely transparent way. In fact, they've created an AI manifesto on their website so their donors can read about how they've used AI to amplify their mission. And in another case, I'll just give you another example. There's a there's a hospital or a foundation, chief development officer who's not a technologist, but she understands the need to experiment and to allow her team to experiment. So she's created failure days, Failure days where where people are encouraged to use AI and share how they failed. Because what she found by people sharing their failures where they either got stuck and someone else said, hey. By the way, I had that same problem, but I tried it another way, and I found this other result. Or in other cases, the person was like, I got stuck. It didn't work, so I tried something else. Or they got stuck, and then they continue to iterate and they figured it out. I think that is, like, the the purest essence of creating that culture of curiosity where you're like, look. Just because we have done it the oh, this certain way in the past doesn't mean we have to do it that way in the future. I believe that we live in a land of unlimited opportunity, to use AI for good in our organizations, and our biggest limitation is just providing the space for people to try new things, to break things, to fail fast, and to share what those learnings are. It's what we do at Virtuous, you know, internally with our team as well. Yeah. Yeah. That's great. We have a bunch of questions. I wanna get to as many as we can. Just a heads up for folks, we won't have time to get to all of them, but we are going to do a podcast where we take a lot of these questions and answer them. And so if you have not already subscribed to our podcast, maybe Riley, I know this is, spur of the moment, but if you could find a link to send people where they can, we'll release this episode, in the next few weeks where we answer more of these. But I wanna jump to this. The first one, does AI have the capability to aggregate publicly available info like platforms on LinkedIn, Facebook, x, all of that, and then automatically integrate this into Virtuous or into a CRM? The answer is yes and no. Right? Because there's different shades of public available data. And so be based on copyright from things like, say, LinkedIn, is, you know, they it's actually illegal to scrape, certain websites, and certain websites block generative AI from scraping. So the answer is publicly available data. In its pure sense, yes. That is actually data that's been made publicly available. In fact, Virtruis now has lots and lots of publicly available data. Now in our in our database, associated with an individual, and that's what we're using to make predictions. And so that's actually helpful when we actually buy and augment. But I think maybe the way you're thinking about that is just, like, all of the, the signal data from, like, Facebook and Instagram and TikTok and all those. The answer is largely no. There are some cases if you own a channel, of course, then you can actually secure the data from your channel, but not, data public you know, that's just made publicly because, certain organizations like Facebook and others prohibit, that data to be scraped. Yep. So I'm gonna ask you if you have an AI policy and are willing to share, you know, to do that. So if anyone on here has one, that you'd like, you know, you is a publicly available way to share it, please put that in the chat. We would love to see it. Another question, actually, someone asked, what features does Virtuous have to use AI? And real quick as as Nathan walks through this, we do have a button on the top of the screen. It says, schedule a Virtuous demo. There'll be a link in the chat. So if you wanna see more of these tools and actually explore, you can sign up for those. But would toss that question to you, Nathan. And I would say that's the number one thing to do because then you're gonna get, you know, firsthand insight into the ways that Vergis is using AI. And all I'll say is that because we have a lot of exciting product launches to to come out very soon. They include predictive AI, which I've talked about machine learning, AIML, which is about predicting which donors are gonna do things, like make a gift or a repeat gift. We have generative AI capabilities that are already in beta, like creating dossiers and doing all kinds of really cool stuff to just save you time and really amplify your work. And then the third part is about AI agents and essentially creating workflows to actually help you, you know, go from point a to z, you know, and and skip a lot of those steps. So we are, our AI road map is extremely, robust, and I am the main reason I'm at Virtuous is because I gotta see this, like, all just come. It's like a flower that's ready to just, like, completely open up and blossom. So, stay tuned for all that. Super exciting. Alright. I know I know we're at time and some people might have to go, but I'm gonna ask just a couple more questions if we can. How do you learn more about your donors without putting all of their donor data into an AI platform? Many donors are individuals and part of our community. Do we need to tell donors that we'll use this info before we do that? It yeah. I hate to use it depends again because, sometimes, you know, sometimes it's appropriate, sometimes it's not. We tend to on the side of caution always, right, is, you know, on your website, if you use AI to enhance the donor experience, then disclose that on your website. And that's absolutely appropriate. In fact, we in our second survey of donor perceptions of AI, it again, donors, number one, they actually feel that the use of AI is very appropriate by nonprofits, but disclosure of using AI is important to them. So I'd say yes. But I would the general rule of thumb, if you're not paying for the product, you are the product, which means if you have any confidential information about your donors and you're not paying for the product, which means you don't know what your privacy settings are, do not put their private data into an AI system. But if you're paying for a product or, say, say, you have Virtuous and all the data is already in a SOC two, which is a secure environment, then, obviously, you can use the data in any kind of iteration that you want. So it it does depend. But if you're thinking, I've got a bunch of data, on donors that is not anonymized and I wanna just put it into a large language model from Excel, I'd probably refrain from that. I would anonymize it. So change the names, create a unique identifier for every constituent. There's so many ways to anonymize data to get the same result and then basically use a key to decode that after the fact. We've done lots and lots of that, over the years, and it works really well really well. Yeah. Well, listen. There's, there's more questions that are that are really good that we just don't have time to get to. And so, again, we're going to, create that podcast episode where we answer these. I think this is first of many, webinars like this too, where we're answering these questions. And so thank you so much for those of you who have submitted a question, who are here today. Again, if you'd like a demo, click that schedule a virtuous demo button. We're also gonna share a survey of today's, webinar, so you can give us some feedback as well on the experience, And so we'll leave that open for a bit. Thank you so much for being here. We are looking forward to continuing to provide as much helpful content as we can. And if you haven't already, again, make sure to download the full guide. You can do that, by clicking docs, downloading that, you can scan this QR code, there'll be a link sent out in the email following, there's a link in the chat, we're giving you all the links, that way you can make sure to get this. And so, thank you again so much for being here. Nathan, thank you for just pouring out so much of your work and your wisdom, your experience. It's really, really needed right now, and so we're really grateful Yeah. For doing that. Well, thanks, Scott. And, you know, just for anyone who's still here, it's, you know, AI is really tough because it's everything and nothing. So this we're not gonna be done. Like, we'll be back. And if this was, you know, too advanced, we're we'll end up going back to the beginning. But if this wasn't advanced enough, we'll go to the next layer. We're not going anywhere. We're gonna continue to do this and really empower your success. Yeah. Absolutely. Absolutely. Alright. Well, thank you all. We wanna honor your time. Thanks for being with us. The recording will come out shortly, and we will see you next time. Thank you.