Video: AI and the Future of Fundraising: A Panel Discussion | Duration: 3520s | Summary: AI and the Future of Fundraising: A Panel Discussion

Transcript for "AI and the Future of Fundraising: A Panel Discussion": Well, good afternoon, good morning, good evening, depending on where you are, and welcome to our session today on AI and the future of fundraising. My name is Bruce McDonald. I'm the president and CEO of Imagine Canada. Delighted to be working with our colleagues at Blackbaud to present this, this webinar for you today and just decided to to join us, for this really interesting conversation. So with that, maybe we'll just, flip to the next slide. Just wanna cover a few housekeeping items as we get started. The webinar audio will be broadcasted through your computer speakers, so hopefully, you can all hear me. Of course, if you can't, you just see my lips moving and don't know what I'm saying at this point. But, by all if you hear captions, you can hover over the stage and kick, click the CC button on the bottom of the screen to turn the captions on and off. 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So before we move into our introducing our panelists and the conversation, I'd just like to start with a land acknowledgment. And for the purpose of today, we'll use Imagine Canada's land acknowledgment that is located on our website, and I'm going to do a section of that land acknowledgment. Imagine Canada staff operations work and network develop on or depend on traditional indigenous territories to provide us physical space, sustenance, safety, and community to work, live, and play. Our physical head office is located located on the traditional territory of many nations, including the Mississaugas of the Credit, the Anishinaabe, the Haudenosaunee, and the Wendat. We acknowledge that these lands are covered by treaty 13 and the Dish With One Spoon Wampum Belt Covenant. We acknowledge that these lands are still home to many diverse First Nations, Metis, and Inuit people. We acknowledge that our ability to live and work on these lands today is a direct benefit of policies of expulsion and assimilation of indigenous peoples during the time of settlement and confederation and since. The harms of these policies are many and are still being felt in indigenous communities today. We express gratitude towards the indigenous peoples who have and will continue to steward these lands. We commit to amplifying the voices of indigenous peoples and working against the everyday forces of white supremacy and colonialism present in the nonprofit sector. And so as I said, you can go on to imagine canada.ca for a full, and complete land acknowledgment that, really states our commitment to, connecting with indigenous communities and those in the nonprofit sector. So with that, we'll move on to in introducing our panelists, and and, there is the opportunity to see full bios online. As I mentioned, my name is Bruce McDonald. I'm the president and CEO of Imagine Canada. With us today, we have Nina Doss, who's the CEO of, Namaste Data. Liz Regeman, who is the internal associate for I'm sorry. I'm just gonna switch screens here a little bit. The interim associate director of donor relations and operations at Georgian College in Ontario, and Sam Venable, senior manager product product senior manager of product management for Blackbaud. So welcome to all of you. It's, gonna be a great conversation, and looking forward to seeing some of the the questions and comments in the chat box, which we'll get to a little bit later. But to kick off, I've got a questions, for all the panelists, and I think, Nina, we'll come to you first on this one. And then I think what we'll do is we'll go to Sam and then Liz and, then Liz, we got another one back to back data that says that 7% of nonprofits say that they're already using AI and 4% say they're planning to use it. How does this align with what you're seeing in the sector and and in the fundraising world in general? So, Nina, over to you for the first response. Thank you, Bruce, so much. Hello, everyone. I am Nina Vas. I'm joining from the beautiful Vancouver, BC. Really excited to be here because this is something I talk in pink and eat and drink. What other words there can be about this, every day of the week? So this question, what am I seeing in this in this space? And so I just finished doing a study called the AI Equity Project. You will have the links, and I'm I'm going to probably Bruce, if it's okay, I'm going to bring the answers from that project because the project was to do some data collection of the nonprofits in states and Canada to understand, where are we with equity inclusion adoption of AI in an ethical manner. And 708 nonprofits participated, so I'm probably going to respond to this question from my end. Of those 708, there are 2 interesting data points that I found. 1, 40% of them said to one of the questions, and I'm not gonna I'm not gonna read the questions, you will have access to them. 40% said that they do not know or I have it in front of me, they do not use AI at all or they are not sure where they use AI. 60% said they use either personal work or both personal and work. Of those 60%, 82% said that they use AI in an ad hoc basis in their work. This is interesting. So the 5 there are 5 things, and I'm a person who speaks in points. So the 5 things I am seeing in this space is people are in our sector. They know about AI. Actually, they are bombarded with AI 500 times from different organizations every day. So we are all of them. We are tired. We are aware of the word AI or artificial intelligence, but we are using it in an ad hoc basis. We don't have consistent language or consistent conversations in our teams around artificial intelligence. We don't have clear values or policies. That's another third thing that I'm seeing in the sector. We don't have clear, common language with our team members. What are we talking about? Which means there is unaddressed concerns and unaddressed opportunities of artificial intelligence lying on the table. Which brings me to the 5th point of my observation, which is when we don't have common language between our team members and we're working on an ad hoc basis at our AI, we are needing some things for general judgment. Like, if you see someone using something that feels like it's coming out of chat gpt or or Gen AI, there's almost a little bit judgment. Oh, I see that that's happening, but it's coming out of this AI tool. So we need a common language. We need to talk to each other. We need more policies or commitment or statements of commitment or AI values. I think that's what I'm seeing from the AI equity project happening in the center. But I'm I'm I'm curious to hear what others have to add to this. Absolutely. Great. Thank you. So, Sam, over to you. Same question. Kind of what are you seeing right now? Yeah. You know, I I talk with a lot of organizations around what's going on in the AI space. And it's funny, you know, you talk to them, and in some ways, they sort of go cross eyed. Right? Because there's just so much happening in the nonprofit space, where folks have a lot of pressing needs. Right? And AI just seems like an ancillary thing that's kind of interesting, but maybe not pertinent to the direct line of their their mission. You know, generally, I think there was a lot of initial interest, as, you know, news sort of spread around, OpenAI's LLM that started to roll out. But it's human nature to sort of focus on the mistakes and the gotchas. So the AI generated images and appeals or, audit automated messaging oopsies. Right? Yeah. I think the the thing to remember about this is the space is moving really fast, and I think a lot of folks forget about where we are in the the journey of AI. It is it is a tool. Right? And there are some things that it is really great for. And that's gonna require a little bit of an instruction and work that comes along with that. So one of the things I think about it so it's a little bit like being a parent. Right? I've got 2 kids at home. And honestly, I'm like, I'm exhausted. I follow them around. I feel like I'm picking up after them all the time. And it drives me crazy. But if I just take a little bit of time and I show them how to do the thing, how to, start to pick up, then they're actually contributing to the whole family, right, to help us operate more efficiently and effectively. And so that upfront investment really pays a lot of dividends down the road. And I think a lot of organizations are falling into that same situation where they're kinda throwing some initial things at it. They get frustrated, and then they kinda pull back, because they just don't have enough time. But I think there's a tremendous opportunity here for the nonprofit landscape. I totally agree with Mina in terms of the conversations that need to be had. But I I think, you know, it's just so important that we start to do some of this experimentation so that we don't miss out on some of the opportunity that's presented there. Just a little bit on my background. I worked for a while in the nonprofit space myself, as both a fundraiser and then leading prospect research and development shop at a large national public here in the the US. You know, our marketing team really cared a lot about these highly public or highly polished publications. Right? These video assets, they were beautiful. Right? But this was right around the time that this, new technology called social media really started to get introduced. And that emphasis on that highly polished mechanism for storytelling meant that we missed out on some of the opportunities that were emerging, from these new capabilities. And so, you know, I take kinda carry that lesson forward as we think about the opportunities here in the the nonprofit, landscape with AI that, I think, there's just a lot that we can do and a lot that that we can explore. Wonderful. Thank you. And so, Liz, over to you. What are you seeing from your vantage point? Thank you, Bruce. Welcome, everyone. I'm joining from, Wasaga Beach, Ontario, the world's longest freshwater beach. So opposite ends from MENA, but not opposite ends in terms of insights and thoughts around it. I have 2 perspectives. 1 from a general fundraising perspective. What I find really interesting is that the tools that we are using, in the nonprofit space, like Blackbaud, like other tools that we use, for online giving, for prospect research, for social media, they use AI. They have it in now kind of embedded in the work, in the the technology and the tools. And so we are using AI. Maybe we just don't necessarily overtly know that we're using AI in a lot of cases. So that's, I think, one of the very interesting perspectives I really think about is and I think it goes to Mina's point around definition and understanding what are you what do you mean by this? What are you referencing? You know, what's the context? From a prospect development perspective, so prospect research, relationship management, and data analytics, it's a really interesting we're always usually early adopters within prospect development around new tools and technology to help, identify potential significant investors to nonprofits, and we always do that from a lens that's very ethical. And so what I'm seeing is really great conversations around the ethics of AI and especially the integration of AI and, personally identifiable data on your donors and how does one use that. So maybe a slightly different lens from, you know, AI generated photos, but just as equally important around, use of data and how it's integrated or not integrated into the AI tools that we have at hand. Thank you. And so so wait again. And we're gonna keep rolling on on this, Liz, a little bit because I I think what we're gonna do now is shift to a specific question for each panelist rather having rather than having each one, take a shot at each question. So what we're really gonna talk about now is the practical applications, and you've started to touch on that, but maybe you can expand upon it a little bit. So so really a specific question is Georgian College itself using AI and its fund development work beyond some of the embedded AI functionality that you've already talked about? And if so, how is it being used and are is it possible to sort of share any results that you might be seeing that you could attribute to the use of AI? For sure. So what I can say from the perspective of Georgian College in terms of, use of AI is that, yes, we are using it, enthusiastically and ethically. We really look at it from that lens of ethics, but then also from the lens of, like, what's the best that can happen. And we really take that approach of experimentation and enthusiastic sort of exploration of it in with that lens of, like, how can this help us be more efficient? How can this allow us to focus in on strategy versus manual manipulation and very tactical tasks? How does this allow our staff to think bigger, bolder, be a little bit more innovative, but in a way that is very manageable and doesn't require significant financial investment, or significant sort of time learning and resources. So 2 things that we use. One of them has we've been an early adopter to Blackbaud's Prospect Insights, which is coming soon to a Canadian nonprofit near you. And, I mean, that is powered by AI, and it allows, you to identify very easily, your top significant major gift prospects and sort of identify a potential ask amount. And in addition to that, also identify who isn't a great major giving prospect. And so that's, you know, again, that from an efficiency perspective, for an organization, it's super helpful. Right? We have to be really efficient with donor dollars, make sure our frontline fundraisers are using them in the most effective ways possible so that the most amount of money can go to the communities we serve. So leveraging a tool like that prospect insights allows you to really focus and to be really efficient with your time and your energy. The other thing that we use, and I've been using it and experimenting it in my work, related to donor relations and stewardship is, like, coming up with unique, interesting, communications, thank you letters, outreach emails. And, again, being able to sort of instead of sitting there for 3 hours staring into space, thinking about when am I gonna write, you know, plugging it into something like, chat GPT, scraping out all, you know, personally identifiable information, and then coming up with a great first draft and then being able to run from there and kind of shortening the time from creation to getting it in the hands of donors. So that's 2 very specific ways that Georgian College is, leveraging AI and testing it out and seeing what the best that could happen. That's great. And and, Sam, before I come over to you for a question, there's one that's come in, on the chat box. I just wanna invite our panelists to think about it. And I'll circle back to it a little bit, but just give you a little time to think about what a response might be. This comes in from Neil. In in a webinar last week on AI, we were warned not to use AI if it is in any any material which is confidential, mainly due to not accepting the right to let the AI system use the material. Is this, is this reality or is this really dangerous for small charities which don't have great secure security checks? So for example, should we be using it to record board minutes which are confidential? So we'll come back to that in a minute because it's a great practical question. But in the meantime, Sam, from Blackbaud's vantage point, you support thousands of organizations. At a high level, what are the most common trends or challenges you're seeing emerge from how organizations are considering adoption or already using AI in their fundraising work? And how are some of Blackbaud solutions supporting fundraisers who are ready to use, or utilize AI and their work and and maybe what are some of those benefits are? Yeah. For sure. I I think, you know, it's not a fundamentally different answer from the first question here just about sort of having that space to be able to experiment, learn, and kinda play with it. One of the things that we are trying to be really intentional with, and I'm glad that Liz mentioned prospect insights, is how we integrate artificial intelligence in a really seamless way in our experience. It's actually focused on driving, outcomes for our customers. So it's not about using AI for the sake of using AI, but how is it actually speeding up that time to action to where, you know, our fundraisers are really focused on getting to those conversations with prospects more quickly and not being paralyzed by just the tons of information that's out there in the world. You know, I I think a lot of folks, just to transition over to the generative AI space, tend to look at, AI sort of in the context of other things that already exist. Again, it's a very natural human tendency. Right? So a lot of folks when they come to generative AI, they sort of look at it as a as a Google, and there are versions of generative AI that can sort of tackle that same approach. But, when you think about somebody new that is coming to your organization and starting to work there, there's a job description. There's a set of instructions and expectations that are communicated to them so they know how to interact and work with your same is true for some of these generative AI tools. You know, we've gotta be really intentional in building that instruction and expectations into the solution so that we actually get better outputs. And so that's another thing that we're taking into account as we're building solutions that are leveraging AI is how behind the scenes can we provide some of that instruction to the generative AI capabilities so that the quality of the output is really meaningful and helpful for our customers. So, generally, you know, we've talked about a couple of things with our solutions that we think are helpful. We also really, wanna be intentional in making sure that our solutions are accessible, they're powerful, and they're responsible. So accessible means there's not a lot of overhead for an organization come to come to and work with, AI, that they're powerful, that they actually are driving outcomes, and that they're respecting the bounds of the regulatory, landscape, so that we are in fact being responsible, with the stewardship and and interaction with with data. So really trying to think about how we can help organizations leverage the tremendous opportunity that's opened up by AI, without necessarily the overhead of having to think about all of the complexities there behind the scenes. So Prospect Insight's one of those solutions. We have some capabilities built in for AI acknowledgments, to Liz's point that helped generate some of those first drafts. We know it's so important to interact with the donor early after they make a gift. And so how do we speed up that time to, that engagement with the constituents? And then Blackbaud Copilot is another thing. So how do we think about maybe alternative ways that we can interact with software? You know, software generally is a pretty interesting thing because humans have to work around it. Right? You have to learn the software. You have to know the right places to go to interact with it to do what you're trying to do. And I think there's some really interesting opportunities, just in terms of that interaction model. You know, almost AirPods look like the shift for this. Right? An audio based interactive model where you just tell it what you're trying to do, and it helps you actually accomplish that task. So there's a ton that we can do here, but we're, like, deeply invested, again, not just in this using AI for its own sake, but thinking about how we can do that in a way that actually helps our customers really meaningfully. Wonderful. Thank you. So, Mina, I'm gonna come to you, for a question before we come back on the one around, you know, privacy and security checks and board minutes. So so, Mina, in in your consulting work, you specialize in advancing data equity for nonprofits and social impact agencies. Yeah. You often use the phrase moving towards human centric AI. What does this mean, and how does it relate to the fund development world, and how should organizations be taking equity into account when using AI in fundraising? Bruce, that's a lot of question in one question. So I'll take one at a time in that. I wanna set some context here first. Why do I say so much about moving towards human centric AI? So my background is I have spent years working behind a computer, designing these algorithms, then a shift happened, and I changed to becoming this consultant who talks and teaches and speaks about these things. At some point, I think I started to question, what does it mean when we stop looking for the largest, highest, biggest in our charts and our datasets? What happens in our in our algorithms when we pass those algorithms into production, which means we are sending it out into the world? We don't just say it has crossed random 70%, 75%. What happens to those percentages which don't fall true to in in our test cases around the algorithms? I started to question a bit kind of, like, as a data scientist, also as a life coach, what does it mean when we talk about these things without considering the people who are in the outliers, who are in small quantities, who are in small numbers? What does that impact mean? And so I started to use the phrase moving towards human centric AI. Now if to be honest, if you ask me the same question, 6 months from now, my answer is going to evolve. And this is it should evolve for everybody, all 7 50 of us, because we are constantly learning and the answers should evolve. At this point, at this moment in time, I would say 4 things stand true to me when I talk about these things. 1 is moving towards human centric AI means listening to your community. Really, truly listening. When we cook for let's say, to give you an example in the family, if you're cooking a meal for your family, right, in the kitchen, we don't just bring a random dish, let's say chicken, in the kitchen and start cooking. We understand how do we cook it so that my mom, who is a diabetic patient, my dad, who has a liver condition, my brother, who has some health conditions, I, who needs something, I would consider what my family needs, and then I would prepare the dish. It's kinda similar. We need to understand what our community needs before we just randomly bring the technology. So listen deeply, carefully to our community, what it means. That's the first thing to move into human centric AI. 2, stop talking or use or not talking, but saying about AI as if it's the magic. What is the inferior? It's just a tool. The question right now is how do we build a good partnership with this technology? Right? It's not no longer about it's superior to us or it's inferior to us. So that brings me to my 3rd point. The question is no longer around AI. How do we keep the human at the center? The question, Denise, how do we keep our humanity at the center? The AI is going to stay, and it's going to stay here for the next 30 years. The question now is, whatever we do with AI, how do we ensure that the good parts of our existence, the the kindness, the compassion, that lives in the things we do with artificial intelligence? That is what I mean by human centric AI. And the last one is what, Sam mentioned at the top is experimentation. Moving towards human centric AI means we would need to learn a lot. We would have to learn to let go of a lot of things, and so it's going to come down to experimentation in a very responsible, community centric way where we learn these things, evolve with it. And so, yeah, ask me again in 6 months and I'll add 6 more points to this. But those four points, what it means to me right now, moving towards human centric AI. That's great. Thank you. And, of course, this is a a webinar for charity, so we try and pack as much into every question as possible. So we'll there's, like, 4 questions embedded in one because we want maximum value. So I Well, thank you for giving me that homework, Bruce. Thank you. Absolutely. So I just wanna maybe throw a little bit of a wave if if anybody wants to respond to this question. I'll just repeat it as it relates to, using material which is confidential. So for small organizations which may not have, you know, robust systems around, security, should they be using AI for something like board minutes, which are confidential? Let me see if anybody wants to take a crack at that one. Okay. Sam, unmuted first, and then we'll go to Liz and then Liz sure if you're raising your answers for just unmuting. That's okay. I'm like an auctioneer. If you twitch, I'll pick that. So Sam and then over to Liz. Great. Yeah. You know, I I think it's really important to come into the use of any AI tool with your eyes wide open. So it is important to kinda read through the t's and c's, see how they're using your data, really being mindful and think about some of the implications. So, I mean, it's great, Neil, that you're bringing up that question about the use of board minutes being confidential, and is it an acceptable use within the balance of an AI solution? And a lot of times, you know, reading and understanding how the company will actually use your data, is gonna be helpful in guiding your decisions. I'm I'm sure we're probably gonna talk about this in a little bit, but that's one of the components that we're really building into the AI solutions that we're designing is how do we take some of that overhead away from the organizations and really build in, some of those principles of data sovereignty and, ownership over that data that it really is, you know, the, the asset of the organization that is producing it. It's not something that we, we own. And so, we need to be very careful that the solutions that we're using do respect that sovereignty of data. So just pay attention, and that'll go a long way in in guiding your decisions. Great. Thank you. Liz, you wanna jump into this? And then I've got a just a quick specific. It's kind of a follow-up question to your earlier comments, which I'll come to in a second. Sam, essentially said the same thing that I was gonna say, which is you really need to read your terms of service. Like, what are what is the technology doing with your data? Can you delete it? Where does it go? Where is it stored? I think for small organizations, you Bruce, your comment around those that may not have formal processes, they should, at the very least, have a technology strategy or a technology philosophy and have, as Sam mentioned, thoughtful, intentional conversations before they leap in. Like, there I don't think there's anything wrong with using any one particular piece of technology, but do it, as Sam said, with your eyes open and with intentionality and understand where the risks are and how that relates to your own risk tolerance in your organization. So it really means communication, as well as reading those pesky terms of service, which are tiny font and really long and and legally. So can't get around that one. Well and and I I think to your point, Liz, one of the things I think we're also hoping for at kind of a macro level is that the funding community will also recognize that as organizations are needing to craft technology, visions, policies, you know, really articulating how they see some of this this emergent technology like AI in their future. Well, that often falls into the cost of administration or cost overhead bucket, but these are really important pieces in order to power future services to make more effective use of donor data, etcetera. So, hopefully, what we'll also see is a response from the funding community that will support organizations in that in that core work in order to maximize their impact. So, Liz, I'm gonna come back to you. It was actually a a follow-up question from your comments about how Georgian College is using AI. Does Georgian College acknowledge their use of AI pub and I'm adding the word publicly to the question, but maybe it's either internally and or publicly. I'll let you choose. So good question. In terms of, like, the if I'm using the specific example of, like, writing a thank you letter or communication, we don't publicly acknowledge the use of AI because by the time we've edited it, and ensured that it's using sort of Georgian branding, Georgian language, we've taken something that was an initial first draft and made it our own. And so we do that, with many of the things, that we work with currently in relation to AI. I think know there's some examples in the fundraising space where especially, social service organizations that may work with vulnerable communities, you know, using there's always the question of, like, do you use the actual individuals and clients and and community members, that, you know, leverage your services, or do you create AI generated in order to sort of not formally expose those individuals to the public. And so, you know, those kinds of questions, it's an interesting one in terms of when do you say this is a representation versus these are actual individuals who have accessed the program. And that's, I think, a decision that each organization needs to make individually, again, through those thoughtful and intentional conversations. Great. Thank you. And and so, I'm gonna ask, I get a question for all of our panelists and then but I do wanna also set up one that's come in from the audience. So first of all, the the question that we ask is gonna address a number, I think, of comments that have come in around kind of the ethics side of things. So that'll be a setup, I think, for some of the questions. But one that I think is a really interesting kind of concrete question that we'll see who wants to jump in on after our kind of round of 3 here is what would be the number one suggestion for a small nonprofit wanting to start in using AI. So if you had one kinda concrete tactical, suggestion, but, Mina, we're gonna start with you on this next question. The most common concerns related to the use of AI, particularly in fundraising, relate to ethics and trust. These areas can be affected by broad perceptions from donors, robust or completely lacking policies related to AI usage, and how transparency is managed. From your perspective, what are you seeing related to those who are doing a particularly good job of managing trust and ethics as they introduce AI into their fundraising efforts? That's such a great question, Bruce. I'm gonna say 3 things from my work that I'm seeing. One of them is transparently sharing, Liz's excellent point that we don't know yet. We are still figuring out where to say we are using AI versus where it's we don't feel it's necessary to include. Because by the time we are putting out something, it already has a voice and tone and a color of our own on on top of the things that the AI has produced. Nevertheless, transparency is the number one thing that I'm seeing organizations are doing great when they say that this is an image that's coming out of AI or this is was the prompt that we use to generate something. 2, that I'm seeing happening really good is when organizations are really strongly committed to asking why again and again and again. The intentionality piece. Why do I need to use this piece of software, piece of program, piece of algorithm? How is it going to help my team? That's number 2. I'm seeing that gives me hope, that gives me some optimism. And 3 is when I see teams doing and talking about AI together. To my original point, the first thing that I mentioned, ad hoc. I'm seeing teams, one person doing something with Chargegbt, the other one using a BitHere, Grammarly, the other person using Canva. It's great. Individually, we are growing, but we need to grow together as a team. And so when we can do this together and leadership can bring teams together around these topics, I think that's something that gives me hope. And I'm seeing a bit of it, and then hopefully, I'll see more of it in the future. Wonderful. Thank you. Sam, same question over to you around kind of this ethics and transparency. Yeah. I think, Minh is touching on a couple of things that are super important. The, again, the the need to be really intentional and have those conversations across the team. Be thoughtful. There's so many great resources out there in thinking about sort of that intersection of humanity and and technology. A couple that I would recommend, predict prediction machines is a book written by a couple of economists that looks at the impact of AI. And and they sort of focus on the economic space, but that's a great read. As well as Co Intelligence Living and Working with AI written by Ethan, Wallach. Just there's so many great books. You know, it's a great opportunity to to drive some dialogue internally at your organization about the impact of this technology in your space. One of the other things that I see organizations that are handling this really well is being really intentional on the front side of these decisions. So one of the tools actually that's recommended in prediction machines is something called an AI canvas. And it really, talks about, it gives you a format, really, for laying out the problem that you're trying to solve, the outcome that you're trying to drive, any potential consequences or downsides to using artificial intelligence to solve for that problem. So it really pulls some of those conversations to the front side of that decision instead of doing it reactively at the end of it. And then, you know, look outside in the world. There's tons of great examples of companies and organizations that have really strong models for how to handle, interacting with AI. Microsoft is a great one with their responsible AI framework that looks at fairness, inclusiveness, transparency, reliability and safety, privacy and security and accountability. That's a model that we've used to guide a lot of our decisions internally at Blackbaud as well. So look outside, see what others are doing. There's a ton of great resources that, help you formulate what your organization's sort of stance is in this, space as as the world is all changing. Thank you. And, Liz, same question around ethics and transparency to you. Definitely in agreement with both what Sam and Mina had to say. I think for any sized organization, a very, very practical thing that you can do is actually embed an ethics session in your team meeting, whether that's on a semiannual or annual basis. So it's wonderful to say you need to talk about it, but I think you need to actually schedule those conversations when there isn't an ethical issue or dilemma. So you can then understand in that session, where where are the values of the leadership? What are what kind of questions or perspectives do people have. It it generates, like, an incredibly lively conversation. I actually just did this recently at Georgian College in October because for AFP, October is ethics month. And we had a very deliberate and intentional ethics conversation. We took some case studies. We, you know, put this together some scenarios, and we asked the question, like, what would you do? And by asking the question and kind of working through it in that model, in a kind of role playing way as opposed to when it's actually happening, you you downplay the risk, and then you can have that sort of thoughtful reflection, on that. So that would be one, like, very practical thing that I would recommend folks do. I'm gonna put in the chat as well. It's interesting that we are having this conversation. APRA International, which is the Association For Prospect Development Professionals, they just released their ethics in AI in fundraising toolkit. And so I popped in the link in the chat. Again, it's the beginning of a great resource in terms of having those very intentional conversations, around ethics in AI. Ethics is a gray area. 1 organization, something could be ethical, and another one, it could be totally not ethical. So it is all very much dependent on the time, the context, the place, the organization. And so having those deliberate planned conversations is really important. Terrific. So I will look to see now just sort of, if if one of our panelists wants to jump in initially on this question around for small nonprofits, what's sort of that first thing, that number one thing they should be doing to to sort of start down this path? Any thought any takers? Oh, no. You got it. Probably the answer holds true regardless of the size of the organization. But the first thing I really want our teams and organizations to do is get some common data values. How do you operate around data as an as a team, as an organization? We don't have that. You and I, for example, Bruce, we we have our human values. Right? Same goes for Sam and Liz. We have our human values, who we are as humans. We don't have similar understanding what makes us who we are around the data. And that's number 1. Because this data that we are talking about, as messy as it is and it will be probably, that is going to be fed into the algorithms. So instead of, simply trying to get access to new tools and experimenting, which is important, by the way, and I'm not diminishing that, we also need to understand who lives in our data and who are we around that data. So I would say the one thing if I have to recommend one thing to organizations, that would be a very healthy intentional conversation. How do we collect data? Who is in our data? How do we do when we have data that is probably giving away the identity of the people and keep and putting them at harm or risk? What do we do then? Just understanding those values. I'll leave it. Great. Thank you. Oh, yeah. So, Sam, Sam, before you answer, though, I know I'm just let you know. I'm coming to you with a question because we're now it's it's, like, really the the, the lightning round of questions from the audience. So just wanna give you a heads up, in terms of a question that's coming around how Blackbaud might be looking to help organizations to understand and do intelligent and effective prop design. So we'll come to that in a second, but maybe you can jump in on the small organizations one. Yeah. I actually kinda wanna piggyback on top of what Nina was saying. I do think having that sort of shared model for how you all look at your data is important. And I would actually encourage folks to experiment in low risk areas as well because that's gonna broaden the conversation about your data approach. You know, just to give you an example from my own house. Right? My wife hates meal planning. I do too. So she started to experiment with throwing some suggestions in the chat gpt in terms of where we shop, our dietary restrictions, and then getting a grocery list and some suggested recipes. So, like, low risk. There's not really any privacy considerations there necessarily, but it gives you an opportunity for the types of things that that technology can solve. And that again informs your perspective when you come back and have a dialogue with your team. So just wanted to throw that in there as well. Great. Thank you. So we're gonna come to one that's come in specifically for you. As Blackbaud starts to integrate AI, how will you be helping organizations learn how to understand and do intelligent and effective prompt design so they are using these tools to the best possible effect? Yeah. Absolutely. So there's a whole series of webinars and conversations that we've been having over the course of the last year or so, as well as some some publications that my team and others in the group have, have published. One of the things that we're trying to do is take some of the overhead off of end users from having to do that. So, you know, it's useful to understand some of the prompting techniques that you can use. But there's also a lot that we can do behind the scenes that sort of build those prompt prompts in automatically, so that it has that sort of baseline instruction set, and then it's adapting maybe some nuance to your specific organization. So, keep an eye out for, again, the the webinars, some of the social posts that we have on MP Engage, but also know that we're we're building some of that behind the scenes into our solutions so that it's taken into account. Great. Thank you. So I'm gonna I I got it now a question again to see who who wants to jump in on it. Kind of an interesting one because it it has a bit of a, I think, attention built inside of it. The question is, it seems counterproductive to use AI with donors who want a personal touch. If they know AI is in play, how do we see that impacting their response? Now this is kind of a take from the old Ferris Bueller movie. Anyone? Anyone Bueller? Anyone? Okay, Liz. I'll kick it off. It's a great question because they the person is absolutely right. Like, again, when it comes to especially when you're talking about significant investments, major gifts, there's an expectation of a much more personalized, customized approach. So there's 2 things that I've been seeing. 1st and foremost, you don't just take what AI gives you and then, like, copy paste it. Like, you would again, it's it's a first draft. You then craft it. You integrate the personalized pieces in there, and and make it your own in that regard. The second thing I'm seeing, and I know, Blackbaud is working on this, and then there are some other, vendors in the space that, you know, kind of initially will write generative AI emails, communications to donors, but they always embed in that the ability for the individual to edit. And so that's the piece where AI will not take away our jobs. It it will simply change our jobs. And so maybe it will do that first draft and make sure it covers sort of, you know, standard aspects of a letter that would be included so you don't have to really think about that and then allow you to kinda really focus in on how do you meaningfully move forward this relationship with this donor. So I think I don't see a tension there. I what I see is AI is, like, a great assistant that remembers, oh, yeah. You put the comma after here or you always start the sentence this way or you make sure you embed this particular language because it's part of your style guide. And then you don't have to manually remember that, and then you can really focus in on the unique aspects of that relationship with a donor to include that in the communications. Wonderful. Just looking to see Mina or Sam. Do you want in on that one at all? Well, I wanna add one thing to that response first. When we speak of personalization, I want to remember I want all of us to remember, 1, why do we want to personalize? Right? Because we care about those relationships. AI is not a competition between us and us versus AI. It's it's a technology. Like, when I mentioned the second point of my moving towards human centered AI, it's not superior or inferior. We are working towards using this technology to build a good partnership. So don't consider this as a as your competition, but think about your strengths and how does it elevate your strengths when you're using this technology. So when it comes to personalization, we personalize because we care. Whether it's about my name, whether it's about where I come from, about my identity, you want to personalize because you care about me. And so use this technology in a way not as is to replace you to but more as a way to, enhance the good parts of your care in your relationship with me. Great. Thank you. Oh, sorry. Sam, please. Oh, sorry. I mean, I I think the other thing is it's important to think about as compared to what. Right? So using AI to interact with donors, you know, in some spaces may not be dissimilar from a broad based, direct marketing appeal or a phoneathon script that's provided. So it's important to sort of right size that conversation instead of making a big scary different thing. Like, how does it compare to sort of proxies that we have in the existing world today? And the other just small thing I would say, think about the impact that that has in terms of the value of that face to face human conversation. Like, the power of a a donor visit and the meaning to an individual when they get that too. I think it becomes even more important as we move into this era. So Yeah. Some some great thoughts raised here. So we had a question earlier about smaller organizations. We've had one come in from the the kind of the perspective of a larger organization. So for larger organizations, how do we ensure that all staff are equipped with the knowledge of AI to make the right decisions in regards to the use of AI so that they do not put the organization or their donors in a kind of in a vulnerable place. And I know, Liz, you had talked about even some training around ethics, but are there other things that and maybe for larger organizations that have more staff to engage in this, what are some of those sort of tactical, practical ideas for making sure that their staff are equipped with the knowledge, so that they're really setting themselves up for success? Yeah. Let me know. First. Let me okay. Larger organizations, they do have access to funding, and I want you to use that strength. Experiment. Use that for a lot of budget. Give people chance and and staff members access to tools so that they can experiment. Again, thoughtfully, intentionally talking to each other, building common language, but then experiment. But it for smaller nonprofits, that's the biggest issue, funding. We don't have enough funding in the sector for nonprofits to experiment with this technology. But for larger nonprofits who do have access to budgets and resources, allot something in the professional development budgets so that they can get access to tools and bring it back to team conversations. So Liz mentioned having an ethics conversation in lunch and learns bring something that the tool of the month that I used in play with or the tool of the month that I learned something. That's where I would start off. But I I I'm curious what Liz and Sam might wanna add. I was gonna add, you know, if it's important, it's worth repeating. And so one of the things I find when it comes to processes, any processes and policies, you've got to repeat it. And whether that's a large organization or a small organization and in a variety of different ways. So email communications, in team meetings, in 1 on ones, embedding in your standard operating procedures. You know, and at Georgian College, one of the things that we have actually started implementing, and I'm sure it will iterate itself, is we actually have at every team meeting a razor's edge in a minute, and we do, like, a little training. As a, you know, midsized organization, yes, you do that initial training, but you you may forget or you may not use it. And so you've gotta have that repetition in order for it to become part of, like, the the daily lexicon within it again, regardless of the size of the organization. Well and it's a great point. When you even just think about staff and volunteer turnover in our sector that you might do it in August, and by next March, a number of those key players who now may have tentacles into this area could have changed. And so, you know, I think your point about, you know, it's it needs to be regular is is really important. So I'm gonna come to another question, and then, just kinda keeping an eye. And I wanna just put a marker down for our last question to all panelists, which, of course, wasn't in the pre information. So I'm springing this one on you. But, you know, it it's interesting that and this next question that is dear deal with hesitancy, kind of in is almost an embedded idea of sort of some fear in here. But the last question will be, what kinda gives you hope and optimism for how AI can transform fundraising in the future? So that'll be kind of our wrap up question. I'll come to each each panelist on there. But so how can an AI transform fundraising, you know, kind of an optimistic view? You know? So but this this question before we get there, how can we get some members? Now that was how it was worded, but I'm gonna take a little liberty of this because if you're not a member based organization, how can we get some individuals, board members, sort of resistors, in in the staff group, you know, on side with using I AI? More specifically, how can we properly address the hesitancy that's coming along with AI? Oh, I'll start again. Okay. It was all it was all it was a race to the clicker there, and and you you crossed the finish line by just just a whisper there. So you and then Sam. I was the one thing I would, suggest is like a show and tell. I think sometimes there's a hesitancy because there's an unknown and there's a fear. And sometimes, if especially if folks are not necessarily technologically savvy or not necessarily early adopters or eager early adopters, doing a little bit of show and tell can be helpful to kind of ease some of that hesitancy or some of that fear or trepidation. And then I think you would also wanna think about the, like, what's in it for me? So in that show and tell, thinking about something like, what's a pain point for a board member, or what's a pain point for a staff member? Can AI as a tool address that pain point and make it their lives easier, more enjoyable, more positive staff experience. And then, you know, think about framing it that way, from that show and tell and they're like, what's in it for me? Again, very something really, really small. You know, don't try again, this isn't how do you eat an elephant? Like, what is it? One bite at a time, which is a horrible visualization, I think, but it it kind of holds true. Like, something small, show and tell, address it as a what's in it for me, and I think that will help sort of acclimatize it and help people see the possibilities. Wonderful. Sam. I honestly, Liz said it so well. I couldn't add anything to it. So well done. Well, I I actually maybe I'll offer a thought on this one because I I'm actually at an event where AI has been a central theme. And one thing I'd heard of this one is for those who are hesitancy in a workspace, just invite them to try it out in a personal way. A low risk opportunity to just try and and practice and get used to the the tools that are out there in whatever their personal interest might be. And then that way, they start to create a little bit of familiarity. So we've only got a few minutes left, so I am gonna come to this last question, around, you know, what gives you hope and optimism as it relates to the use of AI and fundraising? And so, Mina, maybe we'll ask you to to kick off on this one. Sure. Why not? When you're least prepared, you go first. Right? So I think my response and I wanna add one thing to the last question, Bruce, as well, if that's okay. For the hesitancy people who are feeling hesitant in your organizations, please don't dismiss those fears. Create space to acknowledge that. Create space to we are not pushing AI. We are not saying AI is magic. We are bringing people into experiment, meeting where they are. So acknowledge all feelings. To your question, what brings me hope? I would say AI is powerful technology. I have worked with it over for years. I I love the tech part of it, doing things, seeing things, what comes out of it. The part that gives me hope is as we are talking more and more, doing these kind of panel conversations on AI and ethics and inclusion, I think somehow we are deviate just turning a bit from just focusing on technology, but also focusing on people, also focusing on inclusion, asking a bit of why. I think that gives me hope to do these kind of collective learning spaces again and again with all of us thinking what is that we can do that ensures that there's inclusion in these conversations, there's community in these conversations. That gives me hope not to focus solely on technology and profit margins, but also about people. Wonderful. Maybe, Liz, we'll go to you, and then Sam, we'll close off with you on this one. I think for me, I'm very much a a a because I've of my work in operations and process, the thing that frustrates me the most up until now within our nonprofit space is how much we do things manually and how much we really, take the long route and the hard route to get things finished. In an organizations where, you know, that is not to the the that's to the detriment of serving our community as quickly as possible. So what gives me hope is the the the ability of these technology to speed up some of those really processes that are mundane, that really frustrate staff, that, end up taking much longer than they should so that we can get to inspiring more donors to engage in our organization. Being able to serve more in our community and serve, you know, the needs of the the individuals that, you know, our organization's mandated to do. That is what, gets me excited about wanting to dive into this, and and access it. Ultimately, it's to to be able to to have a positive staff experience, a positive donor experience, and a positive experience for the members of the communities we serve. Wonderful. Sam, one minute on hope. Okay. I'll be fast. I the work work in the nonprofit space is so meaningful, and it is also so hard. And I'm betting that every single one of you wishes you had more time, people, or resources. Right? Because we wanna do more. We wanna we wanna deliver more on the mission of our our organization. And this technology is a tool that helps drive our ability to deliver on some of that. And so I just see incredible opportunities for us to continue to deliver, you know, world changing impact that is just extremely exciting. And I think this is really gonna help drive that forward. And I think this is a great way for for us to to bring this great conversation to a close. I think it's it's important to note that I you know, what our hope is is that this conversation today has sparked some ideas, provided context, some resources that you can go to. But I think really just keeping in mind that each situation of every organization is is going to face and is the opportunities before them are unique. And that there's really an official guidance per se, but it's really to hopefully spark ideas and and creativity. And with that, I'd like to thank, Liz, Sam, and Nina for joining us. Also, thank you to our partners at Blackbaud for making this possible. I'm sorry we weren't able to get to all of the questions that, were there. And, really thank all of you for taking time out of your busy days either now live, or in the future as you'll listen to the recording, to participate with us on this really exciting topic. And with that, I wish you the the the best of the rest of your day, however long that might be, from wherever and you are calling in or listening in from. So with that, all the best. Thanks very much, and goodbye. Thank you.