Name: Transparency, trust, and the future of donor relationships
Uploaded: 2026-07-29T16:32:48.690Z
Duration: 37 min 52 s
Description: Transparency, trust, and the future of donor relationships

Transcript for "Transparency, trust, and the future of donor relationships": Hello, everyone, and welcome. Higher, my name is Mary Jones and I am with Blackbaud. Very excited about this session. We're just gonna pause here for a little bit, see if more people are gonna join us. I hope that you are all enjoying your summer. I personally am having fantastic weather here in Greenville, South Carolina and really enjoying it and looking forward to the fall as well. So I will we will get started in a moment. But, I would like to before we I would like to advance the slide. Okay. Here we go. Now let's do some housekeeping. Before we get started, I hope you can hear me. Your audio is coming through your computer. If you have any trouble, hearing me or seeing the slides or anything like that, please put something in the U and a. But the best solution is always to refresh your Raiser. And if you still have not, fixed the issue, you can, leave and come back in. We have quite a few people here days, so our chat is not on. But, if you have any questions throughout the presentation, please, put them in the U and A. You will find that button up in the right hand corner. We've also added several different resources for U, including the report that this information is coming from by my wonderful people at Blackbaud Institute. You can find that as well in the links. We will be going into breakout sessions and sharing with Cover Aid networking. You can find that button right next to the Blackbaud logo at the top left hand corner. I'm really excited. We're recording this, and it will be, sent out to all of you within twenty to forty eight hours. Now I will have to say that our wonderful speakers, Kate and doctor Curry. Doctor Curry is in Glasgow, Scotland, and Kate, unfortunately, has come down with the stomach flu. We I don't know how we even did this or even anticipated it, but we recorded their presentation ahead of time. So I'm going to get that recording loaded for all of us, and, we will move forward. Let me stop sharing my slides Aid now I'm gonna become huge Aid let's All right. Hello Aid welcome to the first, Aid a two part Website series designed to help nonprofit leaders Cover beyond University into Student, responsible action. This webinar is grounded in Search in the Blackbaud Institute. Net today's session, we are going to explore what donors believe, what they worry about, and what they wanna see from organizations who are using AI. You'll learn how transparency, clear communication, and human oversight, as well as data protection can help build confidence and preserve human connection that drives generosity. Today, our goal is to arm you with practical language and decision points that you can use to communicate AI use to donors, reduce trust concerns, align AI enabled engagement with donor expectations. My name is Kate. Averett Foundation. I am the research and publications lead for the Blackbaud Institute, which is Blackbaud, original Search lab Education to the essential business operations of social impact organizations. In addition to my decade as a Search, I also have a background in donor and community engagement. That's where I got my start in Nonprofit. And I currently am chair of, the board for a k through eight arts integration nonprofit in my home of Center North Carolina. And I am extremely honored to be joined by doctor Lorraine Curry, my brilliant colleague, who I'll have to introduce herself. Thanks, Kate. And, yeah, I'm Lorraine Curry, responsible AI research scientist at Blackbaud and research Raiser for AI Information the Blackbaud Institute. So I've spent nearly two decades focused on researching how people experience and respond to emerging technologies. So really focusing on the U aspect and how we can take an intentional and responsible approach to innovation. You all are in for a treat to have Laureen here. And just a tip, Laureen is actually, an instructor in our AI Education for social impact Pro. And the registration for that will be in the resources here if you want to hear even more of her brilliance and be able to get certified in, AI through her. Alright. So the research that we'll be looking at today is primarily drawn from our most recent study Billing the AI effectiveness gap. In this report, we wanted to not only look at AI adoption, but we wanted to look at what sets organizations apart Arts are really seeing ROI when it comes to their AI usage. And so through this research, we identified four key gaps that, organizations need to bridge in order to use AI effectively. Those gaps are in infrastructure, data readiness, effectiveness, and finally, transparency. So days, we'll be taking a deep dive into that transparency gap, focusing specifically on how organizations can grow both donor trust and AI value hand in hand. In just a few weeks, I'll be joined by Laurie poor head of the Blackbaud Institute to discuss the three remaining Campus. And you can find a link to register for that here as well. If you'd like to dive into those more deeply. All right, so let's just dive into things Institute quickly the land as we're all Insights journey with AI Aid its possibilities. Part of our research was really getting a deeper understanding of where social impact professionals and donors are right now. This research in the Blackbaud Institute involved a survey of almost 1,400 social impact professionals. If you are one of them, thank you so much for taking part in our Search. And about a thousand donors and volunteers in The US. We did this study in March of this year. And we'll combine that with Lorraine's deep behavioral insights from hundreds of hours of research and conversations that she's had with people throughout this year as well. What this research is telling us is that AI adoption is widespread. About 85% of social impact, professionals are using AI at work right now. But there's a catch here. Only one in three are using it really Activity, even though they're using it about 50% more than they were the year prior. We're seeing social impact organizations leaning into AI policy creation with about 30% reporting that they had one in place, which is more than double what was reported last year. So congratulations to everyone. That's amazing progress. The study also showed that organizations are seeing time savings. They're averaging about $500 per employee per week saved, and that goes up to $621 for more AI mature organizations. But we wanna identify that the difference there isn't just time and money Giving. It comes down to how they're reinvesting that and making sure that they're doing so effectively. So there's a gap that we've identified. There's a small group of about 10% of organizations that we call AI adaptive. They've moved beyond experimentation into something more intentional and more governed. Most of the social impact Center, about three quarters, are still in that emerging stage. So, the tools are in our hands but the organizational impact hasn't quite caught up yet. So this is when we get into the transparency gap. The data is suggesting that donors are ready, and this is where we see an opportunity to meet them where they are. 76% of donors want to know when and how AI is being U, And our research highlights that only 26% of social impact organizations are currently telling him telling them. That is a big gap that needs to be addressed Aid a good opportunity for everyone who's joined for this webinar. 68% of donors said that protecting their personal data and AI really matters to Team, but only 36% of social impact organizations have reported that they're taking what feel like the right steps there. The greatest gap between donors and organizations' priorities appear to lie Aid best practices and in transparency. Really thinking about security, policies around human input Aid disclosure, and that's the gap to close Aid a real opportunity to continue to support donor trust. So overall, what the insights point to is that donors don't appear to be asking organizations to avoid AI. They're asking organizations to be open about its use. Across Success these findings, donors can see the potential benefits of AI, but they still expect the same things that they've always Connected, clarity, accountability, relevance, and human connection. Ultimately, donors want to know that people are still exercising Management. Somebody is remaining accountable Aid that organizations choose that they choose to support are still visible within the experience. When you tell donors how to use AI, trust is more likely to increase. Aid net 18% positive across every generation based on our research. Aid a public statement about AI responsibility lifts likelihood to keep giving by around 20%. That's most strong around millennials, gen z, and donors who give over $500 a year. So this is critical when you're looking at your future donor pipeline. Only 8% think that this kind of communication is unnecessary, and they'll go looking for it. A third wanna find your policy right on your Website, and transparency is a trust builder, not just a compliance checkpoint. So now I'll pass things over to Laureen to go into her expertise on donor perception and generational differences. Billing. Thanks so much Kate. And to understand how donors really feel about organizations using AI, we also need to understand how they're experiencing AI in their own lives. And what we Foundation a donor population that's already engaging with AI, but but not in a uniform way. So the behaviour, attitudes Aid expectations can look quite different depending on the person. So let's start there. Now whilst the social impact sector appear to be focusing on their readiness, what our insights are highlighting is that donors are already out there living with AI and using it. And that's the current reality. And what we're seeing is that 81% of donors already use AI in their own lives. And if they're employed, that jumps to 90%. And we're seeing Team twothree use it for kind of everyday Inc., but the most frequent uses being things like researching topics and answering questions, writing or editing texts, things like summarizing content and generative ideas. So these are really more generative AI. And I think it's interesting as well when you think about the highest usage being researching topics and how this might translate into how they might research and compare social impact Solutions to better understand them if and how they align with their values and even make comparisons. And another key area that we've seen come up in our behavioral research repeatedly and might be surprising is that seventy one percent reported they're as comfortable or more comfortable with the social impact sector using AI than for profit Complete, and 19% said that they felt less comfortable Aid 10% were unsure. And that's meaningful because it suggests that donors can see the rationale. They understand that social impact Solutions are often working with limited resources and increasing demand Aid in our qualitative research, so our in-depth interviews where I speak with the donors to find out more of the the why behind these numbers, there was almost this quid pro quo mentality. Some donors understood that if AI helps Aid not for profit Education save time or money Aid more resource can go towards the mission, then its use can feel acceptable, understandable, the the idea that it's making use of technology for good. And donor research on agent led communications also found that donors for their relationship with the Education and its mission with AI serving that relationship rather than becoming the relationship. So what I'm saying is that they don't want the relationship to be with the tech. It's always with your Education, irrespective of the technology being U. And that openness creates space for your organizations to introduce new ways of working. And it also comes with responsibility because the the latitude that donors give is connected to the mission and the trust that they already place in you. But Aid we knew there would be a but. It isn't uniform Aid this is where generation becomes a useful lens to look through. So millennials are leading the charge with 94% of them using AI, which is the highest of any group. Gen Z and Gen X aren't far behind at 9089%. And younger donors are also far more comfortable with not for profit organizations using AI than for profits. And boomers are our most cautious Affluence. Around a third don't use AI at all, so they want more reassurance. And the headline for me really is that trust is more likely to rise System with your younger donors when you're open about AI. So a net positive of around 30% amongst millennials plus 29% amongst Gen Z. And that's interesting Inc. you consider this to be the generation of donors that you're trying to build lifetime relationships with. So we've just seen some of the donor headlines from the report that's highlighting donors are using AI, but I just want to spend the next few minutes underneath those averages and some of the why behind them. So when 81% of donors say that they use AI, we're seeing that that yes can mean something different to a 24 year old than it does perhaps to someone in their dev. So the behaviour, the expectations Aid concerns can all shift by generation. And if we design our engagement to the so called average donor, we end up speaking to to nobody in particular. So let's pull that apart. So the donor data shows clear generational Affluence. But what we're seeing isn't simply about, you know, different opinions about AI. Across multiple years of research, we've seen people tend to evaluate AI through slightly different lenses. So for younger generations, the first question is often whether the experience is useful if AI helps them complete a task, find information for engage more effectively with a cause, the technology itself becomes less significant. And as we move towards Gen X and older generations, questions of transparency, oversight and trust become more prominent. And I think that the interesting thing is that all of these groups care about trust. They simply arrive there from different starting points. So some begin with usefulness and then look for reassurance. Others begin with looking for reassurance before they're willing to engage. And there's broadly four mindsets in place. So Gen Z are AI native. They've grown up with certain expectations from technology, so we've observed almost an Connected of AI being part of how they interact with Solutions Aid they're the fastest to disengage if something feels fake or mass produced. And millennials actually appear to be the heaviest everyday users of AI. They're pragmatic. They appear to embrace it when it adds real value, but they've got no patience for a gimmick. And Gen X are more selective, so they're open to AI, but it clearly makes things Center. But they want to know that their data is safe and the outputs are accurate before they trust it. And baby boomers lead with the relationship. They're the most cautious group. They value that human connection above all. And interestingly, they're the ones who most want you to be open about when AI is involved. But the key point is same technology for different sets of expectations. And what's particularly interesting is that transparency appears to serve different purposes for different generations. So for younger donors, transparency often function Solutions as a almost a signal of authenticity. So they expect organisations to be open about how technology is being used. And for millennials, transparency supports choice. They want to understand enough to decide whether the experience works for them. And for older generations, transparency supports trust Team helps answer questions about oversight, accountability Aid data use. And this is why transparency should probably be thought of as more than a disclosure requirement from a behavioral perspective. Transparency helps people calibrate trust appropriately in different contexts Aid it helps donors understand where AI is supporting the work where people remain accountable and how decisions affecting them are being Aid. And ultimately, this is about providing people with enough information to make an informed decision for themselves. This is about donor agency. So kind of what do we actually do with all of this? So you might think about this as four practical shifts. So Direct, lead with the mission, not the mechanics. Donors give to the cause, not the clever School. So we talk about the outcome AI helps us achieve, not the technology for its own sake. And second, be transparent by default. And third, keep humans in the high touch moments. Let AI prepare the ground, but never let it carry the relationship. And fourth, tailor the channel, not the message. The story stays the same and stays authentic, but meet each generation where their comfort already sits. And none of this is about doing less with AI. It's about being Internet. So the trust that you've been given actually grows. So if you take one thing from this section, it's to think about segmenting by behaviour and not just birth year. Generation data helps us think about where a donor is likely to start, how comfortable, how cautious, for example, that is their behaviour and where AI shows up in their journey that helps you consider how to best engage and what might be suitable in any given context. So get that right, lead with the mission, be transparent, keep humans where it Raiser's, and you can more intentionally move from thinking about AI as a as a black box and a risk to the donor relationship and treat it more as a technology that can support Tuition deepening relationships responsibly. So I'll now hand back to Kate to carry on with the findings. Great. Thank you, Laureen. I love this perspective that you're really you're talking Aid I think we all know this Aid our own experience when it comes to donors and supporters is you are talking to individuals. And many times you're talking to individuals that U, you know, maybe they're new to you, but a lot of them also, you know, Institute, they've been a part of your community for a long time. So, after we share a little bit more, you'll have the opportunity to go into breakout rooms and really talk about what this looks like in a real world application Aid your organizations. So just keep that in mind as we go through this as well. So, for the remainder of this, I want to talk a little bit about, some myth busting, specifically looking at general attitudes towards the social sector using AI. So we know that maintaining the trust of your donors is the most critical thing, and there's a lot of curiosity and hesitancy around what donors really think of AI use. And so we really wanted to dig into that and peel back the curtain to see what really matters to them and just where they see AI fitting into their charitable lives. So the most surprising thing that we found to me in this research was that donors are often more optimistic about the impact of it that AI could have on the sector than even those of us that work in it. They see an opportunity for your staff to be less burnt out, to increase your reach, and to improve your record keeping. As Loreen said, there's this practical application on how AI can help move your mission and the causes that they care about forward. But there are really valid concerns. They don't want AI to replace the humans working for the causes that they care about. The relationship and the cultural impact of AI in the workplace are really front of mind for them. This is something that we especially see, in Gen Zers who they're moving into a workplace that is being defined by AI. They really want AI to enhance the work of social impact professionals like yourselves, but they obviously don't want it to replace you. So if it's going to make their donations go further, that's great, but they also wanna make sure that it's enhancing your lives so that you can use AI more effectively and be able to make a greater impact to your organization. And then, of course, privacy and security are pan are paramount. With nearly a quarter of the sector still using free AI tools, this makes donor data really vulnerable. So donors wanna know that their data is secure, that the humans at the heart of the work are empowered, and that AI is used effectively to drive missions further. So the question is, how do you set yourself apart and communicate this to them? As Lorraine mentioned, it's not just about disclosure. I wanna share also just before we dive into really practical Advanced. You know, we're talking about the the individuals, that you you are interacting with every day Aid, the tailoring that goes into that. So I just wanted to share that the report has many insights where you're able to look at really specialized use cases here. This Inc. particular is really showing where donors are most comfortable with AI being used with their within the organizations that they care about. So I just wanted to guide you towards this just to get a little sneak peek at what you'll get if you wanna access the full report. And when you're thinking about System how you bring AI into your workflow. Alright. So let's zoom back out. Your donors do see value of AI use in the Center, but as Lorraine explained, this isn't a blanket statement and there are conditions. And the most important one is transparency. Your donors see value in your AI use, but they want you to be upfront about it. When we ask donors across generations what would earn their trust and increase their likelihood to give again, there was a net positive for organizations that publicly disclose their AI practices with a bonus for those that publicly share the responsible AI practices that they utilize. But, of course, this is an end result. In order to be transparent about your responsible AI use, you first have to have governance and standards in place. And this is where an AI policy is a really good place to start. Once you're aligned internally, you can clearly translate to your donors how you're protecting their data and privacy, and you're able to put it in a place where they wanna see it most, which is your website. And this is a really critical step. And as we shared at the beginning of this session, 76% of donors say it's really important to disclose when AI is being used, especially your higher value donors and those who are earlier in their donor journey that you might wanna cultivate for the long term. But only 26% of your peers are currently disclosing that use, which gives you a really great opportunity to stand out. Closing this transparency gap, I would days, should be a top priority for your organization in 2026. And I know that creating an AI policy can seem like climbing Everest. So we've, included Aid template within the resources for today's webinar coming from Response AI Institute, which is, one of our AI Solutions for social impact, partners. And it really is our go to for a a great standard to start. So if you wanna start those conversations this summer, it's a good place to start. So when we look at best practices for AI use, there is a major gap between donor expectations and organizational actions. And these actions really shouldn't just be considered best practices Inc. AI use. They're now best practices for donor cultivation. So I wanted to point out where we're seeing these gaps Aid things like taking steps to protect sensitive data, including humans Aid review of AI generated outputs, establishing clear policies, assessing systems for bias, and disclosing where AI is used. These are things that you need to consider now as a part of your donor stewardship practices. So having effective AI shouldn't be at the expense of your relationship with donors. The same principles that make for Guided donor strategy will inherently make AI more valuable for your Education. Focusing on clean data, clear governance and policies and infrastructure that supports security will help push you into that top 10% of organizations who are seeing real measurable outcomes from their AI use. Thank you for listening in for us days, and thank you to Loreen for joining and sharing her research. We are now going to move into the portion of this webinar where we pass the mic to you. So U, will be able to join some breakout groups and have these conversations with your peers, and we're really looking forward to hearing your feedback. Thank you again. Thanks, everyone. Higher everyone, welcome back. I hope you enjoyed that presentation. Now we ask that you go into the breakout sessions. Our breakout sessions are Billing out around vertical specifics so you can find the ones that you fit the most Aid, we hope to see you there. Thank you. Hello, everyone, and welcome back. I hope that you had a wonderful time networking and sharing. And now I'm gonna go around the room, and I will ask people to come out on stage and share your top ideas so that we can all learn from each other's group. Evan, I'm gonna call on you first. Can you come on stage and share? Thank you. Oh, good. I'm so glad you called on me first because I had the best group by far. Talked about a lot of things in our group, and, you know, we talked about how transparency, you know, is important Aid, like, we talked about where you can put that in there. But one of the things that we talked about that was actually a big, you know, thing that we really focused in on was the variety of different AIs that are available out there and how some of them come with free subscriptions. And how the free subscriptions don't come with guaranteed privacy. And that, you know, it's really important that you use AI in a way that maintains your data. Inc., you're breaking up. I'm sorry. I said, my we were just talking about data integrity with using good AI and Net, like, the free AI tools a lot. Okay. Alright. Great. Thank you. Appreciate it. We also yeah. We talk I'm sure about that. Okay. Ryan, would you like to go next and share with your group? Thank you, Evan. Thanks, Evan. Hello, everybody. Mary, can you hear me fine? I can. Thank you. Good Team. I see. Excellent. We had Aid good group out a good group breakout. I was in the health care team. Thanks for those who joined me in that, in that room. We talked about transparency. One of the things we found interesting was Team transparency was broadly applicable across generations, but sort of for different reasons depending on the generation. So we enjoyed talking about that. I also noted Critical from the Foundation, I thought it was interesting how organizations, donors, and constituents are more ready for them to U begin using AI or utilizing AI in their in their business than a lot of the organizations themselves are, which I thought was, interesting. Inc., But, of course, being part of the health care group, security, privacy are paramount concerns, to these organizations. And Inc. fact, one of the things I've highlighted brought up was like AI policy. Does your organization have an AI policy? Thankfully, it sounded like four. out of five of my group do have an AI policy. In fact, one of them, talked about they have full on AI counsel to determine what tools can be available. and how they can be used and to vet those tools, before they come in. And I really think that having an AI policy and I know, you know, Purchase, sometimes they're just written Aid they go into nowhere. But I think this is one of the areas where that's very vital and important because if you're not Fundraising offering guidelines on use of AI, your employees are gonna be using it. Your people are gonna be using Inc., and then who knows what you're exposed to. So having sort of an oversight of that being very vital. Agreed very much. Aid, Allison, I'm going to ask you to come up on stage. And since you are in the higher ed group, I have a feeling U going to reiterate a little bit of what Ryan of what Award said. Actually, it was a little bit different. School? Great. lot of fantastic perspectives, good participation. I'm grateful for everybody that joined us. Interestingly, I would say the folks that have a policy in place was the minority, and that wasn't because, you know, they were small or large or one type of Education, a broad array of folks join the breakout. Many had one in progress, but there are so many institutional hurdles to getting kind of a maybe legally binding policy done in a complex organization like higher education Team some of those have been taking quite a while. A really cool insight that they shared and Annual done in a beautiful analogy. Many people talked about how, you know, there days a lot to try and learn with AI communications. They were inspired and intrigued by some of the stuff they learned in the the research today. But the analogy that will stick with me is one gentleman said, the way we're thinking about it is, you know, how you paint the corner of a room to see if you're gonna like the way the paint looks in the whole room, Aid of low Impact. You could put a plant in front of it or something. Yeah. If you really Oh, Aid b d. Oh, it's me. That's excellent. Yeah. I days, Yeah. Good. Got a little gap for me. Sorry. Oh, sorry. I think it was my Internet. you. School, would you also We have so many folks. I Inc.. Nichols, would you like to come up? Oh, you're on mute. There you go. Can can you hear me now? I can. Thank you. Okay. Super. I was blessed to meet with the, k twelve group. Aid, interestingly, they have been worried about AI for their students. Right? So they have a lot of work there as far as we don't want any students cheating with cheating with AI. So more coming up with a policy for for students and ways to block them out of using it on their cell phones and things too, Okay. rather than with donors and things yet. But both of them do not have an external or a school AI policy as a whole, Okay. and Annual haven't been asked about it by parents or or donors or anything like that. So, you know, their start is was identifying the folks in the school who should be part of the group that actually writes the, AI policy. And it was really cool. Cover the, idea of the of the AI council because they were both pulling in, lots of different folks from from, like, tech to the head of schools to, HR, which I thought was really cool. Interesting. So, Yeah. you know, that's that's the best start for them as far as making the biggest m the the biggest impact for sure. Thank you. Thank you so much. And now finally, Nicole, who was working with our largest group of kinda catch all nonprofit. Hey, guys. Yes. We had a great conversation. One of the things that came out early on as we talked about the perceived concerns about AI, from people that they Search, a big one that came up that wasn't on our list was the environmental impact. And in particular, we had a few people who had these big data centers in their in their hometowns, in their areas, and that this is really shaping the Wealth view and the perceptions of the younger people Aid just people in general about, how we should be approaching AI. So that was an Internet takeaway. We also talked a little bit about the generational Affluence, and we had a really good call out on the fact that with boomers being more skeptical than anyone else on this and and not really understanding what's going to happen with this, There's a risk because we know that we're looking for this this great transfer of wealth to happen over the course of the next decade or so. And so. we need we're able to confidently talk to them about how we're using AI and get them to feel comfortable. We also talked about policy and how, how everyone feels like the internal policy is is is starting to get a little more buttoned up, but that how do we talk to our, our donors about what the policy is? And is there a general statement that people are putting on their websites to share externally how they are using AI? Because if the goal is transparency, then how are we being transparent about it? What does that look like? And finally, I think we got down to, like, degree of transparency. Like, how Yeah. do we just need to yeah. Like, do we just need to be transparent when we're using Inc. a a dev Student, or do we need to be transparent when we're using it to analyze spreadsheets? Like, what level, Higher. do we need down to? So. yeah. And somebody Aid posted in the q and a about that same question about what dev, like, they U they're utilizing AI to craft their Donation emails. Do they need to make a statement there? Is that the kind of Activity that our donors are looking for? And. we'll find out that answer for you all and make sure that we post that to this group. And also the to the question of environmental AI where there's a great blog post Inc. our engage, blog post that just went live this week about an environmental impact and how we are monitoring that and how Blackbaud is actively working towards, trying to work, you know, with that, in environmental making sure that it is minimized as much as possible. So, That's been fantastic. Wealth, thank you all. Days we wrap up days, we will be pulling all of this together into a blog post to share out with everyone. Also, we will, be sending you the recording. Aid within twenty four to forty eight hours, you'll receive the recording. And we have posted, the Search. So if you could give us feedback, that'd be great. We're always looking to make sure that we can continue to get better at, offering you this wonderful information. So I hope you all Giving to join us next week for the second part of this series where we will be looking at how our side of the coin is bridging that AI gap and, when we are making differences there. So thank U, everyone. Really appreciate, and have a great day or evening.