AI with intention: from trends to impact

Ann Fellman: Okay, doors are open, we have attendees jumping in — I’m going to go ahead and share, just so everybody knows they’re in the right place. Welcome, welcome, you are in the right spot — we’re going to get started in just a minute.

If you’re joining us, tell us in the chat where you’re at and what your weather is. I happen to be in Dallas today, and it’s like ninety degrees — pretty toasty. Normally I’m in Minneapolis, so I’m a little remote this week. Tell us where you’re at, and welcome — you are in the right place.

We have a jam-packed thirty minutes this week. It’s my favorite week of the quarter — Fundraiser Focus Week — and throughout the week we have experts talking to us on a topic or theme. Obviously, a hot topic right now is AI, and today’s session is AI With Intention: From Trends to Impact. We’ve got Loree Lipstein and Sara LaCava Lieberman here to speak with us — again, welcome, you are in the right place.

Before we jump in, my name is Ann Fellman. I’m the Chief Marketing Officer for Bloomerang. Welcome, welcome — this is all about education, and this is a big week on AI. Before we get started: yes, we’re recording, and yes, we’ll send out the slides afterward, so you’ll get those resources. Stay on, because at the end I’m going to raffle off a fifty-dollar Uber Eats gift card. And stay with us through the week, because on Thursday we’ll do a hundred-and-fifty-dollar giveaway.

For today, as you have questions, make sure you file them in the Q&A section of the Zoom console — look toward the bottom, there’s chat, but then there’s Q&A. Make sure you ask your questions in Q&A, because we’re not going to answer them today — we’re saving them for Thursday. That’s why you’ll want to join us for our roundtable discussion with all of our AI and fundraising experts on Thursday, same time and place. Each day this week we’re talking through a different topic related to AI.

At Bloomerang, we’re all about helping nonprofits raise more. We have fundraising and donor management — what we call the Giving Platform — built from the ground up for fundraisers to help you raise more. If you’re not familiar with Bloomerang, go ahead and take a look.

What I’m really excited about, related to this topic, is Bloomerang’s AI principles — you can find those on our website; scroll to the very bottom of the footer and you’ll see them. As an organization wrapping your arms around AI, or using AI yourselves, it matters to know who your partners are and how they’re handling it responsibly. That’s something we rolled out back in May, and I’m excited about it.

With that, I want to give a warm welcome to Loree and Sara — thank you both for joining us from Thread Strategies, and you can find their contact info here. I’m going to stop sharing and turn the floor over. Loree and Sara, thank you so much for joining us today.

Loree Lipstein: Thank you so much, Ann, for having us, and to the whole Bloomerang team — we love when we get the chance to partner with you all, especially on this topic of being intentional about your AI use. As a quick introduction: I’m Loree Lipstein, and I’m here with my colleague and friend, Sara LaCava Lieberman, and we’re with Thread Strategies.

Thread is a fundraising consulting firm focused on building development capacity for nonprofits, so they can strengthen their fundraising and grow their impact. We partner with nonprofits and fundraising professionals on development strategy, donor engagement, development operations, CRM work, and fundraising training. Over the last ten years we’ve worked with hundreds of nonprofits across all kinds of mission areas and located around the world, and we’re excited to share some of the lessons we’ve learned through that work.

Our core belief is that every nonprofit, regardless of size or budget, can fundraise using sector best practices — no matter how small your team is, tools like Bloomerang and tight strategies can help you function at the level of much larger organizations. Thread works closely with our nonprofit partners to build the strategies, systems, and staffing that make that possible, and as we do, we’re constantly helping organizations navigate emerging tools and challenges — including how to identify meaningful AI use cases and apply AI in ways that are practical, ethical, and mission-aligned. So today we’re bringing you both best practices from the field on AI and fundraising, and real-world experience from putting those practices into action with our partners.

Here’s a quick overview of how we’ll spend our time together: we’ve got four sections to cover, starting with why it’s important to have an AI use case in the first place.

First, a look at the landscape right now for nonprofits and AI use. These statistics come from two recent reports — one from Virtuous on nonprofit AI adoption, and one from CCS. What we’re seeing is that the nonprofit sector isn’t asking whether AI matters anymore, but how organizations are using it — it’s not a question of if, it’s how and why, and it’s about being intentional. What stands out to us is the 92% adoption number, against the gap with the 71% who report having an actual strategy. That tells us most organizations are using AI in some way, shape, or form, but far fewer have the policies, governance, or clear rationale behind why and how they’re using it.

This isn’t unique to the nonprofit world — in general, most professionals in any workplace have been discovering and experimenting with AI tools on their own, and are getting more comfortable with them, naturally using them at work to save time. But meanwhile, there’s a lag in leadership figuring out how AI fits into the organization as a strategy. We hope that through what we share today, you can shift that pattern in your own organization, so AI use is not accidental, but strategic and intentional.

There are a lot of legitimate concerns about AI, which is another reason a thoughtful use case matters so much. Ignoring that AI is out there and part of our lives isn’t going to make those concerns go away — and they are legitimate — but the best way to mitigate the harms of AI while maximizing its impact is having that use case, so you can be intentional and focused as a team, and really think through the potential harms, so your organization can get the best of what AI offers while staying thoughtful about the risks.

On the benefit side, there’s a lot AI can offer fundraising specifically — and while it has benefits elsewhere too, because this is Fundraiser Focus Week, we’re focusing on fundraising use cases today. AI isn’t one single thing; it’s really a category of tools that help across many dimensions. For fundraising, we think about three big buckets of benefit: saving time by automating repetitive, administrative tasks; complex problem-solving that can enhance engagement and help you be more personal with donors and community, even at scale, through personalized content; and, overall, freeing you up for the high-value relationship work that only a trained human professional can do. That’s really the heart of what we mean by being intentional about how, when, and why you incorporate AI into your organization’s processes.

There are four categories that represent some of the most common fundraising use cases. The first is content generation — drafting donor communications, expediting social media posts, blog content, and so on, cutting down the time it takes to create content. The second is predictive giving analytics — a different kind of AI than generative AI, since it uses pattern recognition to predict what’s likely to come next. Applied to donor behavior, that means using AI to predict a donor’s next likely engagement based on their giving cadence, so you can be more personalized and proactive. The third is prospecting — using AI to look for potential donors and funders through data analysis or complex search. And the fourth is project management, which isn’t unique to fundraising, but there’s a lot of it within fundraising, and AI can speed up how you manage that work.

Any one, or all, of these might be relevant to you — but the key, and why we’re really here today, is not to jump in and out of all these potential uses, but to first identify your own needs and strengths as an organization, and then name the specific use case, or cases, most relevant to you. It might be something in these common examples, or something unique to your organization. Next, I’m going to pass it to my colleague Sara, to walk through how you can get specific about your organization’s use case — not just copying what other organizations are doing, but doing what will make the biggest impact for yours. Sara, over to you.

Sara LaCava Lieberman: Thank you. As Loree said, we’re not just using AI for the sake of using AI — it can be exciting, it can be overwhelming, but what we really want you to do is think about how you can use it to actually solve problems and make a difference in your organization. And as Loree said, that’s going to be unique to you.

So to begin, you have to start by identifying your challenges. What’s eating up your time? What problems are you trying to solve? What’s standing between you and your goals? While we often talk about efficiency when we talk about AI, we also want to make sure we talk about joy — what is it you love about fundraising, what energizes you, what are you uniquely good at? AI should protect and expand what you love and what you’re great at, not replace the parts of your job that bring you meaning. So it’s worth taking time to discuss both sides: what you’d like to spend more time on and want to protect, and what the challenges are that get in the way.

One way to do this — certainly not the only way — is a bit of an “AI use case Mad Libs,” adapted from Kindsight. It reads: “I wish I had more time to do ___, which only I can do, or which brings me a lot of joy, or which I am uniquely good at. Doing this other thing takes up a lot of my time and keeps me from getting to that. If I could spend more time on that first thing, the impact for my organization would be ___.” We highly recommend a framework like this to help you and your team think through what your challenges are, what you’re uniquely good at, and how that could create space for more impact — it doesn’t have to be this exact exercise, but the goal is to surface where you’re losing time and where AI could help.

Importantly, it’s not just about what AI could do, but what that would then free you up to do, and what the impact would be for the organization — because we’re not using AI just to use AI or to find efficiencies. As mission-based organizations, we’re focused on the impact we can have for the people we serve. Being able to articulate all of that is also exactly what you’ll need to make the case for AI adoption to your organization’s leaders, if you’re not the one making that decision yourself.

Once you’ve identified your challenge, articulate your end goal: what do you want to be different or better, and what would that actually look like in your fundraising efforts? Once you know the challenge and where you want to be, you can start thinking about how AI can bridge that gap.

At this point, it’s worth auditing what you already have. Tools are releasing new AI functions constantly — Bloomerang and Penny have some really exciting new functions that have rolled out recently. Look at the tools you’re already using day to day, and check whether they need to be turned on, whether there’s an additional subscription required, or whether you need training to get up to speed. Once you know what you’re already working with, you can look for the tools you still need to fill the gap for your main use case.

One thing to remember: data readiness is a critical part of AI adoption. AI tools are only as good as the data you give them — they don’t know what they don’t know. So as you evaluate tools, think about what data you have, whether it’s accurate and organized, and whether you have the inputs necessary to make the tool successful, or whether you need to first clean up how you’re collecting and organizing data. For example, if you wanted to do predictive analytics, you’d need to think about whether you have enough giving information — frequency, patterns — to figure out the most effective future solicitation approach.

The other data piece to think about is measuring success. How would you know if the AI is working, and actually helping you achieve your stated use case? Define your success metrics before you start, and think through what you’ll need to collect and how you’ll collect it. Otherwise, it’s impossible to evaluate whether a tool is worth keeping.

Now Loree is going to talk a bit about choosing a tool, once you’ve gone through this process.

Loree Lipstein: Thank you, Sara. Going back to that Mad Libs structure Sara shared — assuming at this point you’ve identified which problem you’re solving, why, and how it fits into making an impact. As an example: maybe, as a small team, you want to create four different segments for your year-end appeal, but writing all that content is getting in the way of connecting one-on-one with your major donors during the same period. So you’re looking for a tool that helps you write segmented content faster, so you can spend more time in direct, one-on-one meetings with your major donor segment. That would be the ultimate impact — just one of a thousand possible examples of how this framework might play out.

To situate you in that example: you’ve identified the problem, why it matters for your fundraising, and what data you have to work with. If you don’t have organized data about people’s past giving, you might not be able to build four segments in the first place. But assuming you have good data — donors who gave to a specific event, people who’ve never given, small-dollar donors, and so on — you’re now looking for a tool that helps you write content for all those segments faster, and it’s time to explore a specific tool.

The first step is identifying the category of tool based on your use case — in this example, that’s a content-generation tool. Those are tools like ChatGPT, Claude, or Gemini — what are called large language models, or generative AI tools, because they generate content. If you had a different use case, you might be looking for a predictive analytics tool or a project management tool instead — it depends entirely on your use case. Once you know which kind of tool you need, you can build a short list of options to test.

If you’re not sure what the options are, do a quick Google search, or ask a tool like ChatGPT or Claude directly — “what are examples of generative AI tools that can help me write content?” You’ll know how to ask that question once you know what kind of tool you’re looking for and what you need it to do. That’s why identifying the type comes first, before researching a short list. You do want to try more than one option, so you can evaluate cost, user-friendliness, and other factors against each other.

When you get to actually choosing a tool, asking the right questions as a group will help you land on the right one. Especially if you’re rolling something out organization-wide, don’t just jump in because you’ve heard people talking about a particular tool — the reality is there’s rarely just one AI tool that does the one thing you need. There are options, which is nice, but can also be overwhelming. Think about who across the organization will be using the tool and why, and get input from multiple people who’d be using it for the functions you’ve identified. Make sure it’s a working-group decision, not just one person’s preference — be thoughtful about who’s involved, who they represent, and the different considerations across teams.

Some good group questions: Will this tool actually solve the problem we identified? Does it align with our overall strategy? What’s the cost — not just in dollars, but in staff time to implement, learn, and maintain it? Does that fit our budget and capacity? What’s the learning curve? Expect that, like any technology, people will need training and time to practice — be realistic about the timeline to choose and fully adopt a tool, rather than jumping in based on what you’ve heard others use.

Of course, we can’t talk about AI without talking about responsible use and privacy. Responsible use is non-negotiable, especially given the sensitive data nonprofits hold. Many AI systems improve by learning from the data entered into them, so you need to understand exactly what happens with any information you put into a tool. Look for tools built with privacy into their design, not as an afterthought — check for compliance with regulations relevant to you, like HIPAA if you’re a medically oriented nonprofit, and understand how your data is used, and always think about consent.

A simple rule of thumb: if you wouldn’t post it publicly, don’t assume it’s safe to paste into an AI tool, unless you’ve read the privacy policy and understand how it’s using what you share. The good news is many enterprise AI products now offer real privacy protections, especially the paid tiers — the key is understanding exactly what protections you have at whatever level of tool you’re using. Ann mentioned earlier that Bloomerang makes its privacy policies and AI ethical guidelines transparent and available to everyone — we highly recommend seeking that out for any tool you use, so you understand how it handles your data.

Finally: test and analyze. Don’t roll an AI tool out at full scale across your organization without running a pilot first. Check for accuracy — is it doing what it promises? Watch for bias, since all these tools are trained on data that contains bias, so check where that shows up. Make sure it can handle your volume of work, and that you’re on the right version of the tool for your needs. Get input from the team using it, and make sure it’s actually solving the problem you invested in it to solve. Check how it connects to your other systems, and whether that’s working as expected. And overall, calculate the return on investment — time saved, cost — to confirm it’s doing what you intended before rolling it out at full scale.

With one minute left, to wrap up quickly before I hand it back to Ann: don’t just jump in and out of AI tools at a whim — think about your organization’s specific use case or cases, then identify the tool that solves it, evaluating your options against the framework we shared. And we’d encourage you to start small — you don’t have to do everything at once. Adopt one use case, build a real process around it, then repeat with another use case.

With that, Sara and I look forward to answering the questions you’ve submitted when we see you again on Thursday — I’ll let Ann wrap us up.

Ann Fellman: Thank you, Loree and Sara — that was really insightful, a great way to start out. This is a series this week, so we’ll step through a couple of different topics related to AI. Today was about getting started: understanding use cases, and starting with intention around what problems you want to solve in your organization.

Tomorrow we’re going to talk all about prospecting — we’ll be talking with Tim Paris, one of the founders and CEO of Dataro, which focuses on intelligent donor prospecting. You won’t want to miss it, since we’ll be covering precision over volume. On Wednesday, we’ll introduce AI equity frameworks, to help you think about removing bias within AI. And Thursday is our roundtable, where we’ll cover all your questions.

Each day this week we’re giving away a fifty-dollar Uber Eats gift card, and I’m pleased to announce that Gloria Madrill is today’s winner — join us tomorrow for another chance. This is an educational series, and we’ll keep moving through the week that way. On Thursday we’ll also talk through what Bloomerang is doing with AI — we’ve got a number of things rolling out July 1st, including an integration with Dataro, and we have Penny, our AI fundraising strategist. If any of this is new to you, take a look at our AI principles.

What we covered today was the fundamentals — being intentional, and knowing where to start. We’ll go a little deeper each day this week, and finish with a great roundtable discussion. Thank you for joining us, and we’ll see you tomorrow, same time and place, with a session on intelligent donor prospecting. Have a great day.