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- [00:01]
A
Hey. Welcome to Nonprofit Lowdown. Today's presenting sponsor is Zefie.com Zefie is an all in one online donation platform with no platform fees and no credit card fees ever. I've been in your shoes and what I'm not trying to do is spend extra money on platform fees. I don't like it and my donors don't either. We're about to head into year end when 30% of your money comes in. You want every dollar going where it's needed most. Your mission, not lining the pockets of a payment process. Am I right? So check out zefy.com register. That's Z-E-F-F-Y.com register. Make sure to let them know that you got sent from Nonprofit Lowdown. Alright, let's get into today's episode. Welcome to Nonprofit Lowdown. I'm your host, Rhea Wong. Hey, Nonprofit Lowdown listeners, it's Rhea Wong with you once again. And today we have a fan favorite. My biz bestie. You know her, you love her. Brooke. Richie Babbage. We are here to talk about fundraising and AI today. And Brooke, you are the AI Princess. So we're going to do a deep dive. For those of you who don't know Brooke, where have you been? But Brooke is a brilliant strategist executive. She is the founder and CEO of Bending Arc. She is also my business bestie. She's the peanut butter to my jelly. If I'm talking about fundraising, Brooke is talking about the systems and processes that you need to input into your nonprofit disc scale. So together we build beautiful things Together.
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B
Wonderful sandwich.
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A
Wonderful sandwich. That peanut butter and jelly that burnt an Ernie, if you will. All right, let's get into it because I feel like AI is all over the place. We're all talking about it, but what I find is there feels like a disconnect between the promise of AI and the reality of how it's being used. And I think, at least for myself, let's call it 12 to 24 months ago, when AI first really busted on the scene, it was like all the hype, oh, everything is going to be automated. The robots are going to be running the show. Everyone's going to be out of a job. And I just feel like that's not been the reality. So what are you seeing in your part of the world?
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B
100%. I agree that where we thought we'd be and where we actually are, there is a huge gap. And I think that there are two things happening. One, I think that the sort of AI gurus All the people you know that are out in the world being like, AI for everything. And I will admit that sometimes I'm one of those people. I think that a year ago, a lot of people largely underestimated the sort of human concern around AI that there, if you look back in history around the introduction of new technologies, when they introduced the electric elevator, people wouldn't ride it. Right when they started using cars, people were just like, no, this is crazy. So there's always a ebb and flow of interest. And I think that one of the reasons we haven't lived up to, in some ways the potential of AI is that there's very real human concern about, what is this going to mean for my team? What is this going to mean for my job? What is this going to mean for my brain? Right. If I stop doing the brainstorming and the mapping and the strategic thinking? And that concern was just heavier and weightier and slowed progress down. I think the other thing is AI isn't a replacement for thinking. It feels like it is because it moves so quickly and very often, at least in my use cases, says things. I never would have thought to say it initially when we all started using it to like draft emails and say no to people, we didn't want to say no to all of those things that felt uncomfortable as humans, it felt, oh, great, I won't need a copywriter anymore. That's not true. And I think what we're learning, or I should say, that shouldn't be true. That AI is not a good thinker. It is a great partner. It can help you get outside of your own box. But we're rediscovering use cases that aren't as simple as the ones we thought we were going to have. Just as a copywriter or a graphic designer. Is that sort of what's resonating with you and your folks?
- [04:34]
A
Yeah. I sort of think about AI the way that when I was in school and teachers would say, the calculator doesn't replace thinking, so it just calculates it. But we need a human behind it to actually know what to input. We need a human to discern. Like, is this a good answer or is this a bad answer? Talk me through some of your favorite use cases because I know you're very pro AI, as am I, and I'm just curious because you're so good at experimentation and always kind of dabbling in new things. So what are some of your favorite use cases right now?
- [05:06]
B
So I'll name just a couple that are organization wide, or institution wide that I talk to my folks about. And then there are two fundraising specific ones that I'm playing with right now, which I'm sure you are as well, because despite calling me the princess, I would say you're right there out in the AI streets with me. So the first one that I think is really helpful is finance review. So I really like data and I think data is really important if you are talking about scaling and part of your organization, your organization generally. And so one of the things that I have started doing in my own business is every month I download cash flow statements and the profit and loss. I use Kik, but a lot of people use QuickBooks. It doesn't even have to be fancy. I just download it sometimes as a CSV file and then I upload it to ChatGPT with a prompt that basically says the following. Some version of the following. Here are my finances. I want a year over year comparison for this month and this quarter. I want to understand net growth from over the last quarter. I want to understand trends in where my money's coming from and I want to understand trends in where I'm spending. Take all the data and lift up for me in the way that a CFO would. What do I need to be paying attention to in my numbers? And then just like we were saying, because it doesn't replace my thinking, I have to sit with those numbers and I go back and forth and I say, oh, interesting, what do you think these numbers mean? Et cetera. So I think the CFO role has been really helpful. A second big use case that I think is useful with finances and fundraising generally is I really hate Google Analytics. I shouldn't say I hate it. I it continues to be my nemesis in terms of really getting value. But there's really valuable information there. Who is coming to your website? Are they going to your donate page? Are they looking at your blog? You're doing all these things. Is it working? The only way I've come to understand all of that is I go into Google Analytics, I download their report as a PDF, I uploaded ChatGPT and then I talk to ChatGPT and I say, where's my traffic coming from? What are the best pages? Where are they landing? How long are they staying? I just worked with one of the folks in my program to look at their Google Analytics because they couldn't figure out they'd launched this campaign. Their emails were doing great, but their page wasn't converting. And when we looked at traffic, they were getting so much traffic like it was really great. Email was sending people to the page and people were staying on the page for nine seconds. Nine seconds is not long enough to do anything meaningful on a page, which means there's something wrong with the page. So that level of analysis. Yeah, I'll stick with those two. I think thinking about how for me, ChatGPT can help make sense of some data so that you can make smart, strategic choices is one of the best use cases because that will otherwise slow folks down.
- [08:00]
A
Yeah. Let me share two of my favorites that are fundraising related. So one is a data analyst. You upload three years of anonymized data. So just donor ID is no first name, last name, all of the dates and donations and you upload it and you talk to ChatGPT about what are the trends I'm seeing, what are my thresholds for major mid and annual donors, what's my retention rate, where do I see big bumps like generally when does my money come in? So you can start to make sense of your data. Because I think the other thing is like we're all so busy just running that we don't actually have time to step back and really make sense of what we're seeing. So that's the first thing and then the second thing. And I talk about this all the time and I'll put it in the show notes for folks. My ideal donor avatar GP Yes. So yes, you input data about. This is the ideal person who I want to be in my pipeline. Here's what they care about, here are touch points they've had, here are some motivators. And then you can talk to it and say, hey, create an email sequence that might be appealing to this person. Create how might I approach this person? What sort of proposal might be helpful for them? If I have to send an outreach engagement point like how do I phrase that? So I think there's so many things that we don't know. And so in the absence of knowing, we avoid it because it's uncomfortable. And then actually the last thing I'll say is I am. And you and I talked about this. I've started working recently with Practivated and Mallory Erickson. And so we're using AI to have people practice donor conversations before they do it in real time. Because I think a big thing I've seen is people avoid doing it because they're scared and they feel nervous and you don't know what to say. So if you can practice it with an AI robot ahead of time, you walk in having some preparation. So those are some of My fave use cases.
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B
That's right. And just to lift up two through lines that I'm hearing, definitely data analysis. Right. Sort of the stuff that slows us down, that feels overwhelming. ChatGPT can speed that up. Or AI. And then also, and this goes back to our first part of our conversation about like how to think about AI. Generally, it's a partner. I think it's not a replacement, it's a thought partner. You mentioned multiple times you go back and forth with tax routine, you give it data, you ask for analysis, you analyze and go back to ChatGPT. You practice with ChatGPT. So I think that how would you engage or leverage a really smart thought partner. Right. Not somebody who's going to come in and replace you, but somebody that you can go back and forth with is a great way to think generally about coming up with use cases.
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A
Okay, let's talk about donor trust. I talk about donor trust all the time. I think donor trust is an all time low. And yet on the other hand, what I hear from folks is that they don't have enough prospects. Right. So they do things like they pay for wealth screens and they scrape data and they go on LinkedIn. And to me, there's this tension between using AI for insights and then using AI to be creepy.
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B
Yeah, agreed.
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A
Because I come back to this idea of like, how would you treat your own grandmother? Would you want someone scraping your grandmother's data.
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B
Yeah.
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A
And presenting it back. So I'm just curious from your perspective, like especially being trained as a lawyer, I know that you think about these policies and guardrails. So how should we be thinking about the fine line between like, helpful versus creepy? What should be transparent, what should be consent based? What, even though it's technically in the public domain, are things that maybe we just shouldn't do.
- [11:38]
B
I. That's a tricky one because I think I'm thinking Back to when LinkedIn first became really popular. I was on the board of an organization, I won't even say really popular, just the Internet. So this was, I don't know, 15 years ago. I was on the board of an organization and we were interviewing potential board candidates to join the board and this young man came in to be interviewed and one of the first things he said to me was, oh, I researched you and I looked you up and you did this, and you went to Yale and you did this. And I was really creeped out by him and that's not actually fair. Right. So now, fast forward 15 years, if someone came to interview with me and knew nothing about me, I'd be like, you didn't do your homework. So I think I don't have an answer to this part of it, but I do think understanding where we are in our AI journey collectively is important and I try to keep that in mind as I recommend policies because some part of what we're grappling with that feels creepy, may not feel creepy and shouldn't necessarily feel creepy five years from now. So I just want to add that as context. That being said, I think there's some principles that organizations should use. So you mentioned consent based. I think donor trust starts with permission, right? Not assumption. And so when in doubt, just be explicit about what you're doing. Right? You can use plain soft language. Things like we're going to use your data or we're not going to use your data. Right? When you share, if you fill out this donor survey, here is what we're going to do with it. Just naming what you're doing and it doesn't have to be this sort of huge 10 page policy, but assuming that people will feel more comfortable when they can opt out. So naming you can opt out, I think very related to that. You use the word transparency. I think it's about making the why clear, right? So here's what we're doing and here's why we've decided this. And so this is where having a clear AI policy that says here's how we use AI, here's how we use data and here's why, here's what we're trying to do. A lot of times people creeped out because they're like, what are you doing with it? Like why did you. Right? So if that why is there, it can bring people's defenses down and then it feels more like they are in conversation with you. Particularly for donors who need to and ought to feel that they are in partnership with you. So the more of that transparent language you use, I also think, and this will be the last point that I make because I could go on about this forever. I think less is sometimes better. And you know me, I'm a shiny bubble person. I'm always like, more data, more information. But I actually think when we are talking about other people's information, always start with and I say this in my own programs, form should follow function, start with what is the thing we're trying to accomplish and what is the least viable set of information that will allow us to accomplish that? And then ask yourself, do we need to go beyond that? Do we need to collect the Graduation data about my major donor's oldest son. Does that help us get closer to that person? If not, maybe we use some constraint. So those are three guardrails, so to speak, that I've started talking about in my work.
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A
Yeah. And I just want to plug here that Fast Forward has a bot. It's very meta, a bot that you can use to create an AI policy for your organization. So I'll put that in the show notes so you don't need to.
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B
It's great. We used it for a couple of folks that I work with and they really liked it. Like.
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A
Yeah, yeah. So I'll put it in the show notes for folks who are looking to develop their AI policy.
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B
Actually, one more thing.
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A
Yeah, of course.
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B
So I think I. I think that as organizations develop prompts, and a lot of organizations are standardizing prompts for people on their team to use, I would do bias checks. That's one.
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A
So glad you mentioned that. We are getting into that. Say more, friend, say more.
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B
Yeah, I think that auditing, what we're asking AI to do, the assumptions we're making, looking at the responses we're getting, just as in our human interactions, unintended bias, unintended stereotypes are baked into questions we ask, how we frame things, how we share information. That is also true when we are working with ChatGPT or Claude or any AI tool. Except we don't get the feedback loop that we might get from another human who's like, wait a minute, why'd you ask it that way? So I think getting into the habit of doing bias checks and audits of how you're framing questions, what you're asking for, et cetera.
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A
That's a good point. Because I think the other thing that I've heard from folks who are a little more reluctant to jump into the AI pool, the way you and I have, is some real concerns, and they're legitimate concerns around bias and data lakes and who decides what information is in the data lakes and what is considered to be tagged in a certain way, like how the algorithm is designed, et cetera. So how would you counsel people to start thinking about that, given that, especially as women of color, ourselves very aware that the vast majority of people who are designing AI are do not look like us. How can we be careful and mindful and discerning about that at the same time that we want to make use of this very powerful tool?
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B
I'm not sure I have a great answer for that. I think the way that I move through the world is, and the future will Tell me if I'm right about this. I think that the more input I am able to share and the more careful and intentional I am about what I information I'm sending into the system, the better. And that one thing that I am very careful not to do is opt out. Right. Because I don't think we address the concerns you or the problems you identified by not contributing. You can use. I heard this on a podcast with Rick Mulready. He was talking about how you can be explicit about what you want to train, what you want to go into the system and say, remember this? Or this is important data. Or you can say, hey, pay attention to this. So if anything, I'm leaning in and I'm being explicit about why. And then going back to these audits, I think the more careful we are about our prompts and not continuing to introduce bias loops into what we're sharing, I think that matters. Right? Again, I don't know if it's like, what is it? A teaspoon pulling a teaspoon of water out of the ocean? I don't know. But I try to do what I can and I think being intentional and leaning in feels important to me.
- [18:36]
A
Okay, let's pause for a word from our sponsor. Quick Break. Today's episode is brought to you by ze.com if you're running a nonprofit, you already know year end is when 30% of your revenue hits. You want every dollar going to the mission, not bleeding out to platform or credit card fees. That's why I love ZE Z F F Y. It's an all in one online platform with no platform fees and no credit card fees ever. I've been in your shoes and paying fees on donations felt like lighting money on fire. My donors didn't like it either. So do yourself a favor. Head to ze.comregister again. That's z fy.com reg register and keep every cent working for your cause. Be sure to tell them that you heard about it from nonprofit lowdown. All right, back to the show. You brought up something that I feel like is a really important detail that we need to talk about, especially in our sector. As nonprofit folks, I think we are more sensitive than most to the biases in the world, the unintended consequences of AI. I've heard people talk about concerns about environmental impact of AI and I think I want to be careful here to flag like these are concerns and are the concerns enough to not use the tool responsibly? To your point, I think if we are not part of the conversation Then like the tech Bros win.
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B
Absolutely.
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A
AI needs people like us who are discerning and mindful and intentional to be in the conversation.
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B
Yes.
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A
Otherwise there is no data that they're gathering from us.
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B
Absolutely.
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A
Training it on data that we are contributing to it. And then I think the other thing is, at least from my perspective is choose your poison. Right?
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B
Yeah.
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A
Yes. There are unintended consequences of a lot of things. If you drive a car, there are fossil fuels to consider, there's the environmental impact, et cetera, et cetera. And so I don't think this is a pat answer for everything, but I think it's an answer that people have to come to on their own, which is like, what consequences can you live with? Because I think the consequences of not embracing this technology means that you will be left behind.
- [20:57]
B
Yeah. I think the way that I try to frame it for myself and for the folks that have come and asked me about this, I don't think AI let me frame it this way. When I was running my organization, my board and I had really robust conversations about who we would and wouldn't take money from. And as we got bigger, those conversations got more and more robust. When we were a $500,000 organization, I could say we don't take money from anybody that has anything to do with guns, which always actually, I would say remained the case. But what that looks like when you have a corporate sponsorship at say 2 million and a board member finds out that the corporation you're talking to about a partnership is a subsidiary of this other company that has something to do with Johnson and Johnson, which has something to do with guns, which was a real case that happened with us. That's a different conversation. And then it isn't as simple as we never take money. Well, do we want to leave $1.3 million on the table? Maybe, but it's. There's a conversation. So now swap out corporations and swap in AI for me, the guiding question is how do I move through this work and feel like I'm acting from a place of integrity. Integrity doesn't allow for black and whites. It is always gray areas and it grounding every decision we make in intentionality and in an understanding of our purpose. For me, and now the big question is how do we engage with AI I think organizationally and I think individually, people need to sit and say what feels like I am moving forward from a place of integrity. And then you build policies to support that. I think that's the only way. Right. And it's going to be a Bunch of gray areas, especially as you get bigger and are hopefully thinking about using AI in more nuanced ways.
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A
So I want to ask you a question because you're so good at thinking about containers.
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B
I am a Virgo. We like our containers.
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A
What's interesting is I think that leaders at the top may be thinking about AI writ large, but I think the reality is within departments that are already using AI in different ways. And so I'm just curious, how might you, as a leader, let's say you're an ED of an organization and you want to both adopt AI with a lens of intentionality and responsibility and at the same time, innovation. And frankly, sometimes innovation is messy. Sometimes innovation like moves fast and breaks things. What do you do if you're listening to this and you're like, cool, Brooke, I want to lean into this, but I want to do so safely, but I want to do so that encourages people to experiment. What does that look like?
- [23:38]
B
I think it looks like co defining the outer boundaries of what's acceptable. So I think about raising my two boys. And you know them, right? They're little, well, getting bigger. But when they were little, I would take them to a playground or a park. And I wouldn't say, you can go on the swing, on this swing and that slide and that tube. I would say, here is the outer boundaries. Don't go past those boundaries. And most things inside those boundaries are up for grabs. And you may try the slide. And we realize, nope, not going to do that again. Okay, we take that off the list. But as long as you don't go outside of the boundaries of this park, as long as you don't leave this playground, we can experiment within this space. So I think organizationally, as a leader, I'd sit down with my team and this goes back to moving from a place of integrity, shared organizational values. What are the boundaries we're not going to cross? What are the guardrails that feel important to us? Are there certain things we're going to do that always have a human as part of them? Are there certain ways we are not going to use AI? Is there certain data that we're just never going to use? Define those outer boundaries. And then if I were in charge, I would say within those boundaries, play experiment. And we will figure out together through those experiments where the boundaries need to change. But I think if you start too narrow, that's anathema to innovation.
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A
Yeah. I also think building in feedback loops can be a really fun idea because, look, we're all learning AI, right? Absolutely.
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B
So I think you have to. It doesn't work if you don't have the feedback loops.
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A
Yeah. So what if it looks like at every staff meeting you have one person sharing how they used AI this week. Right. Just to share information. Because the truth is nobody is an expert at this really, because the technology is too new and it's changing so rapidly.
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B
That's right.
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A
We're always learning new tools, new techniques, prompting differently. So I think you can have fun with it and you can experiment with it without committing to this is the way that we do it forever and ever.
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B
So I have a question for you. The converse of this, one of the things that I've been hearing, interestingly, is that you have people in departments, people on the ground, who want to experiment with AI, and it's actually the leadership, the board that is saying for privacy reasons, for data, legitimate concerns. They. There are organizations that I talk to where they won't pay for the Pro version of ChatGPT because they actually don't want people uploading information about the organization. I understand concerns about privacy and we've talked about that, but what do you do if it's the reverse? Right. If you have a director of development who wants to use AI for prospecting, for example, but the executive director or the board chair has said no, like we don't do anything having to do with donors on, on ChatGPT, I think that's also pretty common. And what's the sort of narrative there?
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A
Yeah, it depends, right. I think there's a case by case basis to be made, but I think part of it is starts with education. So the education is okay. If we were to upload data, where does the data go? Who owns the data, what is it training? Because I feel perfectly fine. Like when I upload data, for example, I never upload personal names like first name, last name. I upload owner IDs, gift date, gift size, which cannot be tied to any particular person. I also have certain toggles on my ChatGPT which is like, do not share, don't save this conversation. Because then it doesn't enter the data lake and isn't used for training data. So the point is, I think part of it is you need to educate yourself about like, where does the data go and what is private and what is not private and what do I have control over? Once you have clarity about. About that, then I think you can make a informed decision about what we upload and what we do not upload. Because I honestly, I just don't think we don't use AI Is. Not is. That's just not the answer.
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B
Agree.
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A
The answer is we use AI in a very discerning and intentional way because we have evaluated the risks.
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B
Agreed.
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A
And then, you know me, Brooke. I'm more of a ask forgiveness and permission kind of a person. Like me, too.
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B
That's why we have our own little echo chamber. Okay, fine.
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A
It's 20 bucks. You're telling me that you can't pay 20 bucks out of your own pocket? Like, I don't know. Fine. Obviously, my risk tolerance is, like, pretty high. But just really think that if we can free up people, be humans, and to not spend a bunch of time on manual data and manual things that robots can handle better, then do that. Because, look, I've never met a single nonprofit that said, you know what? We have more than enough staff. We don't need more people.
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B
Does no one ever.
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A
Yeah, like, we don't even. We're, like, bored. Because there literally is just not enough work to do if you have that problem, like, good for you. I've never heard anyone having that problem.
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B
I think you're highlighting something actually really important that I just want to make explicit. So when I've heard this, either the executive director wants people to use it and they aren't, or vice versa, they're focused on the cost of using AI, Right? What's going to happen to our data? What's going to happen with bias? What's going to happen with the environment? Those are real things. But there is also a cost, a growing cost to not figuring out how to use AI. And that cost, because it is more invisible, can be ignored. It can feel like, actually, the value equation in your head is, if we don't use AI, everything stays the same. But if we do, there's a cost. That's not the value equation. The value equation, increasingly, is, if we use AI, there's a cost, and if we don't, there's an invisible cost to my team, my time, my energy, my operational efficiency. And so I feel like part of that education you're talking about is getting smart on what are we leaving on the table by not figuring out how to leverage this very powerful tool. What money is this costing us? What time is this costing us? What energy resources, et cetera, make those invisible costs visible.
- [29:58]
A
Yeah. That's so smart. One thing that I've been thinking about recently is I have a program that I'm in which I love. Me a program. And Manny, the guy who runs a program, said this recently, and I've really Been turning it over in my head. He's like, success is not due to hard work. Success is due to your speed. Like, you just need to, like, get stuff done faster.
- [30:19]
B
Make decisions faster. Yep.
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A
Make decisions faster. Put out content faster, Test things faster, get to market faster.
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B
Yes.
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A
Don't overthink the social media thing. Just put it out there. And I think what AI does is helps us be faster. Now I think there's a whole argument to be made about should we be doing faster? Isn't quality better than quantity? Isn't quality better than speed? My answer to that is yes. Both end like, I. I think it's a false dichotomy to really.
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B
Agreed.
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A
You can't have both things. But the thing is, you don't know what is quality. You don't know what's going to hit if people don't see it.
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B
Agreed. Yep. Agreed. It's fail. I call it failing forward.
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A
Yeah.
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B
And I'm a perfectionist type, a procrasta planner. And every single time, whether it's AI or some other decision, I find that I'm procrastina planning. It's slowing me down. And it's because I'm afraid to make a decision that I think will be wrong. And I cannot tell you how many times I've talked to EDS executive directors where really, when we dig down into why things stay on their plate, why they're not making decisions. And a lot of this has to do with creating an AI policy, which is why I love that tool. Right. Get this out of your head. It's because they're afraid the decision is going to be wrong. And I think when you think about experimenting with AI, I have approached using AI tools through the lens of there is leverage to be found if I can do the same amount of work, if I can get the same amount of value from 10 fewer hours a week, that's better. That's just better for everybody. For my kids, for my husband, for my parents, for me. So can this tool help me get there now? 7 times out of 10, no. A lot of these AI tools are a little bit behind where I thought they were. Okay. But I only know that I've only found the ones that have freed up hours of my life. Because I start from, I'm going to try these, I'm going to use them wrong. Some of them are going to be terrible, and then I'm going to find the ones that are great, and it's going to save me time and money.
- [32:25]
A
Yep. And let's go back to this Concept of being afraid to make a mistake. I think that there are very few cases in which a mistake could not be fixable or repairable. Yeah. So I. If y' all are out here listening, like, fear being obsolete over making a mistake.
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B
Yeah.
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A
That's what I would say.
- [32:46]
B
Yeah. You're gonna stay stuck. It's one of the things that I've been talking increasingly with the folks that I coach about is, again, these invisible costs. Right. The invisible danger. The danger is not failing. The danger is not making the wrong decision, hiring the wrong person, bringing the wrong board member. Those are terrible. They feel awful. I've experienced all of them. But those are normal. They are fixable. They will go away. The real danger is that if you don't try things, you will look up two years from now, your budget will be the same, your impact will be the same. Your team will be burned out. You will not have moved or changed our world at all. That's the danger. And that happens every day that goes by that you don't move forward. And I don't mean grow. I don't mean add. I mean make decisions and move forward. So, again, making these sort of dangers visible to folks.
- [33:37]
A
Yeah. And it's so interesting because you and I have now worked with hundreds of nonprofits around the world, and time again, it's not the problem isn't the strategy. The problem is the execution.
- [33:47]
B
Absolutely.
- [33:48]
A
You know what to do. Just do it.
- [33:50]
B
That's right.
- [33:51]
A
Like, most problems would be solved if you just took action on it.
- [33:54]
B
That's right.
- [33:55]
A
And I think one of the things I could imagine people saying is that Facebook move fast and break things. Ethos is why we're here today. And I would say you're right. You're right. But to your point, one of the things you love to say is like, do you want to be right, or do you want to be effective?
- [34:10]
B
That's right.
- [34:11]
A
Choose.
- [34:11]
B
That's right. And I will say one last thing on this topic. And you and I have been business besties, accountability partners for almost a decade now, friends for much longer. There are many ways in which we are similar. And I think one of the ways in which we balance each other is with respect to our relationship, to moving fast and breaking things. And I will just go on record as saying I am. I have traditionally not done that, That I am very careful about everything. Not that you aren't, but I think between the two of us, I'm.
- [34:41]
A
Oh, I'm a shoot first, ask questions later kind of.
- [34:45]
B
And I'm like, what's the path, what are the milestones? I think that somewhere between those two things that there is a dance that has to be done. If you stay in my zone and it becomes procrastinating, you have to make decisions, you have to be willing to try and fail. And to the extent that I was able to grow my organization the way that it was, it's because I calibrated that part of me that wants to know all the things that are going to happen in advance. I calibrated it with. It is the superpower of mine. But I calibrated it with let's see what's going to happen. And I think to your on your end, you are one of the most effective scaling leaders I've ever met. Right. You enter an institution and it gets better. So yes, you move fast and you break things, but there is a deep intuitive strategy. So there's a calibration of the moving fast with a gut checking, a paying attention, a looking at what's working. So I think there's that dance and figuring out for those listening, you'll feel like you're on one end of the spectrum or the other. Both ends are fine. It's that dance, it's being willing to play at both ends of that spectrum that I think is very important.
- [35:51]
A
And also I think a self awareness. Right? Cause I, I think to your point, I've also gotten myself in trouble. Cause I'm like, yeah, I'm sure I'll be fine. We'll figure it out along the way. We have. But it's often like at the human cost of being wide awake at 4 o' clock in the morning. I think this is why we're also a great peanut butter and jelly. Like your strength and my weakness is balance each other out. And this is why we're great. So folks, you have gotten a taste of the Ria and Brooke show or the Brooke and Ria show. It's a fun place to be. But if you are listening and you are thinking about AI dipping a toe in AI or you are all in on the AI, we support you. We think just build some guardrails and play and innovate. Because the organization that you love depends on you.
- [36:39]
B
I have always loved talking with you. This was a great conversation and I look forward to more of the same.
- [36:45]
A
Of course, as always. And I will make sure to put all of your info in the show notes for folks who but who doesn't know about you. If there's a world that you don't know about Brooke, Richie Babbage I don't know where you've been, but you got to know broke Richie Babbage. So Brooke, as always, love, bye bye. Hey fundraisers. Looking to nail those big big fundraising asks? Check out my Big Ask gift program@riawong.com bag. Say goodbye to uncertainty and hello to confidence with my program. Get expert strategies and personalized support to secure those game changing donations. Don't let fear hold you back. Join me and take your fundraising to new heights. We're enrolling now@riawong.com bag. That's riawong.com bag. So if you like big asks and you cannot lie, I'll see you in the program.