Latest / Money to Give / Ep. 61 How AI Is Transforming Nonprofit Fundraising And Donor Engagement with Kevin Dean

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Ep. 61 How AI Is Transforming Nonprofit Fundraising And Donor Engagement with Kevin Dean · Money to Give

• 0:03
  Speaker 1: As nonprofits face growing demands with limited staff and resources,
• 0:07
  Speaker 1: one question keeps rising to the surface. How can organizations
• 0:10
  Speaker 1: work smarter without losing the human connection that makes philanthropy meaningful.
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  Speaker 1: Welcome to Money to Give with me Rick Peck, the
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  Speaker 1: philanthropy guy with over twenty years in the nonprofit sector.
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  Speaker 1: I've seen hundreds of millions of dollars transferred from donors
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  Speaker 1: to nonprofits. I've learned a lot along the way that
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  Speaker 1: i want to help convey to you, my loyal listeners.
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  Speaker 1: In this podcast series, we talk about one how nonprofits
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  Speaker 1: can most effectively showcase their mission and vision and raise
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  Speaker 1: as much money as possible. Two how donors and potential
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  Speaker 1: donors can make the greatest impact possible in the world.
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  Speaker 1: And three how professional advisors, including philanthropic advisors, can offer
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  Speaker 1: the most up to date information and services to their clients.
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  Speaker 1: So join us here each episode for more of that
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  Speaker 1: giving feeling. In today's conversation, recorded at the Wall Street
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  Speaker 1: Conference in West Palm Beach in May twenty twenty five,
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  Speaker 1: I'm joined by Kevin J. Dean, founder of Manabyte, to
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  Speaker 1: explore how artificial intelligence otherwise known as AI, can help
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  Speaker 1: nonprofits become more efficient, more targeted, and more personal in
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  Speaker 1: their fundraising and engagement. Kevin explains why AI isn't something
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  Speaker 1: to fear and why it's not about replacing people but
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  Speaker 1: augmenting them, from smarter donor targeting, to personalized messaging and
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  Speaker 1: even AI agents that act like digital interns. This conversation
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  Speaker 1: reframes AI as a practical tool nonprofits can use right
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  Speaker 1: now to grow and scale with intention. Let's begin, So, Kevin,
• 1:45
  Speaker 1: thanks for stopping by the booth here at the Wall
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  Speaker 1: Street conference here in West Palm Beach. We had a
• 1:50
  Speaker 1: great conversation. I wanted to get you on the mic
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  Speaker 1: here and talk. So you and I were talking about
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  Speaker 1: AI as it relates to nonprofits. So I'll just kind
• 1:58
  Speaker 1: of let you take it away. Just give me your
• 2:00
  Speaker 1: sort of high level perspective.
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  Speaker 2: Yeah. I think that there's an opportunity for nonprofit to
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  Speaker 2: really leverage AI in order to really grow their reach
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  Speaker 2: right and to help them in every process in the
• 2:17
  Speaker 2: finding out how to raise funds, who do you need
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  Speaker 2: to be targeting, how do you make sure that you're
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  Speaker 2: utilizing those funds in the proper way. I think there's
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  Speaker 2: so many opportunities for people to for nonprofit organizations to
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  Speaker 2: be leveraging AI in a way that's very specific to
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  Speaker 2: their what we're they're doing in order to grow and scale.
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  Speaker 3: Yeah, that makes sense.
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  Speaker 1: Would you say, with as much talk as there is
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  Speaker 1: about AI, would you say that nonprofits are a little
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  Speaker 1: bit afraid of it?
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  Speaker 3: I mean, I just asked, because.
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  Speaker 1: I'm sure they're probably thinking at some level, like we
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  Speaker 1: should do this, but we don't know where to start,
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  Speaker 1: we don't know what we're getting into. I think a
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  Speaker 1: lot of people think about time. How much time are
• 2:57
  Speaker 1: we going to put into this relative to how much
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  Speaker 1: we're going.
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  Speaker 3: To get out of it? So I don't know, maybe
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  Speaker 3: just talk about.
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  Speaker 2: Yeah a lot. So there's a couple of things I
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  Speaker 2: don't I wouldn't say that it's necessarily fear. I think
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  Speaker 2: one of the big things is a lack of understanding, right,
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  Speaker 2: And so you hear a lot today about AI, gen AI,
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  Speaker 2: agendic AI and all these terms can be very very
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  Speaker 2: confusing and right, and so some of the things that
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  Speaker 2: you may hear in the mainstream market, you may say, well,
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  Speaker 2: that doesn't necessarily apply to what we're trying to do,
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  Speaker 2: and it's because it's not tied to our specific business goals,
• 3:33
  Speaker 2: and you're one hundred percent correct right that what you're
• 3:36
  Speaker 2: seeing and hearing a lot of really isn't tied to
• 3:39
  Speaker 2: your specific goals. What you need to do is take
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  Speaker 2: a step back and understand what is the art of
• 3:45
  Speaker 2: the possible and then use it to go after specific
• 3:50
  Speaker 2: business problems that you guys are facing, and then from
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  Speaker 2: there make the application. Because a lot of the things
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  Speaker 2: that you see, there's a lot of hype out there,
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  Speaker 2: and I think that's where it gets confus using. So
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  Speaker 2: nonprofits have real problems that they face on a day
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  Speaker 2: to day basis, and it's probably very hard for them
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  Speaker 2: to translate, well, this is the problem that I have.
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  Speaker 2: How is this going to actually help me solve that
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  Speaker 2: problem and achieve our goals as an organization.
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  Speaker 3: That's interesting. I'm almost hearing you say.
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  Speaker 1: I can see a nonprofit sitting in front of you
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  Speaker 1: and saying, here are all the pain points, here are
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  Speaker 1: other things we're struggling with, and then you could be
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  Speaker 1: viewing it through an AI lens and say, well, I'm
• 4:27
  Speaker 1: not sure if a I can help you with that,
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  Speaker 1: but a I can definitely help you with these things.
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  Speaker 1: So just to round out maybe what they don't know
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  Speaker 1: and round out how they can possibly work toward more efficiencies, right.
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  Speaker 2: Yeah, I think that that's definitely the point I think
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  Speaker 2: it's important for organizations to understand is what are the
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  Speaker 2: real capabilities and how it can really help them move forward.
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  Speaker 2: And one of the biggest challenges but also a great
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  Speaker 2: opportunity for nonprofits is data really is the key for
• 4:59
  Speaker 2: them to be success. And I think that there's a
• 5:02
  Speaker 2: misunderstanding of what really powers AI, and it really is
• 5:06
  Speaker 2: their unique data set. And so understanding your data as
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  Speaker 2: a nonprofit and understanding how you can leverage your data
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  Speaker 2: to be a grand generator, right, is super important, And
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  Speaker 2: so that's what's important. And all the other stuff that
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  Speaker 2: they may be hearing is kind of peripheral, and so
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  Speaker 2: when you think about how you're going to use it,
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  Speaker 2: there's different applications and different types of AI in order
• 5:35
  Speaker 2: to solve specific problems. And so a lot of times
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  Speaker 2: people are just generalizing it all right to oh, it's
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  Speaker 2: just chat GPT, right, And that's kind of like what
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  Speaker 2: I like to say is like it's like the Internet
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  Speaker 2: of AI, right, And what I mean by that is
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  Speaker 2: everybody says, I'm on the Internet, Well, what does that
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  Speaker 2: really mean? Right, But there's a core underlying, you know,
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  Speaker 2: technology that drives the Internet. On top of the Internet,
• 6:01
  Speaker 2: you've got websites that provide information, You've got apps that
• 6:05
  Speaker 2: provide functions, right, You've got directories. There's so many things
• 6:09
  Speaker 2: that are built on top of it that have specific functions. Yeah,
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  Speaker 2: might want to think about AI from that perspective. You know,
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  Speaker 2: AI as a general term, it's just through base foundation.
• 6:19
  Speaker 2: But on top of that, you can and should be
• 6:22
  Speaker 2: thinking about domain specific applications that allow you to accomplish
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  Speaker 2: what you need to accomplish as a whole position.
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  Speaker 1: Like almost like subsets getting into this place of like
• 6:34
  Speaker 1: you said, this AI application solves these types of problems,
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  Speaker 1: and I feel like someone like you is very sophisticated
• 6:41
  Speaker 1: in the space, and you could say, you know what,
• 6:43
  Speaker 1: it sounds like what you think even if they just
• 6:46
  Speaker 1: start with a cursory knowledge of how AI can help them,
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  Speaker 1: you can say, actually, I think we should pivot over here.
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  Speaker 1: This is actually more about how we can help you,
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  Speaker 1: how AI can be a benefit to you. Rather than
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  Speaker 1: feeling like like you said, it's just like AI does this,
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  Speaker 1: It's more like there's new u wance is of different
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  Speaker 1: types of AI that can help you with different things.
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  Speaker 2: That is correct, right.
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  Speaker 1: I mean, I'm just going to sort of roll off
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  Speaker 1: a bunch of different things that I think about. When
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  Speaker 1: I think of nonprofits, they think of their database, right,
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  Speaker 1: and I think of cultivation and stewardship of donors.
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  Speaker 3: I think of their website.
• 7:14
  Speaker 1: I think of offering up non cash asset donations. I
• 7:18
  Speaker 1: think about working with volunteers. I think about working with
• 7:21
  Speaker 1: professional advisors, you know, I think about just all those
• 7:25
  Speaker 1: types of things. I can imagine you would come in
• 7:27
  Speaker 1: and say, I'm thinking about this particular issue in this way.
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  Speaker 1: I'm thinking of about this type of AI that would
• 7:35
  Speaker 1: probably be a benefit. So it's really that customization I
• 7:37
  Speaker 1: think of your approach that's helping them versus being like
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  Speaker 1: a blanket a blanket solution.
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  Speaker 2: Yeah. So I love that you talked about looking at
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  Speaker 2: the organization's database, right, because really that's where the gold
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  Speaker 2: is and that's where the opportunity is. And I'll give
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  Speaker 2: you some specific examples of things that nonprofits can do
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  Speaker 2: when they think about leveraging AI well in their spaces.
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  Speaker 2: For instance, like you've got your database, AI can help
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  Speaker 2: you identify when was the last time someone contributed, Like,
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  Speaker 2: what are the things that they're not going to contribute to?
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  Speaker 2: You know, what are opportunities? Who are the connections that
• 8:15
  Speaker 2: they have in order to expand right out? And so
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  Speaker 2: those are things that AI can surface up to organizations
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  Speaker 2: that help them to be more successful. That it's not
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  Speaker 2: just a matter of oh, I want to go in
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  Speaker 2: and do a B and C. You can leverage AI
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  Speaker 2: to bubble up information that you didn't know before to
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  Speaker 2: help you be more specific to They help you be
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  Speaker 2: more targeted, to have the right conversations with the donor
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  Speaker 2: right to bubble up information that allows you to really
• 8:43
  Speaker 2: have a better approach to it.
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  Speaker 3: Yeah, that makes toenmes of sense.
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  Speaker 1: And you know, a common theme, not surprisingly is this
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  Speaker 1: is what I see. Donors are looking for a great
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  Speaker 1: nonprofit or a series of nonprofits to benefit because they're
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  Speaker 1: trying to make a difference. Nonprofits, of course are looking
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  Speaker 1: for donors. They're looking for that matchmaking and I you know,
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  Speaker 1: just to state the statistics this, I know them. In
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  Speaker 1: twenty twenty three, five hundred and fifty seven billion dollars
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  Speaker 1: was donated, up from four hundred and ninety nine billion
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  Speaker 1: the year prior. So money is out there, people are
• 9:16
  Speaker 1: giving away money, but non profits have this bit of
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  Speaker 1: a scarcity mentality to say, well, it's not coming to us,
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  Speaker 1: like who's giving away that money? And how come we
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  Speaker 1: can't be part of that? And but I hear what
• 9:27
  Speaker 1: you're saying is just because maybe they're just barking up
• 9:30
  Speaker 1: the wrong tree. They're going to the wrong people. They're
• 9:32
  Speaker 1: trying to source their donors in not great ways. And
• 9:37
  Speaker 1: efficiencies lie on both sides, right, the efficiencies of nonprofits
• 9:40
  Speaker 1: trying to do better and efficiencies of donors trying to
• 9:43
  Speaker 1: and funders trying.
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  Speaker 3: To do better.
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  Speaker 2: Right, Yeah, And it's also like making making sure that
• 9:50
  Speaker 2: you're going after the right donor at the right time
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  Speaker 2: for the right reasons. Right, So all of those things
• 9:56
  Speaker 2: have to align up, as you know. And so AI
• 9:59
  Speaker 2: can help you by doing things like lead scoring, like, oh,
• 10:03
  Speaker 2: this donor right here is the perfect donor for us
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  Speaker 2: to go after, but we don't want to talk to
• 10:08
  Speaker 2: him in August. We need to talk to him in September, right,
• 10:12
  Speaker 2: So these are the types of things that AI can
• 10:15
  Speaker 2: help you with. AI can then help you craft like
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  Speaker 2: the perfect message to send to them that's personalized instead
• 10:21
  Speaker 2: of having a ton of people who are trying to
• 10:23
  Speaker 2: go around and think about something that's right like this
• 10:26
  Speaker 2: can really help you to get the right message, to
• 10:29
  Speaker 2: get right to right in front of the right people.
• 10:31
  Speaker 1: Yeah, totally, and you're right, there are a lot of work
• 10:35
  Speaker 1: is being done to kind of craft messaging and wordsmith,
• 10:39
  Speaker 1: when in fact AI feels like it's seen that one
• 10:42
  Speaker 1: hundred thousand times over and says this is the messaging
• 10:46
  Speaker 1: I think you want to use for this particular audience. Now,
• 10:50
  Speaker 1: I would think that the irony of AI is that
• 10:52
  Speaker 1: on one level, people would think, oh, it's very impersonal.
• 10:55
  Speaker 1: It's a computer, it's a you know, a system that's
• 10:57
  Speaker 1: coming up with this. But in fact, the irony I
• 11:01
  Speaker 1: would think is that you can actually make it more
• 11:03
  Speaker 1: personal for people because they feel like you're hearing me,
• 11:05
  Speaker 1: you're seeing me.
• 11:07
  Speaker 3: Which is the difference.
• 11:08
  Speaker 1: Right If people donor is essentially at one point they're
• 11:11
  Speaker 1: going to want to get really serious. If they're going
• 11:13
  Speaker 1: to make a big gift, chances are they're going to
• 11:15
  Speaker 1: probably want to talk to somebody. If they feel like
• 11:17
  Speaker 1: you've warmed them up, well, you know, you're communicating with
• 11:20
  Speaker 1: me in the way that I like to be communicated
• 11:22
  Speaker 1: with the messaging that it's resonating with me and so on.
• 11:25
  Speaker 1: Maybe I'm a visual learner, maybe I'm maybe I need
• 11:28
  Speaker 1: to see numbers, maybe I need to see heartwarming stories.
• 11:32
  Speaker 1: But you're giving me what I need as a donor
• 11:34
  Speaker 1: in ways that other nonprofits are not giving those things
• 11:37
  Speaker 1: to me.
• 11:37
  Speaker 3: So therefore I'm.
• 11:38
  Speaker 1: Closer to you, even though behind the scenes AI might
• 11:41
  Speaker 1: have been the majority of that work. Now that's obviously
• 11:44
  Speaker 1: informed by a human being or people who are using
• 11:47
  Speaker 1: AI as a helping resource. Rather than saying, like AI,
• 11:52
  Speaker 1: you do it, it's more like somebody's still coaching the AI, right.
• 11:55
  Speaker 2: Yeah. So one of the things to think about from
• 11:59
  Speaker 2: your nowogy and what you're talking about here is that yes,
• 12:02
  Speaker 2: AI has the ability to do personalization at scale. But
• 12:07
  Speaker 2: what we want everybody to understand is this isn't just
• 12:11
  Speaker 2: going into chat GPT and saying, oh, personalize this message
• 12:15
  Speaker 2: for this person. No, we're talking about bringing this down
• 12:19
  Speaker 2: to a smaller level, not a larger level. And this
• 12:21
  Speaker 2: is where the data that organizations have they can really
• 12:25
  Speaker 2: leverage that to make personal lives messaging based on interactions
• 12:30
  Speaker 2: that they've had in the past. So AI can go
• 12:33
  Speaker 2: through and say yep, they've donated this amount at this time,
• 12:36
  Speaker 2: and this was the reason why things that it's hard
• 12:39
  Speaker 2: for us to do at scale. So we may have
• 12:41
  Speaker 2: an individual who can go through and find all these correlations,
• 12:45
  Speaker 2: but how much times does that actually take you to
• 12:47
  Speaker 2: do it? A lot?
• 12:48
  Speaker 3: It takes a lot of time. Right, they might not
• 12:49
  Speaker 3: be qualified.
• 12:50
  Speaker 2: Correct, and they may not have the correct context, and
• 12:52
  Speaker 2: they may be missing pieces of data. So this is
• 12:55
  Speaker 2: where nonprofits databases are so important, right to make sure
• 13:00
  Speaker 2: that they've got quality information, good data that is clean,
• 13:04
  Speaker 2: that is duplicated, because that's going to be the driving
• 13:07
  Speaker 2: force behind ensuring that you can do personalization at scale.
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  Speaker 2: And then once you put in a system that is
• 13:15
  Speaker 2: powered by AI, what's going to happen is in the future.
• 13:18
  Speaker 2: While as you start, you're going to have humans who
• 13:21
  Speaker 2: are going through and validating, ensuring that the quality is right,
• 13:25
  Speaker 2: insuring that the message just right initially, but then AI
• 13:29
  Speaker 2: have the power to learn. And so what that means
• 13:32
  Speaker 2: is after performance, after performance is oh, this email didn't work.
• 13:35
  Speaker 2: Why didn't network? Okay, this is why it didn't work,
• 13:37
  Speaker 2: and let's try this next email message. Oh this one worked,
• 13:40
  Speaker 2: Why did this one work? And so now it can
• 13:43
  Speaker 2: self learn on itself to say okay. Over time, I've
• 13:47
  Speaker 2: learned that for John this type of message works, for
• 13:50
  Speaker 2: married this type of message work, and for you know, Peter,
• 13:53
  Speaker 2: this is the type of theme which is hard for
• 13:56
  Speaker 2: us to do as humans at scale. Right A, I
• 13:58
  Speaker 2: can do that very easy, easily to make sure that
• 14:01
  Speaker 2: you've got these personalized messages that hit them at the
• 14:04
  Speaker 2: right time for the right reasons.
• 14:06
  Speaker 3: That's amazing.
• 14:07
  Speaker 1: And I can see it going both ways, like you
• 14:09
  Speaker 1: not only are you super personalizing it, but you also
• 14:12
  Speaker 1: may be able to go back into stretch and sort
• 14:15
  Speaker 1: of I analyze later generally speaking, messaging that goes out
• 14:20
  Speaker 1: on Wednesdays at noon tends to get more eyeballs than
• 14:24
  Speaker 1: something that goes out on a Sunday at three or something,
• 14:26
  Speaker 1: you know what I mean, like those types of things,
• 14:27
  Speaker 1: or men seem to respond more for this type of messaging,
• 14:31
  Speaker 1: but women respond more generally. Speaking to this, maybe there's
• 14:34
  Speaker 1: some things like that that I can imagine being helpful
• 14:38
  Speaker 1: so that maybe when even when they're starting out, they say, well,
• 14:41
  Speaker 1: the data so far has said these things. Who are
• 14:44
  Speaker 1: we to blow against the wind, Like, let's start with
• 14:46
  Speaker 1: that and go there rather than just starting completely from scratch,
• 14:49
  Speaker 1: unless you disagree, unless you think maybe you should start
• 14:52
  Speaker 1: completely from scratch.
• 14:53
  Speaker 2: No, So what I'm saying, and the readers listeners aren't
• 14:57
  Speaker 2: seeing my face right, my head nod was this is
• 15:03
  Speaker 2: that it's actually two levels deeper, meaning that it's not
• 15:07
  Speaker 2: men respond to this message, or women respond to this message,
• 15:11
  Speaker 2: or this time. It is John Smith responds to this
• 15:15
  Speaker 2: message at this time. John Brown responds to this message
• 15:20
  Speaker 2: at this time. And so now it's taken this not
• 15:23
  Speaker 2: at a higher level, but this is on an individual level.
• 15:28
  Speaker 2: It knows exactly when to send the message for you,
• 15:31
  Speaker 2: exactly when to send it for me, And that is
• 15:34
  Speaker 2: the beauty of AI. Right, So it's really like it's personal.
• 15:38
  Speaker 1: Better than generalization. It's better than the trends are. It's
• 15:41
  Speaker 1: more like it's almost like we were saying, there are
• 15:44
  Speaker 1: no trends. I know this is going to sound like
• 15:45
  Speaker 1: a weird analogy, but I went to a conference I
• 15:49
  Speaker 1: don't know, maybe fifteen years ago, and it was a
• 15:53
  Speaker 1: cancer director and he threw out a question to the crowd.
• 15:56
  Speaker 1: He said, how many different types of breast cancer do
• 15:58
  Speaker 1: you think there are? And somebody said, I don't know
• 16:01
  Speaker 1: twenty he said, Actually, there's a different type of breast cancer.
• 16:04
  Speaker 1: For every person who has breast cancer, there's a different
• 16:07
  Speaker 1: type of treatment. That's why it's so hard to cure
• 16:09
  Speaker 1: breast cancer. And so I was like, wow, that just
• 16:12
  Speaker 1: kind of made me step back and go, that's how
• 16:14
  Speaker 1: individualized it is. And you hear about individualized medicine these days,
• 16:17
  Speaker 1: but that's what we're talking about.
• 16:18
  Speaker 3: Everybody has a different genetic code, and.
• 16:20
  Speaker 1: Therefore to combat that disease or fight it is going
• 16:23
  Speaker 1: to take a very individualized approach. I feel like, again,
• 16:27
  Speaker 1: strange analogy, but coming back to donating.
• 16:29
  Speaker 2: Perfect analogy. Yeah, it's the absolute perfect analogy. So when
• 16:32
  Speaker 2: it comes to donating and leveraging AI, there's a different
• 16:37
  Speaker 2: driver for every single individual, and AI can bubble that
• 16:40
  Speaker 2: up and help you help nonprofits to really be personalized
• 16:45
  Speaker 2: in their messaging and in their targeting.
• 16:47
  Speaker 1: Yeah, it's incredible. And then obviously the results of that
• 16:50
  Speaker 1: are everybody's happy. The nonprofit's getting the money that it needs.
• 16:53
  Speaker 1: It's the donors are feeling like you heard me, And
• 16:57
  Speaker 1: then I would think that donors would also say, you
• 16:59
  Speaker 1: got to really work with this organization. They're going to
• 17:01
  Speaker 1: source referrals and connections in ways that the institution wouldn't
• 17:06
  Speaker 1: have been able to do on its own, you know,
• 17:07
  Speaker 1: those warm referrals.
• 17:08
  Speaker 2: Yeah, that is great. And I think the other thing
• 17:11
  Speaker 2: when we think about sourcing new referrals is you can
• 17:14
  Speaker 2: begin to leverage AI to go out and source those
• 17:18
  Speaker 2: new referrals for you just say, oh, you know, John
• 17:22
  Speaker 2: Smith has been a great donor. Would you like for
• 17:26
  Speaker 2: me to go out and find more John Smith people
• 17:28
  Speaker 2: who are just like John Smith and start messaging those
• 17:32
  Speaker 2: individuals using the type of messages that we know already worked.
• 17:36
  Speaker 3: Wow.
• 17:37
  Speaker 1: So that's like very powerful. Well, and that is universal.
• 17:40
  Speaker 1: There is a woman actually, when you and I were talking,
• 17:43
  Speaker 1: a woman came up. She was saying almost exactly that.
• 17:46
  Speaker 1: She says, our message is pretty locked down, We're pretty
• 17:48
  Speaker 1: confident in everything we're doing. We just need to find
• 17:51
  Speaker 1: new people. And that feels like in some ways it
• 17:54
  Speaker 1: can feel like a mystery, like I don't know. I mean,
• 17:56
  Speaker 1: there's a lot of people out there that have money,
• 17:58
  Speaker 1: but they aren't necessarily interested in your organization. But a
• 18:01
  Speaker 1: lot of these nonprofits they want a fast track. You know,
• 18:04
  Speaker 1: what if we had an event, what if we had
• 18:05
  Speaker 1: a gala, what if we got twenty people in the
• 18:08
  Speaker 1: room of capacity, they would all want to give to us, right.
• 18:12
  Speaker 3: It's like not necessarily, not necessarily right.
• 18:14
  Speaker 1: So I think that speaks to like going back to
• 18:16
  Speaker 1: that targeting, which is you want what if you had
• 18:18
  Speaker 1: twenty people who were just salivating waiting to hear everything
• 18:22
  Speaker 1: you had to say because you got the right twenty
• 18:24
  Speaker 1: people in the room, rather than twenty people who just
• 18:26
  Speaker 1: responded to your invitation or feel like they're doing you
• 18:29
  Speaker 1: a favor. The other thing you're making me think about
• 18:32
  Speaker 1: is at galas I've seen and I saw this not
• 18:36
  Speaker 1: too long ago, it seems like the right people in
• 18:39
  Speaker 1: the room, they're all wearing tuxes and dresses and beautiful program.
• 18:44
  Speaker 3: The silent auction is not working.
• 18:46
  Speaker 1: The live auction didn't work, the raffle didn't really work,
• 18:50
  Speaker 1: the outright giving component of the program didn't really work.
• 18:54
  Speaker 3: It's like, what happened.
• 18:55
  Speaker 1: They did a beautiful job with everything on every level
• 18:59
  Speaker 1: to the nine its beautiful, It just didn't resonate. So
• 19:03
  Speaker 1: then they sit there and they go, well, what do
• 19:05
  Speaker 1: we do wrong? And it's like, maybe you didn't do
• 19:07
  Speaker 1: anything wrong except that you didn't have the right people
• 19:09
  Speaker 1: in the seats, right, or you weren't, or even the
• 19:11
  Speaker 1: messaging wasn't necessarily on target for what they were looking for.
• 19:15
  Speaker 1: So I can see what you're talking about with AI
• 19:17
  Speaker 1: as being like, we're just going to help you refine
• 19:20
  Speaker 1: and you know, for lack of a better way to
• 19:21
  Speaker 1: describe it, waste less time doing something that you think
• 19:24
  Speaker 1: is going to be productive when in fact it was
• 19:26
  Speaker 1: a little bit off target.
• 19:28
  Speaker 2: Yeah, And so one of the great things that I
• 19:31
  Speaker 2: would love for nonprofits to start thinking about is using
• 19:34
  Speaker 2: AI throughout the entire life cycle. Right. And so if
• 19:38
  Speaker 2: you begin to use AI in your planning phase and
• 19:42
  Speaker 2: you say, okay, we're planning this, Gala, here's the information
• 19:45
  Speaker 2: that we're going to use. Help me understand you know,
• 19:49
  Speaker 2: what are we missing, what are we what should what
• 19:51
  Speaker 2: are the other things that we should be considering, using
• 19:53
  Speaker 2: it as a protagonist to say, okay, this is what
• 19:56
  Speaker 2: we're thinking. Let me know if you see something that
• 19:59
  Speaker 2: we're missing. So I think that's going to be great
• 20:01
  Speaker 2: where you can use AI in that planning process, then
• 20:04
  Speaker 2: during the event, leveraging AI to evaluate what's going on
• 20:08
  Speaker 2: in real time, right, And so this is great to
• 20:11
  Speaker 2: say Okay, here's what we're doing, here's what we're thinking,
• 20:13
  Speaker 2: here's what I'm seeing, and what can I be doing,
• 20:15
  Speaker 2: what should I be changing? And then at the end
• 20:19
  Speaker 2: using it for you know, a recap and an analysis,
• 20:22
  Speaker 2: say here's what happened. Here is the results based on
• 20:24
  Speaker 2: everything that we did in the planning stage and enduring it,
• 20:27
  Speaker 2: tell us what went wrong and tell us what you
• 20:29
  Speaker 2: think that we should do in the future in order
• 20:31
  Speaker 2: to improve right. And so AI had the power and
• 20:34
  Speaker 2: the ability to do all of that. But again, the
• 20:36
  Speaker 2: key is ensuring that as you're using it throughout the
• 20:39
  Speaker 2: entire life cycle, you're putting in the information so that
• 20:42
  Speaker 2: you can go back and analyze it and give you
• 20:45
  Speaker 2: a good results in the future.
• 20:46
  Speaker 1: It's almost like you'll be a learning system and it
• 20:49
  Speaker 1: will be a learning system with you, and then I
• 20:51
  Speaker 1: can just see again, it's not only what you do
• 20:55
  Speaker 1: that the efficiencies of AI helping you walk through the
• 20:58
  Speaker 1: pros and cons and but it's also maybe the capacity itself. Again,
• 21:03
  Speaker 1: the thing that a lot of nonprofits say is we
• 21:05
  Speaker 1: don't have time for this, and the reality is okay, understood,
• 21:09
  Speaker 1: So how can I help you do things so that
• 21:12
  Speaker 1: you can leverage it and free up your time for
• 21:16
  Speaker 1: other things, because otherwise you're going to feel like you're
• 21:18
  Speaker 1: going down this rabbit hole. And back to my I
• 21:23
  Speaker 1: said before being scared, I think scared being like, what
• 21:26
  Speaker 1: does this mean? It's just like a tidal wave of
• 21:30
  Speaker 1: knowledge and application that I don't have time for. I mean, yes,
• 21:34
  Speaker 1: it sounds exciting and unusual, but really, ultimately what you're
• 21:38
  Speaker 1: talking about is just like it's actually helping you save
• 21:41
  Speaker 1: time and be better on every level, both on the
• 21:45
  Speaker 1: strategic side of things as well as on the tactical
• 21:48
  Speaker 1: side of things.
• 21:48
  Speaker 2: Yeah, one hundred percent. A lot of times when we
• 21:50
  Speaker 2: talk to nonprofits, one of the biggest concerns that they
• 21:54
  Speaker 2: bring up is that they are resource constrained. Yeah, right,
• 21:58
  Speaker 2: every one that I've ever talked the resource constraint. They
• 22:02
  Speaker 2: just don't have the manpower, they don't have the financial
• 22:05
  Speaker 2: resources to throw at the challenges that they're trying to face.
• 22:09
  Speaker 2: So now one of the things that we want to
• 22:12
  Speaker 2: just throw out this term is a gentic AI and
• 22:15
  Speaker 2: what agentic AI is the use of agents AI agents
• 22:20
  Speaker 2: that can actually perform on its own specific goals and tasks.
• 22:25
  Speaker 2: And so basically you think of it as you're going
• 22:28
  Speaker 2: to hire a human to do a task, or you
• 22:31
  Speaker 2: can't outsource it to an AI agent who can do
• 22:34
  Speaker 2: that exact same task, right, except for it's a lot
• 22:37
  Speaker 2: cheaper to hire an AI agent than it is to
• 22:39
  Speaker 2: hire a human to do that task. And so with
• 22:43
  Speaker 2: the thought of agents, just so that we can explain
• 22:45
  Speaker 2: to your audience how an agent works, it's similar to
• 22:49
  Speaker 2: the way it would be is if you're going to
• 22:51
  Speaker 2: hire a new intern, right to come in and do
• 22:54
  Speaker 2: some work for you. You've got a one onboard and
• 22:57
  Speaker 2: into your organization, you've got to give background of your
• 23:01
  Speaker 2: to give it the background, like, here's what our organization does,
• 23:04
  Speaker 2: here's what we're trying to accomplish. Here's our rules, here's
• 23:07
  Speaker 2: our guidelines. There's what we say, here's what we don't say.
• 23:10
  Speaker 2: You've got to tell an AI agent all that information.
• 23:13
  Speaker 2: Then you have to give it access, right, so you've
• 23:15
  Speaker 2: got to give it access to the right tools, to
• 23:17
  Speaker 2: the right systems. Right. Then you've got to say, okay, here,
• 23:20
  Speaker 2: now that you have all of this, here's what I
• 23:22
  Speaker 2: want you to go and do. And just like you
• 23:24
  Speaker 2: would hire an intern within your organization, you wouldn't bring
• 23:27
  Speaker 2: in an intern and say, okay, we're going to put
• 23:29
  Speaker 2: you on our strategic planning board for what we're going
• 23:32
  Speaker 2: to do next year. No, you're going to give it
• 23:33
  Speaker 2: a small task. Let it do that task, and then
• 23:36
  Speaker 2: as that intern learns, you're going to professorly give it
• 23:38
  Speaker 2: more tasks. Same thing with an AI agent. You start
• 23:41
  Speaker 2: by giving it a small task or a goal and
• 23:44
  Speaker 2: say I want a lot for you to go and
• 23:45
  Speaker 2: do this task. It goes out and does that tax.
• 23:48
  Speaker 2: Then it learns how to do that task, and you
• 23:49
  Speaker 2: give it another task. Right, And so these are AI
• 23:52
  Speaker 2: agents that you can bring into your organization. Because you're
• 23:56
  Speaker 2: human constrained, you can get these AI agents that will
• 23:59
  Speaker 2: come in and perform these tasks for you to be
• 24:02
  Speaker 2: able to help you be more efficient to do the
• 24:04
  Speaker 2: things that you need to do. But because in the
• 24:06
  Speaker 2: past you've been restrained, now you're not anymore, because now
• 24:09
  Speaker 2: you can leverage AI to do.
• 24:11
  Speaker 3: A lot of the work for you. That's amazing.
• 24:13
  Speaker 1: I met a lot of people that didn't know that
• 24:15
  Speaker 1: you could almost think of AI as as a receptive
• 24:18
  Speaker 1: intern that's doing the work and learning and trying to
• 24:20
  Speaker 1: get better and better. And that's again not to necessarily
• 24:23
  Speaker 1: displace humans. No, it's hmenting, it's augmenting. And also it
• 24:27
  Speaker 1: goes back to maybe there are some things that AI
• 24:30
  Speaker 1: isn't well suited for today at least right now, that
• 24:33
  Speaker 1: the humans are the best application for that. But then
• 24:36
  Speaker 1: there are other things that the humans are not. And
• 24:38
  Speaker 1: I think that's where the business or the nonprofit needs
• 24:40
  Speaker 1: to be thinking about what that looks like.
• 24:42
  Speaker 2: Yeah, and I think the good way, a good way
• 24:45
  Speaker 2: to get started is think about repetitive tasks that can
• 24:48
  Speaker 2: get done pretty simply by giving someone a set of instructions.
• 24:52
  Speaker 2: If you can get those are the things to go
• 24:54
  Speaker 2: after first. Right, So let's think about some of your
• 24:57
  Speaker 2: tasks that you want to just say I'm going to repeat.
• 25:00
  Speaker 2: You can have AI do those paths, and over time,
• 25:03
  Speaker 2: just like an intern does, they're going to learn those tests,
• 25:05
  Speaker 2: they're going to learn the backward, they're gonna let everything
• 25:07
  Speaker 2: thing to know. Then they're going to grow. Right, they're
• 25:10
  Speaker 2: not going to be an intern agent forever. And then
• 25:12
  Speaker 2: once they've learned that, you can give them another task,
• 25:14
  Speaker 2: and then you can give them another test, right, and
• 25:16
  Speaker 2: pretty soon, like you're saying, they'll be able to high handle,
• 25:20
  Speaker 2: you know, more challenging tasks over time. But this isn't overnight.
• 25:25
  Speaker 2: And what this does is this now frees up nonprofit
• 25:28
  Speaker 2: organizations to be able to be more effective, not just
• 25:33
  Speaker 2: externally but internally in the way that they operate, which
• 25:35
  Speaker 2: is where a lot of organizations struggle.
• 25:39
  Speaker 1: So a gentleman I just talked to you today said
• 25:42
  Speaker 1: the majority of nonprofits their revenues he named about one
• 25:46
  Speaker 1: point seven I think it's probably closer to two million.
• 25:48
  Speaker 1: Now revenues are twenty five thousand dollars or less, and
• 25:53
  Speaker 1: I'm thinking about revenue and personnel constrained. I'm imagining that
• 25:58
  Speaker 1: it's almost like, why would you not use AI to
• 26:01
  Speaker 1: help you get there? Because you almost have no other choice.
• 26:04
  Speaker 1: And the good news is AI is going to be
• 26:07
  Speaker 1: one of the best options for you regardless because of
• 26:10
  Speaker 1: all of its efficiencies. Just it seems like if people
• 26:13
  Speaker 1: aren't using it's because they don't know how. They don't
• 26:16
  Speaker 1: know how, And actually probably that's a good lead into manibyte,
• 26:19
  Speaker 1: like tell me how they would work with you, Like
• 26:22
  Speaker 1: what wouldn't if I'm a nonprofit green as I could
• 26:25
  Speaker 1: possibly be. I'm going to my board and saying we've
• 26:28
  Speaker 1: got to raise more money. I know that AI has potential.
• 26:31
  Speaker 1: We really don't know where to start. Would they go
• 26:33
  Speaker 1: to Manibyte, Yeah, most definitely. So they've come to us
• 26:36
  Speaker 1: and we'd help them put together a strategy. We'd help
• 26:38
  Speaker 1: them plan it out, would help them to design what that.
• 26:42
  Speaker 2: What that system looks like for them. Then we'd help
• 26:45
  Speaker 2: them build it, and then we help them implement it,
• 26:47
  Speaker 2: and then we put it in place an option for
• 26:49
  Speaker 2: to help them watch that scale, right, And the way
• 26:52
  Speaker 2: that we do that is, you know, you know, it's
• 26:54
  Speaker 2: going to be cheaper for them to work with us
• 26:58
  Speaker 2: and get agents within the organization would to be to
• 27:01
  Speaker 2: hire you through a four or five people, because you
• 27:03
  Speaker 2: can hire you know, an agent that can do the
• 27:05
  Speaker 2: job or a lot of the tasks of these people
• 27:08
  Speaker 2: that you would hire.
• 27:09
  Speaker 1: Yeah, well, this is fantastic. I really appreciate time here, Kevin.
• 27:13
  Speaker 1: It is so insightful. Yeah, and I love the fact
• 27:16
  Speaker 1: that you are saying this is a viable solution. It's
• 27:20
  Speaker 1: just a matter of exploring the options, just understanding how
• 27:24
  Speaker 1: AI works on all of its nuances, and how it
• 27:27
  Speaker 1: really is sort of a playbook and a step by
• 27:29
  Speaker 1: step process that organizations can go through probably to the
• 27:33
  Speaker 1: point that two years from now I look back and go,
• 27:35
  Speaker 1: you know, I wish we'd done it sooner.
• 27:37
  Speaker 3: Yeah, I mean, I know ai as evolved me all
• 27:39
  Speaker 3: the time.
• 27:39
  Speaker 1: But the idea of like, what if we had started
• 27:41
  Speaker 1: doing this a little earlier, would we have raised more money,
• 27:43
  Speaker 1: would would we have found more of our select audience
• 27:46
  Speaker 1: than if we had you know, still done things the
• 27:48
  Speaker 1: old fashioned way and really not gotten any further.
• 27:51
  Speaker 3: So it sounds like you're.
• 27:52
  Speaker 1: Really helping them see the potential and walking them through
• 27:55
  Speaker 1: at every step of the way, which is very much appreciated.
• 27:58
  Speaker 2: Yeah, one hundred percent. We love helping folks, you know,
• 28:01
  Speaker 2: grow and scale when they're you know, constrained, and we
• 28:04
  Speaker 2: want to do that without having them be tied to
• 28:07
  Speaker 2: you know, outdated processes, systems that don't work in the
• 28:11
  Speaker 2: fear of change.
• 28:12
  Speaker 3: Yeah, you know.
• 28:12
  Speaker 2: And I'll say just one other thing is that as
• 28:17
  Speaker 2: people start evaluating AI solutions, these solutions aren't like solutions
• 28:21
  Speaker 2: in the past where you will think that, oh, this
• 28:24
  Speaker 2: solution is going to take you know, six months to
• 28:27
  Speaker 2: implement and another six.
• 28:28
  Speaker 3: Months before we start to see results.
• 28:30
  Speaker 2: Right, These are systems that people are getting up in
• 28:33
  Speaker 2: place within thirty days and seeing results in sixty days.
• 28:36
  Speaker 3: Oh, that's amazing.
• 28:37
  Speaker 2: Yeah, I've been.
• 28:38
  Speaker 1: It's good to know that because I'm sure people made like, well,
• 28:40
  Speaker 1: how long is this going to take before it starts
• 28:43
  Speaker 1: back to the little successes. That's really what at the
• 28:45
  Speaker 1: essence of a lot of what I'm hearing you say
• 28:47
  Speaker 1: is like a little success, a little success builds on,
• 28:50
  Speaker 1: builds on, and then pretty soon we're more complex tasks,
• 28:54
  Speaker 1: more of wide variety of tasks, deeper level thinking and
• 28:58
  Speaker 1: just and kind of that learning system mentality. Right, Yeah,
• 29:01
  Speaker 1: that's the key, because you know, especially right now, as
• 29:05
  Speaker 1: things are continuing to change and progress, right, we're seeing
• 29:09
  Speaker 1: in in my world in the AI.
• 29:11
  Speaker 2: Space now every thirty days there's another new major development
• 29:15
  Speaker 2: that's bringing in more capabilities. So it's those small successes
• 29:20
  Speaker 2: now that are going to lead you.
• 29:21
  Speaker 1: To a much better future, right, And you'll be acclimated
• 29:25
  Speaker 1: enough so that when those changes, yeah, start to happen,
• 29:27
  Speaker 1: it's like now we can navigate instead of feeling like, boy,
• 29:30
  Speaker 1: on'm mine behind the eight ball, if you like, probably
• 29:32
  Speaker 1: trying to learn how to use a computer in twenty fifteen,
• 29:35
  Speaker 1: it's like you waited.
• 29:36
  Speaker 3: A little too long. Computers have been around for a while.
• 29:38
  Speaker 1: I've been moving with the computers or getting a cell
• 29:41
  Speaker 1: phone in your hand today and being like, how do
• 29:43
  Speaker 1: I use this? It's like you probably should have been
• 29:45
  Speaker 1: learning how to use this a long time ago, so
• 29:46
  Speaker 1: then you could, you know, leverage it to your be one.
• 29:50
  Speaker 1: So how do people reach you? And how do they
• 29:53
  Speaker 1: reach manibike?
• 29:54
  Speaker 2: Yeah, so they can go out to my website, Kevinjdean
• 29:57
  Speaker 2: dot com. That's my personal website where you can go
• 30:00
  Speaker 2: and see our company's website, mantibyte dot com. We'd be
• 30:03
  Speaker 2: more than glad to help talk you through how you
• 30:06
  Speaker 2: can leverage AI to automate your processes and grow and scale.
• 30:11
  Speaker 3: Cool And Kevin J. D D E A N and
• 30:14
  Speaker 3: Manobyte M A N O B y t E. You
• 30:17
  Speaker 3: got it all right, cool er.
• 30:18
  Speaker 1: Thanks Kevin, thanks for stopping by, and I look forward
• 30:20
  Speaker 1: to catching up with you in the future and hearing
• 30:22
  Speaker 1: even more great stories about how AI can be a
• 30:26
  Speaker 1: friend to nonprofits.
• 30:27
  Speaker 2: This has been awesome. Thanks man. All right.
• 30:28
  Speaker 3: Thanks.
• 30:32
  Speaker 1: That wraps up today's conversation with Kevin Jdan and it
• 30:35
  Speaker 1: leaves us with a clear message technology When you thoughtfully constrengthen,
• 30:40
  Speaker 1: not replace, the human side of philanthropy. Here are three
• 30:44
  Speaker 1: key takeaways from our discussion. First, AI works best when
• 30:48
  Speaker 1: it's tied to real problems. Nonprofits don't need hype, they
• 30:52
  Speaker 1: need tools that help them target the right donors, send
• 30:55
  Speaker 1: the right messages, and steward relationships more effectively. Second, and
• 31:00
  Speaker 1: data is the engine clean well managed donor data is
• 31:04
  Speaker 1: what allows AI to personalize outreach at an individual level,
• 31:08
  Speaker 1: making donors feel seen and understood. And Third, AI can
• 31:13
  Speaker 1: free up time for what matters most. By handling repetitive
• 31:16
  Speaker 1: tasks and analysis, AI allows nonprofit leaders to focus on relationships, storytelling,
• 31:23
  Speaker 1: and mission driven leadership. Thanks for listening to Money to Give.
• 31:27
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• 31:31
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• 31:34
  Speaker 1: or colleague, and most importantly, keep that giving feeling going.
• 31:38
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• 31:42
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• 31:45
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• 31:49
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• 31:52
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• 32:15
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• 32:32
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