September 19, 2025 • 26 mins
Large corporations and startups are dominating the AI conversation. But how do non-profits adopt AI without compromising trust?
In this episode of "What’s the BUZZ?," host Andreas Welsch welcomes Scott Rosenkrans, VP of AI Innovation at DonorSearch, to the show to discuss how non-profits use AI to make a measurable impact.
Catch the BUZZ:
• Why does “just because you can” not always mean you should?
• How can predictive and generative AI work together to support human fundraisers?
• Why must donor relationships come before fundraising transactions?
• How does culture readiness in addition to tech readiness drive AI success?
• Which success metrics in fundraising go beyond total dollars raised?
If you’re a non-profit leader or change manager tasked with delivering results from AI, this conversation will help you set yourself up for success.
Questions or suggestions? Send me a Text Message.
***********
Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Andreas Welsch (00:00):
Today we'll talk about how AI innovation helps
nonprofits pursue their mission,and who better to talk about it
than someone who's activelyworking on that.
Scott Rosenkranz.
Hey, Scott.
Thank you so much for joining.
Scott Rosenkrans (00:11):
Hey Andreas, thanks for having me.
I'm very excited to be here.
Andreas Welsch (00:14):
Hey, wonderful.
Why don't you tell our audiencea little bit about yourself, who
you are and what you do.
Scott Rosenkrans (00:19):
Sounds great.
I'm Scott Rosencrans.
I'm VP of AI Innovation at DonorSearch.
I've been in the nonprofitsector my entire career.
I actually went to grad schoolto be a therapist to help people
solve problems and realized inabout six to nine months that I
was gonna burn out too quicklyand found myself in the
nonprofit sector.
So I've been in there for 15years now working with
(00:41):
nonprofits both.
In a nonprofit doing a lot ofcharitable fundraising, working
behind the scenes on identifyingwho we should be asking for
gifts and so on.
And then also working as aconsultant to help nonprofits do
more with the limited resourcesthat they have.
But for the past eight years,I've been prioritizing and
focused on predictive machinelearning models.
(01:03):
So building custom machinelearning models for nonprofits
to predict who's likely to makea gift next 12 months, who's
likely to make their first giftand help.
Now help organizations reallyutilize, again, those limited
resources.
They're usually working overbudget.
They have limited staff, peoplewearing a lot of hats, so been
helping them just make the mostoutta what they have.
(01:24):
And then also doing a lot ofconsulting about how to adopt
AI.
We know that a lot of work youtalk about too, just because
they have tools doesn't meanthat they're seeing the su the
success with it.
So how do we get them to justmove a little bit further in
their AI journey?
Andreas Welsch (01:40):
We met in Phoenix at Machine Learning Week
a couple weeks ago, and said, weshould definitely record an
episode together.
But you also mentioned thatyou're working on a book, right?
Or you actually have a book out?
Scott Rosenkrans (01:49):
Yeah we, have a book out Nonprofit AI.
It came out about two monthsago.
Comprehensive Guide toimplementing Artificial
Intelligence for Social Good.
So my colleague and I, my, myfriend, my mentor, Nathan
Chappelle, we've been in thisspace together for the past
eight years, worked withhundreds of nonprofits and only
a few of them have really seenthe success that we would expect
(02:10):
them to see with thesepredictive models.
And then it even expanded, asyou would expect with generative
AI, right?
Everybody wants to jump on thebandwagon.
They talk, they hear about howthe ROI is limitless, right?
And it's just such an easy thingto adopt, but nobody's really
seeing that come out.
So this book is really to helpanyone in the nonprofit space.
Move a little bit further along,identifying the challenges that
(02:32):
are getting in the way, whetherthey're cultural, technical.
Spoiler alert, they're usuallycultural, right?
And then giving you someexamples of how to actually
implement, whether it'spredictive, generative, or
automation.
And then once and you're seeingsuccess, go back to the book,
read a little bit to how to getfurther along the journey and
then see what's coming down thepike.
And AI is constantly evolving.
(02:53):
New technologies are coming out,so this isn't a read it once and
you're done.
It's a, guide to help you alongthe way.
Andreas Welsch (03:00):
That sounds great.
And, by the way, overall, I lovethe mission of helping others
help others as well, that youshared as working with
nonprofits.
So as I said, super excited tohave you on.
So now Scott, should we play alittle game to kick things off?
Scott Rosenkrans (03:14):
Love it.
Absolutely.
Andreas Welsch (03:16):
So let's do this in good old fashion.
So this game when I hit thebuzzer, you'll see a sentence
and I'd like for you to answerwith the first thing that comes
to mind and why, in your ownwords.
And to make it a little moreinteresting, you'll only have 60
seconds for your answer.
Okay?
Scott, are you ready for, what'sthe buzz?
Scott Rosenkrans (03:36):
As ready as it'll ever be.
Yes.
Andreas Welsch (03:39):
Alright, so here we go.
If AI were a vehicle, what wouldit be?
Vehicle.
Okay.
60 seconds on the clock.
Go.
Scott Rosenkrans (03:50):
First thing this, I'm not sure if this
answer applies, but we're gonnago with it anyway.
I'm gonna say a transformer.
It could be a lot of things allat once.
It's whatever it needs to be inthe moment, whether it's a semi
or a bot.
Whatever it is, it's, there toget you a little bit further and
help you along and can changeand evolve and adapt to the
(04:13):
surrounding circumstances.
Andreas Welsch (04:16):
Perfect.
Sounds like a optimal primeanswer.
Fantastic.
Thank you so much for doing thison on the fly.
I really enjoy seeing how mydifferent guests answer these,
questions and what comes tomind.
Yeah.
Great analogies.
Yeah.
We're here to talk more aboutnonprofits.
(04:38):
And one of the first questionsthat comes to mind for, me is
I've spent 25 years working in,corporate, in for-profit a lot
of time on on, AI projects, AIprograms, strategy enablement
and so on.
But I'm wondering what does itactually look like when you work
for a nonprofit?
How, is it different in thatenvironment from traditional
(04:59):
corporate for profit?
Scott Rosenkrans (05:02):
Yeah, the most obvious thing is that our goals
are not profit based, right?
It's not just increasingrevenue, having better more
returns for stakeholders andshareholders and so on.
It's doing good.
It's completing your mission.
The goal of most nonprofits isto put themselves out of
business because they've solvedthe problem, right?
(05:24):
And when I, as a consumer givemoney to a for-profit company, I
get something in return, right?
I get a iPad, an iPhone.
I get I access to a social mediaplatform or whatever it is.
I get something in, in return toevaluate.
I.
Whether I thought my investmentwas worth it.
(05:44):
For a nonprofit, all we get togive in return is trust.
If you give money to anonprofit, you are trusting that
nonprofit is doing what theysaid they would do with it and
doing it efficiently.
Trust is very easy to break.
And so there's a classic exampleof a nonprofit that, like many
others was under-resourced andso they got rid of, this was a,
(06:06):
for an eating disorder.
National Eating Disorder,nonprofit.
They got rid of their callcenter and replaced it with an
AI chat bot to save resourcesand see all the impact that AI
has and so on.
And within three or four daysthat chat bot was giving harmful
advice, that would be fine foreveryone else, but the audience
(06:27):
in terms of if you wanna loseweight, just check the scale or
eat less calories.
It's not helpful advice.
It's not beneficial for thosesuffering from an eating, eating
disorder, right?
And so that model may havepassed ethical guidelines,
responsible guidelines, but itwasn't beneficial, right?
And as a result, that nonprofitlikely suffered in terms of
(06:47):
their fundraising performance.
And other nonprofits similar tothem probably also suffered.
If I'm reading that in anewspaper, I might not pay
attention to who it is, but Iknow it's a nonprofit.
And then even more so in, intoday's political climate,
there's a lot of a lot of.
Information coming out about hownonprofits have been misusing
(07:10):
funds in certain circumstances,right?
Or a lot of allegations alongthose lines.
So there's a lot of trustmistrust there.
And so we need to make sure thatwhat we're doing with nonprofits
with artificial intelligence,that we're always putting trust
first and we relationshipsfirst.
And we're not just going forwhat's a quick win and what will
prioritize transactions overrelationships.
(07:30):
So another thing is.
Nonprofits are veryoverburdened.
There's a study that came outrecently in our space that 75%
of nonprofit employees arelooking to leave their job in
the next 12 months.
60% of them are considering notcoming back to the nonprofit
sector overall, and theoverwhelming majority by 60% is
(07:52):
they're overworked andunder-resourced.
And then again, you have AI thatsays it'll give you 98% of your
time back, right?
It's$20 a month, like all thesethings, but it's just not
connecting.
And so sometimes people justjump in that AI pool without
thinking what?
How should we be employing this?
How should we be using this?
And not just oh, here's a cooluse case.
(08:13):
Let's try it and see whathappens, and we'll figure out
the rest afterwards.
I think that's the first superimportant distinction that
you're making, right?
It's about what's the mission?
How do we bring good to thecause that, we're advocating
for, that we're trying to,solve.
And it's interesting to, to hearhow that changes, how you
(08:33):
approach innovation, how youapproach things like AI and,
what really needs to be at theforefront.
Yeah, and we've been working alot of colleagues and I have
been working on this programcalled Fundraising AI, right?
We know that many, there's nogovernmental regulation on AI,
right?
There's the NIST framework.
(08:53):
EU has the EU AI Act, but in theUS we don't really have any
specific guidelines orframeworks on how to employ
this.
So we built fundraising AI,which is the first of its kind.
It's a responsible, beneficialframework for nonprofit
artificial intelligence, andgives those organizations
governance template, a tool tosay, this is how we should be
(09:15):
looking at it, how should weshould be using it, and how we
should be evaluating it.
Constantly being adapted andevolved as technology moves on
so that we're providing thatresource and saying, you can't
just go with what Microsoft saysis, responsible or meta is
responsible, right?
We need to build this for ourown and have our own
construction around it.
Andreas Welsch (09:35):
Now, Earlier you mentioned it's not just about
quick wins and showing somevalue, but it's in ensuring that
you do that, right?
That you do it in the rightspaces, that you connect it
deeply to the nonprofit'smission.
So I'm, curious, what does AIinnovation look like in this
space and how do you evenprioritize what should pursue
when, especially you need tocheck it for additional
(09:58):
dimensions?
Scott Rosenkrans (10:00):
Yeah.
Great question.
So it, there's a lot of thatjust because we should.
Just because we can, should we,right?
Chatbots are coming out virtualassistants are coming out.
There's all these technologiesand all these use cases in the
for-profit world that we see youturn on your TV and there's 40
(10:21):
new applications of artificialintelligence, right?
You, can't separate yourselffrom it.
So an example that's coming outmore recently in the nonprofit
sector is an autonomousfundraiser, right?
Fundraising is.
Should be about human to humanrelationships, right?
It should be that I'm seeing youas a potential supporter of
(10:42):
organization.
I'm trying to find out whatmakes you tick.
How would you want to supportthis, and how can we establish a
strong relationship between thetwo of us to further this
mission and help you feel likeyou're expressing your
generosity in a positive way.
If I say, you know what, I'm notgonna do that and I'm just gonna
replace myself in this with aautonomous bot that can work all
(11:03):
the time and knows exactly whatto say.
It also is manipulative bynature and is more successful at
being manipulative and has to begoal oriented.
And that goal is raising dollarslike.
I'm not treating you as anindividual that I want to
establish a relationship with,right?
I'm not, putting trust in thisrelationship between us and I am
(11:25):
in some senses misrepresentingthe organization because I'm
saying what matters more istransactions and getting dollars
in the door than an actual humanto human in relationship.
So where we say AI should beused is more informational.
Use it to save time and thosethings that you don't want to do
that are menial tasks.
(11:45):
That are manual tasks, likeputting together a report,
right?
Or processing a gift.
But when it comes to relational,that's where AI should stay off
to the side.
Keep that for the humans to,continue on and do what we do
best.
Andreas Welsch (11:59):
I think there's this debate on the corporate and
on the for-profit side, whatdoes AI do, what do people do?
To me, the way that you arearticulated that just now makes
it very, clear, right?
It's, a relationship based, it'speople based business in that
sense or relationship.
We need to have this transactionbetween people or not even the
(12:21):
transaction, but therelationship between people and
AI can support us on everythingelse that we do to, run our
nonprofit.
Scott Rosenkrans (12:28):
Exactly.
And the nonprofit sector isagain, like I mentioned, under
attack, but it's also been on adownward trend for a long time,
right?
20 years ago, two thirds ofpeople, if you ask them, they
said that they're making giftsto nonprofits.
Now it's less than half, right?
And if this trend continues in40, 50 years, there's gonna be
(12:48):
no individuals giving tononprofits.
The problem is there's a lot ofproblems, but part of the
problem is that we've been usingthe wrong tools to identify who
we're gonna ask.
We prioritize wealth overrelationships.
We prioritize transactions overrelationships.
And so if you throw in AI to abroken system, it's just gonna
make that broken system.
(13:09):
More broken and faster, right?
It's not gonna make it strongerand it's not gonna fix it.
So you need to reevaluate whatdo we want to prioritize and how
do we use the AI to do that asopposed to just come in and, do
things quicker and move usfaster towards that, brick wall
that we're already gonna hit.
Andreas Welsch (13:25):
Again I see so many parallels here.
On the for-profit side, we talkabout don't just automate a
process.
First of all, look at first doyou need it?
Do you need all the steps?
Can you make it leaner?
And then once it's optimized,yeah, automated.
So some of that is, is what I'mhearing from you as well in the
nonprofit space.
So it doesn't just help to throwmore technology at it.
(13:49):
It might just give you worseoutcomes faster.
Scott Rosenkrans (13:54):
Exactly, Nobody wants worse outcomes
faster.
Andreas Welsch (13:56):
No, please, no.
But then I'm curious on in, inthe organization that you work
for at, donor search, what aresome of the other organizational
capabilities do to even bringnew AI features, models and
initiatives to market?
What are the skills?
(14:16):
What does the culture look like?
What does the datainfrastructure look like to
bring AI for nonprofits out inthe world?
How does it change?
Scott Rosenkrans (14:24):
Yeah, I, again, that's something that's
always changing, right?
It's not it's not like theinternet.
The internet came out howevermany years ago, and people are
like what is this thing?
But then once you learn it, youlearn it, right?
Like it doesn't change all thatmuch.
Maybe there's new, like socialmedia is a new thing, but it's
still based on the internet,right?
Electricity hasn't changed muchsince it's came out.
So it's, not like it's, youlearn it and then you're done
(14:47):
and you're caught up.
You never have to think about itagain.
AI is constantly evolving and.
AI is now building new forms ofAI, right?
Like it's, this exponentialtechnology that we've never
really seen before.
So you always have to stay up todate and always be, treat it as
like an iterative process,right?
Not just in terms of the tech,but also the culture.
We know that we.
(15:09):
A number that we keep hearing isthat 70% of successful AI
adoption for-profit nonprofitdoesn't matter is based on
culture, not data or models,right?
So it doesn't matter what toolyou have, if your organization
is not set up in a way to reallychange their processes.
And this is one thing we talkabout often, like predictive AI,
(15:29):
which is again where, we spendmost of our time and what we do
at donor search is it makes thedecisions for you, right?
A lot of people have valuedthemselves as being able to make
decisions with a lot ofinformation, but if you don't
have to do that anymore, wheredo you put your value?
And so people wanna hold ontothat and keep AI like at arm's
(15:53):
length because it's threateningto their own view of self.
But instead, if you say, okay,now if someone else is making
decisions for me, I get to freeup my time to do other things
that I can do better, right?
And leverage other skill setsthat I haven't been able to.
To flex as much because I'vespent so much of my time making
(16:16):
decisions in this sea of data,right?
And oftentimes decisions werewrong anyway, right?
Or at least wrong to the extentthat they could be now with,
artificial intelligence.
And so it's constantly goingback to say how can we treat
this as iterative process?
How can we focus on, sometimesnot focus on the outcome, but
the process itself.
To see how we can make thosetweaks like you're talking
(16:36):
about, right?
Oh, there's new ways to dothings.
There's new technologies that,that you can apply.
In terms of what we're producinghere, we're we have, again,
these predictive models customfor each organization.
'cause we know no twoorganizations are alike.
But now knowing that generativeand agent are coming into play
as well, how do we combine thetwo?
We, see most value, mostorganizations.
(16:58):
Really move the needle whenthey're using predictive AI,
right?
Generative is incrediblyvaluable and efficient, but it's
not gonna turn a million dollarorganization to a billion dollar
organization, right?
Am Apple, Amazon, Netflix,they're not.
Billion, trillion dollarcompanies because of generative
AI, they're there because ofpredictive AI, right?
Generative just allows you to,speak more individually to each
(17:22):
person and so on, and treat themas an n of one as opposed to a
cohort of individuals.
So like combining the two sothat nonprofits can really dig
in and hone their human skillsets, right?
And offload anything that couldbe.
Offloaded elsewhere to somethingthat's more efficient in doing
so.
Andreas Welsch (17:42):
That sounds great.
The part especially around howyou do that in a nonprofit I
think many parallels.
Again, you still need the data.
You still need the skills andthe resources.
It's interesting to, to hear youtalk more about predictive
analytics or predictive AI, butI'm assuming if you work with
(18:03):
numbers and you want to makepredictions about how do people
likely behave and what are the,dollar amounts that they might
be likely to donate and to giftand, give.
genuinely, I really doesn't helpas much.
It's not the computation.
It's maybe the email or thereport or something around it
that articulates when or putsthese numbers in context.
Scott Rosenkrans (18:28):
Yeah it's funny, we again, we've been
building these models for eightyears now, right?
So before November, 2022, right?
When ChatGPT came out, and atthat time we used to have to in
our sales pitch, we would've tosay AI is not this scary, like
science fiction thing.
You're already using it.
When you use Google Maps orApple or Netflix or Amazon,
(18:48):
you're using it.
So it's here ready.
It's just now we can use it forour own purposes, november, 2022
came around and now we said no.
AI is not just Chacha.
Bt there's two different things.
There's generative predictive.
And so and everyone now thinksthat they have a different
concept and a grasp on what thisthing is, but it muddies the
water even more because they aretwo very distinct tools with two
(19:11):
very distinct outcomes andpurposes.
And it's about knowing what'simportant for what.
And so that's, that was a bigpart of.
When we wrote nonprofit AI,because you'll see a lot of if
you were to Google, how dononprofits use AI?
99 times out of a hundred, it'sgonna be a blog that has no real
examples.
And it's saying a list ofgenerative AI use cases, but not
(19:34):
an organization that's actuallydone it.
So we wanted to put emphasis onpredictive, but also not forget
that generative exists and thenalso.
Automation, right?
Automation plays a big part withAI and leveraging that type of
technology.
So make it more holistic andmore of a, broader view on all
the different capabilities thatit can do, right?
(19:55):
And not just hone in on this isthe end all, be all, it's
ChatGPT and that's all you needto know.
Andreas Welsch (19:59):
So how do you then in this environment measure
the impact and for.
It's not, or it's maybe notcustomer tickets solved or call
volume reduced or, somethingelse.
What's the impact that youmeasure and for.
Scott Rosenkrans (20:17):
Yeah.
Yeah.
So most of my work in nonprofitspace is more on the fundraising
side, right?
The, again, the charitablegiving.
So how do we identify the peopleto make the gifts, right?
And obviously a, very clearmetric would be dollars raised.
But my, my friend often quotesCharlie Munger, show me thy thy.
(20:40):
And I'll show you the incentiveor show me the incentive and
I'll show you the outcome,right?
If you're prioritizing dollarsraised, then you're just gonna
look to try to find dollars,right?
And you're gonna speed over anyhurdles, speed over any
relationships to really getthere.
So a lot of what we're trying todo, not a result of AI, but in
conjunction with employing AIis.
(21:01):
To change the metrics that arebeing prioritized from, again,
transactions to relationships.
So instead of looking at howmuch did you raise this year,
what's your three year rollingaverage, right?
So that way you could see ifit's increasing and you, could
prioritize more long-termrelationships, more sustainable
relationships.
What's your retention rate?
What's your acquisition rate?
(21:22):
So bringing new people in, butthen keeping them along right,
is gonna be, is gonna helpnonprofits stay and stay
stronger and be able to committo their mission better than
just saying, I raised X thisyear and now I'm gonna try to
raise X more because that'syou're, not.
You're either going back to thesame wealthy people and then
(21:43):
they get tired of giving to you,or you're not looking to bring
in someone that's maybe asmaller level now, but could
grow to be a stronger donor,because that's not what your
incentive is.
Your incentive is just dollarsnow.
Andreas Welsch (21:55):
So it's almost like a customer lifetime value.
Scott Rosenkrans (22:00):
Exactly.
So that's another model that webuild long term.
RFM is a pretty traditionalmodel, right?
Recency, frequency, monetary,but it keeps bringing the same
names in terms of a, fundraisingperspective.
It keeps bringing the same namesover.
Over and over again, right?
So you keep going back to samepeople.
So we put that into kind of anAI environment to say who's most
(22:23):
likely to be at the higher end?
Just hasn't given enough runwayto get there yet.
And so it helps peopleprioritize those that are flying
under the radar that are alittle bit newer in their
relationship, but showing upreally strong and wouldn't be
prioritized in other methodswith FM or anything like that.
So we're helping to lead the waywith that customer value, right?
(22:44):
Customer lifetime value and, getthere through the technology
that we can offer.
Andreas Welsch (22:50):
Awesome.
That sounds really great and Ithink puts the technologies, the
models the, kind of techniquesthat you can apply, in a really
good context to show how that isconnected and again, drives the
outcomes that you actually wantto achieve.
Now, Scott, we're getting closeto the end of the show and I was
wondering if you can summarizethe key three takeaways for our
(23:10):
audience today.
Scott Rosenkrans (23:12):
Yeah.
Key three takeaways.
Nonprofits prioritize trust.
They're built on trust, and soif trust is our currency, we
need to go in with a differentapproach to how we're treating
AI.
Especially again, in terms ofwhat's responsible, what's
beneficial than those that arepresented in the for-profit
space.
(23:34):
That would be one.
Two is to.
Prioritize relationships overtransactions, right?
So whenever you're using AI, howcan you do it in a way that
keeps the human in the centerright?
And isn't just saying, how do weget more money quicker?
And then three is just becausewe can, does it mean that we
(23:54):
should.
So identifying those ways thatyou want to grow and want to
build a program that'ssustainable.
And again, isn't heading towardsthis brick wall just on a,
faster moving train, right?
But something that allows you toget over the hurdles of what we
refer to as the generositycrisis.
And move in a way that, that.
(24:16):
Changes that trajectory ofdecreasing dollars, decreasing
donors year after year, andhelps you establish
relationships with people whowanna maintain and watch you
grow.
Andreas Welsch (24:27):
Wonderful.
Thank you so much for paintingthis broad picture for us.
What doing AI in a nonprofitlooks like and doing it for
nonprofits.
Looks like I certainly learned alot.
I think there are a lot ofparallels that I could see
between corporate, for-profitand nonprofit, how you apply
them, but how you apply them inthe context of this particular
(24:47):
domain, and especially the partabout fostering the human
relationships and puttingemphasis on them.
When technology seems to makeeverything go faster and cheaper
and more efficiently, it'sactually still the human to
human relationship that mattersat the end of the day.
I love coming back to.
Scott Rosenkrans (25:06):
Yeah.
Absolutely.
Thank you Andreas.
I really appreciate the time andit was great speaking with you,
and talking to you, and yourcommunity.
I love what you built here.
Thank you so much.
Today we'll talk about how AI innovation helps
nonprofits pursue their mission,and who better to talk about it
than someone who's activelyworking on that.
Scott Rosenkranz.
Hey, Scott.
Thank you so much for joining.
Scott Rosenkrans (00:11):
Hey Andreas, thanks for having me.
I'm very excited to be here.
Andreas Welsch (00:14):
Hey, wonderful.
Why don't you tell our audiencea little bit about yourself, who
you are and what you do.
Scott Rosenkrans (00:19):
Sounds great.
I'm Scott Rosencrans.
I'm VP of AI Innovation at DonorSearch.
I've been in the nonprofitsector my entire career.
I actually went to grad schoolto be a therapist to help people
solve problems and realized inabout six to nine months that I
was gonna burn out too quicklyand found myself in the
nonprofit sector.
So I've been in there for 15years now working with
(00:41):
nonprofits both.
In a nonprofit doing a lot ofcharitable fundraising, working
behind the scenes on identifyingwho we should be asking for
gifts and so on.
And then also working as aconsultant to help nonprofits do
more with the limited resourcesthat they have.
But for the past eight years,I've been prioritizing and
focused on predictive machinelearning models.
(01:03):
So building custom machinelearning models for nonprofits
to predict who's likely to makea gift next 12 months, who's
likely to make their first giftand help.
Now help organizations reallyutilize, again, those limited
resources.
They're usually working overbudget.
They have limited staff, peoplewearing a lot of hats, so been
helping them just make the mostoutta what they have.
(01:24):
And then also doing a lot ofconsulting about how to adopt
AI.
We know that a lot of work youtalk about too, just because
they have tools doesn't meanthat they're seeing the su the
success with it.
So how do we get them to justmove a little bit further in
their AI journey?
Andreas Welsch (01:40):
We met in Phoenix at Machine Learning Week
a couple weeks ago, and said, weshould definitely record an
episode together.
But you also mentioned thatyou're working on a book, right?
Or you actually have a book out?
Scott Rosenkrans (01:49):
Yeah we, have a book out Nonprofit AI.
It came out about two monthsago.
Comprehensive Guide toimplementing Artificial
Intelligence for Social Good.
So my colleague and I, my, myfriend, my mentor, Nathan
Chappelle, we've been in thisspace together for the past
eight years, worked withhundreds of nonprofits and only
a few of them have really seenthe success that we would expect
(02:10):
them to see with thesepredictive models.
And then it even expanded, asyou would expect with generative
AI, right?
Everybody wants to jump on thebandwagon.
They talk, they hear about howthe ROI is limitless, right?
And it's just such an easy thingto adopt, but nobody's really
seeing that come out.
So this book is really to helpanyone in the nonprofit space.
Move a little bit further along,identifying the challenges that
(02:32):
are getting in the way, whetherthey're cultural, technical.
Spoiler alert, they're usuallycultural, right?
And then giving you someexamples of how to actually
implement, whether it'spredictive, generative, or
automation.
And then once and you're seeingsuccess, go back to the book,
read a little bit to how to getfurther along the journey and
then see what's coming down thepike.
And AI is constantly evolving.
(02:53):
New technologies are coming out,so this isn't a read it once and
you're done.
It's a, guide to help you alongthe way.
Andreas Welsch (03:00):
That sounds great.
And, by the way, overall, I lovethe mission of helping others
help others as well, that youshared as working with
nonprofits.
So as I said, super excited tohave you on.
So now Scott, should we play alittle game to kick things off?
Scott Rosenkrans (03:14):
Love it.
Absolutely.
Andreas Welsch (03:16):
So let's do this in good old fashion.
So this game when I hit thebuzzer, you'll see a sentence
and I'd like for you to answerwith the first thing that comes
to mind and why, in your ownwords.
And to make it a little moreinteresting, you'll only have 60
seconds for your answer.
Okay?
Scott, are you ready for, what'sthe buzz?
Scott Rosenkrans (03:36):
As ready as it'll ever be.
Yes.
Andreas Welsch (03:39):
Alright, so here we go.
If AI were a vehicle, what wouldit be?
Vehicle.
Okay.
60 seconds on the clock.
Go.
Scott Rosenkrans (03:50):
First thing this, I'm not sure if this
answer applies, but we're gonnago with it anyway.
I'm gonna say a transformer.
It could be a lot of things allat once.
It's whatever it needs to be inthe moment, whether it's a semi
or a bot.
Whatever it is, it's, there toget you a little bit further and
help you along and can changeand evolve and adapt to the
(04:13):
surrounding circumstances.
Andreas Welsch (04:16):
Perfect.
Sounds like a optimal primeanswer.
Fantastic.
Thank you so much for doing thison on the fly.
I really enjoy seeing how mydifferent guests answer these,
questions and what comes tomind.
Yeah.
Great analogies.
Yeah.
We're here to talk more aboutnonprofits.
(04:38):
And one of the first questionsthat comes to mind for, me is
I've spent 25 years working in,corporate, in for-profit a lot
of time on on, AI projects, AIprograms, strategy enablement
and so on.
But I'm wondering what does itactually look like when you work
for a nonprofit?
How, is it different in thatenvironment from traditional
(04:59):
corporate for profit?
Scott Rosenkrans (05:02):
Yeah, the most obvious thing is that our goals
are not profit based, right?
It's not just increasingrevenue, having better more
returns for stakeholders andshareholders and so on.
It's doing good.
It's completing your mission.
The goal of most nonprofits isto put themselves out of
business because they've solvedthe problem, right?
(05:24):
And when I, as a consumer givemoney to a for-profit company, I
get something in return, right?
I get a iPad, an iPhone.
I get I access to a social mediaplatform or whatever it is.
I get something in, in return toevaluate.
I.
Whether I thought my investmentwas worth it.
(05:44):
For a nonprofit, all we get togive in return is trust.
If you give money to anonprofit, you are trusting that
nonprofit is doing what theysaid they would do with it and
doing it efficiently.
Trust is very easy to break.
And so there's a classic exampleof a nonprofit that, like many
others was under-resourced andso they got rid of, this was a,
(06:06):
for an eating disorder.
National Eating Disorder,nonprofit.
They got rid of their callcenter and replaced it with an
AI chat bot to save resourcesand see all the impact that AI
has and so on.
And within three or four daysthat chat bot was giving harmful
advice, that would be fine foreveryone else, but the audience
(06:27):
in terms of if you wanna loseweight, just check the scale or
eat less calories.
It's not helpful advice.
It's not beneficial for thosesuffering from an eating, eating
disorder, right?
And so that model may havepassed ethical guidelines,
responsible guidelines, but itwasn't beneficial, right?
And as a result, that nonprofitlikely suffered in terms of
(06:47):
their fundraising performance.
And other nonprofits similar tothem probably also suffered.
If I'm reading that in anewspaper, I might not pay
attention to who it is, but Iknow it's a nonprofit.
And then even more so in, intoday's political climate,
there's a lot of a lot of.
Information coming out about hownonprofits have been misusing
(07:10):
funds in certain circumstances,right?
Or a lot of allegations alongthose lines.
So there's a lot of trustmistrust there.
And so we need to make sure thatwhat we're doing with nonprofits
with artificial intelligence,that we're always putting trust
first and we relationshipsfirst.
And we're not just going forwhat's a quick win and what will
prioritize transactions overrelationships.
(07:30):
So another thing is.
Nonprofits are veryoverburdened.
There's a study that came outrecently in our space that 75%
of nonprofit employees arelooking to leave their job in
the next 12 months.
60% of them are considering notcoming back to the nonprofit
sector overall, and theoverwhelming majority by 60% is
(07:52):
they're overworked andunder-resourced.
And then again, you have AI thatsays it'll give you 98% of your
time back, right?
It's$20 a month, like all thesethings, but it's just not
connecting.
And so sometimes people justjump in that AI pool without
thinking what?
How should we be employing this?
How should we be using this?
And not just oh, here's a cooluse case.
(08:13):
Let's try it and see whathappens, and we'll figure out
the rest afterwards.
I think that's the first superimportant distinction that
you're making, right?
It's about what's the mission?
How do we bring good to thecause that, we're advocating
for, that we're trying to,solve.
And it's interesting to, to hearhow that changes, how you
(08:33):
approach innovation, how youapproach things like AI and,
what really needs to be at theforefront.
Yeah, and we've been working alot of colleagues and I have
been working on this programcalled Fundraising AI, right?
We know that many, there's nogovernmental regulation on AI,
right?
There's the NIST framework.
(08:53):
EU has the EU AI Act, but in theUS we don't really have any
specific guidelines orframeworks on how to employ
this.
So we built fundraising AI,which is the first of its kind.
It's a responsible, beneficialframework for nonprofit
artificial intelligence, andgives those organizations
governance template, a tool tosay, this is how we should be
(09:15):
looking at it, how should weshould be using it, and how we
should be evaluating it.
Constantly being adapted andevolved as technology moves on
so that we're providing thatresource and saying, you can't
just go with what Microsoft saysis, responsible or meta is
responsible, right?
We need to build this for ourown and have our own
construction around it.
Andreas Welsch (09:35):
Now, Earlier you mentioned it's not just about
quick wins and showing somevalue, but it's in ensuring that
you do that, right?
That you do it in the rightspaces, that you connect it
deeply to the nonprofit'smission.
So I'm, curious, what does AIinnovation look like in this
space and how do you evenprioritize what should pursue
when, especially you need tocheck it for additional
(09:58):
dimensions?
Scott Rosenkrans (10:00):
Yeah.
Great question.
So it, there's a lot of thatjust because we should.
Just because we can, should we,right?
Chatbots are coming out virtualassistants are coming out.
There's all these technologiesand all these use cases in the
for-profit world that we see youturn on your TV and there's 40
(10:21):
new applications of artificialintelligence, right?
You, can't separate yourselffrom it.
So an example that's coming outmore recently in the nonprofit
sector is an autonomousfundraiser, right?
Fundraising is.
Should be about human to humanrelationships, right?
It should be that I'm seeing youas a potential supporter of
(10:42):
organization.
I'm trying to find out whatmakes you tick.
How would you want to supportthis, and how can we establish a
strong relationship between thetwo of us to further this
mission and help you feel likeyou're expressing your
generosity in a positive way.
If I say, you know what, I'm notgonna do that and I'm just gonna
replace myself in this with aautonomous bot that can work all
(11:03):
the time and knows exactly whatto say.
It also is manipulative bynature and is more successful at
being manipulative and has to begoal oriented.
And that goal is raising dollarslike.
I'm not treating you as anindividual that I want to
establish a relationship with,right?
I'm not, putting trust in thisrelationship between us and I am
(11:25):
in some senses misrepresentingthe organization because I'm
saying what matters more istransactions and getting dollars
in the door than an actual humanto human in relationship.
So where we say AI should beused is more informational.
Use it to save time and thosethings that you don't want to do
that are menial tasks.
(11:45):
That are manual tasks, likeputting together a report,
right?
Or processing a gift.
But when it comes to relational,that's where AI should stay off
to the side.
Keep that for the humans to,continue on and do what we do
best.
Andreas Welsch (11:59):
I think there's this debate on the corporate and
on the for-profit side, whatdoes AI do, what do people do?
To me, the way that you arearticulated that just now makes
it very, clear, right?
It's, a relationship based, it'speople based business in that
sense or relationship.
We need to have this transactionbetween people or not even the
(12:21):
transaction, but therelationship between people and
AI can support us on everythingelse that we do to, run our
nonprofit.
Scott Rosenkrans (12:28):
Exactly.
And the nonprofit sector isagain, like I mentioned, under
attack, but it's also been on adownward trend for a long time,
right?
20 years ago, two thirds ofpeople, if you ask them, they
said that they're making giftsto nonprofits.
Now it's less than half, right?
And if this trend continues in40, 50 years, there's gonna be
(12:48):
no individuals giving tononprofits.
The problem is there's a lot ofproblems, but part of the
problem is that we've been usingthe wrong tools to identify who
we're gonna ask.
We prioritize wealth overrelationships.
We prioritize transactions overrelationships.
And so if you throw in AI to abroken system, it's just gonna
make that broken system.
(13:09):
More broken and faster, right?
It's not gonna make it strongerand it's not gonna fix it.
So you need to reevaluate whatdo we want to prioritize and how
do we use the AI to do that asopposed to just come in and, do
things quicker and move usfaster towards that, brick wall
that we're already gonna hit.
Andreas Welsch (13:25):
Again I see so many parallels here.
On the for-profit side, we talkabout don't just automate a
process.
First of all, look at first doyou need it?
Do you need all the steps?
Can you make it leaner?
And then once it's optimized,yeah, automated.
So some of that is, is what I'mhearing from you as well in the
nonprofit space.
So it doesn't just help to throwmore technology at it.
(13:49):
It might just give you worseoutcomes faster.
Scott Rosenkrans (13:54):
Exactly, Nobody wants worse outcomes
faster.
Andreas Welsch (13:56):
No, please, no.
But then I'm curious on in, inthe organization that you work
for at, donor search, what aresome of the other organizational
capabilities do to even bringnew AI features, models and
initiatives to market?
What are the skills?
(14:16):
What does the culture look like?
What does the datainfrastructure look like to
bring AI for nonprofits out inthe world?
How does it change?
Scott Rosenkrans (14:24):
Yeah, I, again, that's something that's
always changing, right?
It's not it's not like theinternet.
The internet came out howevermany years ago, and people are
like what is this thing?
But then once you learn it, youlearn it, right?
Like it doesn't change all thatmuch.
Maybe there's new, like socialmedia is a new thing, but it's
still based on the internet,right?
Electricity hasn't changed muchsince it's came out.
So it's, not like it's, youlearn it and then you're done
(14:47):
and you're caught up.
You never have to think about itagain.
AI is constantly evolving and.
AI is now building new forms ofAI, right?
Like it's, this exponentialtechnology that we've never
really seen before.
So you always have to stay up todate and always be, treat it as
like an iterative process,right?
Not just in terms of the tech,but also the culture.
We know that we.
(15:09):
A number that we keep hearing isthat 70% of successful AI
adoption for-profit nonprofitdoesn't matter is based on
culture, not data or models,right?
So it doesn't matter what toolyou have, if your organization
is not set up in a way to reallychange their processes.
And this is one thing we talkabout often, like predictive AI,
(15:29):
which is again where, we spendmost of our time and what we do
at donor search is it makes thedecisions for you, right?
A lot of people have valuedthemselves as being able to make
decisions with a lot ofinformation, but if you don't
have to do that anymore, wheredo you put your value?
And so people wanna hold ontothat and keep AI like at arm's
(15:53):
length because it's threateningto their own view of self.
But instead, if you say, okay,now if someone else is making
decisions for me, I get to freeup my time to do other things
that I can do better, right?
And leverage other skill setsthat I haven't been able to.
To flex as much because I'vespent so much of my time making
(16:16):
decisions in this sea of data,right?
And oftentimes decisions werewrong anyway, right?
Or at least wrong to the extentthat they could be now with,
artificial intelligence.
And so it's constantly goingback to say how can we treat
this as iterative process?
How can we focus on, sometimesnot focus on the outcome, but
the process itself.
To see how we can make thosetweaks like you're talking
(16:36):
about, right?
Oh, there's new ways to dothings.
There's new technologies that,that you can apply.
In terms of what we're producinghere, we're we have, again,
these predictive models customfor each organization.
'cause we know no twoorganizations are alike.
But now knowing that generativeand agent are coming into play
as well, how do we combine thetwo?
We, see most value, mostorganizations.
(16:58):
Really move the needle whenthey're using predictive AI,
right?
Generative is incrediblyvaluable and efficient, but it's
not gonna turn a million dollarorganization to a billion dollar
organization, right?
Am Apple, Amazon, Netflix,they're not.
Billion, trillion dollarcompanies because of generative
AI, they're there because ofpredictive AI, right?
Generative just allows you to,speak more individually to each
(17:22):
person and so on, and treat themas an n of one as opposed to a
cohort of individuals.
So like combining the two sothat nonprofits can really dig
in and hone their human skillsets, right?
And offload anything that couldbe.
Offloaded elsewhere to somethingthat's more efficient in doing
so.
Andreas Welsch (17:42):
That sounds great.
The part especially around howyou do that in a nonprofit I
think many parallels.
Again, you still need the data.
You still need the skills andthe resources.
It's interesting to, to hear youtalk more about predictive
analytics or predictive AI, butI'm assuming if you work with
(18:03):
numbers and you want to makepredictions about how do people
likely behave and what are the,dollar amounts that they might
be likely to donate and to giftand, give.
genuinely, I really doesn't helpas much.
It's not the computation.
It's maybe the email or thereport or something around it
that articulates when or putsthese numbers in context.
Scott Rosenkrans (18:28):
Yeah it's funny, we again, we've been
building these models for eightyears now, right?
So before November, 2022, right?
When ChatGPT came out, and atthat time we used to have to in
our sales pitch, we would've tosay AI is not this scary, like
science fiction thing.
You're already using it.
When you use Google Maps orApple or Netflix or Amazon,
(18:48):
you're using it.
So it's here ready.
It's just now we can use it forour own purposes, november, 2022
came around and now we said no.
AI is not just Chacha.
Bt there's two different things.
There's generative predictive.
And so and everyone now thinksthat they have a different
concept and a grasp on what thisthing is, but it muddies the
water even more because they aretwo very distinct tools with two
(19:11):
very distinct outcomes andpurposes.
And it's about knowing what'simportant for what.
And so that's, that was a bigpart of.
When we wrote nonprofit AI,because you'll see a lot of if
you were to Google, how dononprofits use AI?
99 times out of a hundred, it'sgonna be a blog that has no real
examples.
And it's saying a list ofgenerative AI use cases, but not
(19:34):
an organization that's actuallydone it.
So we wanted to put emphasis onpredictive, but also not forget
that generative exists and thenalso.
Automation, right?
Automation plays a big part withAI and leveraging that type of
technology.
So make it more holistic andmore of a, broader view on all
the different capabilities thatit can do, right?
(19:55):
And not just hone in on this isthe end all, be all, it's
ChatGPT and that's all you needto know.
Andreas Welsch (19:59):
So how do you then in this environment measure
the impact and for.
It's not, or it's maybe notcustomer tickets solved or call
volume reduced or, somethingelse.
What's the impact that youmeasure and for.
Scott Rosenkrans (20:17):
Yeah.
Yeah.
So most of my work in nonprofitspace is more on the fundraising
side, right?
The, again, the charitablegiving.
So how do we identify the peopleto make the gifts, right?
And obviously a, very clearmetric would be dollars raised.
But my, my friend often quotesCharlie Munger, show me thy thy.
(20:40):
And I'll show you the incentiveor show me the incentive and
I'll show you the outcome,right?
If you're prioritizing dollarsraised, then you're just gonna
look to try to find dollars,right?
And you're gonna speed over anyhurdles, speed over any
relationships to really getthere.
So a lot of what we're trying todo, not a result of AI, but in
conjunction with employing AIis.
(21:01):
To change the metrics that arebeing prioritized from, again,
transactions to relationships.
So instead of looking at howmuch did you raise this year,
what's your three year rollingaverage, right?
So that way you could see ifit's increasing and you, could
prioritize more long-termrelationships, more sustainable
relationships.
What's your retention rate?
What's your acquisition rate?
(21:22):
So bringing new people in, butthen keeping them along right,
is gonna be, is gonna helpnonprofits stay and stay
stronger and be able to committo their mission better than
just saying, I raised X thisyear and now I'm gonna try to
raise X more because that'syou're, not.
You're either going back to thesame wealthy people and then
(21:43):
they get tired of giving to you,or you're not looking to bring
in someone that's maybe asmaller level now, but could
grow to be a stronger donor,because that's not what your
incentive is.
Your incentive is just dollarsnow.
Andreas Welsch (21:55):
So it's almost like a customer lifetime value.
Scott Rosenkrans (22:00):
Exactly.
So that's another model that webuild long term.
RFM is a pretty traditionalmodel, right?
Recency, frequency, monetary,but it keeps bringing the same
names in terms of a, fundraisingperspective.
It keeps bringing the same namesover.
Over and over again, right?
So you keep going back to samepeople.
So we put that into kind of anAI environment to say who's most
(22:23):
likely to be at the higher end?
Just hasn't given enough runwayto get there yet.
And so it helps peopleprioritize those that are flying
under the radar that are alittle bit newer in their
relationship, but showing upreally strong and wouldn't be
prioritized in other methodswith FM or anything like that.
So we're helping to lead the waywith that customer value, right?
(22:44):
Customer lifetime value and, getthere through the technology
that we can offer.
Andreas Welsch (22:50):
Awesome.
That sounds really great and Ithink puts the technologies, the
models the, kind of techniquesthat you can apply, in a really
good context to show how that isconnected and again, drives the
outcomes that you actually wantto achieve.
Now, Scott, we're getting closeto the end of the show and I was
wondering if you can summarizethe key three takeaways for our
(23:10):
audience today.
Scott Rosenkrans (23:12):
Yeah.
Key three takeaways.
Nonprofits prioritize trust.
They're built on trust, and soif trust is our currency, we
need to go in with a differentapproach to how we're treating
AI.
Especially again, in terms ofwhat's responsible, what's
beneficial than those that arepresented in the for-profit
space.
(23:34):
That would be one.
Two is to.
Prioritize relationships overtransactions, right?
So whenever you're using AI, howcan you do it in a way that
keeps the human in the centerright?
And isn't just saying, how do weget more money quicker?
And then three is just becausewe can, does it mean that we
(23:54):
should.
So identifying those ways thatyou want to grow and want to
build a program that'ssustainable.
And again, isn't heading towardsthis brick wall just on a,
faster moving train, right?
But something that allows you toget over the hurdles of what we
refer to as the generositycrisis.
And move in a way that, that.
(24:16):
Changes that trajectory ofdecreasing dollars, decreasing
donors year after year, andhelps you establish
relationships with people whowanna maintain and watch you
grow.
Andreas Welsch (24:27):
Wonderful.
Thank you so much for paintingthis broad picture for us.
What doing AI in a nonprofitlooks like and doing it for
nonprofits.
Looks like I certainly learned alot.
I think there are a lot ofparallels that I could see
between corporate, for-profitand nonprofit, how you apply
them, but how you apply them inthe context of this particular
(24:47):
domain, and especially the partabout fostering the human
relationships and puttingemphasis on them.
When technology seems to makeeverything go faster and cheaper
and more efficiently, it'sactually still the human to
human relationship that mattersat the end of the day.
I love coming back to.
Scott Rosenkrans (25:06):
Yeah.
Absolutely.
Thank you Andreas.
I really appreciate the time andit was great speaking with you,
and talking to you, and yourcommunity.
I love what you built here.
Thank you so much.
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