December 24, 2025 • 36 mins
Karl W. Kuhnert, Ph.D. is Professor of the Practice of Organization and Management in the Goizueta Business School at Emory University. Karl’s research focuses on how leaders cognitively, interpersonally, and emotionally develop over the life course. Karl has published over 80 peer-reviewed articles, 13 book chapters and made over 100 conference presentations, and served on numerous editorial and review panels. He teaches industrial and organizational psychology, leadership, organizational change, and professional ethics. Karl has won numerous awards for teaching and research. Karl also regularly teaches leadership development in the Executive Ed. Programs at Emory, UCLA, HEC Paris, and UGA. He has served as a consultant with many large and small corporations, non-profit and government organizations including, United Parcel Service, The U.S. Dept. of Treasury, Siemens, The Jet Propulsion Lab, and Cox Automotive.
A Few Quotes From This Episode
- “Every time I have done this, it has freed up experts to do the work they actually want to do.”
- “Tacit knowledge is lived wisdom—it’s what makes an expert an expert.”
- “AI is a tool, it is not truth.”
- “We need to ask how judgments are made, not just whether AI can render them.”
Resources Mentioned in This Episode
- Book: Personal Knowledge by Michael Polanyi
- Book: The MAP: A Practical Guide to Leadership Development by Keith Eigel & Karl Kuhnert
- Article: Training Innovative AI to Provide Expert Guidance on Prescription Medications by Kuhnert
- Article: Teaching Leadership: Where Theory Bridges Practice by Kuhnert
About The International Leadership Association (ILA)
- The ILA was created in 1999 to bring together professionals interested in studying, practicing, and teaching leadership.
About Scott J. Allen
- Website
- Weekly Newsletter: Practical Wisdom for Leaders
My Approach to Hosting
- The views of my guests do not constitute "truth." Nor do they reflect my personal views in some instances. However, they are views to consider, and I hope they help you clarify your perspective.
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Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Scott Allen (03:19):
Okay, everybody.
Welcome to Practical Wisdom forLeaders.
Thank you so much for checkingin wherever you are in the
world.
I have Dr.
Karl Kuhnert.
He is professor in the practiceof organization and management.
And he is located at EmoryUniversity.
Have had another episode withhim.
It might be in the 90s.
So check that out.
(03:39):
Great conversation.
Karl has been, and one thing Ilove about Karl is I've known
his work for years.
And he, if there's one word Iwould use to describe Karl,
curiosity is certainly one ofthem.
He has stayed curious over thecourse of his career.
And Karl, you've had a numberof adventures you've been
(03:59):
engaged in recent times.
And so that's where we'rereally going to take the
conversation today.
But I gave you very little ofan introduction.
What should listeners knowabout you?
What do you want to add to yourtitle?
Karl Kuhnert (04:14):
Other than the fact that I've been at this for
a long time.
And uh and the work I'm doingnow, I feel is very exciting and
has it has a lot ofimplications for how we think
about AI and how we're going touse AI in the future.
Yes.
Oh, yeah.
I think you mentioned thatbefore we started recording,
your wife has asked you toretire three times.
And you I have retired threetimes.
(04:35):
I started this retirementprocess.
Actually, I retired and I thinkit was 2016 or something like
that.
I started this process.
And every time I every time, bythe way, it's interesting.
Again, this is for everybodyout there.
(04:57):
Every time I want to retire, Ifound something new to keep me
here.
So artificial intelligence,leadership, coaching.
Let's talk a little bit aboutthat.
And maybe let's start with thekernel of the idea and just take
me through what you've beenlearning.
I'm so excited.
Okay, let me start somewhere atthe beginning here, which was
(05:19):
really about eight or nine yearsago.
I got this call from a friendof mine, Mark Keith, and he was
a financial person, and we enjoyeach other's company.
And then he left where I was atthe time after Georgie moved
away.
And then about a year or solater, he calls me and he says,
Karl, I've got this softwarefrom it's called Merlin Inc.
(05:40):
And uh the guy who actuallycreated the software, his name
is Karl Wocke, W-O-C-K-E.
And I should also warn you thatif I say, Scott, that uh Karl's
a genius, I'm referring to him,not to me in a third person.
But anyhow, it and so Markshowed me this.
And basically the idea was, andhe's very simple.
(06:02):
He says, you know what we cando with AI?
He says, we can actually pointit to a person.
Not to the data set, but to aperson.
And I went, okay, that'sinteresting.
And he says, what we want todo, and what we can do, is we
want to duplicate or digitize adecision that an expert makes.
(06:24):
And what we can do with that isthen scale that decision to the
benefit of others.
I said, Mark, I mean, I said,okay, I have to see how this
works.
I had these ideas from actuallygraduate school about how
decision making is done.
And of course, this blew awayeverything that I had known.
(06:44):
And I was like, I'm trying toput this all together.
Where's the statistics?
What kind of models are weusing here?
And so I had to get, I had tobasically learn all over again
what this does.
But I started working with himand Karl Wilka.
And I found myself in a numberof different places where I was
actually duplicating people'sdecisions.
And it was absolutelyfascinating.
And so let me just give youjust one example of this.
(07:06):
Yeah.
And I was working for HomelandSecurity.
Okay.
I I look, I've done ethicists,I've done psychiatrists, I've
done a whole lot of people givetheir decisions.
And by the way, as you said,I'm curious.
Every time I sit down withsomeone, I'm I'm like having a
birthday or something.
This is oh my gosh, this is youcan't get better than this.
It's all working with homelandsecurity and feeling, oh, this
(07:26):
is really cool.
And so, anyhow, I'm I'minterviewing this person, and
it's interesting because Italked to the supervisor and
some people at the airport, it'sactually at the airport, and
this was an agriculturalinspector.
And her job is people come inand she has lots and lots of
data that she looks at to decidewho might have bad peanuts from
(07:50):
Brazil coming back from.
Yeah, I always see those signs,but I've never paid.
Do you have a live chicken?
Nope.
I'm good.
But anyhow, she's better thanthis in anybody.
Yeah.
And they said, you have tointerview her.
You have to see if we candigitize her.
And it just to cut the storyshort, I did.
(08:11):
And I asked her afterwards whenI was done.
I showed her I showed herdecision basically and
algorithm.
And I said to her, Is this you?
Does this look like you?
Does this sound like you?
She comes over and hugs me.
Wow.
And she goes, Oh my gosh.
She says, You know how muchtime I spend teaching people how
(08:31):
what I know?
And all these new people thatcome in and they're new to this
job.
And she goes, You know what I'mgonna do now?
I can do what I'm really paidto do.
Try to figure out what the badguys are gonna do next.
Yeah, yeah.
And I want I want to make thispoint.
Every time I have done this, ithas freed up these experts to
(08:55):
do things that they wanted todo.
Interesting.
And so very quickly, and again,I'll I this is on our we can
share, I'll share a paper if youwant to email me.
But what's very cool about thisis that what I'm really tapping
into, and think about anexpert, and that we can have fun
with this just for a second,Scott.
I could say to you, hey Scott,you told me recently that you
(09:19):
decided to quit your job, stopyour podcasting, and what you
want to do is flip homes for aliving.
And uh, and you're like, I'mreally interested in learning
more about this.
And he also says, there's a guythat's down the street, and
he's been flipping homes for 25years.
And we can go on with thescenario, but I'll ask him, I'll
ask you, I'll say, What wouldyou rather get your information
(09:40):
from?
Would you rather get it fromAI?
Oh, by the way, it's gonna giveyou a lot of information.
Yes.
About flipping homes.
You're gonna get a lot ofinformation.
But the other thought you haveis maybe I should talk to my
neighbor about flipping homes.
And the point I want to makehere is what would you gain from
talking to your neighbor?
(10:00):
Localized expertise.
Perfect.
Localized wisdom, specificwisdom.
So this person could tell methat Elyria is a great hot spot
in Northeast Ohio, so to speak.
But it would also be bounded insome fact, in some way, like
that it's it's not just someuniversal Chat GPT output, it's
(10:21):
contained in some ways, right?
Yeah, but that's the point, isthat you're getting data, right,
from this guy, that you can'tget from AI.
And we make this distinction,and this distinction has been
around since at least 1957.
And the guy's name is MichaelPogliani, and he brought this
(10:43):
idea up in a paper of his calledpersonal knowledge.
And he actually referred tothis personal knowledge as tacit
knowledge, and essentially it'slived wisdom and it's in it's
intuition, right?
And you're gonna get that fromyour neighbor, then it's not
gonna be an AI.
And so this really justfascinated me because when
(11:04):
you're working with experts, andyou can say this: what makes an
expert?
It's not explicit knowledge, itis, you know, that they have
the explicit knowledge becauseeverybody has explicit
knowledge.
But how do you do this?
How do you get at this tacitknowledge?
Because that is what makes anexpert an expert.
How do we get into thatdimension, that practical
(11:24):
wisdom, that lived wisdom?
The when is this appropriate,where is this appropriate, how
is this appropriate?
And wait, the way you've hit onthis, Scott, is the way to
think about this is what you getfrom AI, and again, this is
awesome.
It's knowing what.
Yes.
From the expert in tacitknowledge, you get to know how.
(11:47):
Yeah.
You're you're never right now,again, who knows what's gonna
happen in six months or a yearwith AI, but there isn't really
a way.
I don't think you can know whatthat tacit knowledge is because
it's not in the data.
It's not in the data.
And so fast forward.
Yeah.
I'm beard emory.
I'm in one of the greatresearch hospitals in the world.
(12:08):
And so I'm working with thisdoctor.
And at the time, this is abouta year ago, she was actually a
student in my class because Iteach executive MBA students,
which is just a joy, by the way.
Just a joy.
And she comes up to me.
I have this uh simulation Icreate with AI and what I'm
doing.
I create the simulation for myclass.
And she comes up to me andsays, Karl, we got to talk.
(12:31):
She says, I'm one of theexperts at this time on GLP1s.
Oh, okay.
She's again, knowledge, she isthe person at this point about
GLP 1.
And she's telling people allover the country about when you
have a patient, how do you givethem a GLP 1 or not?
And so I sit down with her, andI'll just do this very quickly.
(12:51):
Is it takes me about an hourand a half, and I sit down with
her to tell me what her keyvariables are, as well as the
measures.
And it's fun because I ask herabout I ask her tacit knowledge
questions, what makes youspecial here?
And what do you think aboutwhen you're seeing a patient?
And it's really interesting,right?
It's more, she's giving me morethan just what is in explicit
(13:14):
knowledge.
Yes.
She's giving me other things.
And so we create this algorithmfor her.
And oh, I should probably giveyou one more how we do this.
And this is a little hard toexplain, but this is the real,
again, this is the real geniuswithin the software, and which
we call Tom, which is tacitobject modeler.
Okay, all right.
(13:34):
Oh, nice.
Yeah, tacit object modeler,tacit.
And so what the software doesis it actually takes all the
variables, it takes all themeasures, right?
And just input it.
And what Tom does, and this isagain really unbelievable.
I have Dr.
Collins actually sitting besideme, and what it does is it, if
(13:56):
you will, it creates a scenario,one scenario, and it has all
the variables, and one, but ithas some of these, by the way,
she had something like 22variables, which is a lot for an
expert, by the way.
This wasn't yes or noquestions, these were sometimes
six different options for ameasure.
Um so all of this is in there.
(14:17):
And so what Tom does is itgives us a scenario, and the
question at the bottom is do youprovide or do you give a GOP1
or not to this patient?
She looks at the data, no,fine.
Next scenario, differentscenario.
And oh, by the way, I'll get tothe bottom of this 220
(14:38):
scenarios, right?
It's just cranking outscenarios, it's just crank
scenario.
It is, by the way, here's thething, here's what's important
it's learning about what youvalue.
Oh wow.
And so what you get, and by theway, this is so cool.
At the end, I actually have Tomtest her.
Oh, wow.
Okay.
(14:58):
And oh, by the way, she had ahundred scenarios, she got a
hundred.
And for her, it sounds likethis would be weeks or months.
No, she's looking at thesethings and making decisions in
about 10 seconds.
Wow.
And so this whole process, bythe way, can take I don't know.
I usually, it's about it takethree or four days, but it
doesn't take that long becausewhat I do is when someone gives
(15:18):
me, when I interview them andget their explicit and tacit
knowledge, I actually send themhome just to think about it.
Is anything that you missed orwhatever?
We have this great algorithmthat now can be shared with
physicians all over the country.
But by the way, there arephysicians at this time that
aren't really up on GOP wants.
(15:40):
This is unbelievable.
And the way we talk about this,and it's this is again very
important.
We offer this to physicians asa second opinion.
That's all second opinion.
But I'm saying, hey, listen,here's a second opinion from a
leading expert, major researchhospital.
See what you think.
And what they're doing, by theway, they're putting in their
(16:02):
own data when they have thealgorithm.
It's like they put in it, soit's a personalized decision.
And they also see, and by theway, the people who are who are
using this actually see what shevalues in terms of the
variables.
So everything here istransparent.
You actually know who youactually can look up who the
position is.
So it's it's this is not, and Iusually I like to talk about
(16:23):
this in terms of trust becauseyou never know where the data's
coming from AI.
It's coming from this person.
Scott Allen (16:28):
Yes.
Karl Kuhnert (16:29):
And here's it's a second opinion.
You can make do what you want.
So the way I talk about it, thedata is sourced, it's
transparent, and it'spersonalized.
Because what the doctor's usingis their own explicit
knowledge.
They're putting in whether thepatient has medullary cancer in
the family, all they're puttingin that data or their own data.
But this is more the herjudgment and her decision.
(16:52):
And capturing that.
Yeah, maybe because think of Ihave this visual in my head
right now of all of theknowledge in people's minds
walking around Earth right now.
That is in some cases nevercaptured.
That could be making whiskey,that could be GOP1s, that could
(17:13):
be decision making.
Scott, I don't have I don'thave time for this, but do you
know how many companies havecalled me?
Small businesses who said, Hey,we have this person who's
retiring.
We can't lose her.
She's an accountant, and wecan't we can't replace her right
now.
We need an app that what wouldSharon do?
(17:33):
That's what it is.
And so that's happening allover the country, all over the
world, this baby boomers.
And I would love there's folksagain, you're right.
There's folks that I would loveto be able to digitize.
And so where we are today andwhere I am right now with this,
is this, by the way, this workthat I did with the GLP 1o
(17:57):
actually got me into EmoryHospital.
And so now I'm actually uhinvolved, I'm involved in
clinical trials.
That has been quite a uh anordeal going through this.
But we're gonna end up with notonly with the digitized expert,
but we're also gonna have theopportunity, and I've already
I've done this with other kindsof institutions like banking and
(18:19):
insurance, but I'm so excitedabout being able to collect the
validity data that looking backon past decisions with
physicians.
It's really because there's alot of things I can do, but
what's really important for meis being able to validate their
decision.
And one more thing, this is itagain, it's amazing, is that Dr.
Collins came to me, I think itwas about two months later after
(18:40):
we digitized her.
She says, Karl, there's somenew science.
And she says, I got to changemy model.
I said, Okay, cool.
And it took us about a coupleof hours.
And the important thing is thatthis technology is actually
moving at the pace of science.
And you think, and I I makejokes with my physician friends,
I said, How long does it takefor you to get a paper out that
we're distribute knowledge?
And you say, Oh, a coupleyears.
(19:02):
I mean, like, I can we can makethis happen in a day.
So this is this is where I amwith this, and I'm excited.
And maybe you can invite meback in a year or so and I'll
explain exactly how this workedhow this worked out.
How do you see this connectingto leadership?
I I'd like to get into yourmind about that for a little
bit.
I see possibilities in my mind,but what are you seeing?
(19:25):
All right.
Let me tell you the otherproject that I'm currently
working on.
And I think I sent you a paperon this.
There's a short, very shortpaper that went into the like
was that a business magazinehere at Emory.
I talked to one of theprofessors here who works in AI,
and he says, Hey, listen, Karl,you ought to look at this, what
they call a GPT, which isessentially an app that you can
(19:47):
create.
And what I did is I said, Okay,this ought to be interesting.
So I took, I don't know, ittook me about a month or so.
But what I was able to do, it'sessentially I created kind of
my history and all my work overthe past 30 years with
leadership, right?
Using constructivedevelopmental theory.
I have a strong theory abouthow leaders grow and mature and
(20:08):
all of the work of RobertKeegan, that kind of stuff,
being able to show again howleaders grow over the life
course and mature.
And we know that more matureleaders make um the better
leadership decisions.
And I I put like all this inthere.
I've also been an executivecoach for over 25 years.
And so I was I started puttingin all my transcripts from all
(20:31):
my coaching sessions.
I'm actually coaching in thesesessions, and what they're
getting is my coaching withinthe transcript.
And I have outcomes and allthat kind of stuff and what that
what I think they should do andall this kind of stuff.
But it's cool because I'mactually giving them their own
language back to them.
So it helps in terms offeedback, where they are and
where they need to be.
And so, anyhow, so I have aboutI think around 80 of these
(20:54):
transcripts.
I can actually have more, but Ihave about 80 of these.
But I've also put in myclasses, I have the book with
Keith Eigel, the map, greatbook, and I put that in there.
And so I essentially, mycareer, right?
And what I really set out to dowas can I use this as a way to
(21:15):
help coach my students withtheir dilemmas currently?
And again, these are executiveMBAs, they got problems, they
got issues.
And it was interesting.
I had this thought, they I'mprobably gonna have to teach an
undergraduate class next year.
I'm thinking, look, what am Igonna do?
And I'm thinking, oh yeah,that's it.
This is it.
Let's talk about yourroommates.
(21:35):
But the athletics, theroommates in your student
organization.
I'm not sure I could use it forthat.
But it is so what is what hasabsolutely blown me away is I
had a student the first week Italked about this, and I
literally was building thiswhole semester, right?
That I'm teaching.
And I kept adding things as theweeks went by and had different
content that I could put thingsin that I had talked about
(21:58):
previously.
And in this woman, this is likethe first week I talked about
it, and what she did is that shehad a problem at work.
And what it did, and what theapp did was say, essentially,
here's a number of ways to thinkabout the problem, depending on
who your audience is, and youhave to basically identify
(22:20):
characteristics of youraudience.
And it gave her three differentways, and this third way, which
was essentially the most matureway to handle this, she goes,
Oh my god, Karl, I used it.
I wasn't really sure I could dothis.
And she says, Not only I'mgonna give you the problem, is
(22:44):
her boss kept giving her moreprojects.
Scott Allen (22:49):
Okay.
Karl Kuhnert (22:50):
She was overwhelmed.
Scott Allen (22:51):
Yep.
Karl Kuhnert (22:52):
And guess what?
She was angry at him forkeeping just giving her so much
to do that she couldn't evenworking the whole time.
Yeah.
And of course, she's angry andshe just goes, she wants to go
talk to him about this.
Right.
And what the app does is itsays, Hey, hold on here for a
(23:14):
second.
You don't know why he's givingyou that information all the
time.
All those things to do.
She may he may be thinkingyou're the next best person for
a leadership position.
Okay.
Do you know that?
No, I don't know that.
And then, and then I'm just I'mgonna go to the end here.
Is that one that sort of themost mature would call level
five way of thinking about this?
(23:36):
Is how can you create this,create a dialogue with him?
And it gives you an example ofwhat the dialogue would actually
look like.
Oh, I love that.
What the dialogue would looklike, and what she does is she
uses the dialogue and she comesback to me, she's Karl.
Not only did I betterunderstand my boss and what he
(23:59):
was doing, we had a greatconversation, and the
conversation that meeting tookus off to how we can make a
bigger difference in ourorganization.
It changed her career path atthat moment.
And I'm doing this, and thinkabout this in real time, I'm
coaching 60 students.
(24:22):
Yes, it's scaled.
Well, okay.
Some people, some listeners, Iimagine right now are thinking,
oh, AI, it's gonna hallucinate,it's gonna give bad advice.
Would you address that a littlebit?
How are you thinking aboutthat?
Okay, so you have to understandhere I am on one side
(24:42):
criticizing AI, what it can'tdo.
Now I'm telling you what I cando.
I had to hold those things inmy mind.
Okay, but here's thedistinction.
And I'll use this word becauseI don't have a better way to
really talk about this.
But with my GPT, there was itwas bounded.
It's bounded by me.
(25:03):
It's not open to the internet.
And so what I was most curiousabout, and I kept getting things
every week from students whoknew I was gonna take it's gonna
take more time for me to gothrough all this stuff.
But it was so fascinatingbecause I was just curious how
well does their problem and thesolution align with what I would
(25:24):
say.
What was that alignment like?
And I'll just tell you one ofthe things that really flips me
out in this thing, by the way,in this GPT, I call it the
leadership growth lab, my LGL.
The thing that really flips meout is seeing my own words
coming out of a machine.
Let's take a quotes out.
Well, you don't just take myquote.
(25:44):
It's a good quote, I can't useit in class anymore.
It's uh it's it kind of it isweird, right?
And it's using the theory, andand by the way, let's just let's
call this on and out.
How much time as anorganizational psychologist, an
academic organizationalpsychologist, and we've talked
about how do we link theory andpractice?
(26:04):
Yes, I'm doing it.
I'm taking the constructivedevelopmental theory, tying it
directly to people.
My content is real to them inreal time because they're using
it when they get out of class.
Exactly.
In their problem.
Now, when this woman was usingthe software, here's three ways
(26:28):
to think about this.
And you and she goes with thelevel five.
How is the system thensuggesting paths forward for
her?
Is that also based on your workor is it doing some inferring
at that point?
I'm just really superinterested in that.
Like as it gets into providingher with actionable advice, is
that still 100% you?
(26:49):
Oh, yes.
Wow.
Oh, I have to tell you, it willit will modify my language at
times that actually makes itbetter.
Wow, it does make it better.
I'm like, oh, that's really Ilove the way you said that.
And I'll tell you something isjust I could enough about the
theory that we're using.
And I I just was playing withthis, and I decided, okay, LGL,
(27:14):
what I'd like for you to do istell me on the move from level
two to level three, I would likeyou to explain this to me in
the form of a poem.
Yeah.
You know, let me get to thelast point of the story.
This is a big point here, isthat when we think about AI, and
I'm now at this place where Iknow it can't know what tacit
(27:40):
knowledge is.
And this is these are this iswhat experts bring to their
knowledge, to their to what theyknow.
What we need is not to askwhether large language models
can render expert judgments, buthow are those judgments made?
And you think about yourself,think about man, when you make a
judgment, what do you what areyou considering?
You're considering a lot ofthings based on your experience.
(28:04):
And so I say I try to contrastthis with the L LMs, is that
fluency is not understanding,speed is not wisdom, certainty
is not truth.
What do we need to ask now whenit matters most that human
judgment is in the little Iguess what I'm saying here is
(28:25):
when human judgment is there, wewe probably need that for
making these critical decisions.
And think about healthcare.
And I haven't really I haven'tpushed this yet.
We're using a lot of AI inhealthcare now, and I'm not sure
that we should be makingdecisions, critical decisions
(28:46):
with it, because it can't get tothat tacit knowledge.
I think that's a reallyimportant element to tease out
of this whole conversation isthat whole tacit domain, right?
That's and so many otherthings.
It is a tool, it is not truth.
And to your point, at least fornow, that human judgment is
(29:08):
incredibly important.
And you gotta know what's thesource, where's it coming from?
I want to know that.
At least for now, there's apartnership.
It's uh because we can boththink of plenty of decisions
humans have made that were notgood decisions and could have
been informed by a little bitmore wisdom.
(29:28):
And maybe just even the I thinkof your student who maybe
wouldn't have even conceived ofthat level five option.
She was just mad, she was justangry, and this tool is now
providing her an opportunity topotentially practice what it's
(29:50):
like to work at level five waysof thinking, ways of engaging,
ways of approaching some ofthese challenges, which I think
is brilliant, it's awesome.
And it's a tool, it's not truthnecessarily.
It's not true.
No, it's a tool.
And and again, this I knew thisto actually talk about this,
but what she told me, she goes,I had no idea that I was gonna
(30:12):
come out of that meeting with awin-win.
Yes.
And what happens at level fiveis trying to figure out what a
win-win looks like.
That is this not just for her,but for a company.
Karl, I so appreciate, I thinkfor listeners, you can very
(30:32):
clearly see that curiosity inplay and that experimentation.
And for years and years, thatexperimentation, I just
absolutely love it.
I think we're so lucky to haveindividuals like you exploring
some of these tools,understanding what are those
benefits, what are the potentiallimitations, and you're at the
forefront.
(30:53):
You're at the forefront of thiswork, and that's just
incredible.
And I will reach out again inthe future.
I do want to have a follow-upconversation to track your
adventures, see what your mostrecent learnings are, because I
think this is super important.
And I'm gonna put a couplearticles in the show notes for
listeners so that they canexplore and see some of this in
(31:15):
action as well.
As I always close out anepisode by asking guests what
they've been listening to,streaming, reading, what's
caught their attention in recenttimes.
It may have to do with whatwe've just discussed.
It may have nothing to do withwhat we've just discussed.
But what's been on your radar?
What's really energized me overthe past week, and I'll send
(31:36):
you another article that I thatis actually under review right
now.
And and this guy, you can findhis work actually on LinkedIn.
And his name is Okay, QuattroCOC.
Okay, that's the best I can do.
And he's Italian, so everythingI everything on LinkedIn, thank
(31:59):
goodness they have a translatorbutton.
They can translate all thisbecause I couldn't read it.
But he has this article, and itactually you'll see the amount
of traction he's actuallygetting on LinkedIn um just over
the past few weeks.
But this article appears, andI'll send it to you, Scott.
Uh it actually appears justrecently in the proceedings of
(32:22):
the National Academy ofSciences.
Okay.
He takes this farther right nowthan I do, but he his general
premise that what we actuallyhave now is basically synthetic
knowledge coming out of AI.
And he makes a brilliant casefor this.
It's worth a read.
(32:42):
By the way, I have to also letme also say it's very tough to
read.
Okay.
It's a it's a dense article,but hey, it's going to get a lot
of play.
And it's really is going tocall it basically, it brings
into question this idea of man,there are probably decisions
that we need a human in a loop.
Other things we don't need,right?
We don't need.
And uh so that that's thearticle that I would like to
(33:04):
just have people share.
Scott Allen (33:05):
Awesome.
I will put that in the shownotes as well.
Karl, thank you so much.
Appreciate you, appreciate yourwork.
And I know that listeners areextremely intrigued.
So I will have some links inthe show notes for all of you.
And as always, everyone, thanksso much for checking in.
Take care.
Be well.
Thank you, Scott.
(33:25):
Okay, before we get to mysummary of that episode, I have
a special guest, and this is Dr.
Marcy Levy Shankman.
And we have been colleagues,co-authors, friends since
probably like 2006, back in theday, back in 06.
She is helping with ILA'sdialogue lab.
And so, Marcy, tell listeners alittle bit about this
(33:47):
opportunity and how they can getinvolved, how they can get
engaged.
New Orleans in January soundspretty good to me.
Tell us a little bit more.
Marcy Shankman (33:57):
Scott, thanks for asking me to talk a little
bit about the dialogue lab.
This is a really excitingexperience.
It's only offered every otheryear.
We're going to be in NewOrleans, as you said.
So this three-day dialogue lab,which is going to be in New
Orleans, is focused on dialogueas a form of leadership.
So that means we're not goingto have panels, we're not going
to have workshops, we're notgoing to have presentations.
(34:19):
What we're going to have istrue deep engagement.
So individuals will sponsorinquiry sessions, and those
individuals are the participantsthemselves.
And if you're interested inattending the dialogue lab, you
can come and participate as afull-fledged member of the
community.
This is a full-on, co-createdlearning community.
(34:41):
And if you want to bump up yourlevel of engagement, then you
can propose a topic to discuss.
And the proposal is simply aquestion.
And that's what we call ourinquiry sessions.
We're also going to takeadvantage of being in New
Orleans, which means we're goingto have this experience
grounded in music, food, andcivic life.
And we'll have opportunities toengage with members of the New
(35:04):
Orleans community.
So we think this is the righttime for this gathering.
Dialogue's needed in this timeof polarization, of complexity,
and of disconnection.
And the dialogue lab is anantidote, of sorts, to that.
We want people to come who areinterested in expressing their
curiosity, who have courage toask deep questions, practice
(35:26):
deep listening, express theirvulnerability.
Expertise is not a requirement.
A growth mindset is.
So we're really excited toinvite your listeners to apply
to participate.
The gathering is three days, asI mentioned earlier, January
30th to February 1st of 2026.
(35:47):
And all who are interested inleadership are invited to
attend.
Scott Allen (35:52):
Awesome.
And I what I love in there isyour you mentioned the
opportunity to practice.
And we can practice listeningand practice engaging and
practice discernment and trulybeing present and mindful.
Absolutely love it.
So for listeners, there is allkinds of information in the show
(36:12):
notes.
So please feel free to checkthat out there.
And you know what, Marcy, thankyou so much for being a part of
the leadership team that'sputting this on.
And thanks so much for stoppingby today.
I hope it goes awesome.
Marcy Shankman (36:25):
Thanks, Scott.
Karl Kuhnert (36:27):
I don't have too much to add other than
reinforcing the word curiosity.
And I think Karl beautifullyrepresents that word.
He is an individual who, overthe course of his career, has
stayed curious and is continuingto experiment, learn.
(36:48):
And what I love is that he isrunning toward this technology,
trying to learn better how itworks, and continuing to see how
it can be leveraged, butleveraged in an ethical way.
And that notion of having ahuman in the loop, I think it's
critical.
I think it's incrediblyimportant.
And when the technology ismoving much more quickly than
(37:11):
the policy or the ethics, havingpeople like Karl better
understand this technology, GaryLloyd, Jonathan Reams, Dan
Jenkins, some individuals who inour space, that leadership
space, who are really, reallytruly exploring this.
I just, my hat's off because Ithink it's all of you who are
(37:33):
going to help us betterunderstand what all of that
means.
As always, thanks so much forchecking in.
Take care.
Be well.
Okay, everybody.
Welcome to Practical Wisdom forLeaders.
Thank you so much for checkingin wherever you are in the
world.
I have Dr.
Karl Kuhnert.
He is professor in the practiceof organization and management.
And he is located at EmoryUniversity.
Have had another episode withhim.
It might be in the 90s.
So check that out.
(03:39):
Great conversation.
Karl has been, and one thing Ilove about Karl is I've known
his work for years.
And he, if there's one word Iwould use to describe Karl,
curiosity is certainly one ofthem.
He has stayed curious over thecourse of his career.
And Karl, you've had a numberof adventures you've been
(03:59):
engaged in recent times.
And so that's where we'rereally going to take the
conversation today.
But I gave you very little ofan introduction.
What should listeners knowabout you?
What do you want to add to yourtitle?
Karl Kuhnert (04:14):
Other than the fact that I've been at this for
a long time.
And uh and the work I'm doingnow, I feel is very exciting and
has it has a lot ofimplications for how we think
about AI and how we're going touse AI in the future.
Yes.
Oh, yeah.
I think you mentioned thatbefore we started recording,
your wife has asked you toretire three times.
And you I have retired threetimes.
(04:35):
I started this retirementprocess.
Actually, I retired and I thinkit was 2016 or something like
that.
I started this process.
And every time I every time, bythe way, it's interesting.
Again, this is for everybodyout there.
(04:57):
Every time I want to retire, Ifound something new to keep me
here.
So artificial intelligence,leadership, coaching.
Let's talk a little bit aboutthat.
And maybe let's start with thekernel of the idea and just take
me through what you've beenlearning.
I'm so excited.
Okay, let me start somewhere atthe beginning here, which was
(05:19):
really about eight or nine yearsago.
I got this call from a friendof mine, Mark Keith, and he was
a financial person, and we enjoyeach other's company.
And then he left where I was atthe time after Georgie moved
away.
And then about a year or solater, he calls me and he says,
Karl, I've got this softwarefrom it's called Merlin Inc.
(05:40):
And uh the guy who actuallycreated the software, his name
is Karl Wocke, W-O-C-K-E.
And I should also warn you thatif I say, Scott, that uh Karl's
a genius, I'm referring to him,not to me in a third person.
But anyhow, it and so Markshowed me this.
And basically the idea was, andhe's very simple.
(06:02):
He says, you know what we cando with AI?
He says, we can actually pointit to a person.
Not to the data set, but to aperson.
And I went, okay, that'sinteresting.
And he says, what we want todo, and what we can do, is we
want to duplicate or digitize adecision that an expert makes.
(06:24):
And what we can do with that isthen scale that decision to the
benefit of others.
I said, Mark, I mean, I said,okay, I have to see how this
works.
I had these ideas from actuallygraduate school about how
decision making is done.
And of course, this blew awayeverything that I had known.
(06:44):
And I was like, I'm trying toput this all together.
Where's the statistics?
What kind of models are weusing here?
And so I had to get, I had tobasically learn all over again
what this does.
But I started working with himand Karl Wilka.
And I found myself in a numberof different places where I was
actually duplicating people'sdecisions.
And it was absolutelyfascinating.
And so let me just give youjust one example of this.
(07:06):
Yeah.
And I was working for HomelandSecurity.
Okay.
I I look, I've done ethicists,I've done psychiatrists, I've
done a whole lot of people givetheir decisions.
And by the way, as you said,I'm curious.
Every time I sit down withsomeone, I'm I'm like having a
birthday or something.
This is oh my gosh, this is youcan't get better than this.
It's all working with homelandsecurity and feeling, oh, this
(07:26):
is really cool.
And so, anyhow, I'm I'minterviewing this person, and
it's interesting because Italked to the supervisor and
some people at the airport, it'sactually at the airport, and
this was an agriculturalinspector.
And her job is people come inand she has lots and lots of
data that she looks at to decidewho might have bad peanuts from
(07:50):
Brazil coming back from.
Yeah, I always see those signs,but I've never paid.
Do you have a live chicken?
Nope.
I'm good.
But anyhow, she's better thanthis in anybody.
Yeah.
And they said, you have tointerview her.
You have to see if we candigitize her.
And it just to cut the storyshort, I did.
(08:11):
And I asked her afterwards whenI was done.
I showed her I showed herdecision basically and
algorithm.
And I said to her, Is this you?
Does this look like you?
Does this sound like you?
She comes over and hugs me.
Wow.
And she goes, Oh my gosh.
She says, You know how muchtime I spend teaching people how
(08:31):
what I know?
And all these new people thatcome in and they're new to this
job.
And she goes, You know what I'mgonna do now?
I can do what I'm really paidto do.
Try to figure out what the badguys are gonna do next.
Yeah, yeah.
And I want I want to make thispoint.
Every time I have done this, ithas freed up these experts to
(08:55):
do things that they wanted todo.
Interesting.
And so very quickly, and again,I'll I this is on our we can
share, I'll share a paper if youwant to email me.
But what's very cool about thisis that what I'm really tapping
into, and think about anexpert, and that we can have fun
with this just for a second,Scott.
I could say to you, hey Scott,you told me recently that you
(09:19):
decided to quit your job, stopyour podcasting, and what you
want to do is flip homes for aliving.
And uh, and you're like, I'mreally interested in learning
more about this.
And he also says, there's a guythat's down the street, and
he's been flipping homes for 25years.
And we can go on with thescenario, but I'll ask him, I'll
ask you, I'll say, What wouldyou rather get your information
(09:40):
from?
Would you rather get it fromAI?
Oh, by the way, it's gonna giveyou a lot of information.
Yes.
About flipping homes.
You're gonna get a lot ofinformation.
But the other thought you haveis maybe I should talk to my
neighbor about flipping homes.
And the point I want to makehere is what would you gain from
talking to your neighbor?
(10:00):
Localized expertise.
Perfect.
Localized wisdom, specificwisdom.
So this person could tell methat Elyria is a great hot spot
in Northeast Ohio, so to speak.
But it would also be bounded insome fact, in some way, like
that it's it's not just someuniversal Chat GPT output, it's
(10:21):
contained in some ways, right?
Yeah, but that's the point, isthat you're getting data, right,
from this guy, that you can'tget from AI.
And we make this distinction,and this distinction has been
around since at least 1957.
And the guy's name is MichaelPogliani, and he brought this
(10:43):
idea up in a paper of his calledpersonal knowledge.
And he actually referred tothis personal knowledge as tacit
knowledge, and essentially it'slived wisdom and it's in it's
intuition, right?
And you're gonna get that fromyour neighbor, then it's not
gonna be an AI.
And so this really justfascinated me because when
(11:04):
you're working with experts, andyou can say this: what makes an
expert?
It's not explicit knowledge, itis, you know, that they have
the explicit knowledge becauseeverybody has explicit
knowledge.
But how do you do this?
How do you get at this tacitknowledge?
Because that is what makes anexpert an expert.
How do we get into thatdimension, that practical
(11:24):
wisdom, that lived wisdom?
The when is this appropriate,where is this appropriate, how
is this appropriate?
And wait, the way you've hit onthis, Scott, is the way to
think about this is what you getfrom AI, and again, this is
awesome.
It's knowing what.
Yes.
From the expert in tacitknowledge, you get to know how.
(11:47):
Yeah.
You're you're never right now,again, who knows what's gonna
happen in six months or a yearwith AI, but there isn't really
a way.
I don't think you can know whatthat tacit knowledge is because
it's not in the data.
It's not in the data.
And so fast forward.
Yeah.
I'm beard emory.
I'm in one of the greatresearch hospitals in the world.
(12:08):
And so I'm working with thisdoctor.
And at the time, this is abouta year ago, she was actually a
student in my class because Iteach executive MBA students,
which is just a joy, by the way.
Just a joy.
And she comes up to me.
I have this uh simulation Icreate with AI and what I'm
doing.
I create the simulation for myclass.
And she comes up to me andsays, Karl, we got to talk.
(12:31):
She says, I'm one of theexperts at this time on GLP1s.
Oh, okay.
She's again, knowledge, she isthe person at this point about
GLP 1.
And she's telling people allover the country about when you
have a patient, how do you givethem a GLP 1 or not?
And so I sit down with her, andI'll just do this very quickly.
(12:51):
Is it takes me about an hourand a half, and I sit down with
her to tell me what her keyvariables are, as well as the
measures.
And it's fun because I ask herabout I ask her tacit knowledge
questions, what makes youspecial here?
And what do you think aboutwhen you're seeing a patient?
And it's really interesting,right?
It's more, she's giving me morethan just what is in explicit
(13:14):
knowledge.
Yes.
She's giving me other things.
And so we create this algorithmfor her.
And oh, I should probably giveyou one more how we do this.
And this is a little hard toexplain, but this is the real,
again, this is the real geniuswithin the software, and which
we call Tom, which is tacitobject modeler.
Okay, all right.
(13:34):
Oh, nice.
Yeah, tacit object modeler,tacit.
And so what the software doesis it actually takes all the
variables, it takes all themeasures, right?
And just input it.
And what Tom does, and this isagain really unbelievable.
I have Dr.
Collins actually sitting besideme, and what it does is it, if
(13:56):
you will, it creates a scenario,one scenario, and it has all
the variables, and one, but ithas some of these, by the way,
she had something like 22variables, which is a lot for an
expert, by the way.
This wasn't yes or noquestions, these were sometimes
six different options for ameasure.
Um so all of this is in there.
(14:17):
And so what Tom does is itgives us a scenario, and the
question at the bottom is do youprovide or do you give a GOP1
or not to this patient?
She looks at the data, no,fine.
Next scenario, differentscenario.
And oh, by the way, I'll get tothe bottom of this 220
(14:38):
scenarios, right?
It's just cranking outscenarios, it's just crank
scenario.
It is, by the way, here's thething, here's what's important
it's learning about what youvalue.
Oh wow.
And so what you get, and by theway, this is so cool.
At the end, I actually have Tomtest her.
Oh, wow.
Okay.
(14:58):
And oh, by the way, she had ahundred scenarios, she got a
hundred.
And for her, it sounds likethis would be weeks or months.
No, she's looking at thesethings and making decisions in
about 10 seconds.
Wow.
And so this whole process, bythe way, can take I don't know.
I usually, it's about it takethree or four days, but it
doesn't take that long becausewhat I do is when someone gives
(15:18):
me, when I interview them andget their explicit and tacit
knowledge, I actually send themhome just to think about it.
Is anything that you missed orwhatever?
We have this great algorithmthat now can be shared with
physicians all over the country.
But by the way, there arephysicians at this time that
aren't really up on GOP wants.
(15:40):
This is unbelievable.
And the way we talk about this,and it's this is again very
important.
We offer this to physicians asa second opinion.
That's all second opinion.
But I'm saying, hey, listen,here's a second opinion from a
leading expert, major researchhospital.
See what you think.
And what they're doing, by theway, they're putting in their
(16:02):
own data when they have thealgorithm.
It's like they put in it, soit's a personalized decision.
And they also see, and by theway, the people who are who are
using this actually see what shevalues in terms of the
variables.
So everything here istransparent.
You actually know who youactually can look up who the
position is.
So it's it's this is not, and Iusually I like to talk about
(16:23):
this in terms of trust becauseyou never know where the data's
coming from AI.
It's coming from this person.
Scott Allen (16:28):
Yes.
Karl Kuhnert (16:29):
And here's it's a second opinion.
You can make do what you want.
So the way I talk about it, thedata is sourced, it's
transparent, and it'spersonalized.
Because what the doctor's usingis their own explicit
knowledge.
They're putting in whether thepatient has medullary cancer in
the family, all they're puttingin that data or their own data.
But this is more the herjudgment and her decision.
(16:52):
And capturing that.
Yeah, maybe because think of Ihave this visual in my head
right now of all of theknowledge in people's minds
walking around Earth right now.
That is in some cases nevercaptured.
That could be making whiskey,that could be GOP1s, that could
(17:13):
be decision making.
Scott, I don't have I don'thave time for this, but do you
know how many companies havecalled me?
Small businesses who said, Hey,we have this person who's
retiring.
We can't lose her.
She's an accountant, and wecan't we can't replace her right
now.
We need an app that what wouldSharon do?
(17:33):
That's what it is.
And so that's happening allover the country, all over the
world, this baby boomers.
And I would love there's folksagain, you're right.
There's folks that I would loveto be able to digitize.
And so where we are today andwhere I am right now with this,
is this, by the way, this workthat I did with the GLP 1o
(17:57):
actually got me into EmoryHospital.
And so now I'm actually uhinvolved, I'm involved in
clinical trials.
That has been quite a uh anordeal going through this.
But we're gonna end up with notonly with the digitized expert,
but we're also gonna have theopportunity, and I've already
I've done this with other kindsof institutions like banking and
(18:19):
insurance, but I'm so excitedabout being able to collect the
validity data that looking backon past decisions with
physicians.
It's really because there's alot of things I can do, but
what's really important for meis being able to validate their
decision.
And one more thing, this is itagain, it's amazing, is that Dr.
Collins came to me, I think itwas about two months later after
(18:40):
we digitized her.
She says, Karl, there's somenew science.
And she says, I got to changemy model.
I said, Okay, cool.
And it took us about a coupleof hours.
And the important thing is thatthis technology is actually
moving at the pace of science.
And you think, and I I makejokes with my physician friends,
I said, How long does it takefor you to get a paper out that
we're distribute knowledge?
And you say, Oh, a coupleyears.
(19:02):
I mean, like, I can we can makethis happen in a day.
So this is this is where I amwith this, and I'm excited.
And maybe you can invite meback in a year or so and I'll
explain exactly how this workedhow this worked out.
How do you see this connectingto leadership?
I I'd like to get into yourmind about that for a little
bit.
I see possibilities in my mind,but what are you seeing?
(19:25):
All right.
Let me tell you the otherproject that I'm currently
working on.
And I think I sent you a paperon this.
There's a short, very shortpaper that went into the like
was that a business magazinehere at Emory.
I talked to one of theprofessors here who works in AI,
and he says, Hey, listen, Karl,you ought to look at this, what
they call a GPT, which isessentially an app that you can
(19:47):
create.
And what I did is I said, Okay,this ought to be interesting.
So I took, I don't know, ittook me about a month or so.
But what I was able to do, it'sessentially I created kind of
my history and all my work overthe past 30 years with
leadership, right?
Using constructivedevelopmental theory.
I have a strong theory abouthow leaders grow and mature and
(20:08):
all of the work of RobertKeegan, that kind of stuff,
being able to show again howleaders grow over the life
course and mature.
And we know that more matureleaders make um the better
leadership decisions.
And I I put like all this inthere.
I've also been an executivecoach for over 25 years.
And so I was I started puttingin all my transcripts from all
(20:31):
my coaching sessions.
I'm actually coaching in thesesessions, and what they're
getting is my coaching withinthe transcript.
And I have outcomes and allthat kind of stuff and what that
what I think they should do andall this kind of stuff.
But it's cool because I'mactually giving them their own
language back to them.
So it helps in terms offeedback, where they are and
where they need to be.
And so, anyhow, so I have aboutI think around 80 of these
(20:54):
transcripts.
I can actually have more, but Ihave about 80 of these.
But I've also put in myclasses, I have the book with
Keith Eigel, the map, greatbook, and I put that in there.
And so I essentially, mycareer, right?
And what I really set out to dowas can I use this as a way to
(21:15):
help coach my students withtheir dilemmas currently?
And again, these are executiveMBAs, they got problems, they
got issues.
And it was interesting.
I had this thought, they I'mprobably gonna have to teach an
undergraduate class next year.
I'm thinking, look, what am Igonna do?
And I'm thinking, oh yeah,that's it.
This is it.
Let's talk about yourroommates.
(21:35):
But the athletics, theroommates in your student
organization.
I'm not sure I could use it forthat.
But it is so what is what hasabsolutely blown me away is I
had a student the first week Italked about this, and I
literally was building thiswhole semester, right?
That I'm teaching.
And I kept adding things as theweeks went by and had different
content that I could put thingsin that I had talked about
(21:58):
previously.
And in this woman, this is likethe first week I talked about
it, and what she did is that shehad a problem at work.
And what it did, and what theapp did was say, essentially,
here's a number of ways to thinkabout the problem, depending on
who your audience is, and youhave to basically identify
(22:20):
characteristics of youraudience.
And it gave her three differentways, and this third way, which
was essentially the most matureway to handle this, she goes,
Oh my god, Karl, I used it.
I wasn't really sure I could dothis.
And she says, Not only I'mgonna give you the problem, is
(22:44):
her boss kept giving her moreprojects.
Scott Allen (22:49):
Okay.
Karl Kuhnert (22:50):
She was overwhelmed.
Scott Allen (22:51):
Yep.
Karl Kuhnert (22:52):
And guess what?
She was angry at him forkeeping just giving her so much
to do that she couldn't evenworking the whole time.
Yeah.
And of course, she's angry andshe just goes, she wants to go
talk to him about this.
Right.
And what the app does is itsays, Hey, hold on here for a
(23:14):
second.
You don't know why he's givingyou that information all the
time.
All those things to do.
She may he may be thinkingyou're the next best person for
a leadership position.
Okay.
Do you know that?
No, I don't know that.
And then, and then I'm just I'mgonna go to the end here.
Is that one that sort of themost mature would call level
five way of thinking about this?
(23:36):
Is how can you create this,create a dialogue with him?
And it gives you an example ofwhat the dialogue would actually
look like.
Oh, I love that.
What the dialogue would looklike, and what she does is she
uses the dialogue and she comesback to me, she's Karl.
Not only did I betterunderstand my boss and what he
(23:59):
was doing, we had a greatconversation, and the
conversation that meeting tookus off to how we can make a
bigger difference in ourorganization.
It changed her career path atthat moment.
And I'm doing this, and thinkabout this in real time, I'm
coaching 60 students.
(24:22):
Yes, it's scaled.
Well, okay.
Some people, some listeners, Iimagine right now are thinking,
oh, AI, it's gonna hallucinate,it's gonna give bad advice.
Would you address that a littlebit?
How are you thinking aboutthat?
Okay, so you have to understandhere I am on one side
(24:42):
criticizing AI, what it can'tdo.
Now I'm telling you what I cando.
I had to hold those things inmy mind.
Okay, but here's thedistinction.
And I'll use this word becauseI don't have a better way to
really talk about this.
But with my GPT, there was itwas bounded.
It's bounded by me.
(25:03):
It's not open to the internet.
And so what I was most curiousabout, and I kept getting things
every week from students whoknew I was gonna take it's gonna
take more time for me to gothrough all this stuff.
But it was so fascinatingbecause I was just curious how
well does their problem and thesolution align with what I would
(25:24):
say.
What was that alignment like?
And I'll just tell you one ofthe things that really flips me
out in this thing, by the way,in this GPT, I call it the
leadership growth lab, my LGL.
The thing that really flips meout is seeing my own words
coming out of a machine.
Let's take a quotes out.
Well, you don't just take myquote.
(25:44):
It's a good quote, I can't useit in class anymore.
It's uh it's it kind of it isweird, right?
And it's using the theory, andand by the way, let's just let's
call this on and out.
How much time as anorganizational psychologist, an
academic organizationalpsychologist, and we've talked
about how do we link theory andpractice?
(26:04):
Yes, I'm doing it.
I'm taking the constructivedevelopmental theory, tying it
directly to people.
My content is real to them inreal time because they're using
it when they get out of class.
Exactly.
In their problem.
Now, when this woman was usingthe software, here's three ways
(26:28):
to think about this.
And you and she goes with thelevel five.
How is the system thensuggesting paths forward for
her?
Is that also based on your workor is it doing some inferring
at that point?
I'm just really superinterested in that.
Like as it gets into providingher with actionable advice, is
that still 100% you?
(26:49):
Oh, yes.
Wow.
Oh, I have to tell you, it willit will modify my language at
times that actually makes itbetter.
Wow, it does make it better.
I'm like, oh, that's really Ilove the way you said that.
And I'll tell you something isjust I could enough about the
theory that we're using.
And I I just was playing withthis, and I decided, okay, LGL,
(27:14):
what I'd like for you to do istell me on the move from level
two to level three, I would likeyou to explain this to me in
the form of a poem.
Yeah.
You know, let me get to thelast point of the story.
This is a big point here, isthat when we think about AI, and
I'm now at this place where Iknow it can't know what tacit
(27:40):
knowledge is.
And this is these are this iswhat experts bring to their
knowledge, to their to what theyknow.
What we need is not to askwhether large language models
can render expert judgments, buthow are those judgments made?
And you think about yourself,think about man, when you make a
judgment, what do you what areyou considering?
You're considering a lot ofthings based on your experience.
(28:04):
And so I say I try to contrastthis with the L LMs, is that
fluency is not understanding,speed is not wisdom, certainty
is not truth.
What do we need to ask now whenit matters most that human
judgment is in the little Iguess what I'm saying here is
(28:25):
when human judgment is there, wewe probably need that for
making these critical decisions.
And think about healthcare.
And I haven't really I haven'tpushed this yet.
We're using a lot of AI inhealthcare now, and I'm not sure
that we should be makingdecisions, critical decisions
(28:46):
with it, because it can't get tothat tacit knowledge.
I think that's a reallyimportant element to tease out
of this whole conversation isthat whole tacit domain, right?
That's and so many otherthings.
It is a tool, it is not truth.
And to your point, at least fornow, that human judgment is
(29:08):
incredibly important.
And you gotta know what's thesource, where's it coming from?
I want to know that.
At least for now, there's apartnership.
It's uh because we can boththink of plenty of decisions
humans have made that were notgood decisions and could have
been informed by a little bitmore wisdom.
(29:28):
And maybe just even the I thinkof your student who maybe
wouldn't have even conceived ofthat level five option.
She was just mad, she was justangry, and this tool is now
providing her an opportunity topotentially practice what it's
(29:50):
like to work at level five waysof thinking, ways of engaging,
ways of approaching some ofthese challenges, which I think
is brilliant, it's awesome.
And it's a tool, it's not truthnecessarily.
It's not true.
No, it's a tool.
And and again, this I knew thisto actually talk about this,
but what she told me, she goes,I had no idea that I was gonna
(30:12):
come out of that meeting with awin-win.
Yes.
And what happens at level fiveis trying to figure out what a
win-win looks like.
That is this not just for her,but for a company.
Karl, I so appreciate, I thinkfor listeners, you can very
(30:32):
clearly see that curiosity inplay and that experimentation.
And for years and years, thatexperimentation, I just
absolutely love it.
I think we're so lucky to haveindividuals like you exploring
some of these tools,understanding what are those
benefits, what are the potentiallimitations, and you're at the
forefront.
(30:53):
You're at the forefront of thiswork, and that's just
incredible.
And I will reach out again inthe future.
I do want to have a follow-upconversation to track your
adventures, see what your mostrecent learnings are, because I
think this is super important.
And I'm gonna put a couplearticles in the show notes for
listeners so that they canexplore and see some of this in
(31:15):
action as well.
As I always close out anepisode by asking guests what
they've been listening to,streaming, reading, what's
caught their attention in recenttimes.
It may have to do with whatwe've just discussed.
It may have nothing to do withwhat we've just discussed.
But what's been on your radar?
What's really energized me overthe past week, and I'll send
(31:36):
you another article that I thatis actually under review right
now.
And and this guy, you can findhis work actually on LinkedIn.
And his name is Okay, QuattroCOC.
Okay, that's the best I can do.
And he's Italian, so everythingI everything on LinkedIn, thank
(31:59):
goodness they have a translatorbutton.
They can translate all thisbecause I couldn't read it.
But he has this article, and itactually you'll see the amount
of traction he's actuallygetting on LinkedIn um just over
the past few weeks.
But this article appears, andI'll send it to you, Scott.
Uh it actually appears justrecently in the proceedings of
(32:22):
the National Academy ofSciences.
Okay.
He takes this farther right nowthan I do, but he his general
premise that what we actuallyhave now is basically synthetic
knowledge coming out of AI.
And he makes a brilliant casefor this.
It's worth a read.
(32:42):
By the way, I have to also letme also say it's very tough to
read.
Okay.
It's a it's a dense article,but hey, it's going to get a lot
of play.
And it's really is going tocall it basically, it brings
into question this idea of man,there are probably decisions
that we need a human in a loop.
Other things we don't need,right?
We don't need.
And uh so that that's thearticle that I would like to
(33:04):
just have people share.
Scott Allen (33:05):
Awesome.
I will put that in the shownotes as well.
Karl, thank you so much.
Appreciate you, appreciate yourwork.
And I know that listeners areextremely intrigued.
So I will have some links inthe show notes for all of you.
And as always, everyone, thanksso much for checking in.
Take care.
Be well.
Thank you, Scott.
(33:25):
Okay, before we get to mysummary of that episode, I have
a special guest, and this is Dr.
Marcy Levy Shankman.
And we have been colleagues,co-authors, friends since
probably like 2006, back in theday, back in 06.
She is helping with ILA'sdialogue lab.
And so, Marcy, tell listeners alittle bit about this
(33:47):
opportunity and how they can getinvolved, how they can get
engaged.
New Orleans in January soundspretty good to me.
Tell us a little bit more.
Marcy Shankman (33:57):
Scott, thanks for asking me to talk a little
bit about the dialogue lab.
This is a really excitingexperience.
It's only offered every otheryear.
We're going to be in NewOrleans, as you said.
So this three-day dialogue lab,which is going to be in New
Orleans, is focused on dialogueas a form of leadership.
So that means we're not goingto have panels, we're not going
to have workshops, we're notgoing to have presentations.
(34:19):
What we're going to have istrue deep engagement.
So individuals will sponsorinquiry sessions, and those
individuals are the participantsthemselves.
And if you're interested inattending the dialogue lab, you
can come and participate as afull-fledged member of the
community.
This is a full-on, co-createdlearning community.
(34:41):
And if you want to bump up yourlevel of engagement, then you
can propose a topic to discuss.
And the proposal is simply aquestion.
And that's what we call ourinquiry sessions.
We're also going to takeadvantage of being in New
Orleans, which means we're goingto have this experience
grounded in music, food, andcivic life.
And we'll have opportunities toengage with members of the New
(35:04):
Orleans community.
So we think this is the righttime for this gathering.
Dialogue's needed in this timeof polarization, of complexity,
and of disconnection.
And the dialogue lab is anantidote, of sorts, to that.
We want people to come who areinterested in expressing their
curiosity, who have courage toask deep questions, practice
(35:26):
deep listening, express theirvulnerability.
Expertise is not a requirement.
A growth mindset is.
So we're really excited toinvite your listeners to apply
to participate.
The gathering is three days, asI mentioned earlier, January
30th to February 1st of 2026.
(35:47):
And all who are interested inleadership are invited to
attend.
Scott Allen (35:52):
Awesome.
And I what I love in there isyour you mentioned the
opportunity to practice.
And we can practice listeningand practice engaging and
practice discernment and trulybeing present and mindful.
Absolutely love it.
So for listeners, there is allkinds of information in the show
(36:12):
notes.
So please feel free to checkthat out there.
And you know what, Marcy, thankyou so much for being a part of
the leadership team that'sputting this on.
And thanks so much for stoppingby today.
I hope it goes awesome.
Marcy Shankman (36:25):
Thanks, Scott.
Karl Kuhnert (36:27):
I don't have too much to add other than
reinforcing the word curiosity.
And I think Karl beautifullyrepresents that word.
He is an individual who, overthe course of his career, has
stayed curious and is continuingto experiment, learn.
(36:48):
And what I love is that he isrunning toward this technology,
trying to learn better how itworks, and continuing to see how
it can be leveraged, butleveraged in an ethical way.
And that notion of having ahuman in the loop, I think it's
critical.
I think it's incrediblyimportant.
And when the technology ismoving much more quickly than
(37:11):
the policy or the ethics, havingpeople like Karl better
understand this technology, GaryLloyd, Jonathan Reams, Dan
Jenkins, some individuals who inour space, that leadership
space, who are really, reallytruly exploring this.
I just, my hat's off because Ithink it's all of you who are
(37:33):
going to help us betterunderstand what all of that
means.
As always, thanks so much forchecking in.
Take care.
Be well.
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