July 18, 2025 • 52 mins
Karrie Sullivan has been on the edge of digital transformation since the earliest days of digital media, with a stint at Cars.com during the late ‘90 internet boom. She knows about getting people to change behavior, to adopt the new, to do things differently.
As she said, people won’t change behavior just because you ask them. People will change behavior to fit in. Especially if you hand them proven peer produced playbooks.
Today, Karrie leads Culminate Strategy Group, where she and her team specialize in accelerating new technology adoption by identifying and working with key change agents. That influential 15% sits between the early adopters (the “bleeding edge” 5–7%) and the risk-averse majority (the “we’ve always done it this way” 80%). The Resilient middle 15% hold the key to broader adoption of the new, in this case, more people in the organization putting the power of AI to work for them in their daily tasks.
Please join me in welcoming Karrie Sullivan to the Work 20XX podcast.
In this wide ranging conversation, we cover the interaction of workplace, AI, transformation, psychology, behavioral science, change, adaptability, and a whole lot more. Karrie brings clarity to the chaos of transformation.
The through line: how to increase AI adoption, and more broadly, how to assimilate change faster, so your people can do more, with less, faster, by putting the right tools to work.
Karrie Sullivan: Results, Resilient, Reluctant, Readiness | Work 20XX podcast with Jeff Frick Ep51
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(00:00):
All right.
So I will
count us down
and we'll go
in three, two, one.
Hey welcome backeverybody.
Jeff Frick here
coming to you from the home studio
for another episode of Work 20XX.
And I thinkyou know
when I started this podcasta couple of years ago,
the focus of the futureof work was all around
distributed teams and hybrid work
(00:20):
and everyone trying to figure out
how to react to kind of the
Covid emergency and urgency.
But as we're like 3or 4 years down the road
or five years,I guess, since Covid,
hard to believe
it's really focusing onAI and AI adoption.
And how do we get people to use AI?
And everybody'sscared of AI.
And what is it exactly
and how do we get itin our workflow?
So I think that's really
(00:40):
kind of come tothe forefront.
And so I'm really excited
to have this next guest.
She's an expert at it
not only in AI specifically, but helping organizations assess
how theycan increase
the probabilityof success
in trying to get things going
inside theorganization.
So joining us
all the way from Chicagothe Windy City,
she’s Karrie SullivanFounder and CEO
of Culminate Strategy Group.
(01:01):
Karrie, greatto see you this morning.
Thank you.
It's so great to be hereI appreciate it Jeff.
Oh my pleasure.
So I'm just curious,
were you alwaysin this space
or did the
was it is one of theseclassic cases
where the marketjust came to you
over the last several years?
It's more like that.Yeah.
I started outas a marketer, oddly,
and turned into a database marketer
(01:22):
during the, you know, dot com boom.
I was an early stage employee at Cars.com
Okay
and I found my
I found my space,my niche in digital.
And that's really
I've been doing a lot of transformation
into digital for the last 20, 25 years.
All right. Great.
So you are very activeon LinkedIn.
(01:43):
You're a great follow.
If people don'tfollow you,
check out Karrie'sfeed on LinkedIn.
That's where I kind ofcame across her.
And you've got thisinteresting process.
Where you helppeople do an assessment
of their people
to then figure out
who are the best people
to start their kind of AI journeyor to have success.
So before we get intosome of the details
(02:05):
I wonder if you could share
where that came from and how do you
got started on this?
On this process?
One of the things that Iwas trying to figure out
as I was working on scalefor my own business
was what's our superpower, right?
It's a good research question.
You got to ask it.
And the answerwas my weird EQ.
(02:28):
I didn't realize it was weird at the time.
But it was a little weird.
I have this, apparentlyI have this ability to
read the room really fastand compile teams
together really fast
and specifically teams that do a really good job of change and
adapting to ambiguity, adapting to uncertainty
(02:51):
things like that.
That's kind of whatI look for in humans,
when I recruit them.
That was kind of my
‘Oh, crap! What if?’ moment.
Oh, crap.
My EQ doesn't scale
to every project that we're going to be on.
But thenwhat if
What if my EQ could scale to other
(03:12):
all the projectsthat would be on?
And what would I do with that?
And I reached outto a colleague of mine.
His name is Christopher Skinner
also a good follow.
And he actually is a he's a data scientist.
Computational linguistics wizard guru.
And he invented this model.
(03:33):
He mapslanguage
to developmental psychology
and essentially we can get a bead
on individualsin any company.
Frictionlessly in a couple of hours.
I know the EQof most people.
So one of the things
that Brian Elliottalks about
Another great kind of voice on the future of work
(03:53):
is that in terms of management,
capabilities andmanagement propensities,
that it's the same kind of
characteristics of good managers
that we're able to kind of
deal with the ambiguity around
Covid and changing rules and
[Jeff] and in that[Karrie] Absolutely
which is kind ofthe same attributes
of helping organizationsimplement AI
(04:14):
which in a lot of waysis the same thing.
It's kind of ambiguous.
Nobody knows really what to do
or how to start.
So what are some of thosecharacteristics in,
I guess boththe managers
as well asthe individuals
who are going to havea higher propensity
to be successfulwith new things?
I can give you percentages, how’s that?
About 7% of any company population
(04:36):
is made up of people who,
are already really good
So do you remember Maslow's Hierarchy
from high school?
[Jeff] Yep, yep.[Karrie] Right, so
like the 5% to 7% have gotten kind of close
to the top of Maslow's hierarchy.
Kind of that self actualization-ish.
[Jeff] Right[Karrie] right?
Or cognitive.
(04:57):
And what you're actually doing
is dropping the emotional baggage
that people are learning to get past
over the courseof their lives.
And earlierin earlier stages at
part of what we have to learn is
comfort with ambiguity orcomfort with uncertainty.
And, you know,80% of the population
(05:18):
is deeply uncomfortablewith uncertainty.
And that's really the biggest
challenge with any change.
I don't care if it's future work
or AI or a big pandemic
or even justimplementing
sales for a certainother system
inside your organizationor M&A, right?
The fact that people don't like change and uncertainty
(05:39):
is universal
amongst about 80%of the population.
The other 20%,
gets into varyingdegrees of
that you know, the middle layers of,
you know, esteemand belonging,
in on Maslow and in the upper layers
of cognitive and self-actualized.
(06:00):
And that's essentiallywhat we're looking for.
We're lookingfor grit
and levels of griton kind of a continuum.
In terms of successof the project.
If the 7% are successful
I mean, obviouslythose other
90% are not going to change their fundamental
kind of psychologicalmakeup.
Is it just because it gets
beyond the uncertainty
(06:21):
that then that's what drives the adoption
beyond that first seven and it's
You hit it
less crazy?
Is that why it then works
with those other people?
Because they don't really change?
Well, we're triggeringa couple of things.
So they’re not goingto change their behavior
just becauseyou tell them to
or just because you train them to.
Right.
What we'redoing is
it's a kind of boil the frog scenario, right?
(06:43):
So you takethe 7%
and those are the folkswho jump straight
to experimentation on theon the change curve.
Okay
I call this hackingthe change curve.
And very, very few people
can jump straight over thattrough of the change curve
into experimentation and just acceptance and moving forward
And that's our 7%, right?
(07:04):
Those are theearliest adopters.
They are super comfortable with experimentation.
They automate themselvesout of a job.
They're super comfortablewith ambiguity.
Their innovationmindsets even
but they're also incredibly goodat creating leverage
They know how to
generate a lot of result
for very little or as little effort as possible.
(07:27):
And that's kind of what you're looking for in any
any optimizationof a population.
So we get them to do theexperimentation with us
and tell us what the most
productive use casesare in an organization
because that's problemnumber two with AI, right?
[Jeff] Right.
Is coming with the right use cases
for yourorganization.
So they came up
they come up withthe use cases.
(07:48):
And then group two is about 15% of the population.
And they are smart.
They are intellectually curious.
They liketo learn.
They liketo teach.
And it's thatlearn and teach
tipping point that isreally important here
because they're the translators
between the two groups.
[Jeff] Right.
So if the first group does our
(08:10):
experimentationand they're
they're running offon innovation.
You're not goingto hand
just hand those innovation projects to the 80%, right?
So we work with that 15%.
And they’rea natural
kind of crossover between a proof of concept group.
Right.
And our change champion group that’s
there essentially the same thing psychologically.
(08:30):
And that's really
that's essentially what we're doing.
So if you want to map this to
Kotter or pro Sci,
that's kind of that second group that you want
and what they're doing is
proving outthe concept
proving outthe use cases
and then giving us the detailed process maps
the playbooks,the checklists,
the detail that everybody else needs
(08:53):
so that with the 80%what we're doing is
taking the ambiguity out.
So we're creatingprescriptive
here's how you adoptfor your function.
And we're triggering their need to belong or fit in.
They won’t change their behavior because you tell them to.
They will change their behavior to fit in.
So if you give them people to fit in with
(09:15):
and you give them a prescriptive way to fit in
They’ll do it. They will absolutely do it.
Especially if theirexecutives are saying,
this is our expectation.
This is the behavior we want.
These are the kinds of things
we need to do.This is who we are now.
Right.
And then what aboutthe difference
between the managersversus the people
within that 7%?
Because it's one thing to have somebody who's, you know
(09:37):
always lookingfor high leverage.
Not lazy, but looking for ways
to be much more efficient.
[Karrie] Yeah
With that comesexperimentation.
And with experimentationcomes failure.
Just as apercentage.
And if you're not failing on some things
you're not really trying
hard enough to get out on the edge.
[Jeff] So it's one thing [Karrie] That’s right
for the person to do that.
It's a different thing
for the culture or the management to be
(09:59):
okay with some failurealong the way
knowing that with
aggressive experimentationand with trial and with
you know, kind of charting new paths,
that not everythingis going to work out
and there's going to be
a couple hiccupson the road.
So are the,
are the profilesof the managers
and the people within those early adopter teams the same?
Or is there's a slightly different twist
between managersand peeps.
(10:21):
They're the same.
The biggest challengethat we run across
is that outside ofthe tech industry,
leadership teamsare a bit of a mixed bag,
and middle managementtends to be,
not quite so
top of Maslow
that we see a lot of safety and security
(10:42):
in middle managers.
And thereinlies your problem.
What's essentially happening is
that you've got a gap between the
high resilientindividual contributors
that may be sitting at thebottom of the organization
or they're buried,
and those resilient
experimenting,you know,
transformation or growth or innovation oriented leaders
(11:04):
Right
They’re at the topof the organization.
And then the middlemanagement layer
they're not bad per se it’s just that
what is typicalin organizations
outside of the tech industry
is that people are promoted for IQ, not EQ.
So they are promoted for being detail oriented, methodical,
(11:28):
process orientedand things like that.
Again, there's nothing wrong with that.
The challengeis that as you're going
through changeor transformation
or growth or big velocitykinds of things,
those folks are not wired
to handle that easily.
And that's a lot of thechallenge that you see
in future of work and remote
and Covid andthings like that
(11:48):
but alsowith AI.
Yeah.
So it’s about finding
surgically finding that 7%
wherever they sit in the organization.
And to your point, no.
They're not really fundamentally different psychologically.
They're probably just
a little bit differentwhen it comes to
experience ormaturity level.
Yeah. Interesting.
(12:09):
Okay. So youdid the test.
You did the test on me.
So one ofthe interesting
things about your test isyou don't necessarily
I don't haveto sit down
and fill out like a Myers-Briggs or
and as you've said in someof your other episodes
other things I've seen gettingready for this, you know,
psychological assessmentis not new to HR,
and trying to figure out
people's strengths and weaknesses
is not new to HR.
(12:29):
And there's been lotsof different ways
that people have triedto figure that out.
Your approachis different
cause you just basically
mine, scratch, scrape, I don't know what’s the right verb
existing content that people have already published on
on the web whether it's LinkedIn
or other social media
or maybe youcan explain
kind of, where do you get the
where do you getthe data?
Where’s the data come from?
(12:49):
So the datathat we get
is through the API.
That like LinkedIn or other
other organizations would send to the search engine.
Okay.
so itsyou know
you show up on Google,on your LinkedIn profile,
that's essentiallywhat we're getting.
So it's not a scrape or anything.
It's just purepure data.
And then what we do is
(13:11):
he maps it into,
the model that getsyou know, 60 or so
columns of traitspersonality traits
and scores in those
and then maps thatto a summary of
developmental psychology, like the one I sent to you.
Right? Right. Okay, so
I'm going to read the categories.
I'm going to bethe guinea pig here.
(13:31):
Which isreally interesting.
So I think there's about 6 or 7
opportunity, disciplined,expert, results,
empathy, systematic,and holistic.
And my range, my scores ranges
anywhere from 2, which is not great, to 15.
And the one that
(13:51):
when we shared thisyou know, before today
I was like, oh my gosh
my empathy scoreis only like seven.
That'sthat feels horrible.
I thought I was moreempathetic than that.
I try to be empathetic.
So I wonderif you can explain,
a little bit behindthese characteristics
and where do they fall
and how arethey important?
For people
(14:11):
in terms of what you'retrying to measure,
which isyou know
propensity for success with adopting AI or other new things.
Absolutely.
So if I remember yours actually
I'm going to pull you up here.
Okay.
One sec.
This should be called empathetic.
The fact that we're doing my psychological
breakdown here onmy own podcast is crazy.
Well, it's that results score is where your EQ comes from.
(14:35):
Okay. Soyou are
you are a lovely individual.
That's that was
that was oneof the things
that actually drew me to you
because of your language
people recognize oneanother through language.
Okay, so
One of the things that I recognized in you
was that resultsscore that you have
and that makes youa really solid CEO, right?
(14:57):
The that discipline scorethat I showed you
in your results score
and then yourexpert score
kind of those three things clustered together
Okay.
It's a really niceprofile for a CEO.
So for you opportunistic was an 11
discipline was a 15
expert 9
results 13.
So what'sthe range?
What's the topend of the score?
What's the most you canscore on the category?
(15:18):
You have your own scores.
So, if you add them all together
that's essentiallywhat we call capacity.
Okay.
So yourtotal score
is kind of looselyrelated to IQ
But it’s really how much capacity you have
[Jeff] Okay.
To grow your EQand to evolve yourself
(15:40):
and yours ispretty high.
You've got afairly high score
and you're good at
clearly are good atevolving yourself.
So you've gotyou're also analytical.
We know that, so
Flattery will get youeverywhere, keep going.
So, and you've got enough empathy
to be introspectivetoo, so
we kind of look for that
kind of a high-ish score.
(16:02):
We look forlittle bits of empathy
or enough empathyor introspection
and we lookfor analytical
and what we'relooking for
is that ability orthat capacity or aptitude
to keep evolving yourself
and doing the workon yourself.
And it's usually the workthat comes along with
a therapist or a coach or a guru
or whoeverit is that
(16:23):
that you mighttend to work with
or you just went to the school of hard knocks
and had plenty of adversity in your life
and or perhaps a littlebit neurodiverse.
I don't know ifyou are or not, but,
neurodiversity is a bit of a superpower.
Steve Jobs was dyslexic,Henry Ford was dyslexic.
Neurodiversitydoes tend to push
or seem to push up
(16:45):
emotional psychologicaldevelopment
a little bit earlierin some folks.
But for you,
you've got a nice high score.
And, so your main score is discipline.
Your secondary scoreis results
and in that whatyou will have is
this person who is able to create
(17:07):
a very efficient machine
around pretty muchanything that you do.
So you probably havea bit of a playbook.
You probably havea bit of a method
for doing things.
It's probablypretty repeatable
and you’ve turned it intosomething incredibly efficient.
I imagine that the
editorial process behindyour podcast is
(17:29):
down to a scienceand incredibly efficient.
And almost
I wouldn't sayidentical every time
Right, right.
but probably continuouslyimproving every time.
Right. You're improvingupon that playbook.
And that's kind of how you're wired.
And then that results score
again we call thatkind of the CEO mindset.
It's, that's about leverage.
(17:49):
That's theone.
And it's over an eight.
So anything overan eight on the
on the scoreboardif you will
is impactfulto your behavior.
Your empathy is a little bit slightly different.
Empathy is right on thatborderline of impactful.
You've got enough to be introspective
and read the room, right.
You may still use some rules in your head
(18:09):
to read the room a little bit now and again
but for the most part
you're probably readingthe room pretty fast.
That resultsis where your EQ
comes from, though,
and that'swhere your complex
problemsolving comes from.
So as you seethose complex problems
emerging in the marketin technology
and things like that,
you're easily ableto understand them
and go andrun after
(18:31):
folks that can help you findanswers for those problems.
So then whereare red flags?
What?
Where are red flags that people are just not
not necessarily mebut what are
what are scoresor categories
on this thingthat jump out?
This is not the person
that you want to leadyour first AI project.
So typically what we're going to do.
(18:51):
So what I do withthese scores is actually
segment the employee base into three groups.
Okay.
We call them results, resilient, and reluctant.
So results is goingto be literally results.
You know they're goingto be high results
plus empathyplus systems.
Maybe someexpert
So you would be what we call resilient
because you haveplenty of results.
(19:13):
And you'vegot a discipline score
that's almost equal to results and your expert scores
pretty high up there.
So I call you resilient.
And so you'd bein that 15% group.
And thenthe reluctance
would look a little bit more like
heavier disciplineheavier opportunistic.
And those folksthat are
(19:33):
just not comfortablewith uncertainty.
And most people arepretty darn self-aware.
They know who they are.
So when we startto break down
the employee baselike this
nobody tends to be super offended
[Jeff] Mis-categorized? [Karrie] if you will buy that, right?
So there's no good or bad.
It's just where you are right now.
(19:53):
Right.
And what your comfortable with.
And I kind of call those like
our transformand grow team
and our sturdy and stable team
you always want inany transformation
whether it's returnto office or whether it's
I adoption or just abusiness transformation,
you kind ofwant to have
and eye on who'sin those two teams
(20:15):
because you need a team
that's going to do a really great job of
driving the ship
and making sure it's steady and stable and that we’re
maintaining revenueand we're not
losing anythingor losing any customers.
And you all, but you also need that group
that you identify
that is comfortablewith ambiguity
and comfortablewith uncertainty.
And they're going to run after the
the change or the optimization
(20:36):
but they're going to alsodo a pretty good job
of translating and normalizing all that
for the rest of the team.
And if you start an engagement
do you do a more direct assessment
to validate against whatyou've gotten off the API
in creating something like this
or is this accurate enough thatyou can move forward?
Or do you have to do
[Jeff] something a little bit more direct[Karrie] you know
(20:57):
[Jeff] like the old Myers-Briggs?[Karrie] If I’m doing
If I'm doing recruiting or something like that
and I often get asks for recruiting or organization design
or things like that.
If I see any red flags
or if I don't have enoughdata for somebody or
if it's a reallycritical role.
I’d staff, I have psychologists
and we will interviewand confirm
(21:17):
that our scoring is right.
I can also do the same thing
with spoken word and transcript
if we've got some,you know, recordings.
I can ask simplequestions on a meeting
and get basicallythe same thing
as a confirmationof somebody.
[Jeff] Right.
So if precision is required
we can do that.
It takes an extra step.
But when we're talking about adoption
(21:38):
or change managementor things like that
it’s horseshoesand hand grenades.
It doesn't have to be perfect.
We're looking forbig broad strokes of
of groups of employees
and how we thinkthey're going to behave
and what they're goingto be comfortable with.
And if we find that
there are some differences as we go
we can make thoseadjustments.
And then as part ofyour engagement,
(21:59):
do you help suggest actual
applications or use cases?
Or you work togetherin terms of the project?
I just wantto share a post,
I don't know if you saw it.It’s great,
it was shared by Henrik Jarleskog
I’m probably messing up his name
he's anothergood follow
He's like,this is my team.
And it'sa dozen.
His dirty dozenAI Staff
(22:20):
And he calls them staff
and he even givesthem all names.
[Jeff] So he's like[Karrie] I Love that
ChatGPT is my Chief Strategy and Innovation Officer
Claude is my Executive Editor-in-Residence
Perplexity is my VP ofResearch and Insights
Gemini, my Director of Real-Time Verification
Midjourney, I don't even
I've haven’t heard of a lot of these
Creative Directorof Visual Production
(22:41):
Canva, Head of Rapid Design
Eleven Labs, Senior Manager of Voice Experience
Notebook LM, Chief Knowledge Curator
Gamma AI, Director ofPresentation Development
Otter AI, Chief Meeting Historian
Ambient AI, Chief Workflow Orchestrator and
Veed, Director ofVideo Content Creation
I mean, I just thinkit is such a
(23:03):
such a great postI reached out to him
I’m like, you gotta share thisbecause it just shows you
I’m like, you gotta share thisbecause it just shows you
if you changeyour mindset.
He's operating like this huge staff
of people that are helping him.
And he, you know,he spent the time
to figure out which apps do what.
[Jeff] But it's pretty[Karrie] That’s right
interesting now that, you know,
we should be to the pointwhere there's
enough examplesthat you can point to
(23:24):
to help people start to figure out
where they can start to see some
some real results.
And usually that's kind ofthe big ‘aha’ moment.
As we get further into
I'd say 3 or 4 weeks into any,
any implementationor adoption cycle.
It's the, the moment
for leaders is, oh,
we're not optimizedfor this.
(23:46):
My team is not optimizedfor this.
I'm not organizedfor this or
Ooo, now that we havedigital workers
that we're relying onfor certain things,
what does that meanto my human workers?
How do they work together?
How do I organize them?
What does that
what kind of capacitydoes that create?
Or what does that doto spans and layers
inside my org design.
(24:07):
So those questions really
start to come uppretty quickly.
And we startto answer them
in a bit of a
hey, it's an iterative process.
Right, right.
approach to things, right?
It's, there's no single answer.
And it kind of depends on what's
what cycle you're in
as far as whether you'retrying to grow
or if you arejust trying to
(24:29):
maintain stability and survivein an economic downturn
or things like that.
Yeah.
I had an interviewwith this guy
Charles CorleyHe's great guy and
he's in real estatein Singapore.
But what's interesting is
he was he'sjust a curious guy
But he's been playingwith ChatGPT every day
since it was announced inNovember two years ago.
And so he'sdoing all this stuff
and he's like, Jeff,
(24:49):
I just think of it reallyas a thought partner.
And I runthrough
and you know,we do all the stuff.
And I was likewell Charles
what about hallucinations?What about hallucinations?
And he's likeyou know
you treat it like a really good junior associate.
If you're going to actually do something,
check their workyou know
don't necessarilytake it at face value
but do take it for direction and
(25:10):
do take it for thought process.
And the other thingthat I thought that he
he said that wasreally profound
which I think a lot of people blow
is they're looking for this
like questionand answer
almost like a searchand return
and getthe result.
You got the
‘Can AI Generate Results?’over your shoulder.
But his thing is like
no, it's not like this one time thing.
It's this iterativeprocess.
(25:31):
Both you and the machine
[Jeff] over time[Karrie] that’s right
exploring thingsconversationally.
You probably seethese posts,
you get these ridiculousprompt engineering charts
to put upon the wall
that have a thousand
a thousand linesof you know
nine point font that you can't read
when you're our age but
he's like, no, no,no, no, no.
You know don't try to just be conversational
(25:52):
[Jeff] and work the tool and the[Karrie] just talk to it
tool will start to returninformation back to you.
I call it a good drunken intern.
It's,
it's like it's the perfect
it's like the perfectname for it.
Because that is kind of what it is.
And an LLM
LMS aren't a panacea.
They're not wherewe're going to end up.
The way I look at the large language model tools
(26:16):
is that they're a really good first step
because they're low friction.
Anybody can push a button on a screen
and get started with it
and get to understandhow to use it
how to optimizetheir day with it
if they're wiredto do that
and they're wiredto experiment.
But what we'rereally doing
is conditioningorganizations around
(26:39):
something that most,you know,
office environmentsaren't used to.
Manufacturing has beenyou know
practicing Six Sigma for decades.
Right.
But whatwhite collar
jobs are practicing Six sigma and continuous improvement.
So can we startto introduce concepts,
from other functionsor other industries
(27:03):
that make sense into this process?
And you start to get peopleused to the idea of creating
virtuous cycles
around productivity,improvement, quality.
Value creation, you know.
How do you take a 5x employee and turn into
turn them into a 10x employee?
Right.
So how do you
how do you startto challenge yourself
(27:24):
to do some of those things?
The current tools aren'talways going to do that.
There are tons ofAI tools and models
out there that can be baked into
lots of different systems.
The problemis that
they're higher frictionand higher expense
to implement insidemost organizations.
Not to mentionthe fact
that most organizationsdon't have the data
(27:46):
governanceand organization
to make a lot ofthese tools work.
Right.
So the way I see it is
let's get some of these
easier base hitkind of things right.
Let’s condition the organization and the people
for how they needto think about change,
think about AI
understand that it's not justgoing to take their job
or consume what they do.
(28:08):
And then start toreally do a good job
of comingup with
the high value things,the value creation things
that other AI models and tools
can do forthe organization.
So that if we'rereally successful early
in some of these pilots
and we get ROI and we get lots of adoption
then the CIO or CTO can walk into the CFO's office
(28:30):
and say, hey,this is successful
we know exactlywhere we need to go
and they get the green lightfor the investment
I don't know any CFO
who's going to greenlight
an investmentin any AI tools
or projects that have 20%adoption rates.
So it's kind of breaking that cycle of
(28:51):
innovation projects,getting low adoption
and then justbeing shelved.
And creating higher successor quick wins early
so that they can get yeseson the next investment
Yeah
That's what we're going after.
Yeah, the data governanceand the data process
to feedthese things
is certainly a superimportant part.
(29:12):
I've got some recency bias.
I was recently at theAtlassian ‘Team 25’ show
and they just jumped inwith both feet
because they'rein this unique position
[Jeff] where they've got a lot of[Karrie] What a great organization
[Jeff] They've got all this great data[Karrie] Great minds at Atlassian
[Jeff] inside these tools[Karrie] they’re so good
And so they're attacking
kind of the knowledgediscovery process.
And I forget the numbersbut like, you know
a third ofour time
(29:32):
is spent searchingfor things
searching forinformation, right.
Because you can’t remember
Is it a text, an IM, an email, whatever.
And so that's how
they're really goingafter the opportunity
is to use thetheir internal
they just released
It's called ‘Rovo’
to work within thishuge amount of data
in the systemalready
to start returning,you know, better results
(29:54):
and helpingpeople get their
get their work done.
And the other thingthey've done
I thought waspretty interesting
and I’m curious is
they think you know you got to have this ‘top down’
to go with the‘bottom up.’
So you have to have
you know, senior supportand not only support,
but actually modeling the behavior as we know
all the time for senior leadersto model the behavior
that they wanttheir people to do.
(30:14):
I'm just curious,
do you think in the not too distant future we'll have
like a Chief AI Officer?
BecauseI mean
just like the list of tools I read down
just trying to keep upon what is happening.
You mean you definitelyneed an AI tools friend
who can help
help turn you on to what's
[Karrie] Is just changing too fast to keep up with, yeah[Jeff] is changing too fast, so how do you
How do you see it evolving?
(30:34):
As you knowyour early adopters
have some success?
How does it start to proliferatethrough your organization?
and what will happen atthe top levels in terms of
of driving it from the senior positions?
Yeah.
Atlassian is just such a good example across the board.
They've got a greatleadership team
they've gotgreat mindsets
you know, headingup the company.
(30:55):
and you can see whythey've been successful
and been able to grow and evolve
with the marketas they have.
If I rememberright
weren't they one of the few
that would promiseespecially digital
digital creators or people with digital IP
that they wouldn'tuse their code to
(31:15):
to update the models and
and stuff like thatso that it would be
proprietary to them.
They recognize things quicklywith early adopters,
things quicklywith early adopters
and they respondvery quickly
and that's exactlywhat you want to see
in leadershipteams.
So what I think is goingto happen longer term
is that you're going to see
(31:35):
a bit of a differentialstart to happen,
and it'll be kind oflike your
Go take a look atAutoZone's stock price.
It's just kind of a perfect example that
I don’t know how much AI they use or they don't but
William Rhodes [AutoZone Chair]is so high in his results score
he is, he
(31:57):
has been able to turn a brick and mortar auto retailer
into a company that has a $3,000 plus stock price.
Like, it's unheard ofbut that's the power of what
leadership mindset is actually able to drive
inside anorganization
(32:17):
because they're able to attract similar talent.
Right.
So you're going to start to see
a lot of thattalent collecting.
It's going to look kind of like a weird
people clusteranalysis.
Where you've got talent
that can adopt andadapt to new stuff
clustering together in organizations under leaders.
(32:39):
And you're going to see those organizations
pulling faster aheadof the pack.
You're seeing a little bit of that with Shopify.
They don't want to seeany asked for headcount
unless you can provethat AI can't do the job.
You're going to seesome of those leaders
starting to cluster together and
and pull ahead of competition
or you're also going to see
(33:00):
some mid-market or smaller organizations that are growing
that are ableto grow faster.
Right.
Because the cost to build
and the cost to growis sinking like a stone.
That's the other thingthat AI is going to do
and is doing right now.
The cost to build codeor syndicate
or create leverageis sinking fast.
(33:23):
And those who areable to leverage it
are going to be able togrow and pull ahead of
their competitive setvery, very, very quickly.
Yeah.
And it's also about creativity.
I mean, again,recency bias
Andrew Boyagiat that show
who's in charge of their developer experience evangelism
talked about actuallyfor coding.
He's like, you know mostof the coders are pretty good.
(33:43):
AI is not going to replace the coding.
But there'sa lot of tools.
There’s a lot of steps inthe process that AI
[Jeff] can help.[Karrie] Exactly
So like runa basic review
before you send it outto your peer review.
One of the things I thoughthe said was really creative is
you know, a lot of the non English
for a lot of developers
English is nottheir first language.
So being able towrite nice summaries
(34:04):
about what they did
for everybody elsefor the documentation
like, wow, that's genius.
You know, that is something they struggle with
Exactly
not the coding piece.
So even all thatkind of periphery.
But I'm curious in terms of the adoption
let's just go straight at it.
You just said
you know peoplearen't going to hire
if they can't proveAI can't do the job.
How does thatget resolved
(34:24):
when you'retrying to do this?
You know, it'snot taking your job.
It's anassistant.
Versus, I'm afraidit’s taking my job.
That's all I'mreading about.
See, I think there'sgoing to be
the technology industry
and everybodyelse
Okay
really when itcomes to that.
Because so much ofthe tech industry is
developer led, code,you know, code oriented,
(34:46):
that's what a lot of these models
are kind of optimized for is
is writing code.
So, yeah, they're going to be able
to automate quite a bit.
They're not goingto automate their
10x developers
or even probably their5x developers.
They'll make their 10x developers20x developers
and their 5x, 10x.
So I think that'llhappen a lot
in the techindustry.
And you're alreadyseeing it
(35:06):
almost acrossthe board
where they are rethinkingtheir staffing and their
you know, taking out some of those that
where theycan optimize
and you're seeing
I mean last year we saw more developers on the street.
But they're fairly quicklygoing to find a seat
in other industries,I think.
(35:27):
Yeah.
Because other industriesdon't have that
data sciencedata architecture
data basethe you know
and any of thosefoundational things
most companiesjust don't have,
because they haven'tinvested in it.
Yeah.
they're, that's not who they are.
So those developerswill find other homes
(35:49):
in different spotsin different industries
that are nowstarting to invest in it.
But most of them
if you're in manufacturing,
you're creatingactual physical things.
So, you know, are you going to swap out
your entire back officetomorrow?
Probably not.
Will you in five years?
Maybe.It's possible.
But here's the wayI really look at it, Jeff.
(36:10):
The fact is that our baby boomers are retiring
at 11,000 peopleper day.
So we
and we only haveabout 8,000 Gen Z's
coming of age tobackfill them each day.
So it’s a delta of about3,000 humans a day, right?
If we've got a delta of about 3,000 humans today
(36:30):
that’s about 5 million humans by 2035.
So what that does todifferent industries
and we're already seeing itin manufacturing
in health carein senior health
as baby boomerscontinue to age
and they move intosenior living homes.
Right.
We're goingto have
(36:52):
lots of big shits in wherewe need talent to go.
And it's going to bea lot of skilled labor.
So thosepeople who
maybe either aging outof a white collar job
or thingslike that, you know,
does it need a Gen Z to backfill that?
Maybe. Maybe not.
But do we need Gen Z
(37:12):
or do we need other people to
reskill themselvesinto
home health care or other roles?
Absolutely.
I think that's a lot of what's going to happen
from a humanperspective.
We’re going to see a lot of humans
with AI doingtheir jobs,
and you’re going to see a lot of humans reskilling
either intodifferent industries
(37:32):
or upskilling into theirexisting organizations.
Yeah.
And it's not onlydevelopers too, right
it's creative writersand you know
[Jeff] a lot of the artistic stuff and[Karrie] Totally
you knowit's got to be
just wreaking havoc onthe poor Fiverr subs
that are all overthe developed world
for all thosejust quick little
those quicklittle things.
(37:52):
I want to shiftgears a little bit,
because I knowmost of your clients
are big organizations,big companies.
You're running asmaller organization.
And I know youthink a lot about
you knowkind of this
step function thatsmaller organizations
in terms of numbers of people
can have
using this technologyto grow a really sizable
(38:13):
important andsignificant business.
And, you know
I look no furtherthan that list that
I just read from Henrik with his
giant, you knowhis cabinet of 12 people
that I don’t know what he's paying for all those apps
but it's not hundreds ofthousands of dollars per year
that isfor sure.
So I wonder if you can share as
cause I know youthink about this
how this is going tochange the opportunity
(38:34):
for smaller businesses to really
find opportunitiesor grow in ways
that maybe they didn't think
were possible at all before.
Yeah, actuallyI do
I work with somesmaller organizations, I
Okay.
I didn't actually thinkI was going to, but
I've ended upacquiring a few smaller
(38:54):
clients recentlyeven and
what I'mfinding is that the
scope is just a little bit different
because theywant to jump
a little faster
into org design andoptimization of the team.
So thatespecially if
they've already got abig chunk of their team
already using tools
like ChatGPTor Perplexity
(39:15):
or, you know,Canva, other things,
and they're really juststarting to jump into
how do I optimize?
But they're tryingto grow
and their goal is to growwithout overtaxing
the team thatthey have
but then without actually growing
headcount inways that are
that are onerousto the
to the bottom line either
(39:36):
so thatoptimization
is absolutely happeningright now.
And it just
it happens in varyingdegrees depending on the
the leadersand founders
inside theorganization.
I do think
we've already gotcompanies out there
that are atyou know
$20 million in itin a couple of years
with nominal amounts of VC investment and
(39:59):
things like that
because those foundersare able to leverage the
tools that are available
and the cost of thosetools is not big.
So we willabsolutely see
smaller, you know10 person, 15 people organizations
With $1 billionin revenue
probably in the next 2 to 3 years.
(40:21):
Yeah
I'm pretty surethat's going to happen.
I will probably never have muchof a back office at all.
Right, I heard that in oneof your other podcasts
you said basically you have no back office and you
you know, you chose in termsof a focus on automation.
you know thosethe stuff that nobody likes
to have to deal with likeaccounting and finance.
And you've automated
[Jeff] a lot of that[Karrie] Why would I hire?
[Jeff] within your own organization.[Karrie] Why would I hire for that?
(40:41):
Yeah, I started with RPA years ago.
[Karrie] So [Jeff] Is this RPA
No one ever talk about RPA anywhere.
[Jeff] RPA was supposed to be[Karrie] I know, nobody’s talking about RPA
Supposed to be our greatdigital assistant.
I used to go cover theAutomation Anywhere show
a numberof years
and those were our little peepsthat were supposed to be
you know,our digital employees
as they were soldour digital assistants
Karrie] Totally, they’re perfect little[Jeff] So I don’t know, is this the
(41:01):
They’re perfect little swivel chair employees
If you’re going to take data from one spot
and put it into another.
It's awesome.
you know, now I've
I’m moving on toother other technologies
that are a little bitmore modern.
As my processes changeand as we grow.
But the same principle applies.
I would rather hirea really great CFO
(41:22):
that can think strategicallyabout the money
and think strategicallyabout growth
than I would an army of bookkeepers
to keep track of
all of the you knowinvoices from
you know, contractorsand things like that.
My team is 100% virtual.
I haveit's all 1099.
I train them,I invest in them.
But it'sall 1099.
(41:43):
So it's a very
cost and overheadlight organization.
And that's on purpose.
Right.
Because I don't want to,
because we're a services organization.
I don't want to
I don't want to throw abunch of overhead costs at
at client projects
and have them consume that.
So that's just the way
(42:03):
that I approachproblem solving.
I'm not special necessarily
but that's justhow I think.
And if I think that way
there are other people
thinking in similar ways
that they're going to be ableto grow companies
even faster than I can.
Yeah.
So it's going to happen.
It's going to be reallyinteresting to see
Mid market, I'm actuallyreally optimistic about, too,
(42:26):
because we're goingto see a lot of PE
over the nextten years I think
we're going to see regulatory environment changing
over the next2 or 3 years.
And a lot ofthose boomers
that are runningcompanies right now
are going towant to get out.
And as theydo that
we're going to see a lot of consolidation.
We're going to see a lot ofcompanies being bought
(42:47):
and then technologybeing applied
to generate the value
in that privateequity thesis
that they just haven'tnecessarily done
done as much of before
their cutand burn
[Karrie] kind of approach to things[Jeff] I was going to say
[Jeff] I don’t know what private equity folks [Karrie] isn’t going to work
[Jeff] you're talking to but[Karrie] anymore
[Jeff] most of them seem more[Karrie] It’s gonna
more about the harvesting
and the milkingthe cow than,
[Jeff] investing in growth.[Karrie] It’s going to give birth to
(43:10):
it, this is goingto give birth
to a new generationof private equity.
Yeah
I'm quite certain and that’s we’re gonna
we're goingto see that
in the nextcouple of years to.
I hope soI've seen
I've seen them destroy too many
too many industries from
my favorite is the old rental car, you know
[Jeff] those were all divisions[Karrie] Oh, right
you know, to keepthe factories.
It's a greatillustration
of, you know, whatare you optimizing for?
(43:31):
Or you know, whatthey've done,
you know, kind ofin the radio business.
They won’tthey just
they won't be able to
to follow their oldplaybook anymore.
And that’s exactly the pointbecause the
most of the folksin capital markets
tend to be kind of that
more safety andsecurity oriented mindset
that need that likesthat playbook
(43:52):
and adheres to that playbookkind of religiously.
They love thespreadsheet, right?
So they're goingto end up
breaking outof the spreadsheet
or somebody elseis going to go in
and attract that LP moneybecause the thesis is going to be
much more focusedon leverage.
But that also means
(44:12):
that they're not goingto be able to hire
the same way anymore either.
You know, it'snot going to be
we’ll just go hire my friend
from Wharton orHarvard or whatever.
It's you're going tofind those people
that look morelike that
kind of scrappy versionof a Steve Jobs or a,
you know, Jeff Bezos.
Right.Interesting.
All right, well Karrie, we're getting towards the end of our time.
(44:34):
And, you knowshort of calling
1-800-Karrie-Help-Me
What are somekind of
words of advicethat you have
for leaders as well asyou know,
mid-levelmanagers
and eve, I supposefrontline workers
in terms of how they should
you know,
kind of approachthe challenge
how they should thinkabout the opportunity
and really start getting their feet wet
if they haven'talready.
(44:55):
And if they haven't
or if they have,how do they help their
their compadresget into it?
And if they haven’t,what should
what should people seek even within their own organization
to try to get some help
without necessarilyraising your hand
and saying, I'm scaredto death of this thing?
Greatquestion.
I will give the same advice
that I give tomy college kids.
(45:16):
Every weekI ask them
What risks did you take this past week?
Tell me what risksyou took.
That's a great,great question.
What I’m doing with that is
getting themto be mindful
about the risks they take
and their comfort levelwith uncertainty,
and just getting themto be aware of
(45:37):
their relationshipwith uncertainty.
And if you keep doingthat over and over again
eventually you kind of find that
that you'redealing with
with some of thatemotional baggage
that might have beenin your past.
Aside from thatget a coach.
I love my coach.
I've got a great coach
who used to bea therapist.
Therapists are great.
You know,it but
it's not about what you learn in school necessarily.
(45:59):
Learning isawesome.
But learning and earning
level of resilience and
comfort with ambiguity is
is absolutely whatit's going to take
to be 10x in the next generation of talent.
I'm just curious.
This group of peoplethe young people
Covid was five years ago,
which is hard to believe
Yeah, you’reright
have lived with a lot of uncertainty.
(46:21):
And there’s been a lot of ambiguity
just in the greater global environment.
Things that have beenstable for 80 years
are not necessarilystable that way.
So I wonderif their
you knowis that
is that uplifting forthem in terms of, okay,
we're comfortablewith the uncertainty.
We can deal with itwe've been dealing with it.
Or is it almost like,screw it.
You know
this is such a crazy,bizarre thing
(46:43):
and is changingso fast.
You know,
I'm not necessarilyexcited about investing
when things are going
you know, what's goingto change tomorrow.
I'm curious becausethey've been
they've lived in a very different world
than, say, you and Iwhen we were 20
and you just, kind of followed the
you know, the bouncingball on the script.
You went to schoolyou went to college
you got a job at atraining program.
[Jeff] I went to a great training program, boom[Karrie] Education was a manufacturing plant
(47:05):
Yeah. It was great.
Right?
So different.
So I'm curious, your takeon their perception
of living in a worldof ambiguity
having you knowgot through Covid
and everything else.
We're already seeing the difference in mindset
So we can seemindset differences
kind of in aggregate.
And do some of that analysis.
So we see
weirdly we can seelike migratory patterns of
(47:25):
of people intheir mindsets
going from companyto company
or industryto industry.
But we also can see somegenerational things too.
And Gen Z already hasmore in their savings account
than most of theirmillennial peers.
Gen Z is unique in the adversity that they
experienced in their formative years.
(47:47):
And that adversityis a great teacher.
It sucked for them for sure.
[Jeff] Right.
But it really wasa great teacher.
And they are all
we're already seeing it with them at work to.
There's a reason theykeep kind of turning over
in their jobskind of quickly.
It's because, rememberwhat we talked about
with middle managers
(48:07):
and those middle managers being
pretty focused on playbooks and routine
and processand detail orientation.
Some of those Gen Zshave already
kind of outgrowntheir managers
get frustrated and leave.
So you're when that
more safety andsecurity kind of mindset
(48:27):
tries to put the
a more resilient or a resilient problem solver
into a smaller problem solving box.
They get frustratedit creates friction
and they leave.
Yeah.
But we're already seeing it.
And there's a lot of frustration with Gen Z
There's a lot of Gen Z hate.
I love them.
I hire them all the time.
(48:48):
I think they're great.
And when I score them,they're fabulous.
Like, I clearly find thema little bit easier.
But when I score themthey're fabulous.
So if I'm you
I put Gen Z into
some of those projectsthat are new
that do have a bit ofambiguity around them
that aren't just
they do have to pay their dues for sure.
(49:10):
They need to learnhow to operate inside
an organizationand deal with
you know, the politicsand the culture
and all the thingsand really
you know, get some domain expertise.
They can't justskip ahead.
But to keep theminterested
we need togive them
some interestingthings to do
and give themsome agency
because they alreadyhave agency themselves.
(49:32):
You got to give themsome agency over
in improving their jobs
and improving
the work culturethat they sit in.
Yeah.
It's interesting as you're saying that
I'm thinking ofmy grandfather.
You know,
the Greatest Generationfought in World War II
went to Koreafor a little bit.
And just thein terms of the
uncertainty that they facedin the 30s and the 40s
in terms of building reallystrong resilient people.
(49:54):
Now, what'skind of odd.
Then they all went to
the job marketat that point
you know,through the 50s
that was justrocking and rolling
everything upand to the right
and pretty steadyand constant.
And you
you got that job at AT&Tlike my grandfather did.
And you could have it
for 50 years or 40 years
[Jeff] with lifetime employment[Karrie] I think they also
They also grew up in thatgreat influenza pandemic
or epidemic in theyou know, teens.
(50:16):
[Jeff] Yeah, yeah.[Karrie] Yeah.
They dealt withlots of adversity.
So Gen Z is the first generation
since thegreatest
to have that kind of adversity in their lives.
And I see kind of big things
or similarly big things for them.
Yeah that's great.
Well that's goodbecause we need
[Jeff] as you said the demographic trend[Karrie] We need it
is the biggesttrend of all and
(50:37):
you know especiallyin developed countries
you know there justaren't enough
there aren'tenough people.
So let’s lean heavily on our young people.
And I'm glad to hear that
you've got such great enthusiasm for their
for theircontributions.
[Jeff] Well Karrie, this has been great.[Karrie] Thank you
Thank you fortaking the time today.
Thanks for tryingto make a difference
in the world of AIand AI adoption.
It was really refreshingat this Atlassian event
(50:57):
because they're sothey're so into it.
You know, they
they actually have likeon their internal thing
every morningit starts you know
start your daywith AI.
I mean just like this
[Jeff] constant encouragement[Karrie] That’s awesome
to help peopleget over this hump.
And then you end upwith people like Henrick
who’ve got you know, 12 little AI assistants
helping him get through his dayevery day.
So, reallyappreciate the time.
(51:18):
Well, absolutely.
And next time youneed a bag carrier
at an Atlassian eventyou just let me know,
and I'll be there.
Okay, I'll let you know.
[Jeff] All right. Well, thanks again.[Karrie] You got it
She’s Karrie, I'm Jeff.
She's in Chicago.I’m back in Palo Alto.
Thanks for watchingWork 20XX.
Thanks for listeningon the podcast.
We'll catch younext time.
Thank you.
(51:38):
Excellent.
Thank you guys.
Hey, Jeff Frick Here
big shout out to the podcast audience.
Thanks for listening in.
You can get show notes and transcripts at Work20XX.com
And that also has links to the videos as well.
Appreciate you listeningin on the podcast
Do reach out
say hello, like subscribeand smash that notification bell.
(52:00):
Thanks for listening.Take care. Bye bye.
All right.
So I will
count us down
and we'll go
in three, two, one.
Hey welcome backeverybody.
Jeff Frick here
coming to you from the home studio
for another episode of Work 20XX.
And I thinkyou know
when I started this podcasta couple of years ago,
the focus of the futureof work was all around
distributed teams and hybrid work
(00:20):
and everyone trying to figure out
how to react to kind of the
Covid emergency and urgency.
But as we're like 3or 4 years down the road
or five years,I guess, since Covid,
hard to believe
it's really focusing onAI and AI adoption.
And how do we get people to use AI?
And everybody'sscared of AI.
And what is it exactly
and how do we get itin our workflow?
So I think that's really
(00:40):
kind of come tothe forefront.
And so I'm really excited
to have this next guest.
She's an expert at it
not only in AI specifically, but helping organizations assess
how theycan increase
the probabilityof success
in trying to get things going
inside theorganization.
So joining us
all the way from Chicagothe Windy City,
she’s Karrie SullivanFounder and CEO
of Culminate Strategy Group.
(01:01):
Karrie, greatto see you this morning.
Thank you.
It's so great to be hereI appreciate it Jeff.
Oh my pleasure.
So I'm just curious,
were you alwaysin this space
or did the
was it is one of theseclassic cases
where the marketjust came to you
over the last several years?
It's more like that.Yeah.
I started outas a marketer, oddly,
and turned into a database marketer
(01:22):
during the, you know, dot com boom.
I was an early stage employee at Cars.com
Okay
and I found my
I found my space,my niche in digital.
And that's really
I've been doing a lot of transformation
into digital for the last 20, 25 years.
All right. Great.
So you are very activeon LinkedIn.
(01:43):
You're a great follow.
If people don'tfollow you,
check out Karrie'sfeed on LinkedIn.
That's where I kind ofcame across her.
And you've got thisinteresting process.
Where you helppeople do an assessment
of their people
to then figure out
who are the best people
to start their kind of AI journeyor to have success.
So before we get intosome of the details
(02:05):
I wonder if you could share
where that came from and how do you
got started on this?
On this process?
One of the things that Iwas trying to figure out
as I was working on scalefor my own business
was what's our superpower, right?
It's a good research question.
You got to ask it.
And the answerwas my weird EQ.
(02:28):
I didn't realize it was weird at the time.
But it was a little weird.
I have this, apparentlyI have this ability to
read the room really fastand compile teams
together really fast
and specifically teams that do a really good job of change and
adapting to ambiguity, adapting to uncertainty
(02:51):
things like that.
That's kind of whatI look for in humans,
when I recruit them.
That was kind of my
‘Oh, crap! What if?’ moment.
Oh, crap.
My EQ doesn't scale
to every project that we're going to be on.
But thenwhat if
What if my EQ could scale to other
(03:12):
all the projectsthat would be on?
And what would I do with that?
And I reached outto a colleague of mine.
His name is Christopher Skinner
also a good follow.
And he actually is a he's a data scientist.
Computational linguistics wizard guru.
And he invented this model.
(03:33):
He mapslanguage
to developmental psychology
and essentially we can get a bead
on individualsin any company.
Frictionlessly in a couple of hours.
I know the EQof most people.
So one of the things
that Brian Elliottalks about
Another great kind of voice on the future of work
(03:53):
is that in terms of management,
capabilities andmanagement propensities,
that it's the same kind of
characteristics of good managers
that we're able to kind of
deal with the ambiguity around
Covid and changing rules and
[Jeff] and in that[Karrie] Absolutely
which is kind ofthe same attributes
of helping organizationsimplement AI
(04:14):
which in a lot of waysis the same thing.
It's kind of ambiguous.
Nobody knows really what to do
or how to start.
So what are some of thosecharacteristics in,
I guess boththe managers
as well asthe individuals
who are going to havea higher propensity
to be successfulwith new things?
I can give you percentages, how’s that?
About 7% of any company population
(04:36):
is made up of people who,
are already really good
So do you remember Maslow's Hierarchy
from high school?
[Jeff] Yep, yep.[Karrie] Right, so
like the 5% to 7% have gotten kind of close
to the top of Maslow's hierarchy.
Kind of that self actualization-ish.
[Jeff] Right[Karrie] right?
Or cognitive.
(04:57):
And what you're actually doing
is dropping the emotional baggage
that people are learning to get past
over the courseof their lives.
And earlierin earlier stages at
part of what we have to learn is
comfort with ambiguity orcomfort with uncertainty.
And, you know,80% of the population
(05:18):
is deeply uncomfortablewith uncertainty.
And that's really the biggest
challenge with any change.
I don't care if it's future work
or AI or a big pandemic
or even justimplementing
sales for a certainother system
inside your organizationor M&A, right?
The fact that people don't like change and uncertainty
(05:39):
is universal
amongst about 80%of the population.
The other 20%,
gets into varyingdegrees of
that you know, the middle layers of,
you know, esteemand belonging,
in on Maslow and in the upper layers
of cognitive and self-actualized.
(06:00):
And that's essentiallywhat we're looking for.
We're lookingfor grit
and levels of griton kind of a continuum.
In terms of successof the project.
If the 7% are successful
I mean, obviouslythose other
90% are not going to change their fundamental
kind of psychologicalmakeup.
Is it just because it gets
beyond the uncertainty
(06:21):
that then that's what drives the adoption
beyond that first seven and it's
You hit it
less crazy?
Is that why it then works
with those other people?
Because they don't really change?
Well, we're triggeringa couple of things.
So they’re not goingto change their behavior
just becauseyou tell them to
or just because you train them to.
Right.
What we'redoing is
it's a kind of boil the frog scenario, right?
(06:43):
So you takethe 7%
and those are the folkswho jump straight
to experimentation on theon the change curve.
Okay
I call this hackingthe change curve.
And very, very few people
can jump straight over thattrough of the change curve
into experimentation and just acceptance and moving forward
And that's our 7%, right?
(07:04):
Those are theearliest adopters.
They are super comfortable with experimentation.
They automate themselvesout of a job.
They're super comfortablewith ambiguity.
Their innovationmindsets even
but they're also incredibly goodat creating leverage
They know how to
generate a lot of result
for very little or as little effort as possible.
(07:27):
And that's kind of what you're looking for in any
any optimizationof a population.
So we get them to do theexperimentation with us
and tell us what the most
productive use casesare in an organization
because that's problemnumber two with AI, right?
[Jeff] Right.
Is coming with the right use cases
for yourorganization.
So they came up
they come up withthe use cases.
(07:48):
And then group two is about 15% of the population.
And they are smart.
They are intellectually curious.
They liketo learn.
They liketo teach.
And it's thatlearn and teach
tipping point that isreally important here
because they're the translators
between the two groups.
[Jeff] Right.
So if the first group does our
(08:10):
experimentationand they're
they're running offon innovation.
You're not goingto hand
just hand those innovation projects to the 80%, right?
So we work with that 15%.
And they’rea natural
kind of crossover between a proof of concept group.
Right.
And our change champion group that’s
there essentially the same thing psychologically.
(08:30):
And that's really
that's essentially what we're doing.
So if you want to map this to
Kotter or pro Sci,
that's kind of that second group that you want
and what they're doing is
proving outthe concept
proving outthe use cases
and then giving us the detailed process maps
the playbooks,the checklists,
the detail that everybody else needs
(08:53):
so that with the 80%what we're doing is
taking the ambiguity out.
So we're creatingprescriptive
here's how you adoptfor your function.
And we're triggering their need to belong or fit in.
They won’t change their behavior because you tell them to.
They will change their behavior to fit in.
So if you give them people to fit in with
(09:15):
and you give them a prescriptive way to fit in
They’ll do it. They will absolutely do it.
Especially if theirexecutives are saying,
this is our expectation.
This is the behavior we want.
These are the kinds of things
we need to do.This is who we are now.
Right.
And then what aboutthe difference
between the managersversus the people
within that 7%?
Because it's one thing to have somebody who's, you know
(09:37):
always lookingfor high leverage.
Not lazy, but looking for ways
to be much more efficient.
[Karrie] Yeah
With that comesexperimentation.
And with experimentationcomes failure.
Just as apercentage.
And if you're not failing on some things
you're not really trying
hard enough to get out on the edge.
[Jeff] So it's one thing [Karrie] That’s right
for the person to do that.
It's a different thing
for the culture or the management to be
(09:59):
okay with some failurealong the way
knowing that with
aggressive experimentationand with trial and with
you know, kind of charting new paths,
that not everythingis going to work out
and there's going to be
a couple hiccupson the road.
So are the,
are the profilesof the managers
and the people within those early adopter teams the same?
Or is there's a slightly different twist
between managersand peeps.
(10:21):
They're the same.
The biggest challengethat we run across
is that outside ofthe tech industry,
leadership teamsare a bit of a mixed bag,
and middle managementtends to be,
not quite so
top of Maslow
that we see a lot of safety and security
(10:42):
in middle managers.
And thereinlies your problem.
What's essentially happening is
that you've got a gap between the
high resilientindividual contributors
that may be sitting at thebottom of the organization
or they're buried,
and those resilient
experimenting,you know,
transformation or growth or innovation oriented leaders
(11:04):
Right
They’re at the topof the organization.
And then the middlemanagement layer
they're not bad per se it’s just that
what is typicalin organizations
outside of the tech industry
is that people are promoted for IQ, not EQ.
So they are promoted for being detail oriented, methodical,
(11:28):
process orientedand things like that.
Again, there's nothing wrong with that.
The challengeis that as you're going
through changeor transformation
or growth or big velocitykinds of things,
those folks are not wired
to handle that easily.
And that's a lot of thechallenge that you see
in future of work and remote
and Covid andthings like that
(11:48):
but alsowith AI.
Yeah.
So it’s about finding
surgically finding that 7%
wherever they sit in the organization.
And to your point, no.
They're not really fundamentally different psychologically.
They're probably just
a little bit differentwhen it comes to
experience ormaturity level.
Yeah. Interesting.
(12:09):
Okay. So youdid the test.
You did the test on me.
So one ofthe interesting
things about your test isyou don't necessarily
I don't haveto sit down
and fill out like a Myers-Briggs or
and as you've said in someof your other episodes
other things I've seen gettingready for this, you know,
psychological assessmentis not new to HR,
and trying to figure out
people's strengths and weaknesses
is not new to HR.
(12:29):
And there's been lotsof different ways
that people have triedto figure that out.
Your approachis different
cause you just basically
mine, scratch, scrape, I don't know what’s the right verb
existing content that people have already published on
on the web whether it's LinkedIn
or other social media
or maybe youcan explain
kind of, where do you get the
where do you getthe data?
Where’s the data come from?
(12:49):
So the datathat we get
is through the API.
That like LinkedIn or other
other organizations would send to the search engine.
Okay.
so itsyou know
you show up on Google,on your LinkedIn profile,
that's essentiallywhat we're getting.
So it's not a scrape or anything.
It's just purepure data.
And then what we do is
(13:11):
he maps it into,
the model that getsyou know, 60 or so
columns of traitspersonality traits
and scores in those
and then maps thatto a summary of
developmental psychology, like the one I sent to you.
Right? Right. Okay, so
I'm going to read the categories.
I'm going to bethe guinea pig here.
(13:31):
Which isreally interesting.
So I think there's about 6 or 7
opportunity, disciplined,expert, results,
empathy, systematic,and holistic.
And my range, my scores ranges
anywhere from 2, which is not great, to 15.
And the one that
(13:51):
when we shared thisyou know, before today
I was like, oh my gosh
my empathy scoreis only like seven.
That'sthat feels horrible.
I thought I was moreempathetic than that.
I try to be empathetic.
So I wonderif you can explain,
a little bit behindthese characteristics
and where do they fall
and how arethey important?
For people
(14:11):
in terms of what you'retrying to measure,
which isyou know
propensity for success with adopting AI or other new things.
Absolutely.
So if I remember yours actually
I'm going to pull you up here.
Okay.
One sec.
This should be called empathetic.
The fact that we're doing my psychological
breakdown here onmy own podcast is crazy.
Well, it's that results score is where your EQ comes from.
(14:35):
Okay. Soyou are
you are a lovely individual.
That's that was
that was oneof the things
that actually drew me to you
because of your language
people recognize oneanother through language.
Okay, so
One of the things that I recognized in you
was that resultsscore that you have
and that makes youa really solid CEO, right?
(14:57):
The that discipline scorethat I showed you
in your results score
and then yourexpert score
kind of those three things clustered together
Okay.
It's a really niceprofile for a CEO.
So for you opportunistic was an 11
discipline was a 15
expert 9
results 13.
So what'sthe range?
What's the topend of the score?
What's the most you canscore on the category?
(15:18):
You have your own scores.
So, if you add them all together
that's essentiallywhat we call capacity.
Okay.
So yourtotal score
is kind of looselyrelated to IQ
But it’s really how much capacity you have
[Jeff] Okay.
To grow your EQand to evolve yourself
(15:40):
and yours ispretty high.
You've got afairly high score
and you're good at
clearly are good atevolving yourself.
So you've gotyou're also analytical.
We know that, so
Flattery will get youeverywhere, keep going.
So, and you've got enough empathy
to be introspectivetoo, so
we kind of look for that
kind of a high-ish score.
(16:02):
We look forlittle bits of empathy
or enough empathyor introspection
and we lookfor analytical
and what we'relooking for
is that ability orthat capacity or aptitude
to keep evolving yourself
and doing the workon yourself.
And it's usually the workthat comes along with
a therapist or a coach or a guru
or whoeverit is that
(16:23):
that you mighttend to work with
or you just went to the school of hard knocks
and had plenty of adversity in your life
and or perhaps a littlebit neurodiverse.
I don't know ifyou are or not, but,
neurodiversity is a bit of a superpower.
Steve Jobs was dyslexic,Henry Ford was dyslexic.
Neurodiversitydoes tend to push
or seem to push up
(16:45):
emotional psychologicaldevelopment
a little bit earlierin some folks.
But for you,
you've got a nice high score.
And, so your main score is discipline.
Your secondary scoreis results
and in that whatyou will have is
this person who is able to create
(17:07):
a very efficient machine
around pretty muchanything that you do.
So you probably havea bit of a playbook.
You probably havea bit of a method
for doing things.
It's probablypretty repeatable
and you’ve turned it intosomething incredibly efficient.
I imagine that the
editorial process behindyour podcast is
(17:29):
down to a scienceand incredibly efficient.
And almost
I wouldn't sayidentical every time
Right, right.
but probably continuouslyimproving every time.
Right. You're improvingupon that playbook.
And that's kind of how you're wired.
And then that results score
again we call thatkind of the CEO mindset.
It's, that's about leverage.
(17:49):
That's theone.
And it's over an eight.
So anything overan eight on the
on the scoreboardif you will
is impactfulto your behavior.
Your empathy is a little bit slightly different.
Empathy is right on thatborderline of impactful.
You've got enough to be introspective
and read the room, right.
You may still use some rules in your head
(18:09):
to read the room a little bit now and again
but for the most part
you're probably readingthe room pretty fast.
That resultsis where your EQ
comes from, though,
and that'swhere your complex
problemsolving comes from.
So as you seethose complex problems
emerging in the marketin technology
and things like that,
you're easily ableto understand them
and go andrun after
(18:31):
folks that can help you findanswers for those problems.
So then whereare red flags?
What?
Where are red flags that people are just not
not necessarily mebut what are
what are scoresor categories
on this thingthat jump out?
This is not the person
that you want to leadyour first AI project.
So typically what we're going to do.
(18:51):
So what I do withthese scores is actually
segment the employee base into three groups.
Okay.
We call them results, resilient, and reluctant.
So results is goingto be literally results.
You know they're goingto be high results
plus empathyplus systems.
Maybe someexpert
So you would be what we call resilient
because you haveplenty of results.
(19:13):
And you'vegot a discipline score
that's almost equal to results and your expert scores
pretty high up there.
So I call you resilient.
And so you'd bein that 15% group.
And thenthe reluctance
would look a little bit more like
heavier disciplineheavier opportunistic.
And those folksthat are
(19:33):
just not comfortablewith uncertainty.
And most people arepretty darn self-aware.
They know who they are.
So when we startto break down
the employee baselike this
nobody tends to be super offended
[Jeff] Mis-categorized? [Karrie] if you will buy that, right?
So there's no good or bad.
It's just where you are right now.
(19:53):
Right.
And what your comfortable with.
And I kind of call those like
our transformand grow team
and our sturdy and stable team
you always want inany transformation
whether it's returnto office or whether it's
I adoption or just abusiness transformation,
you kind ofwant to have
and eye on who'sin those two teams
(20:15):
because you need a team
that's going to do a really great job of
driving the ship
and making sure it's steady and stable and that we’re
maintaining revenueand we're not
losing anythingor losing any customers.
And you all, but you also need that group
that you identify
that is comfortablewith ambiguity
and comfortablewith uncertainty.
And they're going to run after the
the change or the optimization
(20:36):
but they're going to alsodo a pretty good job
of translating and normalizing all that
for the rest of the team.
And if you start an engagement
do you do a more direct assessment
to validate against whatyou've gotten off the API
in creating something like this
or is this accurate enough thatyou can move forward?
Or do you have to do
[Jeff] something a little bit more direct[Karrie] you know
(20:57):
[Jeff] like the old Myers-Briggs?[Karrie] If I’m doing
If I'm doing recruiting or something like that
and I often get asks for recruiting or organization design
or things like that.
If I see any red flags
or if I don't have enoughdata for somebody or
if it's a reallycritical role.
I’d staff, I have psychologists
and we will interviewand confirm
(21:17):
that our scoring is right.
I can also do the same thing
with spoken word and transcript
if we've got some,you know, recordings.
I can ask simplequestions on a meeting
and get basicallythe same thing
as a confirmationof somebody.
[Jeff] Right.
So if precision is required
we can do that.
It takes an extra step.
But when we're talking about adoption
(21:38):
or change managementor things like that
it’s horseshoesand hand grenades.
It doesn't have to be perfect.
We're looking forbig broad strokes of
of groups of employees
and how we thinkthey're going to behave
and what they're goingto be comfortable with.
And if we find that
there are some differences as we go
we can make thoseadjustments.
And then as part ofyour engagement,
(21:59):
do you help suggest actual
applications or use cases?
Or you work togetherin terms of the project?
I just wantto share a post,
I don't know if you saw it.It’s great,
it was shared by Henrik Jarleskog
I’m probably messing up his name
he's anothergood follow
He's like,this is my team.
And it'sa dozen.
His dirty dozenAI Staff
(22:20):
And he calls them staff
and he even givesthem all names.
[Jeff] So he's like[Karrie] I Love that
ChatGPT is my Chief Strategy and Innovation Officer
Claude is my Executive Editor-in-Residence
Perplexity is my VP ofResearch and Insights
Gemini, my Director of Real-Time Verification
Midjourney, I don't even
I've haven’t heard of a lot of these
Creative Directorof Visual Production
(22:41):
Canva, Head of Rapid Design
Eleven Labs, Senior Manager of Voice Experience
Notebook LM, Chief Knowledge Curator
Gamma AI, Director ofPresentation Development
Otter AI, Chief Meeting Historian
Ambient AI, Chief Workflow Orchestrator and
Veed, Director ofVideo Content Creation
I mean, I just thinkit is such a
(23:03):
such a great postI reached out to him
I’m like, you gotta share thisbecause it just shows you
I’m like, you gotta share thisbecause it just shows you
if you changeyour mindset.
He's operating like this huge staff
of people that are helping him.
And he, you know,he spent the time
to figure out which apps do what.
[Jeff] But it's pretty[Karrie] That’s right
interesting now that, you know,
we should be to the pointwhere there's
enough examplesthat you can point to
(23:24):
to help people start to figure out
where they can start to see some
some real results.
And usually that's kind ofthe big ‘aha’ moment.
As we get further into
I'd say 3 or 4 weeks into any,
any implementationor adoption cycle.
It's the, the moment
for leaders is, oh,
we're not optimizedfor this.
(23:46):
My team is not optimizedfor this.
I'm not organizedfor this or
Ooo, now that we havedigital workers
that we're relying onfor certain things,
what does that meanto my human workers?
How do they work together?
How do I organize them?
What does that
what kind of capacitydoes that create?
Or what does that doto spans and layers
inside my org design.
(24:07):
So those questions really
start to come uppretty quickly.
And we startto answer them
in a bit of a
hey, it's an iterative process.
Right, right.
approach to things, right?
It's, there's no single answer.
And it kind of depends on what's
what cycle you're in
as far as whether you'retrying to grow
or if you arejust trying to
(24:29):
maintain stability and survivein an economic downturn
or things like that.
Yeah.
I had an interviewwith this guy
Charles CorleyHe's great guy and
he's in real estatein Singapore.
But what's interesting is
he was he'sjust a curious guy
But he's been playingwith ChatGPT every day
since it was announced inNovember two years ago.
And so he'sdoing all this stuff
and he's like, Jeff,
(24:49):
I just think of it reallyas a thought partner.
And I runthrough
and you know,we do all the stuff.
And I was likewell Charles
what about hallucinations?What about hallucinations?
And he's likeyou know
you treat it like a really good junior associate.
If you're going to actually do something,
check their workyou know
don't necessarilytake it at face value
but do take it for direction and
(25:10):
do take it for thought process.
And the other thingthat I thought that he
he said that wasreally profound
which I think a lot of people blow
is they're looking for this
like questionand answer
almost like a searchand return
and getthe result.
You got the
‘Can AI Generate Results?’over your shoulder.
But his thing is like
no, it's not like this one time thing.
It's this iterativeprocess.
(25:31):
Both you and the machine
[Jeff] over time[Karrie] that’s right
exploring thingsconversationally.
You probably seethese posts,
you get these ridiculousprompt engineering charts
to put upon the wall
that have a thousand
a thousand linesof you know
nine point font that you can't read
when you're our age but
he's like, no, no,no, no, no.
You know don't try to just be conversational
(25:52):
[Jeff] and work the tool and the[Karrie] just talk to it
tool will start to returninformation back to you.
I call it a good drunken intern.
It's,
it's like it's the perfect
it's like the perfectname for it.
Because that is kind of what it is.
And an LLM
LMS aren't a panacea.
They're not wherewe're going to end up.
The way I look at the large language model tools
(26:16):
is that they're a really good first step
because they're low friction.
Anybody can push a button on a screen
and get started with it
and get to understandhow to use it
how to optimizetheir day with it
if they're wiredto do that
and they're wiredto experiment.
But what we'rereally doing
is conditioningorganizations around
(26:39):
something that most,you know,
office environmentsaren't used to.
Manufacturing has beenyou know
practicing Six Sigma for decades.
Right.
But whatwhite collar
jobs are practicing Six sigma and continuous improvement.
So can we startto introduce concepts,
from other functionsor other industries
(27:03):
that make sense into this process?
And you start to get peopleused to the idea of creating
virtuous cycles
around productivity,improvement, quality.
Value creation, you know.
How do you take a 5x employee and turn into
turn them into a 10x employee?
Right.
So how do you
how do you startto challenge yourself
(27:24):
to do some of those things?
The current tools aren'talways going to do that.
There are tons ofAI tools and models
out there that can be baked into
lots of different systems.
The problemis that
they're higher frictionand higher expense
to implement insidemost organizations.
Not to mentionthe fact
that most organizationsdon't have the data
(27:46):
governanceand organization
to make a lot ofthese tools work.
Right.
So the way I see it is
let's get some of these
easier base hitkind of things right.
Let’s condition the organization and the people
for how they needto think about change,
think about AI
understand that it's not justgoing to take their job
or consume what they do.
(28:08):
And then start toreally do a good job
of comingup with
the high value things,the value creation things
that other AI models and tools
can do forthe organization.
So that if we'rereally successful early
in some of these pilots
and we get ROI and we get lots of adoption
then the CIO or CTO can walk into the CFO's office
(28:30):
and say, hey,this is successful
we know exactlywhere we need to go
and they get the green lightfor the investment
I don't know any CFO
who's going to greenlight
an investmentin any AI tools
or projects that have 20%adoption rates.
So it's kind of breaking that cycle of
(28:51):
innovation projects,getting low adoption
and then justbeing shelved.
And creating higher successor quick wins early
so that they can get yeseson the next investment
Yeah
That's what we're going after.
Yeah, the data governanceand the data process
to feedthese things
is certainly a superimportant part.
(29:12):
I've got some recency bias.
I was recently at theAtlassian ‘Team 25’ show
and they just jumped inwith both feet
because they'rein this unique position
[Jeff] where they've got a lot of[Karrie] What a great organization
[Jeff] They've got all this great data[Karrie] Great minds at Atlassian
[Jeff] inside these tools[Karrie] they’re so good
And so they're attacking
kind of the knowledgediscovery process.
And I forget the numbersbut like, you know
a third ofour time
(29:32):
is spent searchingfor things
searching forinformation, right.
Because you can’t remember
Is it a text, an IM, an email, whatever.
And so that's how
they're really goingafter the opportunity
is to use thetheir internal
they just released
It's called ‘Rovo’
to work within thishuge amount of data
in the systemalready
to start returning,you know, better results
(29:54):
and helpingpeople get their
get their work done.
And the other thingthey've done
I thought waspretty interesting
and I’m curious is
they think you know you got to have this ‘top down’
to go with the‘bottom up.’
So you have to have
you know, senior supportand not only support,
but actually modeling the behavior as we know
all the time for senior leadersto model the behavior
that they wanttheir people to do.
(30:14):
I'm just curious,
do you think in the not too distant future we'll have
like a Chief AI Officer?
BecauseI mean
just like the list of tools I read down
just trying to keep upon what is happening.
You mean you definitelyneed an AI tools friend
who can help
help turn you on to what's
[Karrie] Is just changing too fast to keep up with, yeah[Jeff] is changing too fast, so how do you
How do you see it evolving?
(30:34):
As you knowyour early adopters
have some success?
How does it start to proliferatethrough your organization?
and what will happen atthe top levels in terms of
of driving it from the senior positions?
Yeah.
Atlassian is just such a good example across the board.
They've got a greatleadership team
they've gotgreat mindsets
you know, headingup the company.
(30:55):
and you can see whythey've been successful
and been able to grow and evolve
with the marketas they have.
If I rememberright
weren't they one of the few
that would promiseespecially digital
digital creators or people with digital IP
that they wouldn'tuse their code to
(31:15):
to update the models and
and stuff like thatso that it would be
proprietary to them.
They recognize things quicklywith early adopters,
things quicklywith early adopters
and they respondvery quickly
and that's exactlywhat you want to see
in leadershipteams.
So what I think is goingto happen longer term
is that you're going to see
(31:35):
a bit of a differentialstart to happen,
and it'll be kind oflike your
Go take a look atAutoZone's stock price.
It's just kind of a perfect example that
I don’t know how much AI they use or they don't but
William Rhodes [AutoZone Chair]is so high in his results score
he is, he
(31:57):
has been able to turn a brick and mortar auto retailer
into a company that has a $3,000 plus stock price.
Like, it's unheard ofbut that's the power of what
leadership mindset is actually able to drive
inside anorganization
(32:17):
because they're able to attract similar talent.
Right.
So you're going to start to see
a lot of thattalent collecting.
It's going to look kind of like a weird
people clusteranalysis.
Where you've got talent
that can adopt andadapt to new stuff
clustering together in organizations under leaders.
(32:39):
And you're going to see those organizations
pulling faster aheadof the pack.
You're seeing a little bit of that with Shopify.
They don't want to seeany asked for headcount
unless you can provethat AI can't do the job.
You're going to seesome of those leaders
starting to cluster together and
and pull ahead of competition
or you're also going to see
(33:00):
some mid-market or smaller organizations that are growing
that are ableto grow faster.
Right.
Because the cost to build
and the cost to growis sinking like a stone.
That's the other thingthat AI is going to do
and is doing right now.
The cost to build codeor syndicate
or create leverageis sinking fast.
(33:23):
And those who areable to leverage it
are going to be able togrow and pull ahead of
their competitive setvery, very, very quickly.
Yeah.
And it's also about creativity.
I mean, again,recency bias
Andrew Boyagiat that show
who's in charge of their developer experience evangelism
talked about actuallyfor coding.
He's like, you know mostof the coders are pretty good.
(33:43):
AI is not going to replace the coding.
But there'sa lot of tools.
There’s a lot of steps inthe process that AI
[Jeff] can help.[Karrie] Exactly
So like runa basic review
before you send it outto your peer review.
One of the things I thoughthe said was really creative is
you know, a lot of the non English
for a lot of developers
English is nottheir first language.
So being able towrite nice summaries
(34:04):
about what they did
for everybody elsefor the documentation
like, wow, that's genius.
You know, that is something they struggle with
Exactly
not the coding piece.
So even all thatkind of periphery.
But I'm curious in terms of the adoption
let's just go straight at it.
You just said
you know peoplearen't going to hire
if they can't proveAI can't do the job.
How does thatget resolved
(34:24):
when you'retrying to do this?
You know, it'snot taking your job.
It's anassistant.
Versus, I'm afraidit’s taking my job.
That's all I'mreading about.
See, I think there'sgoing to be
the technology industry
and everybodyelse
Okay
really when itcomes to that.
Because so much ofthe tech industry is
developer led, code,you know, code oriented,
(34:46):
that's what a lot of these models
are kind of optimized for is
is writing code.
So, yeah, they're going to be able
to automate quite a bit.
They're not goingto automate their
10x developers
or even probably their5x developers.
They'll make their 10x developers20x developers
and their 5x, 10x.
So I think that'llhappen a lot
in the techindustry.
And you're alreadyseeing it
(35:06):
almost acrossthe board
where they are rethinkingtheir staffing and their
you know, taking out some of those that
where theycan optimize
and you're seeing
I mean last year we saw more developers on the street.
But they're fairly quicklygoing to find a seat
in other industries,I think.
(35:27):
Yeah.
Because other industriesdon't have that
data sciencedata architecture
data basethe you know
and any of thosefoundational things
most companiesjust don't have,
because they haven'tinvested in it.
Yeah.
they're, that's not who they are.
So those developerswill find other homes
(35:49):
in different spotsin different industries
that are nowstarting to invest in it.
But most of them
if you're in manufacturing,
you're creatingactual physical things.
So, you know, are you going to swap out
your entire back officetomorrow?
Probably not.
Will you in five years?
Maybe.It's possible.
But here's the wayI really look at it, Jeff.
(36:10):
The fact is that our baby boomers are retiring
at 11,000 peopleper day.
So we
and we only haveabout 8,000 Gen Z's
coming of age tobackfill them each day.
So it’s a delta of about3,000 humans a day, right?
If we've got a delta of about 3,000 humans today
(36:30):
that’s about 5 million humans by 2035.
So what that does todifferent industries
and we're already seeing itin manufacturing
in health carein senior health
as baby boomerscontinue to age
and they move intosenior living homes.
Right.
We're goingto have
(36:52):
lots of big shits in wherewe need talent to go.
And it's going to bea lot of skilled labor.
So thosepeople who
maybe either aging outof a white collar job
or thingslike that, you know,
does it need a Gen Z to backfill that?
Maybe. Maybe not.
But do we need Gen Z
(37:12):
or do we need other people to
reskill themselvesinto
home health care or other roles?
Absolutely.
I think that's a lot of what's going to happen
from a humanperspective.
We’re going to see a lot of humans
with AI doingtheir jobs,
and you’re going to see a lot of humans reskilling
either intodifferent industries
(37:32):
or upskilling into theirexisting organizations.
Yeah.
And it's not onlydevelopers too, right
it's creative writersand you know
[Jeff] a lot of the artistic stuff and[Karrie] Totally
you knowit's got to be
just wreaking havoc onthe poor Fiverr subs
that are all overthe developed world
for all thosejust quick little
those quicklittle things.
(37:52):
I want to shiftgears a little bit,
because I knowmost of your clients
are big organizations,big companies.
You're running asmaller organization.
And I know youthink a lot about
you knowkind of this
step function thatsmaller organizations
in terms of numbers of people
can have
using this technologyto grow a really sizable
(38:13):
important andsignificant business.
And, you know
I look no furtherthan that list that
I just read from Henrik with his
giant, you knowhis cabinet of 12 people
that I don’t know what he's paying for all those apps
but it's not hundreds ofthousands of dollars per year
that isfor sure.
So I wonder if you can share as
cause I know youthink about this
how this is going tochange the opportunity
(38:34):
for smaller businesses to really
find opportunitiesor grow in ways
that maybe they didn't think
were possible at all before.
Yeah, actuallyI do
I work with somesmaller organizations, I
Okay.
I didn't actually thinkI was going to, but
I've ended upacquiring a few smaller
(38:54):
clients recentlyeven and
what I'mfinding is that the
scope is just a little bit different
because theywant to jump
a little faster
into org design andoptimization of the team.
So thatespecially if
they've already got abig chunk of their team
already using tools
like ChatGPTor Perplexity
(39:15):
or, you know,Canva, other things,
and they're really juststarting to jump into
how do I optimize?
But they're tryingto grow
and their goal is to growwithout overtaxing
the team thatthey have
but then without actually growing
headcount inways that are
that are onerousto the
to the bottom line either
(39:36):
so thatoptimization
is absolutely happeningright now.
And it just
it happens in varyingdegrees depending on the
the leadersand founders
inside theorganization.
I do think
we've already gotcompanies out there
that are atyou know
$20 million in itin a couple of years
with nominal amounts of VC investment and
(39:59):
things like that
because those foundersare able to leverage the
tools that are available
and the cost of thosetools is not big.
So we willabsolutely see
smaller, you know10 person, 15 people organizations
With $1 billionin revenue
probably in the next 2 to 3 years.
(40:21):
Yeah
I'm pretty surethat's going to happen.
I will probably never have muchof a back office at all.
Right, I heard that in oneof your other podcasts
you said basically you have no back office and you
you know, you chose in termsof a focus on automation.
you know thosethe stuff that nobody likes
to have to deal with likeaccounting and finance.
And you've automated
[Jeff] a lot of that[Karrie] Why would I hire?
[Jeff] within your own organization.[Karrie] Why would I hire for that?
(40:41):
Yeah, I started with RPA years ago.
[Karrie] So [Jeff] Is this RPA
No one ever talk about RPA anywhere.
[Jeff] RPA was supposed to be[Karrie] I know, nobody’s talking about RPA
Supposed to be our greatdigital assistant.
I used to go cover theAutomation Anywhere show
a numberof years
and those were our little peepsthat were supposed to be
you know,our digital employees
as they were soldour digital assistants
Karrie] Totally, they’re perfect little[Jeff] So I don’t know, is this the
(41:01):
They’re perfect little swivel chair employees
If you’re going to take data from one spot
and put it into another.
It's awesome.
you know, now I've
I’m moving on toother other technologies
that are a little bitmore modern.
As my processes changeand as we grow.
But the same principle applies.
I would rather hirea really great CFO
(41:22):
that can think strategicallyabout the money
and think strategicallyabout growth
than I would an army of bookkeepers
to keep track of
all of the you knowinvoices from
you know, contractorsand things like that.
My team is 100% virtual.
I haveit's all 1099.
I train them,I invest in them.
But it'sall 1099.
(41:43):
So it's a very
cost and overheadlight organization.
And that's on purpose.
Right.
Because I don't want to,
because we're a services organization.
I don't want to
I don't want to throw abunch of overhead costs at
at client projects
and have them consume that.
So that's just the way
(42:03):
that I approachproblem solving.
I'm not special necessarily
but that's justhow I think.
And if I think that way
there are other people
thinking in similar ways
that they're going to be ableto grow companies
even faster than I can.
Yeah.
So it's going to happen.
It's going to be reallyinteresting to see
Mid market, I'm actuallyreally optimistic about, too,
(42:26):
because we're goingto see a lot of PE
over the nextten years I think
we're going to see regulatory environment changing
over the next2 or 3 years.
And a lot ofthose boomers
that are runningcompanies right now
are going towant to get out.
And as theydo that
we're going to see a lot of consolidation.
We're going to see a lot ofcompanies being bought
(42:47):
and then technologybeing applied
to generate the value
in that privateequity thesis
that they just haven'tnecessarily done
done as much of before
their cutand burn
[Karrie] kind of approach to things[Jeff] I was going to say
[Jeff] I don’t know what private equity folks [Karrie] isn’t going to work
[Jeff] you're talking to but[Karrie] anymore
[Jeff] most of them seem more[Karrie] It’s gonna
more about the harvesting
and the milkingthe cow than,
[Jeff] investing in growth.[Karrie] It’s going to give birth to
(43:10):
it, this is goingto give birth
to a new generationof private equity.
Yeah
I'm quite certain and that’s we’re gonna
we're goingto see that
in the nextcouple of years to.
I hope soI've seen
I've seen them destroy too many
too many industries from
my favorite is the old rental car, you know
[Jeff] those were all divisions[Karrie] Oh, right
you know, to keepthe factories.
It's a greatillustration
of, you know, whatare you optimizing for?
(43:31):
Or you know, whatthey've done,
you know, kind ofin the radio business.
They won’tthey just
they won't be able to
to follow their oldplaybook anymore.
And that’s exactly the pointbecause the
most of the folksin capital markets
tend to be kind of that
more safety andsecurity oriented mindset
that need that likesthat playbook
(43:52):
and adheres to that playbookkind of religiously.
They love thespreadsheet, right?
So they're goingto end up
breaking outof the spreadsheet
or somebody elseis going to go in
and attract that LP moneybecause the thesis is going to be
much more focusedon leverage.
But that also means
(44:12):
that they're not goingto be able to hire
the same way anymore either.
You know, it'snot going to be
we’ll just go hire my friend
from Wharton orHarvard or whatever.
It's you're going tofind those people
that look morelike that
kind of scrappy versionof a Steve Jobs or a,
you know, Jeff Bezos.
Right.Interesting.
All right, well Karrie, we're getting towards the end of our time.
(44:34):
And, you knowshort of calling
1-800-Karrie-Help-Me
What are somekind of
words of advicethat you have
for leaders as well asyou know,
mid-levelmanagers
and eve, I supposefrontline workers
in terms of how they should
you know,
kind of approachthe challenge
how they should thinkabout the opportunity
and really start getting their feet wet
if they haven'talready.
(44:55):
And if they haven't
or if they have,how do they help their
their compadresget into it?
And if they haven’t,what should
what should people seek even within their own organization
to try to get some help
without necessarilyraising your hand
and saying, I'm scaredto death of this thing?
Greatquestion.
I will give the same advice
that I give tomy college kids.
(45:16):
Every weekI ask them
What risks did you take this past week?
Tell me what risksyou took.
That's a great,great question.
What I’m doing with that is
getting themto be mindful
about the risks they take
and their comfort levelwith uncertainty,
and just getting themto be aware of
(45:37):
their relationshipwith uncertainty.
And if you keep doingthat over and over again
eventually you kind of find that
that you'redealing with
with some of thatemotional baggage
that might have beenin your past.
Aside from thatget a coach.
I love my coach.
I've got a great coach
who used to bea therapist.
Therapists are great.
You know,it but
it's not about what you learn in school necessarily.
(45:59):
Learning isawesome.
But learning and earning
level of resilience and
comfort with ambiguity is
is absolutely whatit's going to take
to be 10x in the next generation of talent.
I'm just curious.
This group of peoplethe young people
Covid was five years ago,
which is hard to believe
Yeah, you’reright
have lived with a lot of uncertainty.
(46:21):
And there’s been a lot of ambiguity
just in the greater global environment.
Things that have beenstable for 80 years
are not necessarilystable that way.
So I wonderif their
you knowis that
is that uplifting forthem in terms of, okay,
we're comfortablewith the uncertainty.
We can deal with itwe've been dealing with it.
Or is it almost like,screw it.
You know
this is such a crazy,bizarre thing
(46:43):
and is changingso fast.
You know,
I'm not necessarilyexcited about investing
when things are going
you know, what's goingto change tomorrow.
I'm curious becausethey've been
they've lived in a very different world
than, say, you and Iwhen we were 20
and you just, kind of followed the
you know, the bouncingball on the script.
You went to schoolyou went to college
you got a job at atraining program.
[Jeff] I went to a great training program, boom[Karrie] Education was a manufacturing plant
(47:05):
Yeah. It was great.
Right?
So different.
So I'm curious, your takeon their perception
of living in a worldof ambiguity
having you knowgot through Covid
and everything else.
We're already seeing the difference in mindset
So we can seemindset differences
kind of in aggregate.
And do some of that analysis.
So we see
weirdly we can seelike migratory patterns of
(47:25):
of people intheir mindsets
going from companyto company
or industryto industry.
But we also can see somegenerational things too.
And Gen Z already hasmore in their savings account
than most of theirmillennial peers.
Gen Z is unique in the adversity that they
experienced in their formative years.
(47:47):
And that adversityis a great teacher.
It sucked for them for sure.
[Jeff] Right.
But it really wasa great teacher.
And they are all
we're already seeing it with them at work to.
There's a reason theykeep kind of turning over
in their jobskind of quickly.
It's because, rememberwhat we talked about
with middle managers
(48:07):
and those middle managers being
pretty focused on playbooks and routine
and processand detail orientation.
Some of those Gen Zshave already
kind of outgrowntheir managers
get frustrated and leave.
So you're when that
more safety andsecurity kind of mindset
(48:27):
tries to put the
a more resilient or a resilient problem solver
into a smaller problem solving box.
They get frustratedit creates friction
and they leave.
Yeah.
But we're already seeing it.
And there's a lot of frustration with Gen Z
There's a lot of Gen Z hate.
I love them.
I hire them all the time.
(48:48):
I think they're great.
And when I score them,they're fabulous.
Like, I clearly find thema little bit easier.
But when I score themthey're fabulous.
So if I'm you
I put Gen Z into
some of those projectsthat are new
that do have a bit ofambiguity around them
that aren't just
they do have to pay their dues for sure.
(49:10):
They need to learnhow to operate inside
an organizationand deal with
you know, the politicsand the culture
and all the thingsand really
you know, get some domain expertise.
They can't justskip ahead.
But to keep theminterested
we need togive them
some interestingthings to do
and give themsome agency
because they alreadyhave agency themselves.
(49:32):
You got to give themsome agency over
in improving their jobs
and improving
the work culturethat they sit in.
Yeah.
It's interesting as you're saying that
I'm thinking ofmy grandfather.
You know,
the Greatest Generationfought in World War II
went to Koreafor a little bit.
And just thein terms of the
uncertainty that they facedin the 30s and the 40s
in terms of building reallystrong resilient people.
(49:54):
Now, what'skind of odd.
Then they all went to
the job marketat that point
you know,through the 50s
that was justrocking and rolling
everything upand to the right
and pretty steadyand constant.
And you
you got that job at AT&Tlike my grandfather did.
And you could have it
for 50 years or 40 years
[Jeff] with lifetime employment[Karrie] I think they also
They also grew up in thatgreat influenza pandemic
or epidemic in theyou know, teens.
(50:16):
[Jeff] Yeah, yeah.[Karrie] Yeah.
They dealt withlots of adversity.
So Gen Z is the first generation
since thegreatest
to have that kind of adversity in their lives.
And I see kind of big things
or similarly big things for them.
Yeah that's great.
Well that's goodbecause we need
[Jeff] as you said the demographic trend[Karrie] We need it
is the biggesttrend of all and
(50:37):
you know especiallyin developed countries
you know there justaren't enough
there aren'tenough people.
So let’s lean heavily on our young people.
And I'm glad to hear that
you've got such great enthusiasm for their
for theircontributions.
[Jeff] Well Karrie, this has been great.[Karrie] Thank you
Thank you fortaking the time today.
Thanks for tryingto make a difference
in the world of AIand AI adoption.
It was really refreshingat this Atlassian event
(50:57):
because they're sothey're so into it.
You know, they
they actually have likeon their internal thing
every morningit starts you know
start your daywith AI.
I mean just like this
[Jeff] constant encouragement[Karrie] That’s awesome
to help peopleget over this hump.
And then you end upwith people like Henrick
who’ve got you know, 12 little AI assistants
helping him get through his dayevery day.
So, reallyappreciate the time.
(51:18):
Well, absolutely.
And next time youneed a bag carrier
at an Atlassian eventyou just let me know,
and I'll be there.
Okay, I'll let you know.
[Jeff] All right. Well, thanks again.[Karrie] You got it
She’s Karrie, I'm Jeff.
She's in Chicago.I’m back in Palo Alto.
Thanks for watchingWork 20XX.
Thanks for listeningon the podcast.
We'll catch younext time.
Thank you.
(51:38):
Excellent.
Thank you guys.
Hey, Jeff Frick Here
big shout out to the podcast audience.
Thanks for listening in.
You can get show notes and transcripts at Work20XX.com
And that also has links to the videos as well.
Appreciate you listeningin on the podcast
Do reach out
say hello, like subscribeand smash that notification bell.
(52:00):
Thanks for listening.Take care. Bye bye.
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