In this episode of the Throttle Up Leadership Podcast, host Dr. John Dentico interviews Daniel Friker, a global executive strategic advisor and AI workforce futurist. With over 20 years of experience across various industries and AI certifications, Friker discusses how AI is reshaping hiring, HR, and leadership strategies. He emphasizes the critical need for companies to adopt AI technologies early and highlights the importance of building cross-functional AI teams. The conversation also covers the evolving job market, the inefficiencies in current HR practices, and how organizations can adapt strategically to maintain a competitive edge. Dr. Dentico and Friker further explore the contrast between traditional fear-based organizational structures and modern trust-based models, stressing the importance of mutual trust and adaptability in leadership. They conclude by examining the impact of certifications, the human element in hiring, and the future of the staffing industry in the AI era.
00:00 Introduction and Guest Introduction
01:44 Dan Friker's Background and Influences
04:38 AI's Impact on Jobs and Industries
09:59 Leadership Evolution in the AI Era
21:27 AI in HR and Talent Acquisition
32:36 Future of Temporary Staffing and HR Tech
46:43 Conclusion and Final Thoughts
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Dr John Dentico (00:05):
Welcome to the Throttle Up Leadership Podcast.
Our theme for 2025 is The Futureof Work: Meaning is the New Money!
In a world shaped by rapid innovation andconstant change the pursuit of purpose
and impact has never been more important.
I'm Dr John Dentico your host,bringing over 30 years of experience
(00:28):
in leadership, strategic thinkingand purpose-driven innovation.
Here we tackle the pressing challengesof our time-from the rise of
artificial intelligence to the growingneed for meaning in the workplace.
Together we'll uncover how leaderscan ethically integrate AI to enhance
decision-making and keep humanityat the heart of their organizations.
(00:51):
Remember amidst all thetechnological advancements in the
end, it's always about the people.
This podcast is your resource foractionable tools, thought provoking
discussions and inspiring stories.
It's time to go beyond leadershipdevelopment and focus on leadership
(01:11):
impact-creating workplaces where peoplethrive, innovation flourishes and
meaning truly becomes the new currency.
Thank you for joining me on this journey.
Now, let's Throttle Up anddive into today's episode.
Hello again and welcome to theThrottle Up Leadership Podcast.
(01:32):
I am your host, Dr. John DenticoWith me today is Dan Friker, a global
executive strategic advisor and AIworkforce futurist with over 20 years
of experience in professional servicesworking across the energy, oil, and
gas and manufacturing industries.
(01:52):
Known for his ability to lead largescale organizational change and execute
high impact strategic initiatives,
Dan has worked with Fortune500 Giants such as ExxonMobil,
Microsoft, Cisco, 3M, and HP.
A graduate of the CarlsonSchool of Management with
both A BNM master's degree.
Dan combines academic rigor with realworld insight to help organizations
(02:17):
and leaders navigate complexity, fostersustainable growth, and align operations.
With several AI certificationsand a deep passion for workforce
transformation, Dan now focuseson exploring how AI is reshaping
hiring, HR, and leadership strategy.
He speaks and consults on criticalissues like the talent mismatched crisis,
(02:41):
the future of job design and how bothcompanies and individuals can adapt
strategically to stay ahead in the AIera, Dan's mission is clear to bring
the growing divide between organizationsseeking talent and individuals searching
for meaningful careers, empowering leadersto lead with vision and innovation.
(03:02):
I'm truly grateful that hehas taken the time to be here
with me today on the podcast.
Good morning, Dan.
How are you?
Daniel Paul Friker (03:08):
Good morning.
Dr John Dentico (03:09):
Good.
Glad to hear it.
So Dan, I'm adding more of a personaltouch this year to my podcasts.
So I'd like to ask you, tellme a bit about yourself.
Where did you grow up and whatin your mind were some of the
fundamental influences in your life?
Daniel Paul Friker (03:25):
Great question.
I actually grew up on the north sideof Chicago, in a town called Palatine
and turned 18 decided to go to theUniversity of Minnesota, little did
I realize that I would spend the vastmajority of my life here in Minnesota
but I think your question is relevant.
One of the biggest influences in my lifeand my careers, actually, my grandfather.
(03:48):
I come from a long line of teachers.
He happened to be the, PR former principalat SEN High School in the city of Chicago.
think about Chicago in thesixties and seventies, just
a couple of things going on.
But one of the things as he was amajor component of my upbringing
was really around connecting withpeople, it doesn't matter what
(04:11):
kind of socioeconomic, backgrounds.
He is a Swiss German, descendant so forme, that really lit a passion as when
I attended the University of Minnesota.
My junior year, I actuallystudied abroad in Germany.
And so not only did that further fan mydesire to understand, different people and
(04:33):
cultures, but then how to work with them.
And then ultimately when I was at ManpowerGroup for about half my career, I was in
a global role having to work with peoplein the APAC market, the EMEA market, the
Latin American market, and then ultimatelytry to help major corporations Fortune 100
companies really find talent to executeupon initiatives they were doing, be it
(04:55):
for oil and gas, or in the case of 3M,making sure they were producing lifesaving
materials, during the COVID crisis.
it's always been there.
When I went to graduate school, one ofthe things I latched onto was how, even
in graduate school, it was becoming veryapparent that HR departments were really
taking these files that were, , in, insome cases physical file cabinets and
(05:19):
starting to put them into databases.
And then with those databases, theywere starting to use data science
to really help answer a criticalquestion, which is how do we find
the best talent regardless of, wherethey're coming from around the world.
And AI is really justthat extension of it.
So this has been a linear progressionto really understand this critical
inflection point that we're in right now.
Dr John Dentico (05:42):
I agree.
I think, the single greatest,revolution, that the world has
ever seen artificial intelligence.
I, I always refer to JensenHwang's, comment when he said,
speaking about HR you don't haveto worry about HR taking your job.
You have to worry about somebodywho understands how to use AI
Daniel Paul Friker (06:01):
Yeah.
Dr John Dentico (06:01):
So I think, that's a really interesting point.
Let me ask you this, Dan AI is expectedto impact over 70% of jobs worldwide.
From your perspective, which industriesare most vulnerable and how can companies
proactively adapt to this disruption?
Daniel Paul Friker (06:24):
It's a great question and it is actually one that
McKinsey, has published multiplestudies on, So you are right in the
sense that overall AI has the abilityto impact somewhere between 70, 75%.
They would also say that the valuecreation that would come out of AI is
(06:46):
somewhere between 13 and $22 trillion.
The way I like to describe this isthink about within our lifetimes, the
PC revolution, If you used to walkinto major corporations back in the
1980s, you would see hundreds, ifnot thousands of executive admins.
Right.
PC started to come, and majorexecutives are essentially
(07:06):
doing a lot of their own work.
So the executive admin poolessentially went down, Used to walk
into call centers and would seehundreds if not thousands of people.
Now you're able to do the sameamount of work with less people,
I would use a more recent example,which is e-commerce, Growing up in
Chicago, I knew it as the Sears Tower.
Sears, had everything in the palmof their hand to take e-commerce.
(07:28):
And what is Amazon today, fundamentally,they had everything they failed to, they
knew that e-commerce was starting toimpact their business by the mid nineties.
They had quantifiable databy the early two thousands.
Yet it was their inability to adapt to thee-commerce environment and quite frankly.
Jeff Bezos has publicly talked about it.
(07:48):
He is like, I just took it Sears'business model and just put it into
digital age and the rest is history.
So AI has that ability to improveefficiency and I have a sneaking
suspicion, we're gonna talk a lotabout this today, but I think what is
really missing in the marketplace isthe efficiency versus success paradox.
(08:13):
What sectors are goingto be most impacted?
Well, you're gonna see it prob,statistically in the most with retail,
travel, transport and logistics.
Those are the top threeindustries going to be impacted.
But it pairs repeating again,it's gonna impact everything.
And I'm sure we're gonna talk a littlebit more about the efficiency of success
paradox in a moment here, Right now whatwe're really seeing is what I like to
(08:37):
call the clippy, gen one version of AIso as much as these tools are coming out
today, they're still just version 1.0.
it's some of the insights in version 2.0,3.0 that I think are really, incredible.
Dr John Dentico (08:51):
I agree.
it's having an overwhelming effect on,everything we do yet I like to think that
no matter what technology comes about,in the end, it's always about the people.
Daniel Paul Friker (09:03):
Yes.
Dr John Dentico (09:04):
And, and so is so interesting to me that you mentioned,
the computer revolution in the,
late eighties, early nineties.
I remember it all.
I was there.
I remember when browsers were justcoming online, graphical user interfaces.
You mean you have a picture onyour computer and you could just
(09:25):
click these different things.
How cool is that?
I actually sat in someone's office.
And a government office, and behindthem was a computer terminal.
And on that computer terminal wasMilnet, which became the internet.
I saw Milnet working, in small chunks ofemail, and I would not be wrong in telling
(09:48):
you, remembering all the way back to that.
So this is gonna lead to my next question.
I remember I was part of a groupthat used this tool called foresight.
We looked at what was happeningin the world of information
distribution, knowledge distribution.
(10:09):
if this keeps going, that means thepower of information and knowledge is
gonna be in everyone's hands Not justin the hands of the CEO, who's supposed
to be the smartest person in the room,now we don't know who the smartest
person in the room is, because, somebodyworking in AI four to six hours a day,
may have more information at theirhands than the CEO or any one of the
(10:33):
other managers in the organization.
So I would not be wrong in tellingyou, Dan, that I've been waiting 30
years for this moment And the reasonis because from that point, 30 years
ago, I was very concerned, focusedon the e evolution of leadership.
(10:53):
How is leadership have to changeto embrace all these other changes?
And now with AI on the scene, theaftermath of COVID where people are
no longer working for just a paycheckanymore, They want to be involved
in something where they have impact.
(11:14):
I see this evolution of leadershipchanging now to this place where 30 years
ago I started working on this conceptand now it's actually coming to fruition
I'd love to get your perspective on that.
How do you think.
Leadership has changed or has to changein the world in which we live right now.
Daniel Paul Friker (11:34):
So I'm going to borrow from Jack Welsh.
He just put it in a bookand made a lot of money.
If you were to compare US militarydoctrine versus Soviet doctrine.
Russian doctrine, evencurrently, the Chinese doctrine,
(11:55):
you have a pretty rigoroustop down approach.
You have a plan.
That plan is dictated down amongstyour generals, your colonels, your
majors, your captains, and so on andso forth, One of the things that
makes the US military outside of thetechnology is the management of teams,
(12:17):
if you're a sergeant, , you're kind ofsenior, most non-commissioned officer,
lieutenants.
know what the mission is, but they'regiven a fair amount of latitude to
accomplish the mission, and that'snot something that is present.
And you see this kind of playout in the war in Ukraine.
You have a decentralized militaryversus, essentialized military, which
(12:39):
is, using new weapons and tactics.
I mean, Ukrainians don't have a Navy,and yet they've all but destroyed
the Russian Navy in the Black Sea.
And they've been able to do it throughasymmetrical warfare and allowing
frontline workers to make more decisions.
What used to be a top down approachwhere the CEO had the most information,
what you're really, , how you shouldbe looking at AI transformation.
(13:05):
And this is actually was studied prettywell by Andrew Neg, who is not only a
professor at Stanford, although he gothis, I believe, his PhD from MIT, but
he was also in the Google Brain Project,which became, their AI engine, now Gemini.
He actually kind of came up witha framework around what companies
should be doing to adapt AI.
(13:27):
So his first thing was adapt early,so adopt the technology first.
So get your pilots to at leastgain moment, and there's no
shortages of ways of using AI.
It doesn't matter if it's supplychain, if it's hr, , there's whatever.
But the second step, which is socritical, and he talks a lot about
it in his various studies, isbuilding a cross-functional AI team,
(13:51):
so it's not just embeddedwithin the IT department.
You want to get people within anaccounting, you wanna get people
within HR. He also talked about theimportance of making sure that you
have a proper data scientist involved.
So a data scientist is gonna be thekind of person that's gonna look at
the data, structured unstructureddata, because, I mean, AI is core
of what it is, is just software.
(14:13):
It's an algorithm, , goes up one, , threeover, and it comes out with a certain
conclusion to predict a certain result andif you have bad data, you're likely going
to get bad results, so he talks aboutgetting a project going early, building a
good cross, cross-functional team, makingsure that you are at least providing basic
(14:36):
AI training on what it is, what it is not.
And what's interesting is wejust talked about three steps.
It's the fourth step, which isthen you build your AI strategy.
So normally in the business world,you start with strategy and then
you kind of work it way down.
He's like, no, that's withAI because you have CEOs.
(14:57):
And in fact, he actually jokedbecause I took one of his classes.
When you finish the class, he'slike, congratulations, you're
smarter than 80% of the CEOs in AI.
So if the CEO doesn't understandwhat it can and cannot do, then how
do you develop a strategy around it?
And then once you build out the strategyand then making sure that you're
building the external and internalcommunications, and that is becoming
(15:20):
dictated more and more by laws thatare starting to kind of come to bear.
EU passed legislation around ifyou're going to use AI, that you
have to at least create a framework.
So in the HR space, making sure that youdon't have AI create unintentional biases.
In the US and I'm not gonna commentabout whether or not, , the federal
(15:43):
government's doing good things orbad things, but I will talk about
the implications of decisions.
Back in late January, earlyFebruary, the Trp administration
basically said, we're not gonna beputting any regulations on this.
Now, at a state level, you'reseeing, seeing this more and more.
I think there's 15 states that eitherhave laws in the books or will certain
have laws in the book by, by end of 2025.
(16:03):
So again, as companies are adoptingthis, they have to think through it.
But those are really the key steps.
But if you do it right and you doit well now instead of having, so
the CEO will basically be pointed,the mission of the company.
Maybe, kinda even dictating, itcould be revenue growth, gross profit
growth, EBITDA growth could be marketshare, whatever strategy that the
(16:27):
particular CEO is doing, but allowingthe different teams to execute as the
data information and the change inenvironment becomes, , essentially
they're adopting to it, so it's key about.
Adapting the technology, adaptingon how to change your processes,
and then constantly measurethe execution success, so,
Dr John Dentico (16:52):
Okay.
Yeah, I think the the issuesright now as I see it is the
development is happening so quickly.
It's, every day it's different.
Every day you get a change to ChatgPTit seems like, or, , we now we're
at, 4.5, coherence with GROK isgrowing as the nber of chips are now
(17:16):
included in the processing capability.
And I heard yesterday, in a conversation,and I haven't looked into this, I haven't
had time today to look into it, , butNvidia just produced a brand new chip
that, is even more capable , thanthe ones they've already produced.
And most of the AI systems arerunning on Nvidia chips anyway,
(17:38):
so it's a very interestingsituation we find ourselves in.
And yeah, I think there has to besome level of regulation, but I
don't think anybody has a, a grip onwhat that has to look like right now
because the development is so rapid.
What do, by the time you putregulations in place, they're
become overcome by events.
(17:59):
They're old, they're ancientcompared to where the development is.
And my concern, I'm sureit's your concern too, is.
The level of ethical AIexplainable AI transparency, how
is this thing making a decision?
What factors are, is it using?
That I think , AI is movingat 300 miles an hour.
I think it, the, ethical issues of movingat about 80, unless the companies we're
(18:23):
talking about are, have a group that'sconnected directly to the development
and saying we have keep up with you guys.
We gotta stay abreast of eachother as we move down the road.
So, very, very interestingto see how that all occurs.
And, as a former military officerwho taught strategy and tactics, in
(18:43):
the Navy,, I'll tell you one of thebig things, the big changes, that
I've seen and it it, and, and it isat the core of organizations that
a lot of people don't understand.
So we may do a little bit of a deepdive here, just for a couple of minutes.
When you look at organizationstoday, they are still.
(19:07):
structured according toa bureaucratic model.
Daniel Paul Friker (19:10):
Yes.
Dr John Dentico (19:11):
Right.
That.
Okay.
If you want to check it out, anybodywho listens to this, see if they
have an org chart with all thelittle boxes, because the org chart
is the language of bureaucracy.
It's how bureaucracy speaks to everyone.
Okay?
You fill this box, you are over here.
You're in that box.
Okay.
Bureaucracy itself, foundedby Max Weber , in the early
(19:36):
19 hundreds was built on fear.
And he got that from Frederick,the Great of Prussia, who took a
provincial government and turned itinto a world power by reintroducing,
organization onto the battlefield.
And what was that based on?
Well, it was based on the factthat they enlisted people had
to fear the sergeants and thesergeants had to fear the officers.
(19:59):
That is what he borrowed in termsof building the bureaucracy.
Yet today, we know, based on some of themost amazing work done by, for example,
former Air Force officer, now no longerwith us by the name of John Boyd, who's
probably what, if not the greatestAmerican military strategist of our time.
(20:21):
And what Boyd found was thatthe the military organizations.
That were able toovercome odds able to win.
Even when they had decreased nbers,they had less nbers or less technology.
Was was that organization thathad greater level of mutual trust?
(20:44):
Okay, so now.
If you take that and you kinda layit over the way organizations are
today in business or not-for-profit,we're moving from a fear-based
model to a trust-based model.
But many times, and we've done this withthe han relations movement, we take,
(21:04):
the tenants of a trust-based model.
Mix it like oil and water in abottle, and we shake it up really
hard and we sit it down and we go.
Well, why don't we have, whyisn't everybody operating
in this trust-based model?
Well, because what happens is youhave this separation again, you still
(21:25):
have at the baseline, you have thebureaucracy and what, and I don't
think bureaucracy's gonna go away.
I think the structure's gonna be there.
The question is, how can these two thingsactually exist in the same space together?
And if you look at the same bottle withoil and water, they occupy the same space.
But they are, I don't know if you'dcall it a tension, but sort a natural
(21:48):
way that they can work together.
So for me, that's the, whereorganizations have to see themselves.
, the model of leadership that I starteddeveloping 30 years ago is based on
one premise, one foundational piece.
And that is contribution.
It's contribution.
Okay.
We have a problem.
(22:08):
We have an issue.
Anybody got any ideas?
Anybody wanna help us out here?
That kind of a thing.
And I think we have to create that kind ofan environment for people to come togethe
uh, people who understand AI the CEOs, theCOOs in a way, if you will, that really.
(22:29):
Allows them to take from the, takethe best ideas and, and put 'em
together into a strategy that movesthe organization down the road.
I'd love to get your reflection on that.
Daniel Paul Friker (22:42):
Yeah, there's a lot, there's, there's a lot to unpack,
which you, which you talked about there,but I'll try to break it down this way.
So it was about six months ago.
So the area of AI that I wasreally honed in on was within
HR and talent acquisition.
Now I'm gonna go backwards to give youa little bit of a history lesson to like
(23:05):
the, to 2000 when I was in, , like kindafirst going into the staffing industry,
and you have to understand that in 2000,all the major staffing companies, and this
was also by the way, a barrier of entry.
Is they had built these databases thattook them years, if not decades, to
build, so all the way from the 1950s,1960s, all the way through to 2000
(23:29):
people would physically walk in and theywere in, there was an intake process.
They were put intotheir bespoke databases.
And in the early two thousands, ifyou were working for a major staffing
company at Deco, and Manpower Group,you sometimes you would literally get
into these, our database is betterthan their database kind of stuff.
What you had though in the earlytwo thousands is really when
(23:50):
you had the rise of job boards.
So , your early monsters, techies.com,like some of them have come and gone.
Some of 'em are still around, of course.
And you also started to have thesebig staffing companies move away from
client, server based databases intokind of more of a essentially cloud, so
(24:11):
you have this kind of democratization.
So where you can actually create anew staffing company, , essentially
purchase now LinkedIn licenses and havein some regards almost as good if not
better, the ability just to find talent.
Even though these new technologiesare coming out there, the how
they were adapting their businesspractices was slow and coming.
(24:34):
And this kind of leads meto kind of my my point.
The paradox going on right now.
You now.
I, when I was looking to, to do someresearch, I was like, okay, how is AI
making HR and talent acquisition better?
And initially I just thought I wasjust doing bad research and I'm
like, I can't find case studies.
I can find great case studies from likethe Wall Street Journal, , Walmart and
(24:56):
how they're using AI so that they canstock their each Walmart store with
five to 10,000 SKUs that have withina three to four square mile area.
Because now they're able to competewith Amazon so they can do same
day delivery of their products.
But that's been a progression of, theystarted using data science a decade ago.
Now they're using AI tomake the decisions faster.
(25:19):
So the value proposition at the boardpresident, CEO level, they all get it,
, they know what, what the mission is,and they know what success looks like.
But the data that, andusing LinkedIn's own data,
now they're saying there's over athousand applicants for each open role.
So you have these tools that now allowindividual job seekers to apply to
(25:42):
hundreds, if not thousands of jobs.
It's no longer trying tofind a needle in a haystack.
It's literally a needle on top of needles.
And you literally have people able,able submit their rese via AI tools.
And then on the other end, AI tools,reading what those submittals are.
And when you actually lookat, so when you, so in some
regards, is that efficient?
(26:04):
Yes, you're doing more activity,but what's the success?
It's finding the right person for the job.
And so from a management level,and this is why I would point the
finger at executives to go, youhave to define the success metrics.
, Robert McNamara, , when he was lookingto go from the M 14 to the M 16.
(26:26):
Was looking at data.
'cause he was, , kind of a Harvarddata guy and everything else.
And he is like, well if we fire him,we're bullets, we'll kill more people.
So we need guns to fire more bullets.
Correlation is equal to causation.
So like, is that success?
You have to define success at a unitsquad level, , all the way up to, , the
(26:47):
regiment to be able to say like,okay guys, what mountains are we?
What hills are we gonna take?
Right.
And if you're not making it clear on,not necessarily what the activities
are, but what does success look like,then you're constraining via that
bureaucracy that you're talking aboutto move the, the organization forward.
And I think those companies that arestarting to adopt AI and figure out,
(27:10):
okay, these are the pros, these arethe cons, this is how we can improve
the throughput of, of finding thebest talent and doing it more quickly.
In the short term, that's gonnagive you a competitive advantage.
Just like the early adopters of e-commercegave them a competitive advantage.
What you don't want is 2018, which wasthe high watermark in the United States
(27:30):
of retailers going out of business becauseby then they realized if they didn't have
e-commerce, they were out of business.
And what's going to happen likelyby the end of the decade, is the
companies that haven't adopted AInow they're gonna be lapped by it.
Now they're gonna be out of business.
So the short term, this is yourcompetitive advantage moment.
(27:50):
But by the end of the decade, ifyou're not doing it, you, you do,
you're not doing anything 'causeyou're, you're outta business.
, Dr John Dentico (27:58):
it's interesting, I was reading an article the other
day because you, , you referred toit about this woman who's teaching
people how to use Google's Gemini.
To, to, get past the AIresume screening software.
But what I found so ironic, and I thoughtthis was just, I, I just got a great
(28:23):
chuckle out of this, that the hack, inorder to hack the person who's teaching
people how to get through an AI resumescreening system by using AI to do that.
The hack is, well, the way we we getaround this is, we have to call people
(28:43):
in and do person to person interviews.
That's the hack, ? So, so the han elementbecomes the hack to AI in some respects.
And I thought that was justso in incredibly interesting.
And, and, and the point, I justwanna mention this, the point you
made about, about having so manypeople apply to different jobs.
(29:08):
There's this, , as I look alongLinkedIn, there's this tremendous, uh.
If you will, kickback on thenotion of people being ghosted
by organizations, they put inreses and they never hear back.
Well, the answer is becausethere's, , 5,000 people putting
(29:28):
in for this, that kind of a thing.
So that may be one reason, but, but,there's somehow, there has to be some
level of feedback for people to knowthat, hey, it got through, or We got it.
At least go ahead.
Daniel Paul Friker (29:41):
So this is me being very self-aware, and I'm gonna look in
the camera as I say this, so I'm an idiot.
And what I mean, what I'm going with thisis, is that for a guy that went to grad
school at the University of Minnesota.
The guy's been doing all this research.
I, I started to experiment with thismyself, so if you would've asked me,
(30:06):
like, if, am I good at writing reses?
I would've said, absolutely.
I can write a great rese, so there'snow, there's a bunch of these tools.,
the one off the top of my head is calledJob Scan, so you can literally take and
what job scan will do is it'll take a, ajob description, you can cut and paste it
here, and then you can put your rese here.
And essentially it reverse engineers,, very common a ATS platforms like Workday,
(30:31):
like what your score is, and I will tellyou my score was coming at 30% of a match.
And I'm like, how?
How?
Wait a minute, I'm, I'm a smart guy.
This is my area of expertise,
the conclusion.
For looking through these tools isAI power tools in some respects is
(30:54):
actually making it harder becausewhat I found was a job description
that might say go to market strategy.
And it might, so I, it's in myrese, go to market strategy.
But it showed up as godash two dash market.
It didn't recognize it.
So if in my rese I said customerpresentations, but the job description
said client res. So literally justby changing words here, here and
(31:18):
here, it went from 30% to 85%.
So
right now, according to LinkedIn, only 40%of people applying for jobs are using some
type of AI tool to do that comparison.
But you've gotta think of itthis way, if you're on the other
end, if you are a recruiter.
(31:38):
You have a thousand applicants.
What you're likely going to do is to say,I only want to talk to people that are
between a 95 to a hundred percent match,even though the best candidate is probably
in the noise, but because they didn't usea, , essentially a job tool to kind of do
those kind of tweaks and modifications.
So what's the hack?
You go to your network.
(32:00):
So when the Time Magazine came outwith an article on the 24th of March
and they talked about how the datafrom two, from essentially 2020,
2024 most recent data, the nber oneway to get a job is your network.
That has not changed, but yet you haveall these tools and AI so you would
think it would be more efficient still.
So, , face-to-face interactions.
Dr John Dentico (32:21):
Yeah, I think it's, , it's almost like cold calling.
, you're, you're cold calling on,on LinkedIn by providing all this.
I had a guy, a guest on before namedArnaud Lucas, and one of the things that
I, I, I captured from our conversationand he's the, chief Technology Officer
for a, a company in the travel space.
They do buses and trains.
(32:43):
They're called Wanderuu.
He said to me, , there's really, whathe impressed upon me was there's two
job markets, there's the one you hearabout all the time, other job market.
We lost jobs, we gained jobs, we lostjobs, he said, and then the other
market is the talent market, he said,and we're always looking for talent.
Even though the job market may say,oh, we're cutting, , it's 4,000 people
(33:05):
from tech, or we're cutting 300 peoplefrom tech, or whatever it is, we're
always looking for talent, and it'show you can put your best foot forward.
I think that's what AI can help you dois really clean up your presentation
in a way to an, to a company.
But again.
It's the local network.
(33:26):
And , the other thing,,I'll mention this guy is Ed.
His name's Ed Temple.
He's an MCC coach in Canada.
He's been on a while back and hesaid to me, , I think AI's gonna
take over probably 75 to 80% ofthe redundant things that we do.
He said, which makes the other 20%of our hanity even more important.
(33:48):
So it's a very interesting.
Very interesting take on the job market,HR, and and also, and, and I'm just
gonna lead into this just for a second,is the temporary staffing market.
What's that gonna look like?
I mean, with the advent of a AI comingon the scene, doing many of the jobs
(34:10):
perhaps, or, that most people , wouldbe qualified to do, but again, those.
The, the requirements for temporarystaffing may go sky high depending
on what your ability with AI is.
Just an interesting perspective,but any feedback from you on that?
Daniel Paul Friker (34:31):
Yes.
So as a person that worked for a temporarystaffing firm for 20 years,, I've attended
every earnings call, , for the lastdecade and certainly, , if you look
at the top three staffing companies,and I'm using, , earnings report data,
so this is not insider information.
The top three staffing companies are a,a Adecco, Randstad, and Manpower group,
(34:58):
What is interesting and what's been goingon in the staffing space is, there's
a couple of different benchmarks, butI'm gonna use, there's, an industry
magazine called Staffing Industry Analyst.
It's kind of the default expert aboutwhat's the size of the US staffing market,
so with a couple of dips like circa2009, 2010, the staffing market in the
(35:19):
United States continues to grow over thelast 25 years, but what's interesting
is the top three staffing companies,their market share continues to decline.
Now that flies in the phase of MBA101 so what you typically have
is if you have a growing market.
What you typically have is, , thebig companies essentially
(35:39):
gobble up the smaller companies.
Great example, think of the appmarket for streaming, so at first
everyone had their own streaming app.
Then of course, now you'reessentially getting into kind of
the big four, if you will, is , thiscompany's buying this company.
Now it's what you're actually areseeing in the staffing industry space
is entropy, you're actually seeingthe big three losing market share.
(36:03):
Well then now the next question is.
Who is taking their market share, soyes, you can argue that you've got
smaller, mid-sized staffing companiesto kind of take in market share because
they're more focused or more specializedversus kind of the big generic.
And I think that to a certainextent is, is accurate.
But what you're really seeing in themarketplace is HR tech companies are now
(36:28):
actually reaching that market cap of thestaffing space and actually exceeding it.
So I wanna think, think about in thisterm, if you're going to the airport
and you Uber, you're essentially payinga person to take you from your home
or your hotel to the airport, andthat person, by the way, no one called
him, essentially AI said, pick up thisperson here and doing everything else.
(36:51):
DoorDash, same thing.
You're paying a person to bring youyour lunch, dinner, whatever it is.
So HR Tech, the investment in HR Tech.
The company's ability to find theirown talent and then essentially
employ the critical skills of thattalent into their talent ecosystem.
(37:12):
That is really what's kind of going onin the staffing space, So you don't
necessarily need to go to a big staffingcompany to get the talent you need.
Now, there's all thesedifferent types of modalities.
, there's, I mean.
Upwork has been a company that Ibelieve has been around since the
1990s, but , essentially they weredoing gig work in the nineties.
(37:33):
Now they just have the technology tomake it even easier, if you want one
of the best people that knows Claudeor Microsoft Copilot, , and you're
like, Hey, I just want you to buildme this one little thing, and he is
like, that's gonna cost you 10 grand.
It's like, here you go.
Where in the past you might've had togo into a staffing agency to do the
recruitment, talent, everything else.
(37:54):
So like there's so many more modalitiesto your earlier comment about bringing in
good talent, especially in a job market.
So in 2025, we have more generationsof workers in the marketplace today
than at any point in human history.
Like my own father, . PhDMathematics., he retired ultimately
(38:18):
from Northwestern as a professor.
, he probably did consulting foranother three, four years afterward.
, working for Mario Andretti racing,, trying to like figure out like
how to make these, , Indy cars andFormula one cars even faster by
using data science and and whatnot.
But he just did it 'cause he was bored.
And you have that all across thespectrum so I think that's really
(38:40):
what's changing that, that it'sreally disrupting that staffing model
of, , and you can also look at otherfactors that further corroborate this.
In early two thousands, an average UScompany would say about 3% of their
workforce were non full-time employees.
(39:03):
that could be interns, that canbe, maybe you bring back a retiree
or , 1099, whatever you've seen.
That nber steadily increase to 20%.
And right now we're atabout, give or take, 25%.
Now if you look at places like Franceand Italy, they're in the high forties.
And why?
Because it's really expensive tohire and fire full-time employees.
(39:28):
They've adopted all these different typesof modalities to bring in talent so that
they can execute on whatever strategy andbusiness plan that they have, and then
keep the internal full-time employeesto the, essentially the core mission.
So not only are you seeing thetrend line continue to go like this,
but if you look at other marketsthat you can predict likely where
the US market's gonna continue togo through the end of the decade.
Dr John Dentico (39:49):
Yeah, it's interesting.
I, I, , you, you see this termfractional everywhere now.
Fractional, CMO fractional, and , peoplecome in, they do the job, they do
the work, and , all the encbrancesof hiring a full-time employee.
So it's very, very interesting.
I agree.
It's, it's changing rightbefore our very eyes.
(40:13):
, and, and as I said before, everyday you get up and it's different.
It's some new thing happened.
, all these, , everything from quiet,quitting, , right after, COVID and
now we we're moved way beyond that todifferent, different kinds , of work.
Involvement of peopleor engagement of people.
(40:33):
But anyway, it's very interesting.
We're getting close to the end here.
So I would like to ask you my worldfamous question, and that is, in the
staffing world, make it this way.
In the staffing world, HR world, youtalk about mismatches, but if you
had a magic wand and you could changeanything in that world, what would that
(40:54):
issue be and why did you choose that?
Daniel Paul Friker (40:57):
I think we've kind of talked about this a couple of times,
but my magic wand, and to be clear.
Someone in some company willfigure this out, just like
Bezos figured out e-commerce.
And , right now we have a certainamount of anarchy as it relates to
(41:18):
implementing AI in the HR space to beable to find talent, deploy talent.
Like there's, there's alot of inefficiencies there
that will be figured out.
And I think if we can get toeven some type of assemblance.
Right.
And I think I'm just maybeapplying a couple of different
thoughts and ideas here., My
(41:42):
daughter loves playing herNintendo Switch, and the video
games do this all the time.
You get these little power-ups, theselittle certifications, , so you can
upgrade your weapon, you can upgradeyour Mario Kart and everything else.
We're heading towards a worldwhere if you want to get selected
(42:06):
as a job, it's really going to bean acculation of these different
types of certifications, and thosecertifications are gonna be quantifiable,
so imagine a world where you can actuallybetter predict a person's skills because
they have the, , the certifications thatyou're looking for at the right levels.
(42:26):
Now you have better structured data.
So now you have the ability, so insteadof the stress and agony of trying to
find your next job or your career rightnow, you have the ability to go into
a tool or tools to create a customizedlearning plan of what the things you want
(42:47):
to do, what your talent lends you to do.
Then now you have a litany ofcompanies basically saying, we
want your talent in our company,and whatever type of modality.
If it's a full-time job, if it's aconsulting gig, if it's a three month
assignment, six month assignment.
If you can create that type of world.
And I do believe that world is cominglike this isn't just fantasy land's,
(43:11):
Star Wars, Star Trek kind of stuff.
This is, this is ultimately, I thinkwhere the mission is trying to go.
We just have to waddle our way throughit, so that, that's my magic wand.
I would love to start seeing these piecesstart to fall into place where it becomes
more ubiquitous of how you're, how you'refinding your job and what you're doing.
, Dr John Dentico (43:35):
it's very, very insightful because what ha what
clicked in my mind when you weretalking about this in different
certifications is places like Udemy.
And all these online learningplatforms that part of what they
(43:57):
try to do is if you go through acourse, you get a certification.
You, , you get a certificate.
I mean, I, I did an online coursefor the, Artificial Intelligence
Management Professional, certification.
So I got, I got that and,which was very informative.
(44:20):
And I enjoyed the, the online learning,but I have that certification.,
But I could see it in, in, ineverything from digital marketing.
It's to leadership, to all thesedifferent kinds of things to say.
So you show up in a company,for example, and they say, what?
Well.
What can you show me?
(44:41):
What, oh, well I have these fivecertifications to do, , Lean, Six
Sigma, for example, or all thesedifferent kinds of things and go Wow.
or PMP, Project ManagementProfessional, for example, one of those.
And you sit back and youlook, wow, this is great.
This is the kind of skillbase we're looking for it now.
(45:03):
There's other questions that need tobe answered, , things like, what's
the chemistry like of this person?
, will they, will they work with us?
Are they good?
And here's the thing that I focuson all the time, two things that I'm
really zeroing in on, to be honest.
One is, values.
Do the values of the individualmatch the values of the organization?
(45:24):
Can, can we see this , in a waythat they're collaborative together?
And the other thing that I've learned.
In the, in my interviews withso many wonderful people is,
what I call now Hire for heart.
And what I mean by that is has this personbeen through difficult trying times?
Has this person, , forged theirway through a I have had several
(45:48):
guests that are just unbelievable.
In fact, the one I published AriasWebsterBerry, uh was phenomenal.
A phenomenal story about a manwho, who just triumphed over some
of the most difficult, arduouskinds of challenges put before him.
Never said he was a victim.
(46:09):
Never once.
Just moved through it.
Found a way.
To get on the other side andto, and to work through it.
And I think that's important from thehuman, from the human perspective.
So, yeah, I agree.
Skill-based certifications.
I got the numbers punched, if you will.
I got the boxes checked, but at the endtoo, we have to talk about the human side.
(46:32):
Any final words for me on that?
Daniel Paul Friker (46:35):
I think I 100% agree because I think.
I think if there's a theme, it'sadapting with AI and maybe the,
another great example, 'causeyou mentioned the military.
One thing that I don't think themilitary gets credit enough credit
(46:55):
for, , are those after action reviewsand constantly adapting to the
environment that you're in and whatyou're seeing in the current military.
I mean, you even see how the differentunit squad tactics are evolving because
now they want a dedicated drone operator.
(47:17):
Why?
Because they've seen how effectivethat at a squad level, having a drone
operator to be able to know wherethe enemy is how to, , kind of take.
And so those are the kind of things Ithink as companies start to adopt AI
now they have to start thinking through,okay, how do we adapt the to this?
(47:40):
Because if you adapt the rightweapons and tactics, you're going
to have competitive advantage.
And if you're lethargic at it, youcan have the greatest the planet and
people aren't gonna have the success.
Dr John Dentico (47:51):
And be Sears.
You can be Sears.
Daniel Paul Friker (47:55):
Yes.
Dr John Dentico (47:56):
I mean, I always, I I've used this example before.
Can you imagine telling Sears 30 yearsago, you're going outta business?
Daniel Paul Friker (48:01):
Yeah.
Dr John Dentico (48:02):
laughed.
They would've laughed.
It, they, what?
Are you crazy?
We're Sears, we're, we'rebasically indestructible.
No.
Not really.
It's just the way it is.
Listen, Dan, this hasbeen a great conversation.
I can't thank you enough foryour time here today on the
Throttle Up Leadership Podcast.
I wish you all the success in the world,good things to you and your family.
Thank you very much for your time.
Welcome to the Throttle Up Leadership Podcast.
Our theme for 2025 is The Futureof Work: Meaning is the New Money!
In a world shaped by rapid innovation andconstant change the pursuit of purpose
and impact has never been more important.
I'm Dr John Dentico your host,bringing over 30 years of experience
(00:28):
in leadership, strategic thinkingand purpose-driven innovation.
Here we tackle the pressing challengesof our time-from the rise of
artificial intelligence to the growingneed for meaning in the workplace.
Together we'll uncover how leaderscan ethically integrate AI to enhance
decision-making and keep humanityat the heart of their organizations.
(00:51):
Remember amidst all thetechnological advancements in the
end, it's always about the people.
This podcast is your resource foractionable tools, thought provoking
discussions and inspiring stories.
It's time to go beyond leadershipdevelopment and focus on leadership
(01:11):
impact-creating workplaces where peoplethrive, innovation flourishes and
meaning truly becomes the new currency.
Thank you for joining me on this journey.
Now, let's Throttle Up anddive into today's episode.
Hello again and welcome to theThrottle Up Leadership Podcast.
(01:32):
I am your host, Dr. John DenticoWith me today is Dan Friker, a global
executive strategic advisor and AIworkforce futurist with over 20 years
of experience in professional servicesworking across the energy, oil, and
gas and manufacturing industries.
(01:52):
Known for his ability to lead largescale organizational change and execute
high impact strategic initiatives,
Dan has worked with Fortune500 Giants such as ExxonMobil,
Microsoft, Cisco, 3M, and HP.
A graduate of the CarlsonSchool of Management with
both A BNM master's degree.
Dan combines academic rigor with realworld insight to help organizations
(02:17):
and leaders navigate complexity, fostersustainable growth, and align operations.
With several AI certificationsand a deep passion for workforce
transformation, Dan now focuseson exploring how AI is reshaping
hiring, HR, and leadership strategy.
He speaks and consults on criticalissues like the talent mismatched crisis,
(02:41):
the future of job design and how bothcompanies and individuals can adapt
strategically to stay ahead in the AIera, Dan's mission is clear to bring
the growing divide between organizationsseeking talent and individuals searching
for meaningful careers, empowering leadersto lead with vision and innovation.
(03:02):
I'm truly grateful that hehas taken the time to be here
with me today on the podcast.
Good morning, Dan.
How are you?
Daniel Paul Friker (03:08):
Good morning.
Dr John Dentico (03:09):
Good.
Glad to hear it.
So Dan, I'm adding more of a personaltouch this year to my podcasts.
So I'd like to ask you, tellme a bit about yourself.
Where did you grow up and whatin your mind were some of the
fundamental influences in your life?
Daniel Paul Friker (03:25):
Great question.
I actually grew up on the north sideof Chicago, in a town called Palatine
and turned 18 decided to go to theUniversity of Minnesota, little did
I realize that I would spend the vastmajority of my life here in Minnesota
but I think your question is relevant.
One of the biggest influences in my lifeand my careers, actually, my grandfather.
(03:48):
I come from a long line of teachers.
He happened to be the, PR former principalat SEN High School in the city of Chicago.
think about Chicago in thesixties and seventies, just
a couple of things going on.
But one of the things as he was amajor component of my upbringing
was really around connecting withpeople, it doesn't matter what
(04:11):
kind of socioeconomic, backgrounds.
He is a Swiss German, descendant so forme, that really lit a passion as when
I attended the University of Minnesota.
My junior year, I actuallystudied abroad in Germany.
And so not only did that further fan mydesire to understand, different people and
(04:33):
cultures, but then how to work with them.
And then ultimately when I was at ManpowerGroup for about half my career, I was in
a global role having to work with peoplein the APAC market, the EMEA market, the
Latin American market, and then ultimatelytry to help major corporations Fortune 100
companies really find talent to executeupon initiatives they were doing, be it
(04:55):
for oil and gas, or in the case of 3M,making sure they were producing lifesaving
materials, during the COVID crisis.
it's always been there.
When I went to graduate school, one ofthe things I latched onto was how, even
in graduate school, it was becoming veryapparent that HR departments were really
taking these files that were, , in, insome cases physical file cabinets and
(05:19):
starting to put them into databases.
And then with those databases, theywere starting to use data science
to really help answer a criticalquestion, which is how do we find
the best talent regardless of, wherethey're coming from around the world.
And AI is really justthat extension of it.
So this has been a linear progressionto really understand this critical
inflection point that we're in right now.
Dr John Dentico (05:42):
I agree.
I think, the single greatest,revolution, that the world has
ever seen artificial intelligence.
I, I always refer to JensenHwang's, comment when he said,
speaking about HR you don't haveto worry about HR taking your job.
You have to worry about somebodywho understands how to use AI
Daniel Paul Friker (06:01):
Yeah.
Dr John Dentico (06:01):
So I think, that's a really interesting point.
Let me ask you this, Dan AI is expectedto impact over 70% of jobs worldwide.
From your perspective, which industriesare most vulnerable and how can companies
proactively adapt to this disruption?
Daniel Paul Friker (06:24):
It's a great question and it is actually one that
McKinsey, has published multiplestudies on, So you are right in the
sense that overall AI has the abilityto impact somewhere between 70, 75%.
They would also say that the valuecreation that would come out of AI is
(06:46):
somewhere between 13 and $22 trillion.
The way I like to describe this isthink about within our lifetimes, the
PC revolution, If you used to walkinto major corporations back in the
1980s, you would see hundreds, ifnot thousands of executive admins.
Right.
PC started to come, and majorexecutives are essentially
(07:06):
doing a lot of their own work.
So the executive admin poolessentially went down, Used to walk
into call centers and would seehundreds if not thousands of people.
Now you're able to do the sameamount of work with less people,
I would use a more recent example,which is e-commerce, Growing up in
Chicago, I knew it as the Sears Tower.
Sears, had everything in the palmof their hand to take e-commerce.
(07:28):
And what is Amazon today, fundamentally,they had everything they failed to, they
knew that e-commerce was starting toimpact their business by the mid nineties.
They had quantifiable databy the early two thousands.
Yet it was their inability to adapt to thee-commerce environment and quite frankly.
Jeff Bezos has publicly talked about it.
(07:48):
He is like, I just took it Sears'business model and just put it into
digital age and the rest is history.
So AI has that ability to improveefficiency and I have a sneaking
suspicion, we're gonna talk a lotabout this today, but I think what is
really missing in the marketplace isthe efficiency versus success paradox.
(08:13):
What sectors are goingto be most impacted?
Well, you're gonna see it prob,statistically in the most with retail,
travel, transport and logistics.
Those are the top threeindustries going to be impacted.
But it pairs repeating again,it's gonna impact everything.
And I'm sure we're gonna talk a littlebit more about the efficiency of success
paradox in a moment here, Right now whatwe're really seeing is what I like to
(08:37):
call the clippy, gen one version of AIso as much as these tools are coming out
today, they're still just version 1.0.
it's some of the insights in version 2.0,3.0 that I think are really, incredible.
Dr John Dentico (08:51):
I agree.
it's having an overwhelming effect on,everything we do yet I like to think that
no matter what technology comes about,in the end, it's always about the people.
Daniel Paul Friker (09:03):
Yes.
Dr John Dentico (09:04):
And, and so is so interesting to me that you mentioned,
the computer revolution in the,
late eighties, early nineties.
I remember it all.
I was there.
I remember when browsers were justcoming online, graphical user interfaces.
You mean you have a picture onyour computer and you could just
(09:25):
click these different things.
How cool is that?
I actually sat in someone's office.
And a government office, and behindthem was a computer terminal.
And on that computer terminal wasMilnet, which became the internet.
I saw Milnet working, in small chunks ofemail, and I would not be wrong in telling
(09:48):
you, remembering all the way back to that.
So this is gonna lead to my next question.
I remember I was part of a groupthat used this tool called foresight.
We looked at what was happeningin the world of information
distribution, knowledge distribution.
(10:09):
if this keeps going, that means thepower of information and knowledge is
gonna be in everyone's hands Not justin the hands of the CEO, who's supposed
to be the smartest person in the room,now we don't know who the smartest
person in the room is, because, somebodyworking in AI four to six hours a day,
may have more information at theirhands than the CEO or any one of the
(10:33):
other managers in the organization.
So I would not be wrong in tellingyou, Dan, that I've been waiting 30
years for this moment And the reasonis because from that point, 30 years
ago, I was very concerned, focusedon the e evolution of leadership.
(10:53):
How is leadership have to changeto embrace all these other changes?
And now with AI on the scene, theaftermath of COVID where people are
no longer working for just a paycheckanymore, They want to be involved
in something where they have impact.
(11:14):
I see this evolution of leadershipchanging now to this place where 30 years
ago I started working on this conceptand now it's actually coming to fruition
I'd love to get your perspective on that.
How do you think.
Leadership has changed or has to changein the world in which we live right now.
Daniel Paul Friker (11:34):
So I'm going to borrow from Jack Welsh.
He just put it in a bookand made a lot of money.
If you were to compare US militarydoctrine versus Soviet doctrine.
Russian doctrine, evencurrently, the Chinese doctrine,
(11:55):
you have a pretty rigoroustop down approach.
You have a plan.
That plan is dictated down amongstyour generals, your colonels, your
majors, your captains, and so on andso forth, One of the things that
makes the US military outside of thetechnology is the management of teams,
(12:17):
if you're a sergeant, , you're kind ofsenior, most non-commissioned officer,
lieutenants.
know what the mission is, but they'regiven a fair amount of latitude to
accomplish the mission, and that'snot something that is present.
And you see this kind of playout in the war in Ukraine.
You have a decentralized militaryversus, essentialized military, which
(12:39):
is, using new weapons and tactics.
I mean, Ukrainians don't have a Navy,and yet they've all but destroyed
the Russian Navy in the Black Sea.
And they've been able to do it throughasymmetrical warfare and allowing
frontline workers to make more decisions.
What used to be a top down approachwhere the CEO had the most information,
what you're really, , how you shouldbe looking at AI transformation.
(13:05):
And this is actually was studied prettywell by Andrew Neg, who is not only a
professor at Stanford, although he gothis, I believe, his PhD from MIT, but
he was also in the Google Brain Project,which became, their AI engine, now Gemini.
He actually kind of came up witha framework around what companies
should be doing to adapt AI.
(13:27):
So his first thing was adapt early,so adopt the technology first.
So get your pilots to at leastgain moment, and there's no
shortages of ways of using AI.
It doesn't matter if it's supplychain, if it's hr, , there's whatever.
But the second step, which is socritical, and he talks a lot about
it in his various studies, isbuilding a cross-functional AI team,
(13:51):
so it's not just embeddedwithin the IT department.
You want to get people within anaccounting, you wanna get people
within HR. He also talked about theimportance of making sure that you
have a proper data scientist involved.
So a data scientist is gonna be thekind of person that's gonna look at
the data, structured unstructureddata, because, I mean, AI is core
of what it is, is just software.
(14:13):
It's an algorithm, , goes up one, , threeover, and it comes out with a certain
conclusion to predict a certain result andif you have bad data, you're likely going
to get bad results, so he talks aboutgetting a project going early, building a
good cross, cross-functional team, makingsure that you are at least providing basic
(14:36):
AI training on what it is, what it is not.
And what's interesting is wejust talked about three steps.
It's the fourth step, which isthen you build your AI strategy.
So normally in the business world,you start with strategy and then
you kind of work it way down.
He's like, no, that's withAI because you have CEOs.
(14:57):
And in fact, he actually jokedbecause I took one of his classes.
When you finish the class, he'slike, congratulations, you're
smarter than 80% of the CEOs in AI.
So if the CEO doesn't understandwhat it can and cannot do, then how
do you develop a strategy around it?
And then once you build out the strategyand then making sure that you're
building the external and internalcommunications, and that is becoming
(15:20):
dictated more and more by laws thatare starting to kind of come to bear.
EU passed legislation around ifyou're going to use AI, that you
have to at least create a framework.
So in the HR space, making sure that youdon't have AI create unintentional biases.
In the US and I'm not gonna commentabout whether or not, , the federal
(15:43):
government's doing good things orbad things, but I will talk about
the implications of decisions.
Back in late January, earlyFebruary, the Trp administration
basically said, we're not gonna beputting any regulations on this.
Now, at a state level, you'reseeing, seeing this more and more.
I think there's 15 states that eitherhave laws in the books or will certain
have laws in the book by, by end of 2025.
(16:03):
So again, as companies are adoptingthis, they have to think through it.
But those are really the key steps.
But if you do it right and you doit well now instead of having, so
the CEO will basically be pointed,the mission of the company.
Maybe, kinda even dictating, itcould be revenue growth, gross profit
growth, EBITDA growth could be marketshare, whatever strategy that the
(16:27):
particular CEO is doing, but allowingthe different teams to execute as the
data information and the change inenvironment becomes, , essentially
they're adopting to it, so it's key about.
Adapting the technology, adaptingon how to change your processes,
and then constantly measurethe execution success, so,
Dr John Dentico (16:52):
Okay.
Yeah, I think the the issuesright now as I see it is the
development is happening so quickly.
It's, every day it's different.
Every day you get a change to ChatgPTit seems like, or, , we now we're
at, 4.5, coherence with GROK isgrowing as the nber of chips are now
(17:16):
included in the processing capability.
And I heard yesterday, in a conversation,and I haven't looked into this, I haven't
had time today to look into it, , butNvidia just produced a brand new chip
that, is even more capable , thanthe ones they've already produced.
And most of the AI systems arerunning on Nvidia chips anyway,
(17:38):
so it's a very interestingsituation we find ourselves in.
And yeah, I think there has to besome level of regulation, but I
don't think anybody has a, a grip onwhat that has to look like right now
because the development is so rapid.
What do, by the time you putregulations in place, they're
become overcome by events.
(17:59):
They're old, they're ancientcompared to where the development is.
And my concern, I'm sureit's your concern too, is.
The level of ethical AIexplainable AI transparency, how
is this thing making a decision?
What factors are, is it using?
That I think , AI is movingat 300 miles an hour.
I think it, the, ethical issues of movingat about 80, unless the companies we're
(18:23):
talking about are, have a group that'sconnected directly to the development
and saying we have keep up with you guys.
We gotta stay abreast of eachother as we move down the road.
So, very, very interestingto see how that all occurs.
And, as a former military officerwho taught strategy and tactics, in
(18:43):
the Navy,, I'll tell you one of thebig things, the big changes, that
I've seen and it it, and, and it isat the core of organizations that
a lot of people don't understand.
So we may do a little bit of a deepdive here, just for a couple of minutes.
When you look at organizationstoday, they are still.
(19:07):
structured according toa bureaucratic model.
Daniel Paul Friker (19:10):
Yes.
Dr John Dentico (19:11):
Right.
That.
Okay.
If you want to check it out, anybodywho listens to this, see if they
have an org chart with all thelittle boxes, because the org chart
is the language of bureaucracy.
It's how bureaucracy speaks to everyone.
Okay?
You fill this box, you are over here.
You're in that box.
Okay.
Bureaucracy itself, foundedby Max Weber , in the early
(19:36):
19 hundreds was built on fear.
And he got that from Frederick,the Great of Prussia, who took a
provincial government and turned itinto a world power by reintroducing,
organization onto the battlefield.
And what was that based on?
Well, it was based on the factthat they enlisted people had
to fear the sergeants and thesergeants had to fear the officers.
(19:59):
That is what he borrowed in termsof building the bureaucracy.
Yet today, we know, based on some of themost amazing work done by, for example,
former Air Force officer, now no longerwith us by the name of John Boyd, who's
probably what, if not the greatestAmerican military strategist of our time.
(20:21):
And what Boyd found was thatthe the military organizations.
That were able toovercome odds able to win.
Even when they had decreased nbers,they had less nbers or less technology.
Was was that organization thathad greater level of mutual trust?
(20:44):
Okay, so now.
If you take that and you kinda layit over the way organizations are
today in business or not-for-profit,we're moving from a fear-based
model to a trust-based model.
But many times, and we've done this withthe han relations movement, we take,
(21:04):
the tenants of a trust-based model.
Mix it like oil and water in abottle, and we shake it up really
hard and we sit it down and we go.
Well, why don't we have, whyisn't everybody operating
in this trust-based model?
Well, because what happens is youhave this separation again, you still
(21:25):
have at the baseline, you have thebureaucracy and what, and I don't
think bureaucracy's gonna go away.
I think the structure's gonna be there.
The question is, how can these two thingsactually exist in the same space together?
And if you look at the same bottle withoil and water, they occupy the same space.
But they are, I don't know if you'dcall it a tension, but sort a natural
(21:48):
way that they can work together.
So for me, that's the, whereorganizations have to see themselves.
, the model of leadership that I starteddeveloping 30 years ago is based on
one premise, one foundational piece.
And that is contribution.
It's contribution.
Okay.
We have a problem.
(22:08):
We have an issue.
Anybody got any ideas?
Anybody wanna help us out here?
That kind of a thing.
And I think we have to create that kind ofan environment for people to come togethe
uh, people who understand AI the CEOs, theCOOs in a way, if you will, that really.
(22:29):
Allows them to take from the, takethe best ideas and, and put 'em
together into a strategy that movesthe organization down the road.
I'd love to get your reflection on that.
Daniel Paul Friker (22:42):
Yeah, there's a lot, there's, there's a lot to unpack,
which you, which you talked about there,but I'll try to break it down this way.
So it was about six months ago.
So the area of AI that I wasreally honed in on was within
HR and talent acquisition.
Now I'm gonna go backwards to give youa little bit of a history lesson to like
(23:05):
the, to 2000 when I was in, , like kindafirst going into the staffing industry,
and you have to understand that in 2000,all the major staffing companies, and this
was also by the way, a barrier of entry.
Is they had built these databases thattook them years, if not decades, to
build, so all the way from the 1950s,1960s, all the way through to 2000
(23:29):
people would physically walk in and theywere in, there was an intake process.
They were put intotheir bespoke databases.
And in the early two thousands, ifyou were working for a major staffing
company at Deco, and Manpower Group,you sometimes you would literally get
into these, our database is betterthan their database kind of stuff.
What you had though in the earlytwo thousands is really when
(23:50):
you had the rise of job boards.
So , your early monsters, techies.com,like some of them have come and gone.
Some of 'em are still around, of course.
And you also started to have thesebig staffing companies move away from
client, server based databases intokind of more of a essentially cloud, so
(24:11):
you have this kind of democratization.
So where you can actually create anew staffing company, , essentially
purchase now LinkedIn licenses and havein some regards almost as good if not
better, the ability just to find talent.
Even though these new technologiesare coming out there, the how
they were adapting their businesspractices was slow and coming.
(24:34):
And this kind of leads meto kind of my my point.
The paradox going on right now.
You now.
I, when I was looking to, to do someresearch, I was like, okay, how is AI
making HR and talent acquisition better?
And initially I just thought I wasjust doing bad research and I'm
like, I can't find case studies.
I can find great case studies from likethe Wall Street Journal, , Walmart and
(24:56):
how they're using AI so that they canstock their each Walmart store with
five to 10,000 SKUs that have withina three to four square mile area.
Because now they're able to competewith Amazon so they can do same
day delivery of their products.
But that's been a progression of, theystarted using data science a decade ago.
Now they're using AI tomake the decisions faster.
(25:19):
So the value proposition at the boardpresident, CEO level, they all get it,
, they know what, what the mission is,and they know what success looks like.
But the data that, andusing LinkedIn's own data,
now they're saying there's over athousand applicants for each open role.
So you have these tools that now allowindividual job seekers to apply to
(25:42):
hundreds, if not thousands of jobs.
It's no longer trying tofind a needle in a haystack.
It's literally a needle on top of needles.
And you literally have people able,able submit their rese via AI tools.
And then on the other end, AI tools,reading what those submittals are.
And when you actually lookat, so when you, so in some
regards, is that efficient?
(26:04):
Yes, you're doing more activity,but what's the success?
It's finding the right person for the job.
And so from a management level,and this is why I would point the
finger at executives to go, youhave to define the success metrics.
, Robert McNamara, , when he was lookingto go from the M 14 to the M 16.
(26:26):
Was looking at data.
'cause he was, , kind of a Harvarddata guy and everything else.
And he is like, well if we fire him,we're bullets, we'll kill more people.
So we need guns to fire more bullets.
Correlation is equal to causation.
So like, is that success?
You have to define success at a unitsquad level, , all the way up to, , the
(26:47):
regiment to be able to say like,okay guys, what mountains are we?
What hills are we gonna take?
Right.
And if you're not making it clear on,not necessarily what the activities
are, but what does success look like,then you're constraining via that
bureaucracy that you're talking aboutto move the, the organization forward.
And I think those companies that arestarting to adopt AI and figure out,
(27:10):
okay, these are the pros, these arethe cons, this is how we can improve
the throughput of, of finding thebest talent and doing it more quickly.
In the short term, that's gonnagive you a competitive advantage.
Just like the early adopters of e-commercegave them a competitive advantage.
What you don't want is 2018, which wasthe high watermark in the United States
(27:30):
of retailers going out of business becauseby then they realized if they didn't have
e-commerce, they were out of business.
And what's going to happen likelyby the end of the decade, is the
companies that haven't adopted AInow they're gonna be lapped by it.
Now they're gonna be out of business.
So the short term, this is yourcompetitive advantage moment.
(27:50):
But by the end of the decade, ifyou're not doing it, you, you do,
you're not doing anything 'causeyou're, you're outta business.
, Dr John Dentico (27:58):
it's interesting, I was reading an article the other
day because you, , you referred toit about this woman who's teaching
people how to use Google's Gemini.
To, to, get past the AIresume screening software.
But what I found so ironic, and I thoughtthis was just, I, I just got a great
(28:23):
chuckle out of this, that the hack, inorder to hack the person who's teaching
people how to get through an AI resumescreening system by using AI to do that.
The hack is, well, the way we we getaround this is, we have to call people
(28:43):
in and do person to person interviews.
That's the hack, ? So, so the han elementbecomes the hack to AI in some respects.
And I thought that was justso in incredibly interesting.
And, and, and the point, I justwanna mention this, the point you
made about, about having so manypeople apply to different jobs.
(29:08):
There's this, , as I look alongLinkedIn, there's this tremendous, uh.
If you will, kickback on thenotion of people being ghosted
by organizations, they put inreses and they never hear back.
Well, the answer is becausethere's, , 5,000 people putting
(29:28):
in for this, that kind of a thing.
So that may be one reason, but, but,there's somehow, there has to be some
level of feedback for people to knowthat, hey, it got through, or We got it.
At least go ahead.
Daniel Paul Friker (29:41):
So this is me being very self-aware, and I'm gonna look in
the camera as I say this, so I'm an idiot.
And what I mean, what I'm going with thisis, is that for a guy that went to grad
school at the University of Minnesota.
The guy's been doing all this research.
I, I started to experiment with thismyself, so if you would've asked me,
(30:06):
like, if, am I good at writing reses?
I would've said, absolutely.
I can write a great rese, so there'snow, there's a bunch of these tools.,
the one off the top of my head is calledJob Scan, so you can literally take and
what job scan will do is it'll take a, ajob description, you can cut and paste it
here, and then you can put your rese here.
And essentially it reverse engineers,, very common a ATS platforms like Workday,
(30:31):
like what your score is, and I will tellyou my score was coming at 30% of a match.
And I'm like, how?
How?
Wait a minute, I'm, I'm a smart guy.
This is my area of expertise,
the conclusion.
For looking through these tools isAI power tools in some respects is
(30:54):
actually making it harder becausewhat I found was a job description
that might say go to market strategy.
And it might, so I, it's in myrese, go to market strategy.
But it showed up as godash two dash market.
It didn't recognize it.
So if in my rese I said customerpresentations, but the job description
said client res. So literally justby changing words here, here and
(31:18):
here, it went from 30% to 85%.
So
right now, according to LinkedIn, only 40%of people applying for jobs are using some
type of AI tool to do that comparison.
But you've gotta think of itthis way, if you're on the other
end, if you are a recruiter.
(31:38):
You have a thousand applicants.
What you're likely going to do is to say,I only want to talk to people that are
between a 95 to a hundred percent match,even though the best candidate is probably
in the noise, but because they didn't usea, , essentially a job tool to kind of do
those kind of tweaks and modifications.
So what's the hack?
You go to your network.
(32:00):
So when the Time Magazine came outwith an article on the 24th of March
and they talked about how the datafrom two, from essentially 2020,
2024 most recent data, the nber oneway to get a job is your network.
That has not changed, but yet you haveall these tools and AI so you would
think it would be more efficient still.
So, , face-to-face interactions.
Dr John Dentico (32:21):
Yeah, I think it's, , it's almost like cold calling.
, you're, you're cold calling on,on LinkedIn by providing all this.
I had a guy, a guest on before namedArnaud Lucas, and one of the things that
I, I, I captured from our conversationand he's the, chief Technology Officer
for a, a company in the travel space.
They do buses and trains.
(32:43):
They're called Wanderuu.
He said to me, , there's really, whathe impressed upon me was there's two
job markets, there's the one you hearabout all the time, other job market.
We lost jobs, we gained jobs, we lostjobs, he said, and then the other
market is the talent market, he said,and we're always looking for talent.
Even though the job market may say,oh, we're cutting, , it's 4,000 people
(33:05):
from tech, or we're cutting 300 peoplefrom tech, or whatever it is, we're
always looking for talent, and it'show you can put your best foot forward.
I think that's what AI can help you dois really clean up your presentation
in a way to an, to a company.
But again.
It's the local network.
(33:26):
And , the other thing,,I'll mention this guy is Ed.
His name's Ed Temple.
He's an MCC coach in Canada.
He's been on a while back and hesaid to me, , I think AI's gonna
take over probably 75 to 80% ofthe redundant things that we do.
He said, which makes the other 20%of our hanity even more important.
(33:48):
So it's a very interesting.
Very interesting take on the job market,HR, and and also, and, and I'm just
gonna lead into this just for a second,is the temporary staffing market.
What's that gonna look like?
I mean, with the advent of a AI comingon the scene, doing many of the jobs
(34:10):
perhaps, or, that most people , wouldbe qualified to do, but again, those.
The, the requirements for temporarystaffing may go sky high depending
on what your ability with AI is.
Just an interesting perspective,but any feedback from you on that?
Daniel Paul Friker (34:31):
Yes.
So as a person that worked for a temporarystaffing firm for 20 years,, I've attended
every earnings call, , for the lastdecade and certainly, , if you look
at the top three staffing companies,and I'm using, , earnings report data,
so this is not insider information.
The top three staffing companies are a,a Adecco, Randstad, and Manpower group,
(34:58):
What is interesting and what's been goingon in the staffing space is, there's
a couple of different benchmarks, butI'm gonna use, there's, an industry
magazine called Staffing Industry Analyst.
It's kind of the default expert aboutwhat's the size of the US staffing market,
so with a couple of dips like circa2009, 2010, the staffing market in the
(35:19):
United States continues to grow over thelast 25 years, but what's interesting
is the top three staffing companies,their market share continues to decline.
Now that flies in the phase of MBA101 so what you typically have
is if you have a growing market.
What you typically have is, , thebig companies essentially
(35:39):
gobble up the smaller companies.
Great example, think of the appmarket for streaming, so at first
everyone had their own streaming app.
Then of course, now you'reessentially getting into kind of
the big four, if you will, is , thiscompany's buying this company.
Now it's what you're actually areseeing in the staffing industry space
is entropy, you're actually seeingthe big three losing market share.
(36:03):
Well then now the next question is.
Who is taking their market share, soyes, you can argue that you've got
smaller, mid-sized staffing companiesto kind of take in market share because
they're more focused or more specializedversus kind of the big generic.
And I think that to a certainextent is, is accurate.
But what you're really seeing in themarketplace is HR tech companies are now
(36:28):
actually reaching that market cap of thestaffing space and actually exceeding it.
So I wanna think, think about in thisterm, if you're going to the airport
and you Uber, you're essentially payinga person to take you from your home
or your hotel to the airport, andthat person, by the way, no one called
him, essentially AI said, pick up thisperson here and doing everything else.
(36:51):
DoorDash, same thing.
You're paying a person to bring youyour lunch, dinner, whatever it is.
So HR Tech, the investment in HR Tech.
The company's ability to find theirown talent and then essentially
employ the critical skills of thattalent into their talent ecosystem.
(37:12):
That is really what's kind of going onin the staffing space, So you don't
necessarily need to go to a big staffingcompany to get the talent you need.
Now, there's all thesedifferent types of modalities.
, there's, I mean.
Upwork has been a company that Ibelieve has been around since the
1990s, but , essentially they weredoing gig work in the nineties.
(37:33):
Now they just have the technology tomake it even easier, if you want one
of the best people that knows Claudeor Microsoft Copilot, , and you're
like, Hey, I just want you to buildme this one little thing, and he is
like, that's gonna cost you 10 grand.
It's like, here you go.
Where in the past you might've had togo into a staffing agency to do the
recruitment, talent, everything else.
(37:54):
So like there's so many more modalitiesto your earlier comment about bringing in
good talent, especially in a job market.
So in 2025, we have more generationsof workers in the marketplace today
than at any point in human history.
Like my own father, . PhDMathematics., he retired ultimately
(38:18):
from Northwestern as a professor.
, he probably did consulting foranother three, four years afterward.
, working for Mario Andretti racing,, trying to like figure out like
how to make these, , Indy cars andFormula one cars even faster by
using data science and and whatnot.
But he just did it 'cause he was bored.
And you have that all across thespectrum so I think that's really
(38:40):
what's changing that, that it'sreally disrupting that staffing model
of, , and you can also look at otherfactors that further corroborate this.
In early two thousands, an average UScompany would say about 3% of their
workforce were non full-time employees.
(39:03):
that could be interns, that canbe, maybe you bring back a retiree
or , 1099, whatever you've seen.
That nber steadily increase to 20%.
And right now we're atabout, give or take, 25%.
Now if you look at places like Franceand Italy, they're in the high forties.
And why?
Because it's really expensive tohire and fire full-time employees.
(39:28):
They've adopted all these different typesof modalities to bring in talent so that
they can execute on whatever strategy andbusiness plan that they have, and then
keep the internal full-time employeesto the, essentially the core mission.
So not only are you seeing thetrend line continue to go like this,
but if you look at other marketsthat you can predict likely where
the US market's gonna continue togo through the end of the decade.
Dr John Dentico (39:49):
Yeah, it's interesting.
I, I, , you, you see this termfractional everywhere now.
Fractional, CMO fractional, and , peoplecome in, they do the job, they do
the work, and , all the encbrancesof hiring a full-time employee.
So it's very, very interesting.
I agree.
It's, it's changing rightbefore our very eyes.
(40:13):
, and, and as I said before, everyday you get up and it's different.
It's some new thing happened.
, all these, , everything from quiet,quitting, , right after, COVID and
now we we're moved way beyond that todifferent, different kinds , of work.
Involvement of peopleor engagement of people.
(40:33):
But anyway, it's very interesting.
We're getting close to the end here.
So I would like to ask you my worldfamous question, and that is, in the
staffing world, make it this way.
In the staffing world, HR world, youtalk about mismatches, but if you
had a magic wand and you could changeanything in that world, what would that
(40:54):
issue be and why did you choose that?
Daniel Paul Friker (40:57):
I think we've kind of talked about this a couple of times,
but my magic wand, and to be clear.
Someone in some company willfigure this out, just like
Bezos figured out e-commerce.
And , right now we have a certainamount of anarchy as it relates to
(41:18):
implementing AI in the HR space to beable to find talent, deploy talent.
Like there's, there's alot of inefficiencies there
that will be figured out.
And I think if we can get toeven some type of assemblance.
Right.
And I think I'm just maybeapplying a couple of different
thoughts and ideas here., My
(41:42):
daughter loves playing herNintendo Switch, and the video
games do this all the time.
You get these little power-ups, theselittle certifications, , so you can
upgrade your weapon, you can upgradeyour Mario Kart and everything else.
We're heading towards a worldwhere if you want to get selected
(42:06):
as a job, it's really going to bean acculation of these different
types of certifications, and thosecertifications are gonna be quantifiable,
so imagine a world where you can actuallybetter predict a person's skills because
they have the, , the certifications thatyou're looking for at the right levels.
(42:26):
Now you have better structured data.
So now you have the ability, so insteadof the stress and agony of trying to
find your next job or your career rightnow, you have the ability to go into
a tool or tools to create a customizedlearning plan of what the things you want
(42:47):
to do, what your talent lends you to do.
Then now you have a litany ofcompanies basically saying, we
want your talent in our company,and whatever type of modality.
If it's a full-time job, if it's aconsulting gig, if it's a three month
assignment, six month assignment.
If you can create that type of world.
And I do believe that world is cominglike this isn't just fantasy land's,
(43:11):
Star Wars, Star Trek kind of stuff.
This is, this is ultimately, I thinkwhere the mission is trying to go.
We just have to waddle our way throughit, so that, that's my magic wand.
I would love to start seeing these piecesstart to fall into place where it becomes
more ubiquitous of how you're, how you'refinding your job and what you're doing.
, Dr John Dentico (43:35):
it's very, very insightful because what ha what
clicked in my mind when you weretalking about this in different
certifications is places like Udemy.
And all these online learningplatforms that part of what they
(43:57):
try to do is if you go through acourse, you get a certification.
You, , you get a certificate.
I mean, I, I did an online coursefor the, Artificial Intelligence
Management Professional, certification.
So I got, I got that and,which was very informative.
(44:20):
And I enjoyed the, the online learning,but I have that certification.,
But I could see it in, in, ineverything from digital marketing.
It's to leadership, to all thesedifferent kinds of things to say.
So you show up in a company,for example, and they say, what?
Well.
What can you show me?
(44:41):
What, oh, well I have these fivecertifications to do, , Lean, Six
Sigma, for example, or all thesedifferent kinds of things and go Wow.
or PMP, Project ManagementProfessional, for example, one of those.
And you sit back and youlook, wow, this is great.
This is the kind of skillbase we're looking for it now.
(45:03):
There's other questions that need tobe answered, , things like, what's
the chemistry like of this person?
, will they, will they work with us?
Are they good?
And here's the thing that I focuson all the time, two things that I'm
really zeroing in on, to be honest.
One is, values.
Do the values of the individualmatch the values of the organization?
(45:24):
Can, can we see this , in a waythat they're collaborative together?
And the other thing that I've learned.
In the, in my interviews withso many wonderful people is,
what I call now Hire for heart.
And what I mean by that is has this personbeen through difficult trying times?
Has this person, , forged theirway through a I have had several
(45:48):
guests that are just unbelievable.
In fact, the one I published AriasWebsterBerry, uh was phenomenal.
A phenomenal story about a manwho, who just triumphed over some
of the most difficult, arduouskinds of challenges put before him.
Never said he was a victim.
(46:09):
Never once.
Just moved through it.
Found a way.
To get on the other side andto, and to work through it.
And I think that's important from thehuman, from the human perspective.
So, yeah, I agree.
Skill-based certifications.
I got the numbers punched, if you will.
I got the boxes checked, but at the endtoo, we have to talk about the human side.
(46:32):
Any final words for me on that?
Daniel Paul Friker (46:35):
I think I 100% agree because I think.
I think if there's a theme, it'sadapting with AI and maybe the,
another great example, 'causeyou mentioned the military.
One thing that I don't think themilitary gets credit enough credit
(46:55):
for, , are those after action reviewsand constantly adapting to the
environment that you're in and whatyou're seeing in the current military.
I mean, you even see how the differentunit squad tactics are evolving because
now they want a dedicated drone operator.
(47:17):
Why?
Because they've seen how effectivethat at a squad level, having a drone
operator to be able to know wherethe enemy is how to, , kind of take.
And so those are the kind of things Ithink as companies start to adopt AI
now they have to start thinking through,okay, how do we adapt the to this?
(47:40):
Because if you adapt the rightweapons and tactics, you're going
to have competitive advantage.
And if you're lethargic at it, youcan have the greatest the planet and
people aren't gonna have the success.
Dr John Dentico (47:51):
And be Sears.
You can be Sears.
Daniel Paul Friker (47:55):
Yes.
Dr John Dentico (47:56):
I mean, I always, I I've used this example before.
Can you imagine telling Sears 30 yearsago, you're going outta business?
Daniel Paul Friker (48:01):
Yeah.
Dr John Dentico (48:02):
laughed.
They would've laughed.
It, they, what?
Are you crazy?
We're Sears, we're, we'rebasically indestructible.
No.
Not really.
It's just the way it is.
Listen, Dan, this hasbeen a great conversation.
I can't thank you enough foryour time here today on the
Throttle Up Leadership Podcast.
I wish you all the success in the world,good things to you and your family.
Thank you very much for your time.
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