Chris Havrilla - HR Tech 2024 - How to Reduce Technological Adoption Barriers in Organizations - HR Data Labs podcast

Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Announcer (00:01):
The world of business is more complex than ever. The
world of human resources andcompensation is also getting
more complex. Welcome to the HRData Labs podcast, your direct
source for the latest trendsfrom experts inside and outside
the world of human resources.
Listen as we explore the impactthat compensation strategy, data
and people analytics can have onyour organization. This podcast

(00:24):
is sponsored by Salary.com, yoursource for data technology and
consulting for compensation andbeyond. Now here are your hosts,
David Turetsky and Dwight Brown.

David Turetsky (00:38):
Hello and welcome to the HR Data Labs
podcast. I'm your host. DavidTuretsky, here at the HR
Technology Conference 2024 liveand in person at Mandalay Bay
exposition center in beautifulLas Vegas, Nevada. And today, I
have with me one of my BFFsforever. Chris Havrilla from
Oracle.

Chris Havrilla (00:59):
Best friends forever!

Forever. We're yogurt buddies!

Yes we are. Yes we are.

We will always have that New Jersey yogurt
place.

That's right

That will be our Paris!

That'll be our Paris, in our in our quasi
residence in West Orange.

Yeah, that's right. And Marguerite, Santa
Maria

Oh my god

she was our buddy

family. I mean,

Yes, oh my God.
Mishpocha!

Exactly, I learn something new every time I'm
with you.

And I do too, and that's why I appreciate your
friendship. But Chris, as we dowith every podcast, we need to
know one fun thing that no oneknows about Chris Havrilla?

Oh, wow!

I choose the hard ones first.

You do, you do.
What is something fun? Um,

Fun, new, different.

Something new, different, I think. Little known
fact

okay

Little known fact, everybody knows I'm into
F1 right now.

Yes

Everybody knows I am a big supporter of the
Oracle Red Bull team. But very,very few people know how that
got started and where the lovecame from.

Yeah, love to hear Wow

And I will tell you that, from the very
right? where.
And I would go, and I would workat the Road Atlanta track, doing
timing and scoring with, like,probably, maybe 14, I used to go
to dirt tracks because I'm asouthern girl, and we love all
sports, and we love cars, andwe, you know, just the whole
thing, cars and trucks andeverything. So I would start,
you know, I would go to thesedirt track races. And then I

(02:40):
kind of evolved, you know, intosports car racing.
little stop watches, because I'mthat old and we and then I went
into Race Control where, youknow, with our walkie talkies,

(03:03):
we would, you know, find outwhat was happening in other
turns and put it on the masterboard for chief steward. And
then I had race car driver comeand ask me to be on his pit
crew.

Wow.
right. Not a lotof technology at that time

So I could do what is, what is known in like
NASCAR today and stuff like thatas a spotter, but it did not
have a name like that. Iliterally would time and tell
him the differentials betweenthe car in front of him, if
The data and thetechnology and how it's evolved
there was one of the car behindsplit times all the
differentials, how much he'dneed to increase his speed. And

(03:36):
I'd write it on chalkboards.
into how you build a highperforming team,

Yeah

is like, like, people are like, how do you
learn all this about F1? I'mlike, it's like, everything I
love, all coming together.

And that transitions very well into how
to create a high performing teamin the world of business.

That's right, it is because finance and HR and IT
have to work together, don'tthey?

They have to be a big pit crew. Yes

Yes, they really do. And I am the spotter for
now.

There you go, people. It is comes full circle.
That's right, that's ChrisHavrilla. Okay, we're gonna end
the podcast right there. We'redone.

That's all she wrote.

No, I'm kidding.
So one of the beautiful thingsabout being at the HR Technology
Conference is that you lookaround and you see the vastness
of how HR has changed. And youcould say it's HR process. You

(04:46):
could say it is the HRtechnology, but they go one in
hand, right? Go one hand in theother. So Chris, you've been in
HR for a long time, just likeme. What have you seen as far as
evolutions of HR? What are thethings that you're seeing as
trends, as evolution of HR?

I think the thing that really stands out to
me is the fact that it is, Ithink we're finally at this
point where the technology canwork for us.

Okay

Right? It has been the work forever, like we
fed the beast, we fed the beast,we fed the beast and and we just
didn't get, it became the workitself. And for me to see that
in a very organic way, thiscould actually change how people
work and operate, if you letthem, especially.

sure

You know. And that is super exciting to me,
because every time I think aboutsome of these things, the lens I
look at now, when I walk thisshow floor is, will people just
change how they work? Will theysee the opportunity and go, Oh,
my God, I can do this better,faster, this that? Will they see

(05:59):
it and start to explore it thatway, or is it just going to add
another step? Because they'renot going to change working. But
it should be so seamless, itshould be so organic, that I
change how I operate.

right

Right? And, and I see that promise in so many of
these booths. It certainly is atthe heart of what I do when I'm,
you know, thinking about whatour investment strategies is,

sure

Will this actually fundamentally change
the way a manager manages?
Without some big top downtransformation,

right

You know, where everybody takes the same
training and then now you knowhow to go be an empathetic
leader? Yeah, no.

That doesn't work.

It doesn't.

No.

And we saw that over and over and over again,
even in, you know, where we'veworked together in the past,

yes

right?

No names.

So I just that, to me, is the promise. I also
think it's going to change ofthe way HR operates. And I think
that's super exciting, becausewe've said that for a long time.

And by the way, I want to touch on that, if you
don't mind. We just keep takingold process and putting it into
a new technology.

right

And we go, why isn't it getting easier? Why
isn't it getting better?

Why did we add more steps?

We took a Personnel Action form that was a
piece of paper, we put it inthat very secure manila envelope
with the red thread, and thenwe'd send it through into our
office mail. We just haveautomated that thing, and keep
auto maybe we put it in Excelinstead of putting it in through
PeopleSoft or whatever.

Right

But, but we haven't fundamentally changed
how that works. There's stilltransactions in that same facet.
But doesn't that have to breakdown? Don't we have to get rid
Yes! of that shit?

We absolutely do, and I think it's probably a
lot of the FUD, the fear,uncertainty and doubt around AI
and things like that. But thebut the interesting thing to me
right now, and I've and I couldalready see it like, you know,
just even in, you know, aproduct we, you know, we
announce, you know, back in Mayand in, and I saw a lot of the

(08:04):
We have tochange everything. Everything
HR, you know, people look at itand go, that's our job, right?
That's our job. Like we definethe roles. We do this, we do
that. And in, you know, itdefinitely takes some
conversations where you canfinally just say, You know what?
Look at yourself as afacilitator. Think of yourself

(08:27):
as a solution provider, not aservice with answers, but how to
facilitate solutions, becauseyou are operating in the dark.
And if I could show you, in away not to operate in the dark,
where no data is left behind,that will actually make your job
easier. Would you be open tothat? And it's not going to mess

(08:48):
up your structures, so that youstill have the safety blanket of
a structure. But what if we dosomething every year and it's
all the same data, and it reallyis making people step back and
think, and to start kind ofsmall and move fast. Because I
think the thing that is reallymessing people up is this notion

(09:09):
of transformation.
like and so. There's risk, andall of a sudden everything stops
because they can't boil theocean. But if you could show
people how to take baby steps,

right

It actually, the momentum builds pretty quick.

But the key that I love talking about when it
comes to that is the moving ofcheese.

Yes

People freaking hate cheese moving, and it's not
even and sometimes even if youprove to them that it's better
to be in a refrigerator thansitting on a on a plate, out in
the open, where it's gettingmoldy. They don't care, because
it's comfortable there.

Yep

They're okay with it there. They can look at
it. To me, HR is the epitome of,don't move my cheese.
How do you getthem off that? You've, you're

Yes.
speaking really wonderfullyabout all these great benefits
to doing it, but, buteverybody's so resistant to it!
They're they'reresistant to it because the
exact reason they will zig andzag all day long and complain
about it. Right? When you couldshow them a straight line, look,

(10:18):
I can show you a straight lineto get from A to B, but you
can't guarantee it, right?

right

They can. They can see it. It's all but it's
almost like a mirage, right? ButI know, if I zig and zag, then I
know what to expect. You know,even on that journey.

Of course

I think what was fascinating about COVID is that
cheese got moved for them,right?

Yeah. It had to.

And productivity soared, by the way, outside of

yeah

Because we want to, you know, we want to, we
HR, inside of HR, everywhere.
Productivity soared, and then itstarted to drop back down. And
it was right about the timecommand and control came back in
want things to be normal, right?

Quote, unquote, normal. yeah

And I do think you're seeing all these
companies now like people haveto come back to the office. We
have to do this. Things have togo back to normal. And
productivity is going, is goingdown and down and down again,
yet in the same breath saying weneed to be more productive. We
need to be more effective. Andso I do think that the one thing

(11:22):
that we can do is if we can showpeople those baby steps right,
where it takes the risk out.
But, but showsthem that they can get to those

Yeah outcomes a little bit faster.
But that trust has to be built.
That trust has to be built, butthis notion of command and
control does have to go away.
Like the fact that the onlyreason, the only way I can see
forward and where I've seen itwork, and I have seen it work,

(11:49):
we do have customers where wehave gotten traction, but it's
only been when they did babysteps to build momentum. They
did everything with purpose.
They broke it down into, let'ssay, a three month project,
like, we're just going to focuson this. Because once it starts
to elongate, leaders change thischanges the stress comes in that

(12:11):
makes everybody want to go tosafety.
yeah, or you getsome periods of downward
pressure on finances.

Totally.

And it all, everybody goes, Oh, we have to
come back to the office.

Disruptions we always hear about, right?
Whatever form it takes, you haveto just time block stuff out and
have you know, it's kind ofdesign thinking, 101, right? But
let's, let's just get a an MVP,right? An MVP, an MVP, that's
the only thing I've seen workthat are making it, makes it a
little bit more organic, becausepeople can see if I do an

(12:42):
outcome, like an MVP, right? IfI do that, and I get it like I'm
building the trust, right? Andso whether that's in a system or
a process, right? But this, thiskind of notion of a process
needs to go away. I like what Idid this week to get to my
outcome, and what I do next weekis all going to depend on on my

Everything might change. Absolutely.

So we have to get out of this complete notion
of it's got to be this way or noway.

You mentioned this before, and I think it's
very true, that people likecomfort and people like
repeatability. People don't likeshocks to the system. They
don't, they don't do well withchange, and so therefore, every
time something goes wrong, theygo back to their safety. And
safety is control. Safety is,oh, cover my butt or whatever.

(13:33):
And there's been so muchdisruption, there's been so much
change, and we got used to itfor a little bit, and we started
getting pretty good at it, butnow it's all gone away. It's
reverting back to old ways.

It is. And I think a lot of that is, is
trying to kind of bring thatcontrol, you know, back into
things. But I, but I do believethat's why things have to be
kind of broken up.

Yeah.

And you know, this the power of data right
now. I mean, you can't, youcan't deny it. Like you can
start to see, you know, andthank goodness for things like
chat, GPT, that kind of, youknow, there's all kinds of
things you can say about it.
But it reallymade this so mainstream that it

yeah gave people a reason to think
differently and to say, youknow, what, if somebody's going
to try to cheat the system,right? I think, because it's
going to do this paper, but atleast they're thinking outside
the box, and they're just tryingto make something easier for
them. And that's the lens, youknow, and that's what then the
guardrails and controls can comein after that, right? Because

(14:35):
you have to kind of give, like,what's the rules? Where you
can't, you can't copy.
But Chris, youand I both lived through
probably high school when wewere still using typewriters,
but then there were somecomputers around. Some kids used
computers, some usedtypewriters, and we went through
the transition where people werelike, No, you can't use a

(14:57):
computer to send this assignmentthere or to print this
assignment out, because it'sgonna help you with spell check
and whatever.

right

Well, we went through those times. Now there's
Grammarly. Now there's chatGPTand Gemini and blah, blah, blah,
and all of them can help kids toyour point, right? Those are the
tools that those kids haveavailable to them. When it was
us, it was either a Selectric oras a Corona, or it was an apple

(15:23):
two or a TRS 80 from RadioShack.
But those are the tools

Apple 2E for me

I had an apple two plus

oh

but that was before the E, the E came after.
And then there was the Mac thatI actually had in college,

the box!

yes, exactly.
That had the monitor insideAnd it was a
gray screen monitor, if you

Absolutely! remember, I do

black and white, but it was still one little
thing

yeah

And so we've got to get over this, you know, how
work gets done

Yeah

Stop worrying about that!

Announcer (15:55):
Like what you hear so far? Make sure you never miss a
show by clicking subscribe. Thispodcast is made possible by
Salary.com. Now, back to theshow.

Now, there's the other sides of it that I've been
talking about all week here,which is the risk of people
putting stuff in the wild andhaving the walled garden and
having, you know, the datasources that are trusted.

Right

Having the, at least the data be curated to
make sure that we know what'sgoing into these models and
what's going into the algorithms

Right

But for frack's sake, use the tools at your
disposal and get smarter aboutthem before everybody else is
doing it and you're not!

You know, bingo, the key you just said there. I
feel like I preach this day andnight, but if you don't open
your eyes to the technology andlearn what it is, you can't
learn how it can help you. But Ido think the lens we all need to
look through is, well, you know,and. And look, one thing we're
all good at is human nature isbeing selfish.

Yeah

You know, and, and so embrace that and say,
Well, how could this help me?
Like, what if somebody let mehire somebody tomorrow? Well,
what I have them do, right? It'sthe same thing, you know. And so
knowing what I look if I hiredsomebody new tomorrow, it would
be awkward. And, and, and I'mnot gonna trust them, they're
not gonna trust me. We kind ofhave to feel each other out

Right

But we do it, right, and and the faster we do
it, the faster we kind ofembrace that person, you know,
ask some questions. See whatthey're you know, it's the same
thing with this, right?

Right

So, like, be selfish. Go learn about it. Go
see what it can do, play withit, see what it can't do,

right

And then figure out, like, be lazy, be selfish,
right? And figure out how thiscan help you do something
different, if it's creating morework or distracting you from
your outcomes and not helpingyou get there, then move on.
It's not going to be for everything!

So what you just said made a lot of sense, so
much that I started thinking,you know, I could actually ask
these technologies to help mesell.

Yes.

And then I thought, Oh, well, you know, I
also have Salesforce. And I knowsome there are some tools built
in Salesforce that do somethinglike this. We just need to
embrace them.

Right.
Absolutely, it's gonna beawkward.

yeah

Getting to know somebody is awkward. But, you
know, I look at this as I'vegot, I got head count, I just
didn't get before, right? Andnow, what can it do for me?

Right

You know? And I do think that that if everybody
would just take some time andlearn about it, just like you
would, somebody coming on yourteam,

right

How can we help each other?

What's fascinating is, there was that
kerfuffle on LinkedIn not toolong ago. I'm not going to
mention the company name, but acompany said they were hiring an
AI and it was going to have ajob description. They were going
to pay it all that stuff. Peoplelost their freaking mind. Ah,
this is a marketing baloney.
This is, you know, you're, thisis horseshit. Are you gonna do
development for it? Are yougonna, are you gonna give it a

(19:02):
seat? Are you gonna come on,really? You know, even if it is
a marketing thing, bravo!

Right

Bravo. They're thinking about the future. You,
you're worrying about, well, whyis this bothering you that they
did this, right? Why do you haveto beat them up for it?

I actually agree with that. I was really
surprised by that whole thing.
Like I said, you know, I lovedthat they were humanizing it in
a way and I was really shockedby the response. And you know,
in hindsight, I think it didplay into fear and uncertainty
and doubt. But, you know, Iwould say in this world of we

(19:40):
keep talking about skills andcapabilities, is maybe think of
it that way. You know that?
Because I do think machines areworkers in our workforce.

Sure

But if we don't want to humanize it that way,
that's fine. But these machineshave skills and capabilities
that we don't have. And we'vegot ridiculous skills and
capabilities they don't have.

That's right

So if we can start thinking about what, how
does work get done?

Right

And again, how can I embrace this, you know,
set of skills and capabilities,right?

Right

Because I don't have time to call through a
bunch of data in 18 differentsystems.

Exactly.
Yeah, a ton ofresearch, ton of time.

But if this does and can bring it back to me and
put some structure to it, then Ican sit around and argue with
it, and, you know, and thenapply my curiosity and my
critical thinking and getsomewhere, and I just saved two
days worth of research.
Right!

Absolutely, and that's where this is to me, this
is no different than when, youknow, there were cars, when
there were horses, and whenthere was the tractor before
there were people or oxen. Theseare gigantic leaps of
productivity that these aretools that will enable us to do

(21:00):
things. Now, if we have tohumanize them, give them a
social security number, makethem pay taxes. Okay, fine.

Whatever.

I don't care! Makes the tax base richer. But
you know, the selfishness, if Ican get my job done better.

Absolutely.

I'm going to use that tool!

Better, faster, have more of an impact.

Yeah

Hello?

yeah. This is exactly what the words
competitive advantage mean!

Exactly, exactly.

And if you don't embrace it, get the hell out of
my way!

Well, the whole very definition of innovation is
it's going to answer onequestion, and three more are
going to pop up

absolutely!

You know, and, and,

what if I?

We're going to get stale, right? Like we're
we're never going to have animpact!

right

We're never going to have an edge. I want an
edge. You want an edge?

Absolutely.

Thank you.

Yes.

Boom.

Okay, hold on.
We just dropped the mic.

Drop the mic.
Literally.

That's the reason why I love Chris
Havrilla.

This is epic.
You think aboutsomething like performance. I

Yeah mean, if we're gonna, you know,
let's bring it back toperformance and comp, or
something like that. How manytimes have you been in a
performance review where some,literally, the last two months
is the only thing we've talkedabout?
Recency effect, absolutely

Totally but if I can sum, if this, if this
machine summarizes everythingover the last year, probably
brings up the stuff I forgot todocument,

exactly, and the great stuff, yeah,

brings it together and, and now I have a
better conversation. And thethen my boss and myself are not
jaded with trying to go backthrough all this stuff, like, if
it's gonna lead to a betterconversation, like, I'm all good
and, but guess what? Guess what?
If chat GPT comes back and, or,you know, my wonderful Oracle

(22:41):
tool comes back. This the systemformulates a, you know, a base
of a document for my boss toplay with, and now make it her
own. I will know in a heartbeatif it's not her voice.

Yeah

So I, it was funny. I got a lot of media when
we first started to release someof this stuff. Then that said,
Well, you know, then what if themanager never does anything? I'm
like, you think the worker won'trecognize that? Like this will
self manage a little bit. But ifI can get a good, reasonable
performance document that'scomprehensive, that now I can

(23:19):
take into the comp, you know,kind of process and things like
that, I have a much betterchance of getting fair pay.

But Chris, it is exactly what you said though
before. It's better feedbackbecause it's been summarized
over the year.

Yes

We are human! We forget stuff, the good and the
bad, the happy and the sad. I'mgonna break out into a song.
Take the good with the bad, thehappy with the sad. Sorry,
sorry, everybody. I apologizefor that.

It's okay. They didn't see my dance moves.

We'll add video as the as the addendum to this.
No, but seriously though, if youcould give managers and
employees and assist by givingall of the really good
highlights and low lights.
Listen, performance evaluationshave stopped being about how to
be better, so that you canchange for next year and get

(24:17):
more and do more and be better,right? They've become a, well,
my boss hates me because theygave me a one out of five.

Right

No, they don't hate, I mean they might hate
you, but they don't hate you, orthey shouldn't hate you. But it
should be an objectiveconversation about, did you
achieve the things that you hadset out with your boss?

Right

Did that align with your job description, it
should have!

yes

If it doesn't, we got other. I mean, that goes
back to the data being accuratefrom before.

But it also goes back to that conversation,
right? I would tell you that ifyou asked my boss right now what
my PowerPoint skills and what mywhat I would say my PowerPoint.
Skills are, would be two verydifferent scores. But if we, if
I just put a PowerPoint on myprofile tomorrow, and we, and it

(25:10):
drove a better conversation,whether that was actually you
need some development on that,or actually you should be
teaching some other people. Or,you know, I don't, you don't
have all those bells andwhistles. And I was like, Yeah,
but I had a better conversation,right? Like, it's all
perspective that needs to bediscussed when we're thinking
about how to unlock myperformance of potential.

Exactly.

And because that data point could have different
meanings and perspectives anduses. So those are all the
things, but we get so mired inwhat like, if you go back to
skill data, okay, you know, I'veseen this with, you know, with
people as they start to thinkabout adoption, okay, but what
about proficiency? And whatabout validation? Like, what

(25:52):
about the conversation?

Right

And then we can again, those baby steps, yes,
let's just talk. Let's just putthe PowerPoint on there first,
and then we can have aconversation, and then we can
start to see what is, what willsay. This number could represent
what it would be thatperspective. And then you can
start to build accordingly. It'sbaby steps.

It is, but we have so gotten as a people, as a
culture, and it's not just theUS. It's beyond performance
evaluations as a practice isjust garbage these days. Nobody
likes it. Everybody doesn't lookforward to it. I mean, I love
it. I love feedback. I don't getany. You probably get some.

(26:34):
Hopefully it's real, all goodfeedback. But I mean, seriously,
though

I get a lot of feedback.

I'm sure you do.
I get some too, but No, butseriously, it stopped being what
it was. It stopped being about,how do I develop like you were
saying, How do I become better?
How do I get a better career?
How do I get more comp whatever?
And it's all about, do they likeme or not?

Well, I do think that's the promise and the
organic change that I see comingand, or at least when I think
about how we're investing,right? How do we kind of
democratize the data and theinsights to drive better
conversations, you know? And Ithink about, like, what we did
with Oracle grow, right? We're,we're actually putting that

(27:17):
down, and we're saying in this,we're in your role now, but
also, you know, for futurepotential in your org, right?
Doesn't mean everything isright, but it gives that worker
something to talk about withthat manager, because that is
perspective, maybe even thatworker didn't think, because we
still have a tendency to think,not you and I, because when you

(27:39):
look at our job titles over thelast several years have changed
dramatically. So I know you andI don't self identify in one
title, but a lot of people do,right? And and to have that
ability to because we mighttrust the machine better than
even our moms or dads or friendsor colleagues or bosses, right?
And to say, Well, why did it saythat? Let me look into this a

(28:03):
little more, right? But I wouldhave never known to look there
in the first place, right?
That's where I think this willslowly change the way we think,
because we all are inherently wewant to succeed. We want more
money, we want more opportunity.
We want better challenge. We'reselfish. Maybe I, you know, I've
always said I'm a little lazybecause I want to get to this

(28:24):
point B faster, right? But, butit makes me work more
innovatively, because I want to,I want that edge!

Right

So I do think that's the promise of a lot of
this AI right now, even if wehave to argue with it, it's
gonna, it's gonna make us thinkdifferently.

Hey, are you listening to this and thinking
to yourself, Man, I wish I couldtalk to David about this. Well,
you're in luck. We have aspecial offer for listeners of
the HR Data Labs podcast, a freehalf hour call with me about any
of the topics we cover on thepodcast or whatever is on your
mind. Go tosalary.com/HRDLconsulting to

(29:08):
schedule your free 30 minutecall today.
We have to learn how to arguewith it. We have to learn how to
work with it. We have to buildskills to be able to understand
what it even means, becausethere's so much noise,
especially here.

Yeah

Not, I'm not talking about the noise in the
microphones. I'm saying there'sso much noise of the
differences. What is it? What isAI? Because every, every single
except for the Omaha Steakspeople, yeah, everybody's
talking about AI.

And I think we have to ask that every single
person in here has to say, butwhat's it going to do for me?
But what's it going to do forme? Like, give quit thinking
about the buzzwords, and say,what is this going to do for me?
And then I have to think about,will that help, or will that
create more work for me?

Right

Like, we just have to break this down a little
bit more simple. So because, youknow, at the end of the day,
that really is what is going toactually drive the kind of
traction that we need. But Ireally firmly believe, and I
think all of us as vendors, andall of the people here serving,

(30:17):
you know where we are going toclaim AI and black box and and
things like that, is we alsokind of have to have a little
bit of a why box so we cancontextualize.

yeah

If something is giving a recommendation or a
suggestion, why?

Right

Put a why there, so, so you have something to
argue with.

right

Not just, Oh, okay. Or, and maybe even that
kind of fixed box. Well, what ifthat's like wrong? Then there's
something behind it that needsto be fixed.

I learned something new for the first time
at the show, and I forget who itwas who told me it. I think it
was Richard Rosenow. He saidthat there are bots that will
check other bots becausesometimes artificial
intelligence, it dreams up,

It hallucinates.
Right exactly,

Because it's using other AI generated content
to give back an answer.

Right

Okay, holy shit! Because if that's what's
happening, right, sorry for thelanguage, but holy shit. You
know, if it's coming up with ananswer that's a lie, or it's
coming up with an answer that'snot exactly based in fact.
Obviously those two things canexist, co exist, because we know
that, that there's a differencebetween a lie and something

(31:34):
that's not exactly true. My eyesare going back.

That's a whole nother podcast.

That's a political podcast we could have
for like, hours, but soseriously!

Yeah

If the things that we're relying on to give us
true answers that because it's acomputer and how could it lie?
But it's dreaming shit up on itsown? Oh my god!

Well you know what? We argue with each other.
We debate each other all thetime. We've got to learn to do
that with the machines, but themore we can make it easy by
doing that, providing somecontext that why box and fix box
is exactly why I think you knowwe need that, especially while
we're gaining trust with thisyou know this stuff, but also to
know if there is a problem andthat we do need to go back and

(32:19):
check something, fix something.
What's the source? What'sdriving this?

Iguess, at the end of the day, what, what we're
talking about is we still needto do QA on this stuff. We still
need to make sure that theanswers that are being given not
just are accurate. I mean, yeah,we got to check the math. We got
to make sure that we're going tobe able to be okay with when
it's not, and that, if this iswhat we're adopting, and how

(32:47):
we're adopting and how we'regoing to embrace it, unless we
put in those other agents tomake sure that the answers are
correct, and keep doing unittests and keep doing accuracy,
you know, whether it's spotcheck or every single one, we're
going to have to assume thatsome of the stuff is wrong.

We do with people. We should do it with
machines.

Yeah

You know, we it's, oh, we should question
things, and we should trainpeople when they aren't doing
things the right way, ormachines too, right? That's,
it's, you know,

Well the machines are trained by people,
so

they are and data

and data, and we all know the HR data is not
clean.

Yeah, so that's why none of this is perfect. I
don't know that we're going toget answers. We have to look at
it right now, and at least inthe beginning, as suggestions
and recommendations and. Butyeah, I mean us not thinking is
not a good plan for this.

Right. But the world's not perfect.

No.

And we're not perfect.

No.

So why should we assume that the technology is? I
mean, you know, for whateverreason, my iPhone crashes every
once in a while.

Yeah

I love it, but it crashes.

It does.

The Internet crashes. Things happen so we
have to build that into whatwe're doing.

Right. That's it exactly.

Sounds like we're creating religion. Well,
it's perfect, but it's notperfect. It's something we
believe in, but we don't believein it.

I don't ever say anything is perfect.

Yeah

I don't ever think I have the answers

You're perfect, Chris Havrilla.

No.

Yes, you're perfect for me. You are so
beautiful.

I won't even sing. I'm already losing my
voice.

It's me. Can't you see? Actually, it's the
first time I've ever sang on thepodcast.

I was just about to say first time I think I've
ever heard you sing, and it'snot bad!

It's for you, it's serenading. And for those
of you who are still listeningto that, and I haven't actually
made you deaf, I apologize, butthat's what Chris does to me. I
mean, this is my yogurt BFF. So

That's right, yogurt BFF, forever, for sure

Chris, it's always a pleasure.

Thank you. This was awesome.

You know what? I think I should just call you
every once in a while toserenade you, or

Maybe you should.

I won't record it like this, but

we won't subject everyone

but again, thank you very much.

Thank you for having me. This was great.

All right, take care, stay safe.

Announcer (35:19):
That was the HR Data Labs podcast. If you liked the
episode, please subscribe. Andif you know anyone that might
like to hear it, please send ittheir way. Thank you for joining
us this week, and stay tuned forour next episode. Stay safe.

All Episodes

Chris Havrilla - HR Tech 2024 - How to Reduce Technological Adoption Barriers in Organizations

Episode Transcript

Popular Podcasts

Stuff You Should Know

Dateline NBC

On Purpose with Jay Shetty

.css-15opob5{left:0;position:absolute;top:0.8rem;} All Episodes

.css-14f5ked{margin:0;word-break:break-word;display:-webkit-box;-webkit-box-orient:vertical;box-orient:vertical;-webkit-line-clamp:2;overflow:hidden;}Chris Havrilla - HR Tech 2024 - How to Reduce Technological Adoption Barriers in Organizations