October 27, 2023 • 14 mins
Ready to see the future of HR tech through the eyes of an expert? Jenny Cotie Kangas, Founder and Chief Solutions Architect at WhiteRock, takes us on a journey into the world of Generative AI, illuminating its potential in reshaping the recruitment industry.
The future of HR tech is here, and we're ready to embrace it. Are you?
Special mini series recorded with Oleeo at HR Tech 2023 with hosts Ryan Leary, Brian Fink, and Shally Steckerl
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:07):
Hey everybody, welcome back to the Sourcing
School podcast.
I'm Brian Fink, he's Ryan Learyand we are joined by the one,
the only JCK.
We are excited to be on thefloor here, sponsored by the
team at Olio, in the Oliopodcasting booth.
Jenny, what's going on today?
How are you?
Speaker 2 (00:23):
Hi everybody.
This is Jenny Cody-Kangus, orJCK as the nickname Brian
mentioned.
I'm doing very well.
It's been a great HR tech andI'm so excited to have this
conversation today.
Speaker 1 (00:32):
Don't knock the champagne doors.
There's a champagne cart thatis going past JCK as we speak
right now there is champagne.
Speaker 2 (00:38):
there is alcohol everywhere at this conference.
My goodness, it's the last day.
Speaker 3 (00:42):
They're ready to party.
It's 11.44 in the AM y'all.
Can I just say I love yourglasses.
Speaker 2 (00:49):
They are amazing.
They would look like shit on me, but I love them.
Yeah, no, I thank you.
Fun thing, I love differentglasses and I just input my
prescription on to semi-opticalor Zelo and you can get.
I think these are like $45 withthe lenses.
Speaker 1 (01:07):
Really yeah, can't beat that.
Speaker 2 (01:10):
The only problem is you can try these different kind
of glasses out, but I ended upfalling in love with these ones
and now they're discontinued.
So I was like I will find a way.
Speaker 1 (01:17):
There's always a way, so yes, that kind of sounds
like my plight when it comes toApple accessories that are in
this bright orange color overhere, right, we don't like Apple
, stop it.
I love my Apple accessory.
All right, all right.
Speaker 3 (01:29):
So I don't know about you, but this fly is the last
thing I've ever seen.
Speaker 1 (01:32):
Oh yeah, there's a fly that's buzzing around.
Speaker 3 (01:34):
It's like It'll land on your head.
It'll land on your microphone.
Speaker 1 (01:39):
So, jck, you are walking the floor, People are
having lots of conversationswith you.
30,000 foot view you make animpact in this community.
What, from a 30,000 foot view?
What is it that you do thatbrings value to everybody who's
here on the floor today?
Speaker 2 (01:54):
Yeah, great question, brian.
So what I do?
We obviously so.
For those of you who arelistening in, we're sitting in a
booth inside of a giant, giantroom that is filled with 500
different vendors that are inour space, and so what I do is I
work with customers to makesure that they have the
infrastructure to be able toimplement these properly,
(02:16):
because it's not necessarilyabout the technology, it's about
how you implement it and makingsure that you've got like a
foundational data strategy andyou're able to tell stories with
numbers and that things areworking the right way.
And so I'm the person thatcomes alongside of the buyers to
help them learn how to get thatright.
Speaker 1 (02:30):
Okay about getting it right.
What are people getting righthere at HR Tech?
What are they getting correct,come correct, jck.
Speaker 2 (02:41):
I think they're getting right to be here right,
like so, having your growthmindset I mean people listening
to this podcast right now right,and being able to be curious
and willing to learn.
I think that part is really,really important.
Speaker 1 (02:56):
Oh, ry leaned into his mic and I'm like I'm going
to interrupt them.
Speaker 3 (03:00):
No, I was just.
I'm letting you talk, I'm tired.
Speaker 1 (03:04):
All right.
So why are you tired man?
They're passing out the drinks.
They got orange juice overthere.
Speaker 3 (03:09):
We'll get you a little caffeine, they've got the
five hour energy too.
Speaker 2 (03:13):
They have literally catacorn art from us right, yeah
.
Speaker 1 (03:16):
We should just go grab some Energy.
You talk about implementation.
Speaker 2 (03:20):
Yes.
Speaker 1 (03:21):
What are the vibes that you're feeling going from
2023 to 2024 in the TA space?
You got any predictions?
Speaker 2 (03:29):
Do you really want my predictions?
Speaker 1 (03:31):
We want your predictions.
Are you going to be theanti-hero?
Speaker 2 (03:34):
No, I don't know what that means, but also oh wait,
that's a Taylor Swift song,right?
Speaker 1 (03:39):
I think so.
Speaker 2 (03:41):
So for the people listening in a little bit of
context.
I hit my head in 2020.
I remember nothing from beforeit, including popular culture,
and so sometimes these types ofsituations come up and I look at
Brian and like that was aTaylor Swift song.
We're referenced right.
Speaker 1 (03:53):
Okay, so for reference, JCK and I talk all
the time right, so this is likea conversation between friends.
Like, Ryan is the odd man outhere, Ryan.
Speaker 3 (04:01):
that's all I thought.
Speaker 1 (04:03):
I didn't know if this was a Taylor Swift joke,
because you know like Maddie isall about.
So everybody knows my daughter.
If you don't know her from thepodcast, you know her from HRTX
or you know her from mynewsletter where I beg you for
money for her school.
Speaker 3 (04:15):
You know he spent $1,400 on tickets Two.
Speaker 1 (04:19):
We are never ever ever talking about that together
.
We are never ever ever.
Speaker 2 (04:26):
I love it All right.
So sorry everybody for thequick like sidestep on Taylor
Swift, I think there's.
So to go back to your question,which is what do you think is
gonna come in 20, or what's kindof?
Speaker 3 (04:37):
20, 24.
Speaker 2 (04:39):
I think we're about to step into a reckoning.
Speaker 1 (04:42):
A reckoning.
Speaker 2 (04:43):
Yeah, in a pretty big way, and so what I mean by that
is Generative AI has changedthe game in a lot of ways.
Right, Like I'm a hugeproponent of Generative AI, I'm
somebody with a disability andGenerative AI helps me do so
much more.
That's not having to like drainthe batteries of like who I am,
and so I'm able to go so muchfurther and faster.
So, big fan of Gen AI.
(05:04):
However, there's a lot oftechnology that's coming out of
the Gen AI space right now,including technology that's
gonna allow you to, you know,mass apply to like 200 plus jobs
with the click of a button.
Our infrastructures that wehave on the town acquisition
standpoint, they're not readyfor that, and so what I mean is
(05:25):
these companies, probably in thenext, probably three months or
so, are going to be flooded withhundreds and hundreds and
hundreds of thousands ofapplications.
Speaker 1 (05:34):
That are Gen AI.
That are Gen AI bonds.
Yes.
Speaker 2 (05:38):
And so what's gonna have to happen is these, these
applicant tracking systems, thedifferent technologies that
you're using are gonna have tofigure out some way to have like
a digital footprint to assesswhether or not this is like a
bot or not a bot, right?
Speaker 3 (05:53):
Like so that's gonna be a step ahead.
Speaker 2 (05:55):
They're gonna have to .
But, as we know, with applicanttracking systems, when we
implement them and we customizethem, often they are taken off a
vendor roadmap and that makesthem really difficult to play
with right.
Speaker 1 (06:07):
Oh no.
Speaker 2 (06:08):
So there is gonna be.
I think there's gonna be areckoning, and if you think it's
hard to go through theapplications that you have today
for your job, just imagine ifnow I got 400 applications and
I'm gonna get 400,000.
Speaker 1 (06:20):
Okay, so then let me come back to this Is that
there's been a big theme hereand over the past six months.
You and I were at RecFest andthere were a lot of people that
were talking about the death ofsourcing, and I define sourcing
as finding passive talent.
You're talking about an influxof active talent and active
applicants, right?
Speaker 2 (06:38):
100%, I am.
And just to double click on thesourcing piece those of you
listening it turns out humansare not one size fits all, turns
out right, that's what we do inthe sourcing world is being
able to, like, ask right sharpquestions and figure out what's
needed, and then we're gonna goand try to find that needed
thing out there, right?
(06:58):
So Jenn AI today and probablyfor the next several years,
isn't going to be able tounderstand the nuance of a human
.
It's gonna be a lot harder.
And so, yes, there mayeventually be certain things
that aren't gonna be that JennAI will take over.
But sourcing just going awayy'all, like if you know how to
(07:20):
find a purple, sparkly unicornOpenAI is not gonna be able to
help you find a purple, sparklyunicorn Like.
Speaker 1 (07:25):
it's just not how it works Now.
Speaker 2 (07:26):
that being said, sorcerers are so sorry, I'm like
totally like oddest like whatis it speech?
Speaker 3 (07:33):
box, soap box or whatever.
She's about to fire all thesorcerers in the world.
Speaker 2 (07:37):
No, so sorcerers are uniquely positioned to become
super users of Jenn AI because,like, honestly, like Boolean
search string you guys is aprompt right and so ChatGPT does
not do good, boon, they won't.
No, but here's.
I need to connect the dots foryou here.
Speaker 1 (07:58):
Okay.
Speaker 2 (07:59):
So, in order to make Jenn AI work the best, I need to
call out.
So, at its core, sorcerers areable to understand and find
people and the nuance of peopleright.
In order to get Gen AI to workproperly, you need to understand
the nuance of what you'relooking for.
So, if I'm able to call outsimilar to like I need a redact,
(08:19):
whatever right, like if I'mable to call out the hat that I
need the person to wear and thelens.
I want it to look through youare able to get ridiculous
responses in terms of, likeprompt engineering and so like I
truly believe that sourceershave a massive leg up because
you already understand that.
Like it's the right sharpquestions right that you're
(08:39):
gonna need in order to find theright answer.
So I will pause for a second.
But yeah, brian looks like hehas something to say.
Speaker 3 (08:45):
I like it.
I mean I like everything you'resaying, and again I mean a
couple of the products that wespoke with over the last two
days.
They're all saying the samething and they're all preparing
for that.
I don't know that any of themhave accomplished it by the look
on the face.
Speaker 1 (09:01):
I'm saying no.
Jenny is sticking her finger inher mouth as she is going to
throw up.
I'm commenting everywhere.
This is a visual pot.
Oh, okay, I'm sorry.
Speaker 2 (09:08):
So here's the thing?
Speaker 1 (09:09):
I don't think that.
Speaker 2 (09:10):
Jenny has now removed her glasses.
Oh God and Brian knows thatthat's a tell for me that I'm
about to say something.
All right, all right, okay.
There were some incrediblepresentations y'all yesterday,
specifically about like howJenny is gonna change the world
and like all the way through.
Speaker 3 (09:24):
So you're calling BS on all of this.
Speaker 2 (09:26):
No, I'm not calling BS on it.
Speaker 3 (09:27):
Buyer syndrome.
Speaker 2 (09:29):
I am highlighting a very, very, very important flaw
that exists.
So what was said, just to like,connect the dots, was that,
because of the growth of thesegender-divine products, all of
our problems are going to besolved.
Right, which is cool, that'sawesome.
Yes, in order to like, if youhave the proper data
(09:50):
infrastructure and you can youand I know this word, data nerds
right, it's my favorite fourletter word.
But, like, if you have theright data infrastructure, your
data is clean in that lake.
I can put Gen AI or somethingthat's on there to connect the
dots to that.
The problem is, most of thebuyers who are here don't
understand how to build theproper foundation in order to
(10:12):
get the data clean, and if wedon't do that, it's garbage in,
garbage out, y'all.
Speaker 1 (10:17):
It doesn't matter.
Speaker 2 (10:18):
It's such an important piece.
If we want to help ourorganizations get this right, we
have to start storytelling Likewhat does it actually look like
?
Yes, there is this cool, likeyou know, pie in the sky, we can
do this.
It's a mountain with, like thenew world or whatever.
I don't know all the hisms.
I'm playing my head in drink.
Speaker 1 (10:39):
Let's unpack that a little bit.
Let's unpack that a little bit.
Speaker 3 (10:45):
How does an organization with really just
bad data, how do they start toget that prepared for this next
move?
Speaker 2 (10:53):
Number one step y'all is you have to have a map of
your decision-making processesand your experience from high to
fire.
If you don't have that, youreally aren't positioned to be
able to leverage like AI orautomation, because in order to
get AI or automation right, youneed to be putting it where
there's friction, and if youcan't assess where there's
(11:13):
friction, that's the wrong wayto do it.
So step one is going to beutilize those design thinking
skills to understand what theproblem is and actually map it,
and then to be able to ask theright, sharp questions.
And, as we know, I grew up as aproject manager before I hit my
head.
But one of the rules that wehave in the project management
space is, like I was saying,Scrum master.
(11:35):
The problem that presents itselfy'all is often not the problem
you need to fix, and so you'vegot to ask the right sharp
questions to figure that out andreally clarify and verify that
Five wise, five wise.
Yes, and what Brian's referringto is like you have a situation,
you ask, but why?
And then you get an answer andyou ask, but why?
(11:57):
And you ask, and like it soundssilly y'all, but like this is a
really important piece that weneed to go through, because when
resources are lean and ourteams are a lot smaller and
we're having to do more withless, it is imperative that we
are actually working on theright things.
And so, yeah, I know I'm likeprobably way over time, you guys
(12:17):
, but Two minutes and 15 seconds.
Speaker 3 (12:21):
You're good.
Yay, yay, yay, only two minutesover, okay this has been a
really impactful conversation.
Speaker 1 (12:26):
I hope that people will give this a listen and that
people are going to share thisout, because you have laid out
You've laid out what you need todo to be I mean not just what
you need to do to be actionable,but what you need to do to
create success in yourorganization.
And there are a lot ofbuzzwords, there's a lot of junk
.
It is data.
It is good data in good dataout.
(12:47):
It is bad data in bad data out.
This is an interesting time forus in an interesting
environment.
We'll see what the reckoninglooks like in 2024.
Will you come back in 2024 andhave another conversation with
us.
Speaker 3 (12:58):
We need to have you back on and like when we get
home, let's have her back on andget a full 30 minutes.
Speaker 1 (13:04):
Okay, that sounds like a plan.
Speaker 2 (13:05):
Yes, and the last thing that I just want to like
double-click on here not tointerrupt you, brian.
She's going to go to 14 minutesnow you guys, the only way to
solve a hard problem is tochoose to begin, and you're not
going to know the entireblueprint, but when you choose
to begin, you're going to beable to assess what's the first
right step and then, based onagain on data favorite word
(13:26):
right Like where are we drifting, where are we shifting in order
to get that right?
And so these things we'retalking about, like how do you
get work right?
You got to start right.
Choose to begin Amen, and it'snot necessarily about having the
perfect strategy.
It's actually about having theperfect strategy to drift and
shift your strategy when you getit wrong.
And remember that getting itright is gleamed through getting
(13:49):
it wrong y'all.
So failure is the blueprintwhere we can gleam the blueprint
for getting it right.
So be intentional.
Yes.
Speaker 1 (13:58):
Oh, listen to that.
A round of applause for JCK.
Speaker 3 (14:01):
We're going to wrap this up.
Speaker 1 (14:02):
We are coming to you live from the Olio podcasting
booth at HRTech.
Thank you so much, JCK.
Jck, we will see you soon onthe Sourcing School podcast.
Thank you.
Hey everybody, welcome back to the Sourcing
School podcast.
I'm Brian Fink, he's Ryan Learyand we are joined by the one,
the only JCK.
We are excited to be on thefloor here, sponsored by the
team at Olio, in the Oliopodcasting booth.
Jenny, what's going on today?
How are you?
Speaker 2 (00:23):
Hi everybody.
This is Jenny Cody-Kangus, orJCK as the nickname Brian
mentioned.
I'm doing very well.
It's been a great HR tech andI'm so excited to have this
conversation today.
Speaker 1 (00:32):
Don't knock the champagne doors.
There's a champagne cart thatis going past JCK as we speak
right now there is champagne.
Speaker 2 (00:38):
there is alcohol everywhere at this conference.
My goodness, it's the last day.
Speaker 3 (00:42):
They're ready to party.
It's 11.44 in the AM y'all.
Can I just say I love yourglasses.
Speaker 2 (00:49):
They are amazing.
They would look like shit on me, but I love them.
Yeah, no, I thank you.
Fun thing, I love differentglasses and I just input my
prescription on to semi-opticalor Zelo and you can get.
I think these are like $45 withthe lenses.
Speaker 1 (01:07):
Really yeah, can't beat that.
Speaker 2 (01:10):
The only problem is you can try these different kind
of glasses out, but I ended upfalling in love with these ones
and now they're discontinued.
So I was like I will find a way.
Speaker 1 (01:17):
There's always a way, so yes, that kind of sounds
like my plight when it comes toApple accessories that are in
this bright orange color overhere, right, we don't like Apple
, stop it.
I love my Apple accessory.
All right, all right.
Speaker 3 (01:29):
So I don't know about you, but this fly is the last
thing I've ever seen.
Speaker 1 (01:32):
Oh yeah, there's a fly that's buzzing around.
Speaker 3 (01:34):
It's like It'll land on your head.
It'll land on your microphone.
Speaker 1 (01:39):
So, jck, you are walking the floor, People are
having lots of conversationswith you.
30,000 foot view you make animpact in this community.
What, from a 30,000 foot view?
What is it that you do thatbrings value to everybody who's
here on the floor today?
Speaker 2 (01:54):
Yeah, great question, brian.
So what I do?
We obviously so.
For those of you who arelistening in, we're sitting in a
booth inside of a giant, giantroom that is filled with 500
different vendors that are inour space, and so what I do is I
work with customers to makesure that they have the
infrastructure to be able toimplement these properly,
(02:16):
because it's not necessarilyabout the technology, it's about
how you implement it and makingsure that you've got like a
foundational data strategy andyou're able to tell stories with
numbers and that things areworking the right way.
And so I'm the person thatcomes alongside of the buyers to
help them learn how to get thatright.
Speaker 1 (02:30):
Okay about getting it right.
What are people getting righthere at HR Tech?
What are they getting correct,come correct, jck.
Speaker 2 (02:41):
I think they're getting right to be here right,
like so, having your growthmindset I mean people listening
to this podcast right now right,and being able to be curious
and willing to learn.
I think that part is really,really important.
Speaker 1 (02:56):
Oh, ry leaned into his mic and I'm like I'm going
to interrupt them.
Speaker 3 (03:00):
No, I was just.
I'm letting you talk, I'm tired.
Speaker 1 (03:04):
All right.
So why are you tired man?
They're passing out the drinks.
They got orange juice overthere.
Speaker 3 (03:09):
We'll get you a little caffeine, they've got the
five hour energy too.
Speaker 2 (03:13):
They have literally catacorn art from us right, yeah
.
Speaker 1 (03:16):
We should just go grab some Energy.
You talk about implementation.
Speaker 2 (03:20):
Yes.
Speaker 1 (03:21):
What are the vibes that you're feeling going from
2023 to 2024 in the TA space?
You got any predictions?
Speaker 2 (03:29):
Do you really want my predictions?
Speaker 1 (03:31):
We want your predictions.
Are you going to be theanti-hero?
Speaker 2 (03:34):
No, I don't know what that means, but also oh wait,
that's a Taylor Swift song,right?
Speaker 1 (03:39):
I think so.
Speaker 2 (03:41):
So for the people listening in a little bit of
context.
I hit my head in 2020.
I remember nothing from beforeit, including popular culture,
and so sometimes these types ofsituations come up and I look at
Brian and like that was aTaylor Swift song.
We're referenced right.
Speaker 1 (03:53):
Okay, so for reference, JCK and I talk all
the time right, so this is likea conversation between friends.
Like, Ryan is the odd man outhere, Ryan.
Speaker 3 (04:01):
that's all I thought.
Speaker 1 (04:03):
I didn't know if this was a Taylor Swift joke,
because you know like Maddie isall about.
So everybody knows my daughter.
If you don't know her from thepodcast, you know her from HRTX
or you know her from mynewsletter where I beg you for
money for her school.
Speaker 3 (04:15):
You know he spent $1,400 on tickets Two.
Speaker 1 (04:19):
We are never ever ever talking about that together
.
We are never ever ever.
Speaker 2 (04:26):
I love it All right.
So sorry everybody for thequick like sidestep on Taylor
Swift, I think there's.
So to go back to your question,which is what do you think is
gonna come in 20, or what's kindof?
Speaker 3 (04:37):
20, 24.
Speaker 2 (04:39):
I think we're about to step into a reckoning.
Speaker 1 (04:42):
A reckoning.
Speaker 2 (04:43):
Yeah, in a pretty big way, and so what I mean by that
is Generative AI has changedthe game in a lot of ways.
Right, Like I'm a hugeproponent of Generative AI, I'm
somebody with a disability andGenerative AI helps me do so
much more.
That's not having to like drainthe batteries of like who I am,
and so I'm able to go so muchfurther and faster.
So, big fan of Gen AI.
(05:04):
However, there's a lot oftechnology that's coming out of
the Gen AI space right now,including technology that's
gonna allow you to, you know,mass apply to like 200 plus jobs
with the click of a button.
Our infrastructures that wehave on the town acquisition
standpoint, they're not readyfor that, and so what I mean is
(05:25):
these companies, probably in thenext, probably three months or
so, are going to be flooded withhundreds and hundreds and
hundreds of thousands ofapplications.
Speaker 1 (05:34):
That are Gen AI.
That are Gen AI bonds.
Yes.
Speaker 2 (05:38):
And so what's gonna have to happen is these, these
applicant tracking systems, thedifferent technologies that
you're using are gonna have tofigure out some way to have like
a digital footprint to assesswhether or not this is like a
bot or not a bot, right?
Speaker 3 (05:53):
Like so that's gonna be a step ahead.
Speaker 2 (05:55):
They're gonna have to .
But, as we know, with applicanttracking systems, when we
implement them and we customizethem, often they are taken off a
vendor roadmap and that makesthem really difficult to play
with right.
Speaker 1 (06:07):
Oh no.
Speaker 2 (06:08):
So there is gonna be.
I think there's gonna be areckoning, and if you think it's
hard to go through theapplications that you have today
for your job, just imagine ifnow I got 400 applications and
I'm gonna get 400,000.
Speaker 1 (06:20):
Okay, so then let me come back to this Is that
there's been a big theme hereand over the past six months.
You and I were at RecFest andthere were a lot of people that
were talking about the death ofsourcing, and I define sourcing
as finding passive talent.
You're talking about an influxof active talent and active
applicants, right?
Speaker 2 (06:38):
100%, I am.
And just to double click on thesourcing piece those of you
listening it turns out humansare not one size fits all, turns
out right, that's what we do inthe sourcing world is being
able to, like, ask right sharpquestions and figure out what's
needed, and then we're gonna goand try to find that needed
thing out there, right?
(06:58):
So Jenn AI today and probablyfor the next several years,
isn't going to be able tounderstand the nuance of a human
.
It's gonna be a lot harder.
And so, yes, there mayeventually be certain things
that aren't gonna be that JennAI will take over.
But sourcing just going awayy'all, like if you know how to
(07:20):
find a purple, sparkly unicornOpenAI is not gonna be able to
help you find a purple, sparklyunicorn Like.
Speaker 1 (07:25):
it's just not how it works Now.
Speaker 2 (07:26):
that being said, sorcerers are so sorry, I'm like
totally like oddest like whatis it speech?
Speaker 3 (07:33):
box, soap box or whatever.
She's about to fire all thesorcerers in the world.
Speaker 2 (07:37):
No, so sorcerers are uniquely positioned to become
super users of Jenn AI because,like, honestly, like Boolean
search string you guys is aprompt right and so ChatGPT does
not do good, boon, they won't.
No, but here's.
I need to connect the dots foryou here.
Speaker 1 (07:58):
Okay.
Speaker 2 (07:59):
So, in order to make Jenn AI work the best, I need to
call out.
So, at its core, sorcerers areable to understand and find
people and the nuance of peopleright.
In order to get Gen AI to workproperly, you need to understand
the nuance of what you'relooking for.
So, if I'm able to call outsimilar to like I need a redact,
(08:19):
whatever right, like if I'mable to call out the hat that I
need the person to wear and thelens.
I want it to look through youare able to get ridiculous
responses in terms of, likeprompt engineering and so like I
truly believe that sourceershave a massive leg up because
you already understand that.
Like it's the right sharpquestions right that you're
(08:39):
gonna need in order to find theright answer.
So I will pause for a second.
But yeah, brian looks like hehas something to say.
Speaker 3 (08:45):
I like it.
I mean I like everything you'resaying, and again I mean a
couple of the products that wespoke with over the last two
days.
They're all saying the samething and they're all preparing
for that.
I don't know that any of themhave accomplished it by the look
on the face.
Speaker 1 (09:01):
I'm saying no.
Jenny is sticking her finger inher mouth as she is going to
throw up.
I'm commenting everywhere.
This is a visual pot.
Oh, okay, I'm sorry.
Speaker 2 (09:08):
So here's the thing?
Speaker 1 (09:09):
I don't think that.
Speaker 2 (09:10):
Jenny has now removed her glasses.
Oh God and Brian knows thatthat's a tell for me that I'm
about to say something.
All right, all right, okay.
There were some incrediblepresentations y'all yesterday,
specifically about like howJenny is gonna change the world
and like all the way through.
Speaker 3 (09:24):
So you're calling BS on all of this.
Speaker 2 (09:26):
No, I'm not calling BS on it.
Speaker 3 (09:27):
Buyer syndrome.
Speaker 2 (09:29):
I am highlighting a very, very, very important flaw
that exists.
So what was said, just to like,connect the dots, was that,
because of the growth of thesegender-divine products, all of
our problems are going to besolved.
Right, which is cool, that'sawesome.
Yes, in order to like, if youhave the proper data
(09:50):
infrastructure and you can youand I know this word, data nerds
right, it's my favorite fourletter word.
But, like, if you have theright data infrastructure, your
data is clean in that lake.
I can put Gen AI or somethingthat's on there to connect the
dots to that.
The problem is, most of thebuyers who are here don't
understand how to build theproper foundation in order to
(10:12):
get the data clean, and if wedon't do that, it's garbage in,
garbage out, y'all.
Speaker 1 (10:17):
It doesn't matter.
Speaker 2 (10:18):
It's such an important piece.
If we want to help ourorganizations get this right, we
have to start storytelling Likewhat does it actually look like
?
Yes, there is this cool, likeyou know, pie in the sky, we can
do this.
It's a mountain with, like thenew world or whatever.
I don't know all the hisms.
I'm playing my head in drink.
Speaker 1 (10:39):
Let's unpack that a little bit.
Let's unpack that a little bit.
Speaker 3 (10:45):
How does an organization with really just
bad data, how do they start toget that prepared for this next
move?
Speaker 2 (10:53):
Number one step y'all is you have to have a map of
your decision-making processesand your experience from high to
fire.
If you don't have that, youreally aren't positioned to be
able to leverage like AI orautomation, because in order to
get AI or automation right, youneed to be putting it where
there's friction, and if youcan't assess where there's
(11:13):
friction, that's the wrong wayto do it.
So step one is going to beutilize those design thinking
skills to understand what theproblem is and actually map it,
and then to be able to ask theright, sharp questions.
And, as we know, I grew up as aproject manager before I hit my
head.
But one of the rules that wehave in the project management
space is, like I was saying,Scrum master.
(11:35):
The problem that presents itselfy'all is often not the problem
you need to fix, and so you'vegot to ask the right sharp
questions to figure that out andreally clarify and verify that
Five wise, five wise.
Yes, and what Brian's referringto is like you have a situation,
you ask, but why?
And then you get an answer andyou ask, but why?
(11:57):
And you ask, and like it soundssilly y'all, but like this is a
really important piece that weneed to go through, because when
resources are lean and ourteams are a lot smaller and
we're having to do more withless, it is imperative that we
are actually working on theright things.
And so, yeah, I know I'm likeprobably way over time, you guys
(12:17):
, but Two minutes and 15 seconds.
Speaker 3 (12:21):
You're good.
Yay, yay, yay, only two minutesover, okay this has been a
really impactful conversation.
Speaker 1 (12:26):
I hope that people will give this a listen and that
people are going to share thisout, because you have laid out
You've laid out what you need todo to be I mean not just what
you need to do to be actionable,but what you need to do to
create success in yourorganization.
And there are a lot ofbuzzwords, there's a lot of junk
.
It is data.
It is good data in good dataout.
(12:47):
It is bad data in bad data out.
This is an interesting time forus in an interesting
environment.
We'll see what the reckoninglooks like in 2024.
Will you come back in 2024 andhave another conversation with
us.
Speaker 3 (12:58):
We need to have you back on and like when we get
home, let's have her back on andget a full 30 minutes.
Speaker 1 (13:04):
Okay, that sounds like a plan.
Speaker 2 (13:05):
Yes, and the last thing that I just want to like
double-click on here not tointerrupt you, brian.
She's going to go to 14 minutesnow you guys, the only way to
solve a hard problem is tochoose to begin, and you're not
going to know the entireblueprint, but when you choose
to begin, you're going to beable to assess what's the first
right step and then, based onagain on data favorite word
(13:26):
right Like where are we drifting, where are we shifting in order
to get that right?
And so these things we'retalking about, like how do you
get work right?
You got to start right.
Choose to begin Amen, and it'snot necessarily about having the
perfect strategy.
It's actually about having theperfect strategy to drift and
shift your strategy when you getit wrong.
And remember that getting itright is gleamed through getting
(13:49):
it wrong y'all.
So failure is the blueprintwhere we can gleam the blueprint
for getting it right.
So be intentional.
Yes.
Speaker 1 (13:58):
Oh, listen to that.
A round of applause for JCK.
Speaker 3 (14:01):
We're going to wrap this up.
Speaker 1 (14:02):
We are coming to you live from the Olio podcasting
booth at HRTech.
Thank you so much, JCK.
Jck, we will see you soon onthe Sourcing School podcast.
Thank you.
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