February 19, 2025 • 43 mins
This week we welcome David Ellis to the podcast. David started his career as a recruiter in New Zealand, before relocating to Europe to work as a talent consultant. After earning his PhD, he moved to Boston to join Korn Ferry, where he currently sits as SVP of Talent Transformation.
Topics include:
The future of talent acquisition, how AI can streamline processes while enriching candidate experiences, how to strike the right balance between AI and human touch, the ethical considerations when training AI, AI in high volume recruiting vs. executive recruiting, the shift towards skills-based hiring, talent acquisition roles of the future, the debate over hybrid work, the importance of critical thinking, approaches to measuring the ROI of artificial intelligence, and how AI is already empowering “wow” moments in recruiting
David Ellis
SVP of Talent Transformation, Korn Ferry
Articles:
Korn Ferry’s Talent Trends 2025: Progress Over Perfection
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Welcome to the Changing State of Talent
Acquisition, where your hosts,graham Thornton and Martin Credd
, share their unfiltered takeson what's happening in the world
of talent acquisition today.
Each week brings new guests whoshare their stories on the
tools, trends and technologiescurrently impacting the changing
state of talent acquisition.
Have feedback or want to jointhe show?
(00:21):
Head on over to changestateio.
And now on to this week'sepisode.
Speaker 2 (00:27):
All right, and we are back with another episode of
the Changing State of TalentAcquisition Podcast.
Super excited for our nextguest, David Ellis, SVP of
Talent Transformation at KornFerry.
David, super happy to have youhere.
I'm going to start with asoftball.
Why don't you tell us a littlebit more about your background
and how you found your way toKorn Ferry?
Speaker 3 (00:48):
Okay, great.
Well, yeah, first of all,thanks for having me.
It's really, really nice to behere and chat with you guys.
I started my career actuallyback in New Zealand which
matches the accent better thanwhere I live now, which is here
in Boston and I was actuallyworking in a line manager role
in a bank and as part of that Ihad to recruit dozens and dozens
(01:12):
and dozens of people a year,and I was quite fascinated about
how kind of subjective that was.
This was probably, I would say,20 years ago and I thought, well
, this is an area of practicewithin HR that I can probably
get my brain into a bit more andwhere I can make a contribution
(01:34):
.
So I ended up leaving that role, going into recruiting in an
agency and did that for I don'tknow six months.
Then I moved into an in-houserole at one you know one of New
Zealand's biggest companies andI ended up, you know, being a
recruiting leader there.
And then, yeah, I just went offto Europe and worked in
consulting there in bothrecruiting and talent
(01:55):
development, and then I did mydoctorate after that.
So I took a bit of a break.
And then Corn Fairy just poppedup on LinkedIn one day and just
said hey, do you want to talkto us about a job?
That was 10 years ago and I waslike I don't think so, I don't
think I really want to.
No, but actually I'm reallyglad I did, because I just love
working here and I love theclients and love the people I
(02:16):
get to work with.
Speaker 2 (02:17):
No, that's awesome.
Well, I can say, corn Fairy ispopping up in our newsfeed quite
a bit too, and you know, one ofthe reasons we are excited to
have you on is, you know, youjust put out a, you know, 2025
town acquisition trends reportand you know, frankly, I thought
it was, you know, the moremeaty one that we've.
You know, come across too, andyou know some really great
insights.
So, you know, I was reallylooking forward to this
(02:39):
conversation, you know.
I think you know, maybe thebest way to start is with a
softball, david.
So in your trends report, youtalk about what TA is going to
look like in the future.
So maybe a good place to letyou wax poetic is what do you
think talent acquisition isgoing to look like in 2030,
david, yeah, and you thinkthere's a softball.
Speaker 4 (03:04):
I was going to say Graham, I don't think that's a
softball.
Speaker 2 (03:07):
Get your going to print this out too, so we'll
remind you in six years here, orfive years.
Speaker 3 (03:13):
I can't wait to hear what your other question is.
Well, you know, it's reallyinteresting because if you'd
asked me that a couple of yearsago, I might've given you a
different answer than I'm, youknow, than I, than I would give
today.
And, by the way, I heard youguys talking um about our report
too and giving it a bit of a,you know, a bit of a soft
critique, and I I really enjoyedthat.
(03:34):
So, if you know, if peoplehaven't heard that, then it
would be.
It'd be good for them to tohear that too.
But I do think there's kind of a, there's a sweet spot for
talent acquisition here where,you know, efficiency and
experience are moving in thesame direction.
Like that seems to be kind ofthe ultimate aim within at least
(03:55):
forward thinking TAorganizations today.
2030, I mean, I'll be a bitmore bold about it In 2030, it's
going to be at least, you know,50, 60% more automated than it
(04:15):
is today, and I think we'll seea lot of the processes that have
benefited so far from AI, fromautomation, especially in the
high volume hiring space, Ithink we will see them
incrementally, you know, creepover into other segments of
hiring and I think that you knowI'm so optimistic about the
potential that that offersorganizations and offers talent
because, you know, with AI'spotential around data collection
(04:40):
and the insights that can begleaned from that, like I said,
I'm just so optimistic aboutthat potential.
So hopefully that's a start tothe softball.
But I'm interested in what youguys think as well, of course.
Speaker 4 (04:55):
Yeah well, it's a huge question.
I had the same chuckle tomyself when Graham called out a
softball, because I was like boy.
I don't know how I would answerthat question.
It is a very big question.
I thought I have a couple ofintroductions we could go.
David, before we do that, Ijust want to gush on New Zealand
a bit.
For some reason, the universityis sending me New Zealanders.
(05:16):
I have another client with adifferent project who happens to
be from New Zealand.
I've been there twice andsomehow I missed identifying the
accent both times, but anyway,we're thrilled to have you on
the show.
Probably should have startedthere.
As it relates to this questionof what recruiting is going to
look like in 2030, maybe you canunpack something you said.
(05:36):
You said, and correct me if Imisheard you, but I think you
said that efficiency andexperience are aligned or
something, and this kind ofalignment is one way of thinking
about the future.
Did I hear you correctly and,if so, could you just say a
little more about those twoideas and how they relate?
Speaker 3 (05:55):
Yeah, of course, and thanks for the plug for New
Zealand.
I'm sure the tourism I don'tknow if they need help.
Yeah, yeah, we'll push up theprice of the airfares for me to
get home is what will happen.
But so my yeah, my point thereis that you know when, when this
kind of big AI, um, or at leastGen AI sort of blast and fuss
(06:16):
and buzz really came to the forerecently, I think a lot of
organizations were like, how canwe leverage this to do things
faster?
And, and now what we're seeingis more organizations saying how
can we do this better?
And those are, at least to mymind, separate but related
(06:36):
concepts.
Right, speed is one sort ofmeasure, um, and it benefits the
organization, of course, and,in some cases, the talent going
through the process.
But actually, I think the sweetspot for AI is where you
leverage that to elevate theexperience.
And you know, I heard a lot oftalk early on about how we can
(06:57):
either provide, you know, humanexperiences or high touch
experiences, or we can usetechnology more or AI more, and
I actually think that's notbinary like that.
I think the right mix of peopleand AI, sort of almost tailored
to the segment of hiring thatyou're doing, is I mean, I keep
(07:21):
using that word sweet spot, butI think that's the key and I do
think that's where this is goingright.
Organizations won't just focussolely on the efficiency play
anymore, even though that willremain important.
They'll figure out how to useAI to elevate the human
experience.
Speaker 4 (07:38):
Okay, that helps a lot.
I thought that's what you meant, but hopefully for our audience
just to have you expand on thatwas helpful, I think.
Well, you make a good point andit's one that probably our
industry needs to hear.
Hr, talent acquisition, aswe've said endlessly on this
podcast have historically beenseen as cost centers, which
takes us kind of maybe in anunshareable way to this idea of
(08:01):
efficiency.
So organizations and TA leadersare always being pushed to be
more and more efficient.
But I think what you're raisingis a very important point,
which is efficiency is a goodthing unless the model upon
which we're iterating was notvery good to begin with.
And so if the status quo is nottreating candidates very well,
(08:24):
not having a lot of humancontact or that human side to
the recruiter experience or tothe candidate experience, is
that something we want toiterate and be more efficient at
?
Is that a fair way of kind ofdoing that?
Or what would you say to thatassessment?
Speaker 3 (08:43):
Yeah, I think that's fair.
I do think the issue ofmeasurement comes up here too,
because how do we know whetherthis is doing what we want it to
do?
And so we have to figure outfirst of all, I think, what we
want it to do, because otherwiseit makes it harder to measure
the kind of the strategic impactof these kind of paths that
(09:05):
we're on around technology andinnovation.
You know we've got ourtraditional measures of talent
acquisition around time, costand quality, but this is an
opportunity here to help us doit cheaper, better and faster, I
think.
So it actually hits all ofthose notes, and I think that
(09:27):
the organizations shouldn't giveup until they have figured out
how to create that sort ofutopian state.
Speaker 2 (09:36):
Yeah, I want to first , I really want to touch on this
.
You know, measuring, roi andhiring, when you know, through
the lens of adding AI.
But before we do that, like youknow, let's unpack a little bit
more, david, this kind of ideaof you know the human touch
between, or balancing the humantouch between, ai efficiency
gains, you know, and not losingthat human touch.
(09:58):
Can you maybe give an exampleof let's talk about how
organizations are leveraging AIfor efficiency?
But what do we mean by notlosing that human connection
point?
Give me an example of how weroot that piece.
Speaker 3 (10:15):
I think some of it comes in from.
Possibly one of the bestexamples right now is in the
high volume hiring context,because that seems to be the
biggest sort of level ofadoption right now at least, you
know en masse by verydefinition of volume hiring,
where the conversational aspectof how a bot will interact with
(10:35):
the talent gives it a humanflavor, even though there's no
human sitting there or manuallyoperating this process.
There are process steps that arepassed between different pieces
of software and as thecandidate moves through the
(10:56):
process, and that has thepotential to feel pretty human,
as long as it's aimed at theright, you know, as I was saying
earlier, aimed at the rightsegment of talent.
Now, if you try that in anexecutive hiring context and you
run that process, you knowthrough a chat bot and you're
gathering information about themand scheduling interviews and
(11:17):
you know maybe even providingoutcomes of processes to the
talent, then that's going tofall flat on its face.
So in that context, you knowfor that segment, at least today
as it is today, you know youwouldn't typically put such an
automated process because itwould detract from the human
(11:38):
component that's aligned withthe expectations of that segment
, from the human componentthat's aligned with the
expectations of that segment.
And I do think that's reallyimportant here is that the
experience of the talent and theway that you are leveraging AI
and humans through the processthose things all have to sync up
.
Speaker 2 (11:53):
Yeah, no, I think that's helpful and maybe I'll
kind of take a step back on onepiece of you know.
Take a step back on one piece.
So you know, I think one of theexciting aspects you know here
is, you know, if we think aboutAI, you know most HR tech, you
know.
If we look through the lens ofHR technology, you know, and AI
and solutions, a lot of that isreally focused on identifying
(12:13):
patterns from existing.
You know, information orautomating pieces like resume
screening, you know, and that'sjust you know sort of the basic
stuff of, hey, I want to know ifsomeone lives in this state,
you know, has thesequalifications, and like A, if A
, then B, right, and like youknow what.
You know.
What gets more exciting is withgenerative AI.
(12:33):
You know.
We're quite literally, you know, creating entirely new content,
right, entirely new personal.
You know new personalizedcandidate outreach right, it's
interview questions that aregenerated based off of what
we're seeing on resumes or howpeople are answering questions,
and so I think just saying toDavid is like, hey, one of the
(12:56):
reasons that the human touchbecomes more interesting is
generative AI is quite literally, generating new, you know,
content.
Right, it's about creationrather than automation, and so
you know it's, it's difficult,right, but I think, you know, I
think if we think of AI asautomation yeah, there's really,
you know it's hard to see howthat becomes you know, has an
(13:18):
element of human touch or humanconnection to it.
It is.
You know, it is just, you know,moving people through a process,
right, whereas you know,generative AI just opens up a
whole new box of opportunitiesif implemented correctly, and
that's because it's no longerhey, we're, you know, drawing a
map to get from point A to pointB.
It is.
You know, generative AI isalmost, you know, the Sherpa
that is.
You know, drawing a map to getfrom point A to point B.
(13:40):
It is.
You know, generative AI isalmost the you know the Sherpa
that is, you know, walkingpeople through, you know, a
unique process to thatindividual.
Does that resonate?
Speaker 3 (13:49):
I mean, I do agree with that.
I think there's maybe there's abetter example too I can offer,
not than yours, but the one Igave which is, you know the
potential here.
So if I've got like, forexample, I've got a cohort of
professionals that I've hiredinto an organization Say, I've
(14:24):
hired 500 of something you know,whatever sales managers over
the past year here in the US howlikely that cohort of
individuals is to achieve mybusiness goals here, which are
probably, if it's a salespopulation, probably something
to do with growth, then AI givesus new potential to take, you
know, if these people haveundergone assessments.
It gives us potential to takethat data, roll it into
something which gives me acohort level view or an
enterprise level view.
If I've hired an entireenterprise of new people and
(14:45):
then marry that up against mykey business drivers At the same
time at individual level, itwill be able to, and it is able
to throw the new line managersof these people some insights
about them that previously wenever would have done because
hiring was always quite siloed.
(15:06):
It was, you know, we broughtpeople into the organization and
yay, our job's done, ourhandoff is done.
But now the potential goes somuch greater than that because
of what we're learning and howthat can inform development
plans, and I think that that, inits very essence, is human.
Even though it's not likesomeone sitting there at an
(15:27):
Excel workbook doing all thisstuff, it's being done for them
because the organizations haveinvested in that type of
automation.
But the experience for thepeople who are going through
that process I mean that'spretty impressive, as well as
the insights that can beproduced for the business
leaders.
That you could also argue helpswith human aspects of running
(15:51):
business.
I don't know what you guysthink about that, but that
potential is there right now.
Speaker 4 (15:56):
Yeah, I mean that's a fascinating topic, you know.
I think I know enough aboutgenerative AI to be dangerous.
But one of the big topics thatseems to come up in the general
case, not specific to recruiting, is the importance into
training, which in the generalcase for like a chat GPT means
(16:22):
ingest all the information thathumanity has created, that we
have on record and assimilate itand use that as a resource when
somebody asks you a question.
I guess it's a long way ofgetting to a question about
training.
As it relates to generative AIspecifically for recruiting and
(16:43):
maybe that's kind of related tomy question from earlier, Could
you say anything more about that?
How do we train a generative AIin a recruiting context to be
not only considering thebusiness objectives and all
those things, but to be actuallyrepresenting our company in a
human way?
Speaker 3 (17:02):
That's a really good question.
So and there I think you'reyou're talking more about like I
don't know, the out, the, thepersonalization of the outreach
and and the sort of experiencethat the person goes through
when they're going through theprocess steps.
Is that is that right, or Ithink?
Speaker 4 (17:17):
so yeah, well, I mean , yeah, like I said, I know
enough to be dangerous, butthat's the most obvious place.
But yeah, I'm just curious,like, is it much like the chat
GPT general case where we'rejust saying here's all the
outreach that we've done tocandidates in the past and you
can even listen to recordedinterviews, maybe, and here's an
example of really successfulrecruiting outcomes, and then
the AI compiles all of that andgets some sense of how it should
(17:41):
operate and behave on behalf ofyour organization?
Speaker 3 (17:45):
Yeah, I'm probably less expert in the area of how
you train these models.
To be honest, what we hear alot of clients talking about at
the moment is risk that iseither real or perceived around.
You know these machineslearning in a way which is
discriminatory.
I guess you could equally applythat to processes where the
(18:11):
outreach, the style of languageused, you know the very mode of
outreach, whether it's via textmessage, email, whatever
LinkedIn message is selected bya machine and is learnt through
the success that it's had andfailures that it's had, through,
you know, approaching similarpeople for similar roles through
(18:31):
those channels.
I guess that potential's allthere, right for it to get that,
you know, not necessarily rightall the time, but I think that
potential and that risk isprobably much less publicly
talked about than thediscrimination and bias sort of
fear that I think is quiterightly out there in the market
(18:51):
still.
Speaker 2 (18:52):
Yeah, and you know, I don't even think of it.
Like you know, again, similar,you know, not expert in training
, you know AI to do tasks, but,like you know, I think you could
argue, boy, if you just, youknow, pulled in data from your
college recruiting team, right,who went on campus to hire
engineers, like, boy, like theoutput of you know plugging that
you know sort of you knowbackdrop of data into, you know
(19:14):
your screening for candidates oryour outreach, might look a lot
different, right, and you know,and so you know, and that's
just the things that we don'tthink about.
Right, you try to, you know,grab a subset of data and like,
start working through it andlike there are a lot of you know
downstream, you know challengesif you're training, you know
training, your, your tools, youknow to make decisions based on
(19:34):
data and hopefully that's notwhat people are doing.
But who knows, right, I thinkit's just there's certainly
risks and I think arguablythat's probably why most of
these new jobs that we're seeingthat are going to be super high
in demand, it's these jobs likeethics specialists and AI
(19:55):
quality assurance analysts.
It's the roles that people thatcan recognize.
You know, we only used a smallsubset of data and like, hey,
the outputs on that is going tobe drastically different than
you know, if we looked at thingsfrom a different lens.
Speaker 3 (20:10):
Right, I know, I agree, and I do think that's why
the I do think that's why thehuman touch, quite unquote, is
important there as well, becauseanything, any, any use case
where we can gather insightsabout the experiences of people
who have been through ourprocess, is going to be useful,
as long as we don't blanketthrow it into, you know, or
(20:30):
generalize against it and thenthrow it into a population
that's just um, it's just notrelevant, or or it's it has some
danger attached to it becauseit's not, it's not even valid as
to how we're, like you know,approaching that population.
So I think, yeah, I do think,we're on the same page there.
Speaker 2 (20:46):
Oh yeah, and, like you know, I'll just say, like, I
think it's at least it'sexciting to me that, like one of
the most in-demand you know,quote unquote, we'll call it a
skill set right or capability is, like you know, looking five,
10 years out in the future, isjust critically thinking.
You know the critical thinkingright.
And like, boy, like, if you canrecognize that, like hey, this
might not sit right, like youknow, the smartest people in the
(21:09):
room are going to be the onesthat you know have the ability
to say, boy, something justdoesn't pass the sniff test.
You know, on this one too,right.
And like, hey, are we askingthis wrong?
And you know, I'm sure all ofus have.
You know, you know, used GeminiClaude, you know chat GPT and,
like, popped in questions and,you know, looked in an output
and just said, boy, like, areyou sure?
Are you sure this is what youmean?
(21:31):
And you know, a lot of times,like you're back, oh no, I'm.
You know, I missed looking atsomething.
You, your AI model of, missedlooking at something a certain
way.
And so I still love thisanalogy of AI being used as an
unpaid intern.
Boy, you wouldn't take productsto market that an intern
necessarily built withoutlooking at it first.
Last question on AI too is theysaid we'd get back to it.
(21:55):
So, ok, talk a lot about where,how AI is going to be, you know
, used, like some of the risks,like how does Corn Fairy?
Or how do we think about youknow measuring AI's true return
on investment in hiring?
Because, well, I think thereare a lot of different ways that
you know AI is going to beimplemented.
So, you know, how do we thinkabout measuring AI's true impact
(22:18):
on the hiring process?
Speaker 3 (22:23):
measuring AI's true impact on the hiring process.
Yeah, I think we can point ourmeasures at the process itself,
which is, you know, I kind ofspoke a bit about this earlier
around time, cost and quality.
How fast are we doing it, howmuch is it costing us and what's
the quality of the experienceas well as of the hire that you
make?
And so that I mean that's thosemeasures can be a baseline, can
(22:45):
be taken pre-AI and thenpost-AI, again measured, and
then looking at the delta.
I think the other part which iskind of frankly, a bit more
interesting to me is well, it'stwo things.
One is and we're helpingcompanies think this through
right now right, this is, thisis a question that people are
coming to us with, and it kindof covers a couple of your
(23:07):
questions.
One, what's, what does the taorganization look like of you
know, and at the three-year mark, at the five-year mark, and
then second, because part ofthat answer is around more ai.
The question is, then is then,well, how do we measure success,
how do we measure return forour business on what we've done?
And so, yeah, this is where itgets interesting to me.
(23:30):
Why did you do it in the firstplace.
Is it because someone in theC-suite said there's a whole lot
of talk about AI, you need todo something about AI?
Well, I do think we've gone abit beyond that.
I do think it was a lot of that,you know, when this whole thing
kind of blew up and the measurethere should then be sort of
attached to higher level metricswhich are connected to the
(23:51):
strategy of the TA function.
What is the function trying todeliver this year and how does
that support the broader kind ofpeople strategy of the
enterprise?
And so measures, you know, Ithink at least my view of this
is that those measures should inpart connect back to those
higher level measures, um items,strategic items, because
(24:11):
they're the ones that we'vealready decided are going to be
the sort of strategic pillars ofof the business and of the
people strategy.
I do think that's, I do thinkthat's part of this, and I also
think you know we're we'reworking right now on a um, a TA
professional of the futureprofile, cause we're we're quite
forward thinking, like you guysare right, like we're we're
(24:32):
always thinking about future,and we've got this research um,
the corn fairy institute, which,um, they kind of powered the
report that you, you know thatyou uh referenced at the at the
top of the top of this episode,and so we are actually putting a
point of view together abouthow the ta professionals sort of
role can change throughout thisevolution as well, and what we
(24:56):
think that might look like atthe 2030 point.
And the reason I'm raising thatis because I think that should
be one of the measures.
How has AI enabled us to changethe role of the people and
elevate the role of the peoplewho are executing on talent
acquisition?
For us, that's a people-focusedsort of measure and metric that
(25:21):
also speaks to the way that weorganize ourselves to get the
job done.
So I feel like I'm ranting abit here, but does that at least
partly give a view on what Ithink we should be measuring?
Speaker 4 (25:34):
Absolutely.
I think you're speaking to twoguys who think of it in similar
ways.
Yeah, we can take the mostbasic approaches you outlined at
the beginning, which is, usethe same old metrics and see how
they change pre and postimplementing AI.
If we could draw a line in thesand so specifically and that
(25:54):
may be hard, but I love whatyou're saying about.
Sure, we should probably dothat, as some nod to the past,
but this is such a sea changetechnology that has the ability
to impact organizations wellbeyond candidate experience that
we probably need a whole newsuite of metrics.
Some of them are probablyquantitative and others might be
(26:17):
more qualitative, but we preachto our clients all the time,
and I'm sure you guys have asimilar perspective that I think
it's very easy as TA folks toget lost in our silos and just
be so focused on the job at hand.
I have to fill this role today.
I have to get this role today.
I have to get this seat filled.
The C-suite is barking down ournecks about this.
(26:38):
We got to do it faster andcheaper and all these things.
And these things are all fine,of course, but it loses sight of
the whole purpose of why do wehave a talent acquisition
function in the first place,it's not because we want to get
really good at screening throughthousands of candidates I mean,
sure that might be a byproduct,right?
The reason we have it isbecause we're an organization in
(26:59):
the world that exists for thispurpose, with this mission, and
we need great people who arealigned with that mission to
deliver on it.
And so I think maybe that'swhat you were speaking to.
Hopefully I didn't misrepresentit.
One thing I have a question Ihad is and maybe you know, being
relatively close to the AI asit relates to recruiting.
You know anyone who's messingaround with like a chat GPT has
(27:21):
had.
Most people, I would say, haveprobably had like a wow kind of
moment with it, just like youknow, I've shared some on the
podcast in the past.
But you can ask it to dosomething that would require all
of your day, or maybe severalof your days, and it will
deliver an output that's maybe95% as good as what you could
have done in a few minutes, andwe can think of lots of
(27:42):
different examples of that, butit is stunning when you see what
it can do.
Do you think in your experiencethat there are any
organizations delivering wowmoments that are being powered
by generative AI currently?
Or do you think, as an industry, the recruiting function is
still having quite gotten thereyet, where it's not quite
(28:02):
delivering those wow experiencesin a recruiting context?
Speaker 3 (28:05):
I love that question, such a thoughtful question.
So I think, yes, there havebeen wow moments.
I think some of them came earlyon and possibly some of the
ones you were alluding to aroundokay, this thing can actually
generate a job description forme, it can generate interview
questions for me, it canprobably come up with a
simulation exercise for me.
(28:26):
I mean, how valid those thingsare is a whole nother.
But if it's 95% there to yourpoint and all I have to do is go
through and sort of um, youknow, correct it, add to it
whatever, then great.
I think you know my view onthis is that a lot of these wow
moments around what it cangenerate in terms of content are
(28:47):
yet to come.
I mean, I just love some of thememes that it can generate.
I know that's not related torecording, but when I first saw
the potential of that, I waslike, oh my goodness, and it got
me thinking okay, this is whatyou can do today.
What are you going to be ableto do in three years' time?
And when we think about thekind of holy grail of judgment
(29:12):
and empathy and these sort ofhuman qualities that we're told
that AI can't actually help uswith?
And I was sitting, I wassitting in a bar the other day
you might want to edit this outwith um.
He was having relationshipproblems, he goes.
(29:32):
I asked chat gpt what to doabout this and chat gpt told me
to ask you, david, wow.
So that's it.
Hang on a minute.
Maybe this thing is smarterthan I thought, um, but I think
and that's why I think, some ofthe wow moments are like, some
of them are going to be likesmaller wows, and some of them
(29:52):
we just can't even imagine whatthey're going to be, because,
yeah, I mean, we have.
We have to imagine this thing'sgoing to be even bigger than
what we've seen so far, don't we?
I mean, what do you?
What do you guys think?
Speaker 4 (30:05):
Yeah Well, I mean it was interesting hearing you talk
about some of those wow moments, because we want to measure
things.
You know I'm a scientist bytraining.
Talk about some of those wildmoments because we want to
measure things.
I'm a scientist by training, soI certainly resonate with that.
I love measuring things, but mybiggest wild moments with
generative AI have not beenmeasurable.
I mean they have beenmeasurable, I suppose, but when
(30:26):
I asked ChatGPT to create 200multiple choice questions based
on a dense pharmacology textbookfor my partner and it spit it
out in 20 seconds, I wasn'tsitting there thinking, boy, let
me crunch the numbers to seethe ROI on this.
I paid $19.99 and it saved meeight hours and here's my hourly
(30:46):
rate and we could have donethat right.
But it's almost beyond thequantitative at that point when
you have some of these wowmoments.
So I just think it's reallyexciting to hear that some of
them are happening already andeven more exciting to see what
future wow moments might looklike in a recruiting context.
Speaker 3 (31:01):
I agree, and some of the ones that you can use that
are embedded in what you useevery day, like the co-pilot in
Microsoft, and you know what itcan do within Excel, what it can
do to generate PowerPointslides out of Word documents,
you know, like that.
I think some of that stuff waspretty wowing.
Well, it's actually it wasquite wowing for me when I first
(31:22):
used it because it didn't.
It didn't do what I wanted itto, but I think that was more
user error, but but it's goingto get better at that and so
maybe that will produce some ofthe wows.
But okay, this this thing's waybetter at doing this than I,
than it was last time I did it ayear ago, or whatever.
Yeah.
Speaker 2 (31:37):
Yeah, no, I think that's just absolutely spot on
and like I think we can umcontinue that talking point,
these talking points, for awhile.
I want to want to pivotslightly, david, just because
you know I don't want to wrapthis episode.
We're talking a little bit moreabout, you know, skills,
skills-based hiring.
You know, I think you know weall understand that.
You know AI is creating a wholenew you know generation of
(32:00):
skills that are going to berequired, only generation of
jobs.
You know that people are goingto be hiring for.
I'm just curious, you know,where do you think you know
skills-based hiring?
You know this concept that hasbeen talked about ad nauseum
over the last three, four years.
Do you think this is part ofthe impetus where we're going to
see a real changing point onskills-based hiring taking over
(32:24):
the traditional resume?
Do you think AI can speed thatup in the sense that, hey, we're
really starting to look forvery, very specific, very, very
specific skills that are relatedto new roles that you know
arguably didn't exist a year ago.
Right, like, where do we seeskills-based hiring go going
over the next, you know, half adecade?
Speaker 3 (32:43):
Yeah, that's a yes from me, by the way.
I do see what you justdescribed, so this has been so
interesting.
We do talk about this in thereport, as you said, because the
progress on this has not reallymatched the level of ambition
that organizations had in thisarea, and I do think that we
(33:03):
will see this speed up a littlebit, especially in organizations
that aren't afraid to get inand make some changes.
We talk about progress overperfection.
In fact, perfection is theenemy of progress, and so what
can you do now?
What can you do today toadvance your sort of journey
(33:23):
towards this North Star?
And one of the issues that I seeis organizations that are
really big and complex struggleto get the buy-in from.
You know, when we think aboutlarge enterprises as an example,
they struggle to get the buy-infrom the hiring manager
community, which is where this,you know, lives or dies, because
you might encourage them tofocus less on professional
(33:46):
qualifications, where they'reless relevant, or years of
experience in a role, wherethat's less relevant.
And then you know, then thecandidate gets to the hiring
manager interview and well, howmany years did you spend over
here?
And so they just take adifferent lens, which is a bit
more traditional.
So I think pace here can begained from organizations that
(34:10):
address some of those kind ofbuy-in change management aspects
.
Ultimately, I do think you knowif I conceptualize the resume as
a marketing tool, it'ssomething that I can pull
together to sell myself to you,my experience, my whatever my
skill set to you.
And AI potentially enables aprocess which is a bit more
(34:35):
objective because it can bothinfer skills that I've got but
also match me to roles.
I mean, these are well inproduction now, these features,
and so it's almost a case of youknow why hasn't the story here
quite matched the capability?
And I think in someorganizations it has, but others
(34:57):
have got a long way to go.
And it's to those ones we'rekind of saying hey, just make
some, take some steps here, umand and the rest will you know,
and then you'll be able to takemore steps because those ones
will become embedded.
That's kind of how I've beenthinking about this.
But yeah, what do you guysthink?
Speaker 2 (35:14):
Yeah, no, I mean, I completely agree, and I think
you know, hopefully we see moremaybe publicity is a word I'll
use around you knoworganizations that are investing
in we'll call it.
You know skills-based workright or skills-based you know
programs and so, like you know,or skills-based, you know
programs and so, like you know,you see stuff.
(35:35):
Like you know Amazon, you knowinvesting.
You know, arguably, you know,billions of dollars in
upskilling their workers.
You know, and you know, at&tinvesting.
You know a billion dollars intheir future ready program,
figuring out what skills of thefuture are going to look like,
and so I think, like, hopefullyit was as more and more of those
programs you know kind of frontand center, amazon and you know
(35:57):
and AT&T are not saying, hey,like we're going to invest on
sending people to, you know, geta bachelor's degree from you
know a certain university.
Or hey, like, let's make surethat we're investing in, you
know, getting people to do acertain job for the next 18
months.
You know it really is.
You know companies and smartcompanies and large ones are
investing in skills, reskilling,upskilling, and I think that's
(36:17):
a logical, you know connectionpoint to boy, like, if companies
are investing in certain skills.
Why aren't we, you know, as anindustry, investing more in
hiring based off of you knowcertain skills?
Skills-based hiring too.
Does that make sense, david?
Speaker 3 (36:32):
Oh yeah, it does, and I think you know.
The other question is how do weso, how do we define this whole
skills-based hiring anyway?
Is it connected to askills-based organization sort
of programmatic approach?
And, at the same time, what isa skill?
Because you know, I mean, is askill a technical skill?
Is it a behavioral competency?
And our view is that, actually,if you just focus on the
(36:55):
technical skills, then you'renot going to find the
performance sort ofdifferentiation factor that
you're looking for in selection.
If you've got five people whocan all use Excel, how are you
going to figure out which one'sgoing to do the job best?
And it's to be found in theirbehavioral competencies and
their identity, their personalidentity.
So yeah, I think there's a lotof questions that some
(37:20):
organizations have been able toanswer, or at least taken a view
of, whereas others are a littlebit potentially just bogged
down by just the enormity ofthis.
And I think Graham, your dog,agrees.
Speaker 4 (37:43):
Very much, very much.
It's possible.
Normative, um, you know of this, uh, and I think graham, your
dog, um, agrees very much, verymuch possible as well.
No, david, I know we're shorton time, but there's one thing I
really want to get yourfeedback on.
So you were talking abouthiring managers, but how
important it is to bring hiringmanagers along for some of these
evolutions that we're seeingwith AI and skills-based hiring.
Just anecdotally, can youcomment on why you think it's?
What is the hesitation withhiring managers to adopt
(38:04):
skills-based hiring?
Is it purely risk aversion?
We've been doing this a certainway for a long time.
This candidate seems to havegreat skills, but you know what?
They don't have a lot ofexperience.
And you're telling me I'm goingto hire someone for our
organization that does not havea bachelor's degree potentially.
And what happens if it turnsinto an unmitigated disaster for
the organization?
I'm the hiring manager I was incharge of this.
(38:26):
Is that as simple as it is?
Or do you think people havevalid for lack of a better term
not risk associated, but justthey don't trust or don't fully
understand skilled space hiring?
But it's not purely abouttaking the risk averse approach.
Speaker 3 (38:44):
I do think some of it's fair.
I do think some of it ismisunderstanding or potential
underappreciation of thepotential and what I get, what I
get from, like, what's in itfor me, um, so we can help tell
that story better.
And then I think, yeah, it's,it's I do.
I do think in some cases it's abit more complicated as well,
(39:08):
because maybe I've just got adifferent opinion as a hiring
manager than whoever's beencreating this program about what
good looks like for thisposition.
Maybe before I worked here Iworked in the same job, you know
, five for five, exactly thesame job for five years in a
competing firm.
What I do think there's somevalidity in is the notion that
(39:30):
actually skills are bothacquired and developed.
So the proficiency level goesup from experience.
So if I, if I learn a skill andI don't use it, have I learned
the skill but I practice itevery day, there's a better
chance that I'm not justmastered it but I've been able
to build proficiency.
So there is some.
I think there's some validityto the.
(39:51):
So there is some.
I think there's some validityto the, you know, to the sort of
tenure argument, but at thesame time I yeah, I think this
is more about saying, hey, weneed to focus on skills more
than we do some of these otheritems which are perhaps less
relevant to performance, whichis, you know specific tenured
experience in the same job orprofessional qualifications,
(40:13):
unless you're a doctor or youknow whatever, you absolutely
what these roles that actuallydo require you to have
professional qualifications.
Speaker 2 (40:22):
I think that's great.
Well, you know, I think wecould keep this conversation
going for hours.
David, I'm going to ask you onequestion that you know, I think
is just top of mind for me.
You mentioned doctors.
I saw a quote earlier this week.
It's like oh, why aren'tdoctors working remote?
Well, I think there's anobvious answer for that one,
hopefully.
But I'm curious, hey, withstill a lot of chatter,
(40:43):
especially over the last sixweeks, on this future of hybrid
work in office versus remote,just curious, has hybrid work
reached its final form?
Or how is hybrid, you know, orthis, you know, hybrid 360 model
sort of evolving, you know,with the workforce of the future
?
Speaker 3 (41:02):
Well, I love listening to you guys talking
about this I don't know if itwas on the most recent episode
or another one where you werekind of I think one of you was
like, look, if I, if I, hear onemore sort of argument against
hybrid work.
You know, my view of this isthat.
So what we did in the report iswe said that hybrid isn't just
(41:25):
about, you know, whether I'm inthe office or not.
Actually, it's more towardsthis concept of work-life
integration, where I work when,where and how suits me to
produce what I need to produce,for me to get the job done.
And I heard you guys talkingabout, well, what happens if,
with mouse clicks as a check oflike, I've just never subscribed
(41:45):
to that whole notion where wehave to monitor people all the
time.
And I think there's a problem,you know, in professional,
certainly in professionalsegments, where if you don't
trust somebody to get the jobdone and can't measure them by
the work they do, then that's abigger problem than you know.
Anyway, not to re-have theconversation that you had the
(42:08):
other day, but yeah, I justthink the potential here to
allow people to work when, whereand how the best suits them
should be maximized because itmakes business sense.
It makes business sense becauseit's what the talent wants and
it works for them in their lives.
So, yeah, that's kind of myview on that and I think what
(42:31):
we're trying to get through inthe report as well.
Speaker 2 (42:37):
Yeah, no Well.
I think that's kind of my myview on that, and I think what
we're trying to get through inthe in the report as well yeah,
no, well, I think that'sfantastic and I think that's
probably a logical place for usto put a pin in this one.
Last question, david, it's easy.
Where can people find youonline?
Speaker 3 (42:44):
Where can they find me online?
Well, they can find me onLinkedIn and they can find me on
, um, they can actually find meon the corn ferry website as
well.
Um and uh, yeah, I've, I'vereally enjoyed the conversation
today.
Thanks for having me on and I,um, yeah, I hope you hope you
guys, uh, and and whoever'slistening got something out of
it as well.
So, yeah, thanks again thanks,david.
Speaker 2 (43:05):
it's been great, david, and you know we'll make
sure that we're, you know,linking all of these corn fairy
reports, you know, and upcomingwebinars too, because I think
there's just a wealth of contentthat you know we think is some
of the better content that's outthere too.
So this has been a greatepisode and, you know really
appreciate you joining us.
All right, thanks for tuning in.
As always, head on over tochangestateio or shoot us a note
(43:27):
on all the social media.
We'd love to hear from you andwe'll check you guys next week.
Welcome to the Changing State of Talent
Acquisition, where your hosts,graham Thornton and Martin Credd
, share their unfiltered takeson what's happening in the world
of talent acquisition today.
Each week brings new guests whoshare their stories on the
tools, trends and technologiescurrently impacting the changing
state of talent acquisition.
Have feedback or want to jointhe show?
(00:21):
Head on over to changestateio.
And now on to this week'sepisode.
Speaker 2 (00:27):
All right, and we are back with another episode of
the Changing State of TalentAcquisition Podcast.
Super excited for our nextguest, David Ellis, SVP of
Talent Transformation at KornFerry.
David, super happy to have youhere.
I'm going to start with asoftball.
Why don't you tell us a littlebit more about your background
and how you found your way toKorn Ferry?
Speaker 3 (00:48):
Okay, great.
Well, yeah, first of all,thanks for having me.
It's really, really nice to behere and chat with you guys.
I started my career actuallyback in New Zealand which
matches the accent better thanwhere I live now, which is here
in Boston and I was actuallyworking in a line manager role
in a bank and as part of that Ihad to recruit dozens and dozens
(01:12):
and dozens of people a year,and I was quite fascinated about
how kind of subjective that was.
This was probably, I would say,20 years ago and I thought, well
, this is an area of practicewithin HR that I can probably
get my brain into a bit more andwhere I can make a contribution
(01:34):
.
So I ended up leaving that role, going into recruiting in an
agency and did that for I don'tknow six months.
Then I moved into an in-houserole at one you know one of New
Zealand's biggest companies andI ended up, you know, being a
recruiting leader there.
And then, yeah, I just went offto Europe and worked in
consulting there in bothrecruiting and talent
(01:55):
development, and then I did mydoctorate after that.
So I took a bit of a break.
And then Corn Fairy just poppedup on LinkedIn one day and just
said hey, do you want to talkto us about a job?
That was 10 years ago and I waslike I don't think so, I don't
think I really want to.
No, but actually I'm reallyglad I did, because I just love
working here and I love theclients and love the people I
(02:16):
get to work with.
Speaker 2 (02:17):
No, that's awesome.
Well, I can say, corn Fairy ispopping up in our newsfeed quite
a bit too, and you know, one ofthe reasons we are excited to
have you on is, you know, youjust put out a, you know, 2025
town acquisition trends reportand you know, frankly, I thought
it was, you know, the moremeaty one that we've.
You know, come across too, andyou know some really great
insights.
So, you know, I was reallylooking forward to this
(02:39):
conversation, you know.
I think you know, maybe thebest way to start is with a
softball, david.
So in your trends report, youtalk about what TA is going to
look like in the future.
So maybe a good place to letyou wax poetic is what do you
think talent acquisition isgoing to look like in 2030,
david, yeah, and you thinkthere's a softball.
Speaker 4 (03:04):
I was going to say Graham, I don't think that's a
softball.
Speaker 2 (03:07):
Get your going to print this out too, so we'll
remind you in six years here, orfive years.
Speaker 3 (03:13):
I can't wait to hear what your other question is.
Well, you know, it's reallyinteresting because if you'd
asked me that a couple of yearsago, I might've given you a
different answer than I'm, youknow, than I, than I would give
today.
And, by the way, I heard youguys talking um about our report
too and giving it a bit of a,you know, a bit of a soft
critique, and I I really enjoyedthat.
(03:34):
So, if you know, if peoplehaven't heard that, then it
would be.
It'd be good for them to tohear that too.
But I do think there's kind of a, there's a sweet spot for
talent acquisition here where,you know, efficiency and
experience are moving in thesame direction.
Like that seems to be kind ofthe ultimate aim within at least
(03:55):
forward thinking TAorganizations today.
2030, I mean, I'll be a bitmore bold about it In 2030, it's
going to be at least, you know,50, 60% more automated than it
(04:15):
is today, and I think we'll seea lot of the processes that have
benefited so far from AI, fromautomation, especially in the
high volume hiring space, Ithink we will see them
incrementally, you know, creepover into other segments of
hiring and I think that you knowI'm so optimistic about the
potential that that offersorganizations and offers talent
because, you know, with AI'spotential around data collection
(04:40):
and the insights that can begleaned from that, like I said,
I'm just so optimistic aboutthat potential.
So hopefully that's a start tothe softball.
But I'm interested in what youguys think as well, of course.
Speaker 4 (04:55):
Yeah well, it's a huge question.
I had the same chuckle tomyself when Graham called out a
softball, because I was like boy.
I don't know how I would answerthat question.
It is a very big question.
I thought I have a couple ofintroductions we could go.
David, before we do that, Ijust want to gush on New Zealand
a bit.
For some reason, the universityis sending me New Zealanders.
(05:16):
I have another client with adifferent project who happens to
be from New Zealand.
I've been there twice andsomehow I missed identifying the
accent both times, but anyway,we're thrilled to have you on
the show.
Probably should have startedthere.
As it relates to this questionof what recruiting is going to
look like in 2030, maybe you canunpack something you said.
(05:36):
You said, and correct me if Imisheard you, but I think you
said that efficiency andexperience are aligned or
something, and this kind ofalignment is one way of thinking
about the future.
Did I hear you correctly and,if so, could you just say a
little more about those twoideas and how they relate?
Speaker 3 (05:55):
Yeah, of course, and thanks for the plug for New
Zealand.
I'm sure the tourism I don'tknow if they need help.
Yeah, yeah, we'll push up theprice of the airfares for me to
get home is what will happen.
But so my yeah, my point thereis that you know when, when this
kind of big AI, um, or at leastGen AI sort of blast and fuss
(06:16):
and buzz really came to the forerecently, I think a lot of
organizations were like, how canwe leverage this to do things
faster?
And, and now what we're seeingis more organizations saying how
can we do this better?
And those are, at least to mymind, separate but related
(06:36):
concepts.
Right, speed is one sort ofmeasure, um, and it benefits the
organization, of course, and,in some cases, the talent going
through the process.
But actually, I think the sweetspot for AI is where you
leverage that to elevate theexperience.
And you know, I heard a lot oftalk early on about how we can
(06:57):
either provide, you know, humanexperiences or high touch
experiences, or we can usetechnology more or AI more, and
I actually think that's notbinary like that.
I think the right mix of peopleand AI, sort of almost tailored
to the segment of hiring thatyou're doing, is I mean, I keep
(07:21):
using that word sweet spot, butI think that's the key and I do
think that's where this is goingright.
Organizations won't just focussolely on the efficiency play
anymore, even though that willremain important.
They'll figure out how to useAI to elevate the human
experience.
Speaker 4 (07:38):
Okay, that helps a lot.
I thought that's what you meant, but hopefully for our audience
just to have you expand on thatwas helpful, I think.
Well, you make a good point andit's one that probably our
industry needs to hear.
Hr, talent acquisition, aswe've said endlessly on this
podcast have historically beenseen as cost centers, which
takes us kind of maybe in anunshareable way to this idea of
(08:01):
efficiency.
So organizations and TA leadersare always being pushed to be
more and more efficient.
But I think what you're raisingis a very important point,
which is efficiency is a goodthing unless the model upon
which we're iterating was notvery good to begin with.
And so if the status quo is nottreating candidates very well,
(08:24):
not having a lot of humancontact or that human side to
the recruiter experience or tothe candidate experience, is
that something we want toiterate and be more efficient at
?
Is that a fair way of kind ofdoing that?
Or what would you say to thatassessment?
Speaker 3 (08:43):
Yeah, I think that's fair.
I do think the issue ofmeasurement comes up here too,
because how do we know whetherthis is doing what we want it to
do?
And so we have to figure outfirst of all, I think, what we
want it to do, because otherwiseit makes it harder to measure
the kind of the strategic impactof these kind of paths that
(09:05):
we're on around technology andinnovation.
You know we've got ourtraditional measures of talent
acquisition around time, costand quality, but this is an
opportunity here to help us doit cheaper, better and faster, I
think.
So it actually hits all ofthose notes, and I think that
(09:27):
the organizations shouldn't giveup until they have figured out
how to create that sort ofutopian state.
Speaker 2 (09:36):
Yeah, I want to first , I really want to touch on this
.
You know, measuring, roi andhiring, when you know, through
the lens of adding AI.
But before we do that, like youknow, let's unpack a little bit
more, david, this kind of ideaof you know the human touch
between, or balancing the humantouch between, ai efficiency
gains, you know, and not losingthat human touch.
(09:58):
Can you maybe give an exampleof let's talk about how
organizations are leveraging AIfor efficiency?
But what do we mean by notlosing that human connection
point?
Give me an example of how weroot that piece.
Speaker 3 (10:15):
I think some of it comes in from.
Possibly one of the bestexamples right now is in the
high volume hiring context,because that seems to be the
biggest sort of level ofadoption right now at least, you
know en masse by verydefinition of volume hiring,
where the conversational aspectof how a bot will interact with
(10:35):
the talent gives it a humanflavor, even though there's no
human sitting there or manuallyoperating this process.
There are process steps that arepassed between different pieces
of software and as thecandidate moves through the
(10:56):
process, and that has thepotential to feel pretty human,
as long as it's aimed at theright, you know, as I was saying
earlier, aimed at the rightsegment of talent.
Now, if you try that in anexecutive hiring context and you
run that process, you knowthrough a chat bot and you're
gathering information about themand scheduling interviews and
(11:17):
you know maybe even providingoutcomes of processes to the
talent, then that's going tofall flat on its face.
So in that context, you knowfor that segment, at least today
as it is today, you know youwouldn't typically put such an
automated process because itwould detract from the human
(11:38):
component that's aligned withthe expectations of that segment
, from the human componentthat's aligned with the
expectations of that segment.
And I do think that's reallyimportant here is that the
experience of the talent and theway that you are leveraging AI
and humans through the processthose things all have to sync up
.
Speaker 2 (11:53):
Yeah, no, I think that's helpful and maybe I'll
kind of take a step back on onepiece of you know.
Take a step back on one piece.
So you know, I think one of theexciting aspects you know here
is, you know, if we think aboutAI, you know most HR tech, you
know.
If we look through the lens ofHR technology, you know, and AI
and solutions, a lot of that isreally focused on identifying
(12:13):
patterns from existing.
You know, information orautomating pieces like resume
screening, you know, and that'sjust you know sort of the basic
stuff of, hey, I want to know ifsomeone lives in this state,
you know, has thesequalifications, and like A, if A
, then B, right, and like youknow what.
You know.
What gets more exciting is withgenerative AI.
(12:33):
You know.
We're quite literally, you know, creating entirely new content,
right, entirely new personal.
You know new personalizedcandidate outreach right, it's
interview questions that aregenerated based off of what
we're seeing on resumes or howpeople are answering questions,
and so I think just saying toDavid is like, hey, one of the
(12:56):
reasons that the human touchbecomes more interesting is
generative AI is quite literally, generating new, you know,
content.
Right, it's about creationrather than automation, and so
you know it's, it's difficult,right, but I think, you know, I
think if we think of AI asautomation yeah, there's really,
you know it's hard to see howthat becomes you know, has an
(13:18):
element of human touch or humanconnection to it.
It is.
You know, it is just, you know,moving people through a process,
right, whereas you know,generative AI just opens up a
whole new box of opportunitiesif implemented correctly, and
that's because it's no longerhey, we're, you know, drawing a
map to get from point A to pointB.
It is.
You know, generative AI isalmost, you know, the Sherpa
that is.
You know, drawing a map to getfrom point A to point B.
(13:40):
It is.
You know, generative AI isalmost the you know the Sherpa
that is, you know, walkingpeople through, you know, a
unique process to thatindividual.
Does that resonate?
Speaker 3 (13:49):
I mean, I do agree with that.
I think there's maybe there's abetter example too I can offer,
not than yours, but the one Igave which is, you know the
potential here.
So if I've got like, forexample, I've got a cohort of
professionals that I've hiredinto an organization Say, I've
(14:24):
hired 500 of something you know,whatever sales managers over
the past year here in the US howlikely that cohort of
individuals is to achieve mybusiness goals here, which are
probably, if it's a salespopulation, probably something
to do with growth, then AI givesus new potential to take, you
know, if these people haveundergone assessments.
It gives us potential to takethat data, roll it into
something which gives me acohort level view or an
enterprise level view.
If I've hired an entireenterprise of new people and
(14:45):
then marry that up against mykey business drivers At the same
time at individual level, itwill be able to, and it is able
to throw the new line managersof these people some insights
about them that previously wenever would have done because
hiring was always quite siloed.
(15:06):
It was, you know, we broughtpeople into the organization and
yay, our job's done, ourhandoff is done.
But now the potential goes somuch greater than that because
of what we're learning and howthat can inform development
plans, and I think that that, inits very essence, is human.
Even though it's not likesomeone sitting there at an
(15:27):
Excel workbook doing all thisstuff, it's being done for them
because the organizations haveinvested in that type of
automation.
But the experience for thepeople who are going through
that process I mean that'spretty impressive, as well as
the insights that can beproduced for the business
leaders.
That you could also argue helpswith human aspects of running
(15:51):
business.
I don't know what you guysthink about that, but that
potential is there right now.
Speaker 4 (15:56):
Yeah, I mean that's a fascinating topic, you know.
I think I know enough aboutgenerative AI to be dangerous.
But one of the big topics thatseems to come up in the general
case, not specific to recruiting, is the importance into
training, which in the generalcase for like a chat GPT means
(16:22):
ingest all the information thathumanity has created, that we
have on record and assimilate itand use that as a resource when
somebody asks you a question.
I guess it's a long way ofgetting to a question about
training.
As it relates to generative AIspecifically for recruiting and
(16:43):
maybe that's kind of related tomy question from earlier, Could
you say anything more about that?
How do we train a generative AIin a recruiting context to be
not only considering thebusiness objectives and all
those things, but to be actuallyrepresenting our company in a
human way?
Speaker 3 (17:02):
That's a really good question.
So and there I think you'reyou're talking more about like I
don't know, the out, the, thepersonalization of the outreach
and and the sort of experiencethat the person goes through
when they're going through theprocess steps.
Is that is that right, or Ithink?
Speaker 4 (17:17):
so yeah, well, I mean , yeah, like I said, I know
enough to be dangerous, butthat's the most obvious place.
But yeah, I'm just curious,like, is it much like the chat
GPT general case where we'rejust saying here's all the
outreach that we've done tocandidates in the past and you
can even listen to recordedinterviews, maybe, and here's an
example of really successfulrecruiting outcomes, and then
the AI compiles all of that andgets some sense of how it should
(17:41):
operate and behave on behalf ofyour organization?
Speaker 3 (17:45):
Yeah, I'm probably less expert in the area of how
you train these models.
To be honest, what we hear alot of clients talking about at
the moment is risk that iseither real or perceived around.
You know these machineslearning in a way which is
discriminatory.
I guess you could equally applythat to processes where the
(18:11):
outreach, the style of languageused, you know the very mode of
outreach, whether it's via textmessage, email, whatever
LinkedIn message is selected bya machine and is learnt through
the success that it's had andfailures that it's had, through,
you know, approaching similarpeople for similar roles through
(18:31):
those channels.
I guess that potential's allthere, right for it to get that,
you know, not necessarily rightall the time, but I think that
potential and that risk isprobably much less publicly
talked about than thediscrimination and bias sort of
fear that I think is quiterightly out there in the market
(18:51):
still.
Speaker 2 (18:52):
Yeah, and you know, I don't even think of it.
Like you know, again, similar,you know, not expert in training
, you know AI to do tasks, but,like you know, I think you could
argue, boy, if you just, youknow, pulled in data from your
college recruiting team, right,who went on campus to hire
engineers, like, boy, like theoutput of you know plugging that
you know sort of you knowbackdrop of data into, you know
(19:14):
your screening for candidates oryour outreach, might look a lot
different, right, and you know,and so you know, and that's
just the things that we don'tthink about.
Right, you try to, you know,grab a subset of data and like,
start working through it andlike there are a lot of you know
downstream, you know challengesif you're training, you know
training, your, your tools, youknow to make decisions based on
(19:34):
data and hopefully that's notwhat people are doing.
But who knows, right, I thinkit's just there's certainly
risks and I think arguablythat's probably why most of
these new jobs that we're seeingthat are going to be super high
in demand, it's these jobs likeethics specialists and AI
(19:55):
quality assurance analysts.
It's the roles that people thatcan recognize.
You know, we only used a smallsubset of data and like, hey,
the outputs on that is going tobe drastically different than
you know, if we looked at thingsfrom a different lens.
Speaker 3 (20:10):
Right, I know, I agree, and I do think that's why
the I do think that's why thehuman touch, quite unquote, is
important there as well, becauseanything, any, any use case
where we can gather insightsabout the experiences of people
who have been through ourprocess, is going to be useful,
as long as we don't blanketthrow it into, you know, or
(20:30):
generalize against it and thenthrow it into a population
that's just um, it's just notrelevant, or or it's it has some
danger attached to it becauseit's not, it's not even valid as
to how we're, like you know,approaching that population.
So I think, yeah, I do think,we're on the same page there.
Speaker 2 (20:46):
Oh yeah, and, like you know, I'll just say, like, I
think it's at least it'sexciting to me that, like one of
the most in-demand you know,quote unquote, we'll call it a
skill set right or capability is, like you know, looking five,
10 years out in the future, isjust critically thinking.
You know the critical thinkingright.
And like, boy, like, if you canrecognize that, like hey, this
might not sit right, like youknow, the smartest people in the
(21:09):
room are going to be the onesthat you know have the ability
to say, boy, something justdoesn't pass the sniff test.
You know, on this one too,right.
And like, hey, are we askingthis wrong?
And you know, I'm sure all ofus have.
You know, you know, used GeminiClaude, you know chat GPT and,
like, popped in questions and,you know, looked in an output
and just said, boy, like, areyou sure?
Are you sure this is what youmean?
(21:31):
And you know, a lot of times,like you're back, oh no, I'm.
You know, I missed looking atsomething.
You, your AI model of, missedlooking at something a certain
way.
And so I still love thisanalogy of AI being used as an
unpaid intern.
Boy, you wouldn't take productsto market that an intern
necessarily built withoutlooking at it first.
Last question on AI too is theysaid we'd get back to it.
(21:55):
So, ok, talk a lot about where,how AI is going to be, you know
, used, like some of the risks,like how does Corn Fairy?
Or how do we think about youknow measuring AI's true return
on investment in hiring?
Because, well, I think thereare a lot of different ways that
you know AI is going to beimplemented.
So, you know, how do we thinkabout measuring AI's true impact
(22:18):
on the hiring process?
Speaker 3 (22:23):
measuring AI's true impact on the hiring process.
Yeah, I think we can point ourmeasures at the process itself,
which is, you know, I kind ofspoke a bit about this earlier
around time, cost and quality.
How fast are we doing it, howmuch is it costing us and what's
the quality of the experienceas well as of the hire that you
make?
And so that I mean that's thosemeasures can be a baseline, can
(22:45):
be taken pre-AI and thenpost-AI, again measured, and
then looking at the delta.
I think the other part which iskind of frankly, a bit more
interesting to me is well, it'stwo things.
One is and we're helpingcompanies think this through
right now right, this is, thisis a question that people are
coming to us with, and it kindof covers a couple of your
(23:07):
questions.
One, what's, what does the taorganization look like of you
know, and at the three-year mark, at the five-year mark, and
then second, because part ofthat answer is around more ai.
The question is, then is then,well, how do we measure success,
how do we measure return forour business on what we've done?
And so, yeah, this is where itgets interesting to me.
(23:30):
Why did you do it in the firstplace.
Is it because someone in theC-suite said there's a whole lot
of talk about AI, you need todo something about AI?
Well, I do think we've gone abit beyond that.
I do think it was a lot of that,you know, when this whole thing
kind of blew up and the measurethere should then be sort of
attached to higher level metricswhich are connected to the
(23:51):
strategy of the TA function.
What is the function trying todeliver this year and how does
that support the broader kind ofpeople strategy of the
enterprise?
And so measures, you know, Ithink at least my view of this
is that those measures should inpart connect back to those
higher level measures, um items,strategic items, because
(24:11):
they're the ones that we'vealready decided are going to be
the sort of strategic pillars ofof the business and of the
people strategy.
I do think that's, I do thinkthat's part of this, and I also
think you know we're we'reworking right now on a um, a TA
professional of the futureprofile, cause we're we're quite
forward thinking, like you guysare right, like we're we're
(24:32):
always thinking about future,and we've got this research um,
the corn fairy institute, which,um, they kind of powered the
report that you, you know thatyou uh referenced at the at the
top of the top of this episode,and so we are actually putting a
point of view together abouthow the ta professionals sort of
role can change throughout thisevolution as well, and what we
(24:56):
think that might look like atthe 2030 point.
And the reason I'm raising thatis because I think that should
be one of the measures.
How has AI enabled us to changethe role of the people and
elevate the role of the peoplewho are executing on talent
acquisition?
For us, that's a people-focusedsort of measure and metric that
(25:21):
also speaks to the way that weorganize ourselves to get the
job done.
So I feel like I'm ranting abit here, but does that at least
partly give a view on what Ithink we should be measuring?
Speaker 4 (25:34):
Absolutely.
I think you're speaking to twoguys who think of it in similar
ways.
Yeah, we can take the mostbasic approaches you outlined at
the beginning, which is, usethe same old metrics and see how
they change pre and postimplementing AI.
If we could draw a line in thesand so specifically and that
(25:54):
may be hard, but I love whatyou're saying about.
Sure, we should probably dothat, as some nod to the past,
but this is such a sea changetechnology that has the ability
to impact organizations wellbeyond candidate experience that
we probably need a whole newsuite of metrics.
Some of them are probablyquantitative and others might be
(26:17):
more qualitative, but we preachto our clients all the time,
and I'm sure you guys have asimilar perspective that I think
it's very easy as TA folks toget lost in our silos and just
be so focused on the job at hand.
I have to fill this role today.
I have to get this role today.
I have to get this seat filled.
The C-suite is barking down ournecks about this.
(26:38):
We got to do it faster andcheaper and all these things.
And these things are all fine,of course, but it loses sight of
the whole purpose of why do wehave a talent acquisition
function in the first place,it's not because we want to get
really good at screening throughthousands of candidates I mean,
sure that might be a byproduct,right?
The reason we have it isbecause we're an organization in
(26:59):
the world that exists for thispurpose, with this mission, and
we need great people who arealigned with that mission to
deliver on it.
And so I think maybe that'swhat you were speaking to.
Hopefully I didn't misrepresentit.
One thing I have a question Ihad is and maybe you know, being
relatively close to the AI asit relates to recruiting.
You know anyone who's messingaround with like a chat GPT has
(27:21):
had.
Most people, I would say, haveprobably had like a wow kind of
moment with it, just like youknow, I've shared some on the
podcast in the past.
But you can ask it to dosomething that would require all
of your day, or maybe severalof your days, and it will
deliver an output that's maybe95% as good as what you could
have done in a few minutes, andwe can think of lots of
(27:42):
different examples of that, butit is stunning when you see what
it can do.
Do you think in your experiencethat there are any
organizations delivering wowmoments that are being powered
by generative AI currently?
Or do you think, as an industry, the recruiting function is
still having quite gotten thereyet, where it's not quite
(28:02):
delivering those wow experiencesin a recruiting context?
Speaker 3 (28:05):
I love that question, such a thoughtful question.
So I think, yes, there havebeen wow moments.
I think some of them came earlyon and possibly some of the
ones you were alluding to aroundokay, this thing can actually
generate a job description forme, it can generate interview
questions for me, it canprobably come up with a
simulation exercise for me.
(28:26):
I mean, how valid those thingsare is a whole nother.
But if it's 95% there to yourpoint and all I have to do is go
through and sort of um, youknow, correct it, add to it
whatever, then great.
I think you know my view onthis is that a lot of these wow
moments around what it cangenerate in terms of content are
(28:47):
yet to come.
I mean, I just love some of thememes that it can generate.
I know that's not related torecording, but when I first saw
the potential of that, I waslike, oh my goodness, and it got
me thinking okay, this is whatyou can do today.
What are you going to be ableto do in three years' time?
And when we think about thekind of holy grail of judgment
(29:12):
and empathy and these sort ofhuman qualities that we're told
that AI can't actually help uswith?
And I was sitting, I wassitting in a bar the other day
you might want to edit this outwith um.
He was having relationshipproblems, he goes.
(29:32):
I asked chat gpt what to doabout this and chat gpt told me
to ask you, david, wow.
So that's it.
Hang on a minute.
Maybe this thing is smarterthan I thought, um, but I think
and that's why I think, some ofthe wow moments are like, some
of them are going to be likesmaller wows, and some of them
(29:52):
we just can't even imagine whatthey're going to be, because,
yeah, I mean, we have.
We have to imagine this thing'sgoing to be even bigger than
what we've seen so far, don't we?
I mean, what do you?
What do you guys think?
Speaker 4 (30:05):
Yeah Well, I mean it was interesting hearing you talk
about some of those wow moments, because we want to measure
things.
You know I'm a scientist bytraining.
Talk about some of those wildmoments because we want to
measure things.
I'm a scientist by training, soI certainly resonate with that.
I love measuring things, but mybiggest wild moments with
generative AI have not beenmeasurable.
I mean they have beenmeasurable, I suppose, but when
(30:26):
I asked ChatGPT to create 200multiple choice questions based
on a dense pharmacology textbookfor my partner and it spit it
out in 20 seconds, I wasn'tsitting there thinking, boy, let
me crunch the numbers to seethe ROI on this.
I paid $19.99 and it saved meeight hours and here's my hourly
(30:46):
rate and we could have donethat right.
But it's almost beyond thequantitative at that point when
you have some of these wowmoments.
So I just think it's reallyexciting to hear that some of
them are happening already andeven more exciting to see what
future wow moments might looklike in a recruiting context.
Speaker 3 (31:01):
I agree, and some of the ones that you can use that
are embedded in what you useevery day, like the co-pilot in
Microsoft, and you know what itcan do within Excel, what it can
do to generate PowerPointslides out of Word documents,
you know, like that.
I think some of that stuff waspretty wowing.
Well, it's actually it wasquite wowing for me when I first
(31:22):
used it because it didn't.
It didn't do what I wanted itto, but I think that was more
user error, but but it's goingto get better at that and so
maybe that will produce some ofthe wows.
But okay, this this thing's waybetter at doing this than I,
than it was last time I did it ayear ago, or whatever.
Yeah.
Speaker 2 (31:37):
Yeah, no, I think that's just absolutely spot on
and like I think we can umcontinue that talking point,
these talking points, for awhile.
I want to want to pivotslightly, david, just because
you know I don't want to wrapthis episode.
We're talking a little bit moreabout, you know, skills,
skills-based hiring.
You know, I think you know weall understand that.
You know AI is creating a wholenew you know generation of
(32:00):
skills that are going to berequired, only generation of
jobs.
You know that people are goingto be hiring for.
I'm just curious, you know,where do you think you know
skills-based hiring?
You know this concept that hasbeen talked about ad nauseum
over the last three, four years.
Do you think this is part ofthe impetus where we're going to
see a real changing point onskills-based hiring taking over
(32:24):
the traditional resume?
Do you think AI can speed thatup in the sense that, hey, we're
really starting to look forvery, very specific, very, very
specific skills that are relatedto new roles that you know
arguably didn't exist a year ago.
Right, like, where do we seeskills-based hiring go going
over the next, you know, half adecade?
Speaker 3 (32:43):
Yeah, that's a yes from me, by the way.
I do see what you justdescribed, so this has been so
interesting.
We do talk about this in thereport, as you said, because the
progress on this has not reallymatched the level of ambition
that organizations had in thisarea, and I do think that we
(33:03):
will see this speed up a littlebit, especially in organizations
that aren't afraid to get inand make some changes.
We talk about progress overperfection.
In fact, perfection is theenemy of progress, and so what
can you do now?
What can you do today toadvance your sort of journey
(33:23):
towards this North Star?
And one of the issues that I seeis organizations that are
really big and complex struggleto get the buy-in from.
You know, when we think aboutlarge enterprises as an example,
they struggle to get the buy-infrom the hiring manager
community, which is where this,you know, lives or dies, because
you might encourage them tofocus less on professional
(33:46):
qualifications, where they'reless relevant, or years of
experience in a role, wherethat's less relevant.
And then you know, then thecandidate gets to the hiring
manager interview and well, howmany years did you spend over
here?
And so they just take adifferent lens, which is a bit
more traditional.
So I think pace here can begained from organizations that
(34:10):
address some of those kind ofbuy-in change management aspects
.
Ultimately, I do think you knowif I conceptualize the resume as
a marketing tool, it'ssomething that I can pull
together to sell myself to you,my experience, my whatever my
skill set to you.
And AI potentially enables aprocess which is a bit more
(34:35):
objective because it can bothinfer skills that I've got but
also match me to roles.
I mean, these are well inproduction now, these features,
and so it's almost a case of youknow why hasn't the story here
quite matched the capability?
And I think in someorganizations it has, but others
(34:57):
have got a long way to go.
And it's to those ones we'rekind of saying hey, just make
some, take some steps here, umand and the rest will you know,
and then you'll be able to takemore steps because those ones
will become embedded.
That's kind of how I've beenthinking about this.
But yeah, what do you guysthink?
Speaker 2 (35:14):
Yeah, no, I mean, I completely agree, and I think
you know, hopefully we see moremaybe publicity is a word I'll
use around you knoworganizations that are investing
in we'll call it.
You know skills-based workright or skills-based you know
programs and so, like you know,or skills-based, you know
programs and so, like you know,you see stuff.
(35:35):
Like you know Amazon, you knowinvesting.
You know, arguably, you know,billions of dollars in
upskilling their workers.
You know, and you know, at&tinvesting.
You know a billion dollars intheir future ready program,
figuring out what skills of thefuture are going to look like,
and so I think, like, hopefullyit was as more and more of those
programs you know kind of frontand center, amazon and you know
(35:57):
and AT&T are not saying, hey,like we're going to invest on
sending people to, you know, geta bachelor's degree from you
know a certain university.
Or hey, like, let's make surethat we're investing in, you
know, getting people to do acertain job for the next 18
months.
You know it really is.
You know companies and smartcompanies and large ones are
investing in skills, reskilling,upskilling, and I think that's
(36:17):
a logical, you know connectionpoint to boy, like, if companies
are investing in certain skills.
Why aren't we, you know, as anindustry, investing more in
hiring based off of you knowcertain skills?
Skills-based hiring too.
Does that make sense, david?
Speaker 3 (36:32):
Oh yeah, it does, and I think you know.
The other question is how do weso, how do we define this whole
skills-based hiring anyway?
Is it connected to askills-based organization sort
of programmatic approach?
And, at the same time, what isa skill?
Because you know, I mean, is askill a technical skill?
Is it a behavioral competency?
And our view is that, actually,if you just focus on the
(36:55):
technical skills, then you'renot going to find the
performance sort ofdifferentiation factor that
you're looking for in selection.
If you've got five people whocan all use Excel, how are you
going to figure out which one'sgoing to do the job best?
And it's to be found in theirbehavioral competencies and
their identity, their personalidentity.
So yeah, I think there's a lotof questions that some
(37:20):
organizations have been able toanswer, or at least taken a view
of, whereas others are a littlebit potentially just bogged
down by just the enormity ofthis.
And I think Graham, your dog,agrees.
Speaker 4 (37:43):
Very much, very much.
It's possible.
Normative, um, you know of this, uh, and I think graham, your
dog, um, agrees very much, verymuch possible as well.
No, david, I know we're shorton time, but there's one thing I
really want to get yourfeedback on.
So you were talking abouthiring managers, but how
important it is to bring hiringmanagers along for some of these
evolutions that we're seeingwith AI and skills-based hiring.
Just anecdotally, can youcomment on why you think it's?
What is the hesitation withhiring managers to adopt
(38:04):
skills-based hiring?
Is it purely risk aversion?
We've been doing this a certainway for a long time.
This candidate seems to havegreat skills, but you know what?
They don't have a lot ofexperience.
And you're telling me I'm goingto hire someone for our
organization that does not havea bachelor's degree potentially.
And what happens if it turnsinto an unmitigated disaster for
the organization?
I'm the hiring manager I was incharge of this.
(38:26):
Is that as simple as it is?
Or do you think people havevalid for lack of a better term
not risk associated, but justthey don't trust or don't fully
understand skilled space hiring?
But it's not purely abouttaking the risk averse approach.
Speaker 3 (38:44):
I do think some of it's fair.
I do think some of it ismisunderstanding or potential
underappreciation of thepotential and what I get, what I
get from, like, what's in itfor me, um, so we can help tell
that story better.
And then I think, yeah, it's,it's I do.
I do think in some cases it's abit more complicated as well,
(39:08):
because maybe I've just got adifferent opinion as a hiring
manager than whoever's beencreating this program about what
good looks like for thisposition.
Maybe before I worked here Iworked in the same job, you know
, five for five, exactly thesame job for five years in a
competing firm.
What I do think there's somevalidity in is the notion that
(39:30):
actually skills are bothacquired and developed.
So the proficiency level goesup from experience.
So if I, if I learn a skill andI don't use it, have I learned
the skill but I practice itevery day, there's a better
chance that I'm not justmastered it but I've been able
to build proficiency.
So there is some.
I think there's some validityto the.
(39:51):
So there is some.
I think there's some validityto the, you know, to the sort of
tenure argument, but at thesame time I yeah, I think this
is more about saying, hey, weneed to focus on skills more
than we do some of these otheritems which are perhaps less
relevant to performance, whichis, you know specific tenured
experience in the same job orprofessional qualifications,
(40:13):
unless you're a doctor or youknow whatever, you absolutely
what these roles that actuallydo require you to have
professional qualifications.
Speaker 2 (40:22):
I think that's great.
Well, you know, I think wecould keep this conversation
going for hours.
David, I'm going to ask you onequestion that you know, I think
is just top of mind for me.
You mentioned doctors.
I saw a quote earlier this week.
It's like oh, why aren'tdoctors working remote?
Well, I think there's anobvious answer for that one,
hopefully.
But I'm curious, hey, withstill a lot of chatter,
(40:43):
especially over the last sixweeks, on this future of hybrid
work in office versus remote,just curious, has hybrid work
reached its final form?
Or how is hybrid, you know, orthis, you know, hybrid 360 model
sort of evolving, you know,with the workforce of the future
?
Speaker 3 (41:02):
Well, I love listening to you guys talking
about this I don't know if itwas on the most recent episode
or another one where you werekind of I think one of you was
like, look, if I, if I, hear onemore sort of argument against
hybrid work.
You know, my view of this isthat.
So what we did in the report iswe said that hybrid isn't just
(41:25):
about, you know, whether I'm inthe office or not.
Actually, it's more towardsthis concept of work-life
integration, where I work when,where and how suits me to
produce what I need to produce,for me to get the job done.
And I heard you guys talkingabout, well, what happens if,
with mouse clicks as a check oflike, I've just never subscribed
(41:45):
to that whole notion where wehave to monitor people all the
time.
And I think there's a problem,you know, in professional,
certainly in professionalsegments, where if you don't
trust somebody to get the jobdone and can't measure them by
the work they do, then that's abigger problem than you know.
Anyway, not to re-have theconversation that you had the
(42:08):
other day, but yeah, I justthink the potential here to
allow people to work when, whereand how the best suits them
should be maximized because itmakes business sense.
It makes business sense becauseit's what the talent wants and
it works for them in their lives.
So, yeah, that's kind of myview on that and I think what
(42:31):
we're trying to get through inthe report as well.
Speaker 2 (42:37):
Yeah, no Well.
I think that's kind of my myview on that, and I think what
we're trying to get through inthe in the report as well yeah,
no, well, I think that'sfantastic and I think that's
probably a logical place for usto put a pin in this one.
Last question, david, it's easy.
Where can people find youonline?
Speaker 3 (42:44):
Where can they find me online?
Well, they can find me onLinkedIn and they can find me on
, um, they can actually find meon the corn ferry website as
well.
Um and uh, yeah, I've, I'vereally enjoyed the conversation
today.
Thanks for having me on and I,um, yeah, I hope you hope you
guys, uh, and and whoever'slistening got something out of
it as well.
So, yeah, thanks again thanks,david.
Speaker 2 (43:05):
it's been great, david, and you know we'll make
sure that we're, you know,linking all of these corn fairy
reports, you know, and upcomingwebinars too, because I think
there's just a wealth of contentthat you know we think is some
of the better content that's outthere too.
So this has been a greatepisode and, you know really
appreciate you joining us.
All right, thanks for tuning in.
As always, head on over tochangestateio or shoot us a note
(43:27):
on all the social media.
We'd love to hear from you andwe'll check you guys next week.
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