October 15, 2025 • 41 mins
Fresh off conference season, Mark Tomasino and I break down what we actually saw on the show floor. The net out: If your differentiator is "AI-powered," you've already lost.
We dig into the feedback loop nobody's talking about: job seekers using AI to mass apply to jobs, employers using AI to filter the noise, and the whole apparatus just... creating more work for all.
Mark walks through his six-question framework for cutting through vendor pitches, why context matters more than the algorithm, and why the most interesting solutions may come from founders who've never worked in traditional TA.
Also: why everyone's fishing from the same data ocean, what skills taxonomy actually means (and why you're probably not doing it), and Mark's contrarian take on why AI won't take our jobs – but it will expose which work shouldn't have been ours in the first place.
If you're a TA leader trying to figure out where to put your AI budget, this one's for you.
Talivity helps employers see what others miss – brand reputation risks, workforce shifts, and the real value of AI – so you can make smarter decisions and achieve measurable hiring outcomes. Learn more at Talivity.com.
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
SPEAKER_00 (00:00):
Welcome to the Changing State of Talent
Acquisition podcast with yourhost, Grant Thorta.
Each episode brings youunfiltered conversations about
the tools, trends, andtechnologies impacting the
future of talent acquisition.
Our guests share their storieson what's working, what's hype,
and what's actually helpingcompanies hire better and grow
(00:21):
faster.
Have feedback or want to jointhe show?
Head on over to Telivity.com tolearn more.
But now we're on to this week'sepisode.
SPEAKER_02 (00:30):
All right, and we're back with another episode of the
Changing State of TownAcquisition podcast.
Slightly different format withsome exciting news over the last
couple weeks with Change Daygetting acquired and joining the
Recruitics family.
Our next guest is Mark Tomasino.
Mark, I've known for the betterpart of boy, uh two decades at
(00:52):
this point.
And Mark is now in his role asdirector of partnerships.
Uh we'll call it specialprojects still over at Telivity.
Mark, we'd love for you to sharea little bit more about your
career journey, maybe some ofthe pivotal moments that you
know brought you into theTelivity or changed a team
across the board.
SPEAKER_01 (01:10):
Sure.
Thanks for having me, Graham.
Honored to be a guest finally onthis pod.
And um, yeah, a little bit aboutme is I've been in the industry
since 2006, first job out ofcollege.
I made 125 cold calls a day atCareer Builder, and I managed to
not quit or get fired, which ismore than most people could say
at the time.
(01:30):
And uh, you know, so I ended updoing all levels of sales, you
know, from SB up to the largestglobal employers.
When career builders startedbuying a bunch of different
software and technology to bringto market, I was one of the
early solution architects, uh,which is Graham, how you and I
know each other, of workingclosely, of uh bringing that to
(01:50):
market and working withdifferent employers, tech
stacks, uh, which really piquedmy interest into the broader
world of HR Tech andintegrations.
We were acquired by privateequity and 2017.
I then was kind of an internalsales consultant between the
different portfolio companiesthat are owners owned.
And finally in 2020, I becamevice president of partnerships,
which I oversaw our resellerintegrations and agency partner
(02:15):
programs amongst some otherthings, kind of building on all
the things that came beforethat.
I at the time I had atwo-year-old son and I had a
planned sort of career break anduh to spend more time with my
growing family.
So took a break from theworkforce for a few years, but
uh Graham, you know, you atChange State uh brought me over
(02:38):
to help on a consulting basiswith some HR Tech partnerships.
And no surprise we got along sowell and enjoyed working that we
flipped that to full time.
And now here we are, havinggrown change state and now
joined the recruits family andover at Telivity.
SPEAKER_02 (02:51):
Yeah, well, you know, I think you're
underselling, and we're gonnaget into it, you know, your
background and knowledge of youknow the HR Tech space, you
know, in particular in thepre-hire world.
And you know, I I always youknow talk about what gives me
energy, and I think there's alot of problems you know to
solve standing in front of awhiteboard.
And you know, I think that'skind of where we cut our teeth
(03:11):
in general.
But you know, we're gonna divedeep into HR Tech.
So I guess a great startingpoint, Mark, maybe is we just
got back from HR Tech, and Ithink it's fair to say we
probably couldn't walk uh youknow uh 10 feet without seeing
another uh AI-powered booth.
And you know, you were kind ofmy co-pilot through all of this.
(03:32):
So, you know, what was yourbiggest takeaway from HR Tech
from the conference?
What kind of stood out as maybeyou know, genuinely, you know,
innovative versus just uh well,we'll call it well-marketed, you
know, noise?
SPEAKER_01 (03:45):
Sure.
Uh yeah, I mean, you you see thebig players, although I would
say the big players have asmaller presence there than
maybe historic, you know, andI'm talking like the big HCM
systems, but what you did seewas a ton of startups.
So like they have a wholestartup area that was just you
know folks that have pointsolutions trying to solve very
(04:06):
specific problems in the space.
So that was interesting.
So there's a lot of emergentplayers.
I would say like a really commonone that I was almost surprised
to see how many were you knowtrying to go about solving the
same thing were these AIinterview tools.
Um, and so you know, we canprobably get into the nuance of
that a little bit and somethings I learned, but you know,
doing being able to do quickside-by-sides of companies that
(04:29):
are essentially going aboutsolving the same problem in the
same way, but trying to peelback that onion and try to
figure out who's who and what'swhat and who who has a novel way
of solving this problem andwho's really just putting a
wrapper around the same back-endstuff.
SPEAKER_02 (04:43):
Yeah.
Well, you know, I I'll also sayin the startup pavilion and you
know, just in booths in general,maybe you maybe you saw this
too, maybe you didn't.
You know, I think that you know,we saw a lot of new solutions
and you know, coming fromfounders who maybe haven't
worked in traditional TA, right?
And so, you know, we talk a lotabout, you know, everyone talks
(05:05):
about Tesla, you know, comingfrom outside of the you know,
the vehicle industry, Uber, youknow, people coming from outside
of you know the transportation,like disrupting industries as a
whole.
And like, you know, I do hope,and I think there's you know,
some very good, you know,there's a lot of energy behind
you know, people coming fromoutside TA.
And I'm hoping that you know westart to bring more fresh
(05:27):
thinking into you know TAinstead of some of the recaps
that I read where it was, hey,yeah, guess what?
Like someone bought someoneelse.
I realize that might be ironicgiven that we just got acquired,
but hey, like some big, massiveapplicant tracking system um or
HRS system just bolted onanother tool.
So, you know, I'm justwondering, like, fresh thinking,
(05:49):
do you feel like there's alittle bit of a different energy
in HR Tech and you know in theindustry than maybe in years
past?
SPEAKER_01 (05:55):
Yeah, I would say that.
I mean, you know, it's it's adouble-edged sword.
You have this emergence of AI,and so everyone's gonna use the
same words, and you gotta try tofigure out who's actually going
about solving the problems.
And you'll probably hear merefer to this several times on
the pod, but it it all goes backto problems.
And so the founders that I'menergized by or the products
(06:18):
that I found most interestingare the ones that understand the
problem that they're trying tosolve, and they can articulate
it when asked directly.
What problem do you solve?
What are the use cases thatyou're finding the most success
with clients?
And they know exactly whatthey're doing.
You know, almost like startingfrom first principles of hey,
you know, what's really theproblem?
(06:39):
What's what's the underlyingproblem here?
So to me, that's how you youkind of cut through the noise.
And the inverse of that would beif you talk to somebody and it
sounds like they just keepadding features so they can keep
up on an RFP checklist.
Oh, yeah, we do this and we dothat, and we do this over here
too.
And it's almost like you looklose sight of like, yeah, but
what what do you stand for?
Like, what's the ethos here?
(07:00):
What what's the what's thefundamental problem that that
you're trying to address?
Because sometimes it can feellike our fundamental problem is
we need to win more RFPs, or weneed to we need to attract
investors.
So we need to use these words orsay that we can do these big
things because this othercompany said they could do those
and they got a billion dollarvaluation.
So yeah, I always be rooted ininto the problems, and that's
(07:21):
that's what differentiates thesolutions for me, personally.
SPEAKER_02 (07:25):
All right.
Well, you know, let's let'sstick on this problems path
because you know I want to getinto our marketplace a little
bit, but you know, I think thatyou know you've kind of you know
framed this in a unique way forme, right?
And that's as we're walkingthrough these booths, we're
talking to a lot of vendorstalking about how we're gonna
use AI to solve problems.
And I think you know, there'sprobably a little bit of irony
(07:46):
in here in that you know, jobseekers are you know using AI to
apply to a lot more jobs.
And you know, I think you know,we've created a lot of volume
problems for companies in thesense that we see a large, a
much larger volume of candidatesor applicant flow than we have
historically.
And so, you know, on some level,I think you know, we're using AI
(08:10):
to solve problems that have beencreated by AI.
Yeah, but what's yourperspective of you know the
bigger problems that you'rehearing companies solving coming
out of HR Tech or trying tosolve maybe?
SPEAKER_01 (08:20):
Yeah, I think you're you're touching on something
there.
I call it the AI arms race andrecruiting.
But yeah, let's talk about theemployer side.
So, you know, pretty quicklyafter ChatGPT rolled out, people
started using it for things likeyou know, drafting emails and
letters and in mails and thingsof that nature, so candidate
(08:41):
outreach.
And then they thought, oh, youknow, this could probably make
my job description a little morerobust and you know fleshed out.
So now it's it's rewriting thejob descriptions.
And oh, well, it also can helpme filter and match, although
those tools have been around fora while, but you know, they've
definitely grown theirprovidence and uh and more so.
So I don't even have to like gothrough these resumes per se,
(09:03):
because now I can have a toollevel screen and float the best
ones towards the top.
And you know, that's allcelebrated in the name of
efficiency, but you know, it'snot not fun when the other side
has it too.
Um, which then you have then youhave uh you know candidates who
are like, well, hey, I can usethis, I'll rewrite my resume to
perfectly match this jobdescription.
(09:25):
Okay, and you can send that out,you know, help help me uh answer
these interview questions,right?
And those tools are just gettingmore and more sophisticated.
And yeah, send off my resume.
Oh, congratulations, I youapplied to a thousand jobs while
you were sleeping, right?
And now now you do have uh notonly a volume problem, but you
have a noise problem.
(09:46):
Because what is an algorithm, amatching algorithm, no matter
how sophisticated it might be,what's it supposed to do when it
has you know hundred resumesthat were tailor-made to the job
description if that is theunderlying matching criteria,
right?
It's like you know,congratulations, here's you
know, a bunch of hundred percentmatches.
(10:07):
Well that's no longer useful.
And now you have way more to gothrough.
Okay, so what do you do?
Well, now you have a wholecategory of solutions, also AI,
that is is going to interviewthem.
Because hey, I I don't haveenough information and context
to f to know who of this are areactually the best.
(10:28):
But you know what, you know whatI could do is if I had an
ability to have a conversationwith them, I can get more
information on that candidate.
And now you know then I couldfacilitate a better match.
So I'm trying to get moreinformation and context by way
of conversation interviewing.
But well, shoot, my stack ofapplicants is a thousand, two
thousand tall.
(10:49):
I can't interview all thosefolks.
Ah, but AI can.
Okay.
So now we're gonna feed all ofthese candidates who may or may
not deserve or should beinterviewed, but we're gonna
funnel them through this AIinterview tool.
Um, and on the other end, thehope is that now I've I've
really sifted through and I'veI've truly identified, you know,
the best quality and mostengaged and the best fit for our
(11:11):
organization and for this role,you know, by by way of this.
But like I just described awhole apparatus that like just
wasn't there what, three yearsago?
And that's a lot of noise tosift through.
And it makes me question, belike, wait a minute, what are we
doing here?
SPEAKER_02 (11:26):
Well, I guess you know, it reminds me of you know,
maybe a decade ago, and I thinkyou heard this in our past life.
Like, you know, first theproblem was uh, hey, a job ads,
job ads are bad.
You know, so your job ads arebest case, that's a lie.
You know, resumes are are poor,you know, people put you know,
arguably some some fluff inresumes.
So, you know, resumes and andjob ads are both, you know, two
(11:46):
false documents.
You know, so you're matching onelie and with another lie.
And, you know, so it's not asurprise that our matching
algorithms are on, also.
And I think, you know, thiswhole idea of context as we're
getting into you know movingpeople through the process is
one that I don't know, I'mprobably more keen to follow a
little bit too, because I thinkyou're right that like, you
(12:06):
know, it is becomingincreasingly more difficult to
match a generic is the word Iuse, even though I I hesitate,
but a generic profile with ageneric job description and try
and figure out of a thousandpeople that come through, you
know, who really is going to bethe best fit.
SPEAKER_01 (12:21):
Right.
Yeah.
You know, another term I Idiscovered yesterday, and it was
a real uh brilliant article, butit it's basically this age of
quick hit video and algorithmgenerate content and in gen AI.
It's really compression and it'slike a compression of the
average.
So, which just makes everythinglook the same.
(12:42):
And you get like changeblindness.
You can't, you know, you'retrying to compare two things
that are like really, reallysimilar, sounding, looking, etc.
And the you know, the highfidelity, the signal, like the
true uniqueness, the raw file ormaterial, that that's what
doesn't really exist anymore.
Which, you know, we can talkabout that later.
I think that that might be thecounter uh example
(13:04):
differentiator that thatemployers and you know really
anyone can use to stand out.
I would love to dig in oncontext a little bit, if we can.
Yeah, yeah.
Well, where where do you want totake it?
So this was my this was my bigaha doing a lot of side-by-side
comparisons.
And as you can tell, I'm alittle skeptical of the AI
interviewing tools.
Not that they can't work reallywell or be, you know, super
(13:25):
useful.
It's just it's the emergence ofthe solution seems to be trying
to combat a problem of our ownmaking, of you know, AI
everything to you know, createthat volume problem that you're
talking about.
But I do see these things youknow having real great potential
and can help a number of ourclients who we interact with
every day, you know, get throughthat tunnel and and find the
(13:47):
best quality candidates.
However, context is really whatmatters.
And you know, I demoed probablysix to eight of these tools at
the expo, and some of them it'syou know, you upload your job
description, and then fromthere, the AI interview tool
(14:08):
will analyze that and come upwith interview questions that it
can ask to susto if somebodymatches the qualifications to
the job.
And so then they're gonna havesome either plain voice or a
video avatar.
Uh, you can kind of pick yourflavor there, and then that's
what's gonna have theconversation with the candidate,
and they're gonna ask thesequestions and hopefully without
not too much lag time, andthey're gonna ask those
(14:29):
questions.
And now you're basicallyconducting that first screen
interview with the candidate,uh, but uh AI is doing it
instead of a recruiter, so youcan do thousands of these a day
instead of you know dozens,maybe.
So that's the solution, but thecontext that is fed into them is
what I think is thedifferentiator.
So some tools is like upload jobdescription and go.
(14:52):
Some of them are well, we startwith the job description, but
look, now you can toggle on thisor enter this, you know, you can
you can edit, you know, and kindof create your flow after doing
it, which okay, that's that'sgood.
Or better at least.
But I think the best solutionsand the ones that are most
interesting to me are the onesthat take in the full context of
(15:14):
the organization.
Meaning, like, what if you couldupload all of your you know,
recruiting best practices thatyou've established, and you
know, the interview questionsthat you have said, you know,
like that that's what we like toask, or your um what you
consider to be your employerbrand differentiators, um your
(15:36):
employee handbook, your corevalues, sample candidates who
have worked out really, reallygreat in your organization and
you know what what theirbackground is, the conversation
that you have with the hiringmanager in your intake meeting.
Okay, now we've now we'veincluded a lot more context.
And whether it's a human or AI,they're gonna do a lot better
because now they have morecontext to conduct uh a better
(15:59):
interview and to facilitate abetter match.
And so I think that's it, is youhave to you if you're going to
use AI as a layer to try tospeed up things that humans you
know have been doing or could bedoing, the the underlying
context that they rely on issuper important.
But the catch is that is nodifferent.
(16:22):
Whether it's a robot doing it orif it's a human doing it,
everyone performs better withmore context.
Yeah, I'll pause there.
SPEAKER_02 (16:30):
Well, and I have a few different ways I want to
kind of crystallize this, Mark.
So, first, goals a better match,you know, we're talking about AI
interviews in you know, inparticular, and like, you know,
I would argue that that's youknow, by some definition, a new
category of solutions, right?
And I would also say that like,you know, the way we're thinking
(16:50):
about our uh Telity marketplace,and we'll maybe talk about this
a little bit, is you know,really shouldn't be focused on
what category does somethinglive in, but what problems are
we trying to solve?
And like, you know, hearing youdescribe feeding in all these
additional contextual points,you know, your brand
differentiators, your handbook,you know, your green breast
(17:11):
practices and so on, like, youknow, it almost feels like AI
interviews, you know, kind ofblend in with assessments.
And, you know, maybe it's uhit's just a new type or a new
form of candidate assessments.
And like, hey, would you feeldifferent if you're a candidate
and you're going through theinterview process and like, hey,
(17:33):
it's not an AI interviewnecessarily, but like, hey, like
this is our AI assisted uh, youknow, candidate assessment.
It's part of our process.
Like, boy, like, does that makelike are we, you know, so are we
thinking about you know this ina way that is easy, you know,
that is maybe just you know,almost off-putting for
candidates or anyone when youhear, hey, it's AI, everyone
(17:57):
shudders a little bit.
But you know, arguably, like,we're not that far off from
having this just be a new way toassess candidates.
Is it just assessments under adifferent sort of cloak?
SPEAKER_01 (18:08):
Yeah, I think it's probably off-putting if you call
it an interview, and it's lessoff-putting if you call it an
assessment, um, is the truth.
Um, if I were trying tocommunicate this to a candidate,
if I'm an employer, the way Iwould describe it is we get a
lot of job applications.
And while we would want to spendtime with every single one,
(18:28):
that's just not possible.
However, we do believe inallowing all of our candidates
to share their full story, youknow, to get beyond just the
resume, so that way we can havemore information to see if
you're a good match at ourorganization or elsewhere.
You know, so we're using thistool that, you know, to allow
(18:48):
you to put your best footforward in a way that a uh a
sterile application or resumecan't, right?
So that's you know, that'sthat's how I would convey it to
uh to a candidate.
Now, on the employer side, Ithink you're right.
You know, we you you think of itas like the initial phone
screen, you know, is really whatit's it's kind of doing.
But these things do have thecapability to essentially spin
(19:11):
up assessments, you know, typequestions, you know, because
they can be behavioral questionsand then they, you know, they
can be hard skill typequestions.
So, you know, what we considerto be like, oh, you reach this
stage and now this assessmentgets triggered.
I do wonder if this is kind of amerging of two stages in the
workflow, and it just to helpspeed up the process and get to
(19:33):
point B a bit quicker.
And I do wonder too, if you havean assessment that you like, you
know, how how can you marrythose things so that assessment
gets completed and more of thisconversational AI experience
versus, hey, go through and youknow, type out the information
and check the box and you know,et cetera.
Because, you know, if you canget all that done in one fell
swoop, then maybe that's themaybe that's the answer uh
(19:55):
there.
And but then you also want tothink about consistency.
So do you, you know, youprobably don't want AI just
making up ad hoc assessments byyou know by the position and you
know, whatever the hiringmanager had to say, you know,
per se, you know, you I don'twant to necessarily get into it,
but you might be opening a canof worms with compliance and
yeah, and just following astandardized procedure there.
(20:16):
But but yeah, I think you'reright.
I think I think there could be aconvergence of what we're
calling AI interviewing withactually we're talking about
assessments.
SPEAKER_02 (20:24):
Oh, and I also think like, you know, when we think
about where we're findingbudget, you know, sure, AI
interviews like it's taken, youknow, we're saving headcount in
theory, right?
But like, boy, there's a lot ofdollars in assessments too.
And, you know, everyone's alwaystrying to figure out where the
money's gonna come from.
And I wouldn't gloss over thefact that like, boy, there's
gonna be some you know definiteoverlap opportunities with these
(20:47):
AI interview tools, you know,and assessments and where, you
know, uh everything's in thespirit of giving the better can
the right candidates a chance toyou know better align themselves
with opportunities that they'reapplying to, right?
Well, you know, I want I do wantto talk a little bit about a few
things, you know, in themarketplace.
I but I before we get there, I'mgonna ask one more question
though, Mark.
(21:07):
So when we talk about all thesetools at HR Tech, you know, I
think you you brought up aninteresting point.
And I'm gonna paraphrase it andI'm gonna let you explain it.
But when we think aboutdifferentiators, you know, you
see, we see a lot of dataproviders.
Talk about the data providers atHR Tech and what you're seeing
(21:29):
with overlap or maybe you know,sharing um or you know, where
people are getting their datafrom to build some of these
tools.
You know, and I know that's kindof a leading question, but um,
you know, give me um give meyour give me your thoughts, you
know, when it talks to wherepeople are getting their data
from and how we should bethinking about that as TA
practitioners.
SPEAKER_01 (21:49):
Sure.
Okay, so I'm gonna put this in afew different categories.
And one I'm gonna call thecandidate sourcing slash profile
search uh tools.
And you know, for the sake ofthis, I'm gonna avoid naming any
specific providers or vendors.
Um, but you know, I can describethe category.
Like these would be tools thatrecruiters log into.
(22:11):
Uh they're trying to search forcandidates.
There's there's gonna be youknow a certain amount of
matching features, and thesetools are now adding um agentic
AI.
So if your recruiters don't wantto use it, well, we'll have an
AI use it and search for thecandidates and message them and
get them to apply, et cetera.
These are basically a set oftools that are like um we'll
(22:34):
call them LinkedIn competitors,or hey, you know, you're
spending a lot of money over onLinkedIn, you know, here's a you
maybe you could find the samecandidates and others, you know,
in this tool.
I guess the breakdown like forthe data question, well, where
does that data come from inthese tools?
And the answer is most of themare LinkedIn profiles.
(22:55):
Um like they are.
Like that's that's like here's aperson that does this job.
Now that is supplemented withdata behind the scenes.
And there's probably about ahandful, and really a couple
that power the data behind thescenes.
And this is where they're gonnabring in the phone number and
the personal email address andmaybe some other context bits
(23:16):
that they've left on you knowother websites.
This is where you'll get likeGitHub also thrown in there for
the tech positions.
That's why they always demo aJava developer because you got
the hard skills and you getstuff you know over uh from
GitHub.
Now, these tools can be supervaluable and they all have their
own way and workflows and userinterfaces that I think are are
really slick, but like theycan't they can they can't invent
(23:38):
candidates.
They don't have proprietorproprietary databases where
candidates have uploaded theirinformation.
They're scraping.
And you know, like it or not,the biggest source of that
scrape data is LinkedIn, andthen it's enriched with these
other data sources.
So I guess that's that's one,but like, you know, they can put
whatever hundred hundreds ofmillions, a billion candidates
(23:59):
on their on their pitch decksand these sorts of things.
It's really the same.
It's the same candidate pools,enriched with similar data sets.
What's going to make thosesolutions different is the user
interface, the differentcommunication tools, and like
you know, doesn't plug in withyour existing workflow.
So that's one, but I'm wonderingif you were thinking of other
types of data providers.
SPEAKER_02 (24:21):
Oh no, I think that's I think that's exactly
it.
Like, you know, everyone talksabout you know the billions of
different data sources thatthey're pulling things from.
And I think, you know, if we'rebeing honest, it's you know,
everyone's fishing from the sameocean, right?
And you know, I think uh I thinkcunliness is one piece, but I I
I think that there's a lot ofoverlap that we don't really
talk about in this space.
SPEAKER_01 (24:42):
Yeah.
There is another category dataprovider I do want to highlight.
And these this is what I wouldcall going back to the context,
one of the foundational layersof data that people talk about
but don't seem to execute on alot, or at least they they
understand the concept, butthey're not really understand
what that means.
(25:02):
And it has to do with skillstaxonomy.
So going through the exercise ofreally understanding your
organization and the roleswithin it, and this, not not
just the job title and the jobdescriptions, but what are the
actual skills needed to completethis work?
And where does your organizationwant to go and what skills are
needed for that?
(25:23):
Well, you have your big HCMslike a workday or SAP, and
there's there's areas there forskill data, uh, but it's not
populated like out of the box.
Like that's something you got togo do.
And you can kind of likebrainstorm and think and start
typing in skills there, butthere are data providers that
provide this framework of skillstaxonomy, which so essentially
(25:44):
like think about like reallycompleting your work, your
internal workforce data withskills, and then that informing,
i.e., context, of okay, talentmobility, who do we need to go
get from outside?
You know, what are the skillsnecessary?
And now that's gonna facilitatebetter matching and queries like
(26:05):
on those whatever technology youhave layered on top of that.
So I would add that in thecontext bucket, and these
skills, skill type dataproviders can provide that
enrichment so everything elseperforms better.
SPEAKER_02 (26:19):
Yeah, I think we're gonna see quite a shift in um
skills demand over the next youknow year to decade.
You know, we talk about uh, youknow, the example I forever give
as a, you know, when I went toIU, it was you got to get an
informatics degree.
That's what my mom said.
No one knew what it was, butlike, hey, it was had to do with
computers, maybe, you know, andand and obviously I didn't get
(26:40):
an informatics degree, and herewe are.
Um but hey, 10 years later, whatwas the number one degree people
were looking to hire for?
People with informatics degrees.
You know, then like, hey, lastyear it was prompt engineers.
You know, I think you know, theinteresting piece you know that
I'm following is you know, Ithink we saw people putting a
microphone in front of a lot offolks over at HR Tech saying,
hey, what skills do you thinkare gonna be most in demand in
(27:02):
the next, you know, next fiveyears?
And my answer is probablycontrarian, and it's uh reading
and writing, critical thing,being able to critically think,
being able to speak.
I that is those are the skillsthat are gonna be in demand.
You know, everyone can you knowdrop an article into Chat GPT
and say, hey, turn this into aLinkedIn post.
(27:23):
And like uh, you know, the oldadage is hey, I I know what it
is when I see it.
You know, hey, I I I know whatit is.
Um, you know, human wrote it,you know, versus if it was
written by you know ChatGPT andjust popped out.
And I think you know, we'regonna see a pretty big increase
in demand for critical thinking,you know, reading, or well,
maybe reading is not the rightone, but writing and speaking.
(27:46):
Anyway, that's a that's a rabbithole we don't need to go down
today.
Um, probably.
All right.
So coming out, you know, comingout of HRTAC, you know, a lot of
pressure for uh from clientsfrom executive leadership
saying, hey, I got this greatreport from Gardner.
It says we need to do somethingwith AI.
And I would say it's a prettyoverwhelming landscape.
(28:08):
You know, how do you think abouthelping, how do you think about
evaluating maybe AI solutionsfirst?
And you know, if you're a ifyou're a buyer, um, you know,
what are some of the questionsthat you know you ask that kind
of cuts through the marketingspeak, Mark?
You know, helping a how howwould you help a buyer thinking
about you know workflow problemsrather than you know technology
(28:29):
features or benefits?
SPEAKER_01 (28:31):
Yeah, I guess first first I would say don't pay
attention to the Joneses.
I do think there's an element ofthat is part of the marketing
rush is to make everyone feellike they're not participating
in some sort of party.
And if you don't do this, you'reyou're gonna get left behind.
I you know, like yes, you needto be evaluating at all times
(28:55):
how you can um improve yourprocess and be efficient and
learn about new tools andleverage points, you know, to
apply to your business.
So I'm not I'm not saying likejust put your head in the sand
and pretend like this isn't oneof the most amazing times in
technology innovation because itis.
But what I'm what I am saying isis like, well, I heard so-and-so
is using this you know, piece oftech or that piece of tech.
(29:17):
You know, we need to do thattoo, or you get pressure from
your executive team, like, weneed to use AI.
And what's absent of a lot ofthese conversations, and we get
inquiries like this all thetime, is it it's like they're
vaguely in an area, you know,they've been dropped in this
territory, but they don't knowwhere to go.
(29:39):
Like they don't know is it is itwest, is it north, is it east,
you know, is it through you knowthis door behind that tree?
So um and then but the reason isis they haven't they haven't
slowed down and just thought yougo back to the critical
thinking.
What is the problem?
What problem are you trying tosolve?
And so I guess now I'll I'll getinto like what's the framework
(30:03):
that I've been using and uh I Ithink will be really helpful of
of helping TA leaders thinkthrough this, but also helping
partners, you know, vendorsolutions clarify, you know,
their pitch and thinking.
But um I can't take credit forthis, and if we have time, I'll
tell you the story where it camefrom.
But there's this is called thefive questions.
Um I've added a six because Ithink it's important in this day
(30:26):
and age.
SPEAKER_02 (30:27):
It's not to your framework now, Mark.
See, it's six.
It's six questions.
So look at that.
So now it's the Thomas Tinomethod.
There we go.
SPEAKER_01 (30:34):
Oh, great, perfect.
Um, no, I can't take credit forthis.
Um, but uh or you know, I willsay I use it and I have refined
it over the years.
So the first thing is what isthe problem?
And then I would add it, I'd adda kind of an addendum to that.
And what is the impact of thatproblem?
Because if you're in a business,every business has a lot of
(30:54):
problems.
But you have to quantify thatproblem, and and that way you
can prioritize which problemsyou're trying to solve.
Number two is why is what I'mdoing today not working?
What is it about your currentsituation?
Maybe it's your current techstack, maybe it's your current
process, maybe it's the peopleyou have or the roles and
responsibilities you've divviedup, but like why is what I'm
(31:17):
doing today not working?
And that's where you get to theroot cause.
So really it's like the firstone is what's my problem?
Like, what are the symptoms andhow bad are they?
Two is why am I experiencingthose symptoms?
And if you can't get clear onone and two, do not pass go.
Because then you're gonna end uptalking with a bunch of
solutions and they're gonna putall sorts of ideas in your head
of what your problem may or maynot be.
(31:38):
And if you are, you know, end upbuying one that's not actually
mapped to the root cause or theproblem, you're gonna burn a lot
of capital, a lot of time, and awhole year business cycle before
you even realize that.
So, like that is the criticalpoint is the one and two.
What's my problem and why iswhat I'm doing today not
working?
Okay.
From there, now you can go andlook for solutions.
(32:02):
And um, you can make sure thatif you're talking to a solution,
that they match your problemsituation, they match your one
and two.
But three, what will you help medo differently?
And this is where you get todifferentiators.
A lot of solutions.
Okay, you say you can solve myproblem.
What are you gonna help me dodifferently than I'm doing
today?
(32:22):
And how does that compare toother solutions that are in the
market?
Number four, why should Ibelieve you if you're talking to
a solution?
And you know, that can sound alittle crass maybe, but really
what you're asking is like,what's the believability here?
Are you somebody who juststarted and I'm customer number
one?
Have you been in business for awhile?
(32:44):
Where have you had success?
Do you have case studies,testimonials?
Do you have reviews that aren'tjust paid for?
Like, can I talk to somebody?
Like you have a referral.
So that's why, why should Ibelieve you?
That's your trust andcredibility if you're a solution
provider, and that's your BSdetector if you are a buyer of a
solution.
Number five, what are the costs,ROI, and roll-up planning?
(33:07):
So, what's the pricing modelhere?
You know, is it per user, permodule, is it subscription,
annual, you know, et cetera.
What's the ROI?
This goes back to problem.
The question number one what isthe impact of this problem
having on my business?
If you can't connect the ROI ofthat solution to your original
problem statement and theimpact, like that that step is
(33:30):
often missed, but that's how youget stuff done internally.
That's how you're gonna sell itto the board or to your your
CHRO or who's ever making thefinal decision on that.
You have to be able to show theROI as it relates to your
problem.
And then rollout plan.
Is this like going live by theend of the week?
Is this the three, four months?
Do I need to bring in a projectmanager?
Do you provide a projectmanager?
(33:51):
What happens after I'm live?
What sort of ongoing support,training, customer success, and
those sorts of things?
And those things can mitigateyour risk of like, hey, it was
the right solution, wasn't stoodupright.
It was the right solution, butman, that support, you know,
it's just not where we needed,or we didn't have the ongoing
training.
All right.
So that leads to number six iswhat are my risks and how can I
(34:14):
mitigate them?
And some of that is getting therollout plan right.
But the other ones, this isgonna have to do with data
security, uh, compliance.
And you know, in the age of AI,like, you know, we need to have
a conversation about hey, whatare you opening me up to in
terms of you know bias, emerginglaw and compliance and those
sorts of things?
(34:36):
So those are I would call nowthe six questions.
But that's how I talk topartners to evaluate them, to
you know, create this idea of ashort list of who we feel like
we can trust to introducecustomers to.
And on the flip side, when we'retalking with customers, this is
how we can help clarify theirthinking about you know what
they're actually trying tosolve.
SPEAKER_02 (34:57):
Yeah.
Well, I think uh you and I wentthrough those same uh five
question trainings uh for a wella number a number of years, I'd
say.
And I think that you really hasjust helped frame you know how
we approach a lot of our problemsolving too, which has been
super fun.
So um, okay, well, I know thatwe're past time.
I don't think we're gonna getinto our marketplace, Mark, but
(35:20):
you know, I'm gonna say, youknow, I got two questions to
close with.
One, looking forward, so youknow, which what's maybe your
most contrarian prediction abouttalent acquisition technology?
You know, what is what'severyone think will happen that
something that you might bemight might strongly disagree
with?
SPEAKER_01 (35:39):
Um so I would say there's there's a lot of fear,
uncertainty.
You know, AI is gonna take allof our jobs.
And um particularly it's likethe like the idea that there's
gonna be this superintelligenceor AGI that could become
malevolent and want to turn usall into paperclips.
And if we don't, you know, if wedon't stop this now or get
(36:01):
alignment, you know, we're alldoomed.
I I have strong reasons forbelieving, you know, that
stuff's not true.
But really, it it boils down toof just having a fundamental
fundamental understanding ofwhat it means to be human and
what humans uniquely do, andwhat AI or any robots or you
(36:21):
know computer programs, whatthey are, and what they're meant
to do.
And so I'm not scared of that,and that's probably a separate
podcast of digging in the exactreasons why.
But because I'm not scared ofthat, I also view that as the
the quintessential opportunityhere.
There will be tasks that AI willdo, and they will do them
faster, better, cheaper, and atscale that you just can't do as
(36:44):
a human.
And that's a good thing.
There are going to be thingsthat are uniquely human, and
that is the only place whereemployers, candidates,
businesses of all shapes andsizes, and just being a person
in this world, that's where youdifferentiate yourself.
And I I feel like there's goingto be a counter movement, or,
(37:07):
you know, all the everythingwill be AI'd, or you know,
there's going to be agents doingall this stuff.
And really where you're going tostand out is um doing the things
that only humans can do, i.e.,critical thinking, like you
alluded to before, storytelling,uh, making uh making things that
are compelling and providing ahuman connection.
(37:28):
And I think that's where you'llsee people win.
SPEAKER_02 (37:31):
Yeah, well, I guess great.
Well, yeah, last question, youknow, I think you bring some
pretty uh, I think you have avery unique lens, and I know
hey, whatever everyone's readingor listening to kind of drives
how they learn.
So what are you reading orlistening to these days, Mark?
And you know, maybe like wheredo you go when you're trying to
learn or stay ahead of our ourlandscape or this AI landscape
(37:52):
in general?
SPEAKER_01 (37:53):
Yeah.
Well, given the nature of what Ido, I'm plugged into the
industry.
So I'm always talking withindustry peers and you know
folks at these differentproviders.
So that's certainly a source ofinformation that you know it's
not everyone has time to do thatbecause it's not their job.
I would say more broadly is Ireally, I really love taking
ideas from everywhere.
And so not specifically, youknow, industry or you know,
(38:15):
directly, but like, you know,these are you call them your
social media follows, but I Ipride myself on finding
independent thinkers who Iadmire intellectually, and then
I love it when they disagreewith each other on whatever
topic it is, because that givesme an opportunity to like weigh
two really good arguments andthen decide between those.
(38:36):
And so I guess I don't want togive you a list of people to
follow or anything like that,but I I would urge others, you
know, like you know, if you findyourself in an echo chamber
where everyone seems to beagreeing with them themselves,
step outside of that becausethere's always smart people on
both sides of an argument, andgetting good arguments from two
sides helps you find a better,you know, the more correct
(38:56):
answer ultimately.
And a book I'm reading, and Irecommend it to everybody, but
it's you know, it takes a lot toget your brain wrapped around.
It's called The Beginning ofInfinity by David Deutsch.
And you might want to start withsome podcast interviews.
His last name is spelled uhD-E-U-T-S-C-H, but gives you a
real kind of what I was talkingbefore of giving the foundation
(39:18):
of understanding what it meansto be human, what is knowledge,
uh, how do we grow knowledge andyou know why we shouldn't be so
worried about the robots takingover.
SPEAKER_02 (39:28):
Yeah, so just light, light reading, you know, for
your for your for your afternooncoffee, or maybe the perfect
book if uh you know you ifyou're if you can't sleep.
No, David Deutsch is now on myTwitter feed every day, uh since
you and I have been having thoseconversations.
So fantastic recommendation.
And again, I go back to like youknow, the energy at HR Tech is
you know, it it is good that wehave people coming in from
(39:50):
outside of the TA space andbuilding products.
And you know, I think sure,adding a new uh widget or
feature to you know to an ATS orHRS is something that you know
these large vendors want to talkabout.
But like, you know, I thinkwe're gonna have some really
good ideas come from people whoare not, you know, in the weeds
every day, too.
And you know, I love that youknow, we are all trying to
(40:12):
leave, you know, to learn fromoutside of our industry, period.
All right.
Well, I think that's a goodplace to put a pin in this one,
Mark.
Well, I think our six questionshas to turn into a webinar at
some point too, because youknow, I I think we both get
energized talking about problemsolving and and our approach to
it.
So maybe we'll add that one intothe old ticker also.
Well, till next time, we'll linkeverything in the show notes and
(40:35):
you'll know where to find us.
But yeah, thanks, Mark, asalways, for this and the
continual journey we're on withnow with Tellivity.
So onward.
SPEAKER_01 (40:43):
Thanks for having me and looking forward to it.
And I'll see you in the nextone.
All right, thanks for tuning in.
SPEAKER_02 (40:49):
As always, head on over to changestate.io or shoot
us a note on all the socialmedia.
We'd love to hear from you, andwe'll check you guys next week.
Welcome to the Changing State of Talent
Acquisition podcast with yourhost, Grant Thorta.
Each episode brings youunfiltered conversations about
the tools, trends, andtechnologies impacting the
future of talent acquisition.
Our guests share their storieson what's working, what's hype,
and what's actually helpingcompanies hire better and grow
(00:21):
faster.
Have feedback or want to jointhe show?
Head on over to Telivity.com tolearn more.
But now we're on to this week'sepisode.
SPEAKER_02 (00:30):
All right, and we're back with another episode of the
Changing State of TownAcquisition podcast.
Slightly different format withsome exciting news over the last
couple weeks with Change Daygetting acquired and joining the
Recruitics family.
Our next guest is Mark Tomasino.
Mark, I've known for the betterpart of boy, uh two decades at
(00:52):
this point.
And Mark is now in his role asdirector of partnerships.
Uh we'll call it specialprojects still over at Telivity.
Mark, we'd love for you to sharea little bit more about your
career journey, maybe some ofthe pivotal moments that you
know brought you into theTelivity or changed a team
across the board.
SPEAKER_01 (01:10):
Sure.
Thanks for having me, Graham.
Honored to be a guest finally onthis pod.
And um, yeah, a little bit aboutme is I've been in the industry
since 2006, first job out ofcollege.
I made 125 cold calls a day atCareer Builder, and I managed to
not quit or get fired, which ismore than most people could say
at the time.
(01:30):
And uh, you know, so I ended updoing all levels of sales, you
know, from SB up to the largestglobal employers.
When career builders startedbuying a bunch of different
software and technology to bringto market, I was one of the
early solution architects, uh,which is Graham, how you and I
know each other, of workingclosely, of uh bringing that to
(01:50):
market and working withdifferent employers, tech
stacks, uh, which really piquedmy interest into the broader
world of HR Tech andintegrations.
We were acquired by privateequity and 2017.
I then was kind of an internalsales consultant between the
different portfolio companiesthat are owners owned.
And finally in 2020, I becamevice president of partnerships,
which I oversaw our resellerintegrations and agency partner
(02:15):
programs amongst some otherthings, kind of building on all
the things that came beforethat.
I at the time I had atwo-year-old son and I had a
planned sort of career break anduh to spend more time with my
growing family.
So took a break from theworkforce for a few years, but
uh Graham, you know, you atChange State uh brought me over
(02:38):
to help on a consulting basiswith some HR Tech partnerships.
And no surprise we got along sowell and enjoyed working that we
flipped that to full time.
And now here we are, havinggrown change state and now
joined the recruits family andover at Telivity.
SPEAKER_02 (02:51):
Yeah, well, you know, I think you're
underselling, and we're gonnaget into it, you know, your
background and knowledge of youknow the HR Tech space, you
know, in particular in thepre-hire world.
And you know, I I always youknow talk about what gives me
energy, and I think there's alot of problems you know to
solve standing in front of awhiteboard.
And you know, I think that'skind of where we cut our teeth
(03:11):
in general.
But you know, we're gonna divedeep into HR Tech.
So I guess a great startingpoint, Mark, maybe is we just
got back from HR Tech, and Ithink it's fair to say we
probably couldn't walk uh youknow uh 10 feet without seeing
another uh AI-powered booth.
And you know, you were kind ofmy co-pilot through all of this.
(03:32):
So, you know, what was yourbiggest takeaway from HR Tech
from the conference?
What kind of stood out as maybeyou know, genuinely, you know,
innovative versus just uh well,we'll call it well-marketed, you
know, noise?
SPEAKER_01 (03:45):
Sure.
Uh yeah, I mean, you you see thebig players, although I would
say the big players have asmaller presence there than
maybe historic, you know, andI'm talking like the big HCM
systems, but what you did seewas a ton of startups.
So like they have a wholestartup area that was just you
know folks that have pointsolutions trying to solve very
(04:06):
specific problems in the space.
So that was interesting.
So there's a lot of emergentplayers.
I would say like a really commonone that I was almost surprised
to see how many were you knowtrying to go about solving the
same thing were these AIinterview tools.
Um, and so you know, we canprobably get into the nuance of
that a little bit and somethings I learned, but you know,
doing being able to do quickside-by-sides of companies that
(04:29):
are essentially going aboutsolving the same problem in the
same way, but trying to peelback that onion and try to
figure out who's who and what'swhat and who who has a novel way
of solving this problem andwho's really just putting a
wrapper around the same back-endstuff.
SPEAKER_02 (04:43):
Yeah.
Well, you know, I I'll also sayin the startup pavilion and you
know, just in booths in general,maybe you maybe you saw this
too, maybe you didn't.
You know, I think that you know,we saw a lot of new solutions
and you know, coming fromfounders who maybe haven't
worked in traditional TA, right?
And so, you know, we talk a lotabout, you know, everyone talks
(05:05):
about Tesla, you know, comingfrom outside of the you know,
the vehicle industry, Uber, youknow, people coming from outside
of you know the transportation,like disrupting industries as a
whole.
And like, you know, I do hope,and I think there's you know,
some very good, you know,there's a lot of energy behind
you know, people coming fromoutside TA.
And I'm hoping that you know westart to bring more fresh
(05:27):
thinking into you know TAinstead of some of the recaps
that I read where it was, hey,yeah, guess what?
Like someone bought someoneelse.
I realize that might be ironicgiven that we just got acquired,
but hey, like some big, massiveapplicant tracking system um or
HRS system just bolted onanother tool.
So, you know, I'm justwondering, like, fresh thinking,
(05:49):
do you feel like there's alittle bit of a different energy
in HR Tech and you know in theindustry than maybe in years
past?
SPEAKER_01 (05:55):
Yeah, I would say that.
I mean, you know, it's it's adouble-edged sword.
You have this emergence of AI,and so everyone's gonna use the
same words, and you gotta try tofigure out who's actually going
about solving the problems.
And you'll probably hear merefer to this several times on
the pod, but it it all goes backto problems.
And so the founders that I'menergized by or the products
(06:18):
that I found most interestingare the ones that understand the
problem that they're trying tosolve, and they can articulate
it when asked directly.
What problem do you solve?
What are the use cases thatyou're finding the most success
with clients?
And they know exactly whatthey're doing.
You know, almost like startingfrom first principles of hey,
you know, what's really theproblem?
(06:39):
What's what's the underlyingproblem here?
So to me, that's how you youkind of cut through the noise.
And the inverse of that would beif you talk to somebody and it
sounds like they just keepadding features so they can keep
up on an RFP checklist.
Oh, yeah, we do this and we dothat, and we do this over here
too.
And it's almost like you looklose sight of like, yeah, but
what what do you stand for?
Like, what's the ethos here?
(07:00):
What what's the what's thefundamental problem that that
you're trying to address?
Because sometimes it can feellike our fundamental problem is
we need to win more RFPs, or weneed to we need to attract
investors.
So we need to use these words orsay that we can do these big
things because this othercompany said they could do those
and they got a billion dollarvaluation.
So yeah, I always be rooted ininto the problems, and that's
(07:21):
that's what differentiates thesolutions for me, personally.
SPEAKER_02 (07:25):
All right.
Well, you know, let's let'sstick on this problems path
because you know I want to getinto our marketplace a little
bit, but you know, I think thatyou know you've kind of you know
framed this in a unique way forme, right?
And that's as we're walkingthrough these booths, we're
talking to a lot of vendorstalking about how we're gonna
use AI to solve problems.
And I think you know, there'sprobably a little bit of irony
(07:46):
in here in that you know, jobseekers are you know using AI to
apply to a lot more jobs.
And you know, I think you know,we've created a lot of volume
problems for companies in thesense that we see a large, a
much larger volume of candidatesor applicant flow than we have
historically.
And so, you know, on some level,I think you know, we're using AI
(08:10):
to solve problems that have beencreated by AI.
Yeah, but what's yourperspective of you know the
bigger problems that you'rehearing companies solving coming
out of HR Tech or trying tosolve maybe?
SPEAKER_01 (08:20):
Yeah, I think you're you're touching on something
there.
I call it the AI arms race andrecruiting.
But yeah, let's talk about theemployer side.
So, you know, pretty quicklyafter ChatGPT rolled out, people
started using it for things likeyou know, drafting emails and
letters and in mails and thingsof that nature, so candidate
(08:41):
outreach.
And then they thought, oh, youknow, this could probably make
my job description a little morerobust and you know fleshed out.
So now it's it's rewriting thejob descriptions.
And oh, well, it also can helpme filter and match, although
those tools have been around fora while, but you know, they've
definitely grown theirprovidence and uh and more so.
So I don't even have to like gothrough these resumes per se,
(09:03):
because now I can have a toollevel screen and float the best
ones towards the top.
And you know, that's allcelebrated in the name of
efficiency, but you know, it'snot not fun when the other side
has it too.
Um, which then you have then youhave uh you know candidates who
are like, well, hey, I can usethis, I'll rewrite my resume to
perfectly match this jobdescription.
(09:25):
Okay, and you can send that out,you know, help help me uh answer
these interview questions,right?
And those tools are just gettingmore and more sophisticated.
And yeah, send off my resume.
Oh, congratulations, I youapplied to a thousand jobs while
you were sleeping, right?
And now now you do have uh notonly a volume problem, but you
have a noise problem.
(09:46):
Because what is an algorithm, amatching algorithm, no matter
how sophisticated it might be,what's it supposed to do when it
has you know hundred resumesthat were tailor-made to the job
description if that is theunderlying matching criteria,
right?
It's like you know,congratulations, here's you
know, a bunch of hundred percentmatches.
(10:07):
Well that's no longer useful.
And now you have way more to gothrough.
Okay, so what do you do?
Well, now you have a wholecategory of solutions, also AI,
that is is going to interviewthem.
Because hey, I I don't haveenough information and context
to f to know who of this are areactually the best.
(10:28):
But you know what, you know whatI could do is if I had an
ability to have a conversationwith them, I can get more
information on that candidate.
And now you know then I couldfacilitate a better match.
So I'm trying to get moreinformation and context by way
of conversation interviewing.
But well, shoot, my stack ofapplicants is a thousand, two
thousand tall.
(10:49):
I can't interview all thosefolks.
Ah, but AI can.
Okay.
So now we're gonna feed all ofthese candidates who may or may
not deserve or should beinterviewed, but we're gonna
funnel them through this AIinterview tool.
Um, and on the other end, thehope is that now I've I've
really sifted through and I'veI've truly identified, you know,
the best quality and mostengaged and the best fit for our
(11:11):
organization and for this role,you know, by by way of this.
But like I just described awhole apparatus that like just
wasn't there what, three yearsago?
And that's a lot of noise tosift through.
And it makes me question, belike, wait a minute, what are we
doing here?
SPEAKER_02 (11:26):
Well, I guess you know, it reminds me of you know,
maybe a decade ago, and I thinkyou heard this in our past life.
Like, you know, first theproblem was uh, hey, a job ads,
job ads are bad.
You know, so your job ads arebest case, that's a lie.
You know, resumes are are poor,you know, people put you know,
arguably some some fluff inresumes.
So, you know, resumes and andjob ads are both, you know, two
(11:46):
false documents.
You know, so you're matching onelie and with another lie.
And, you know, so it's not asurprise that our matching
algorithms are on, also.
And I think, you know, thiswhole idea of context as we're
getting into you know movingpeople through the process is
one that I don't know, I'mprobably more keen to follow a
little bit too, because I thinkyou're right that like, you
(12:06):
know, it is becomingincreasingly more difficult to
match a generic is the word Iuse, even though I I hesitate,
but a generic profile with ageneric job description and try
and figure out of a thousandpeople that come through, you
know, who really is going to bethe best fit.
SPEAKER_01 (12:21):
Right.
Yeah.
You know, another term I Idiscovered yesterday, and it was
a real uh brilliant article, butit it's basically this age of
quick hit video and algorithmgenerate content and in gen AI.
It's really compression and it'slike a compression of the
average.
So, which just makes everythinglook the same.
(12:42):
And you get like changeblindness.
You can't, you know, you'retrying to compare two things
that are like really, reallysimilar, sounding, looking, etc.
And the you know, the highfidelity, the signal, like the
true uniqueness, the raw file ormaterial, that that's what
doesn't really exist anymore.
Which, you know, we can talkabout that later.
I think that that might be thecounter uh example
(13:04):
differentiator that thatemployers and you know really
anyone can use to stand out.
I would love to dig in oncontext a little bit, if we can.
Yeah, yeah.
Well, where where do you want totake it?
So this was my this was my bigaha doing a lot of side-by-side
comparisons.
And as you can tell, I'm alittle skeptical of the AI
interviewing tools.
Not that they can't work reallywell or be, you know, super
(13:25):
useful.
It's just it's the emergence ofthe solution seems to be trying
to combat a problem of our ownmaking, of you know, AI
everything to you know, createthat volume problem that you're
talking about.
But I do see these things youknow having real great potential
and can help a number of ourclients who we interact with
every day, you know, get throughthat tunnel and and find the
(13:47):
best quality candidates.
However, context is really whatmatters.
And you know, I demoed probablysix to eight of these tools at
the expo, and some of them it'syou know, you upload your job
description, and then fromthere, the AI interview tool
(14:08):
will analyze that and come upwith interview questions that it
can ask to susto if somebodymatches the qualifications to
the job.
And so then they're gonna havesome either plain voice or a
video avatar.
Uh, you can kind of pick yourflavor there, and then that's
what's gonna have theconversation with the candidate,
and they're gonna ask thesequestions and hopefully without
not too much lag time, andthey're gonna ask those
(14:29):
questions.
And now you're basicallyconducting that first screen
interview with the candidate,uh, but uh AI is doing it
instead of a recruiter, so youcan do thousands of these a day
instead of you know dozens,maybe.
So that's the solution, but thecontext that is fed into them is
what I think is thedifferentiator.
So some tools is like upload jobdescription and go.
(14:52):
Some of them are well, we startwith the job description, but
look, now you can toggle on thisor enter this, you know, you can
you can edit, you know, and kindof create your flow after doing
it, which okay, that's that'sgood.
Or better at least.
But I think the best solutionsand the ones that are most
interesting to me are the onesthat take in the full context of
(15:14):
the organization.
Meaning, like, what if you couldupload all of your you know,
recruiting best practices thatyou've established, and you
know, the interview questionsthat you have said, you know,
like that that's what we like toask, or your um what you
consider to be your employerbrand differentiators, um your
(15:36):
employee handbook, your corevalues, sample candidates who
have worked out really, reallygreat in your organization and
you know what what theirbackground is, the conversation
that you have with the hiringmanager in your intake meeting.
Okay, now we've now we'veincluded a lot more context.
And whether it's a human or AI,they're gonna do a lot better
because now they have morecontext to conduct uh a better
(15:59):
interview and to facilitate abetter match.
And so I think that's it, is youhave to you if you're going to
use AI as a layer to try tospeed up things that humans you
know have been doing or could bedoing, the the underlying
context that they rely on issuper important.
But the catch is that is nodifferent.
(16:22):
Whether it's a robot doing it orif it's a human doing it,
everyone performs better withmore context.
Yeah, I'll pause there.
SPEAKER_02 (16:30):
Well, and I have a few different ways I want to
kind of crystallize this, Mark.
So, first, goals a better match,you know, we're talking about AI
interviews in you know, inparticular, and like, you know,
I would argue that that's youknow, by some definition, a new
category of solutions, right?
And I would also say that like,you know, the way we're thinking
(16:50):
about our uh Telity marketplace,and we'll maybe talk about this
a little bit, is you know,really shouldn't be focused on
what category does somethinglive in, but what problems are
we trying to solve?
And like, you know, hearing youdescribe feeding in all these
additional contextual points,you know, your brand
differentiators, your handbook,you know, your green breast
(17:11):
practices and so on, like, youknow, it almost feels like AI
interviews, you know, kind ofblend in with assessments.
And, you know, maybe it's uhit's just a new type or a new
form of candidate assessments.
And like, hey, would you feeldifferent if you're a candidate
and you're going through theinterview process and like, hey,
(17:33):
it's not an AI interviewnecessarily, but like, hey, like
this is our AI assisted uh, youknow, candidate assessment.
It's part of our process.
Like, boy, like, does that makelike are we, you know, so are we
thinking about you know this ina way that is easy, you know,
that is maybe just you know,almost off-putting for
candidates or anyone when youhear, hey, it's AI, everyone
(17:57):
shudders a little bit.
But you know, arguably, like,we're not that far off from
having this just be a new way toassess candidates.
Is it just assessments under adifferent sort of cloak?
SPEAKER_01 (18:08):
Yeah, I think it's probably off-putting if you call
it an interview, and it's lessoff-putting if you call it an
assessment, um, is the truth.
Um, if I were trying tocommunicate this to a candidate,
if I'm an employer, the way Iwould describe it is we get a
lot of job applications.
And while we would want to spendtime with every single one,
(18:28):
that's just not possible.
However, we do believe inallowing all of our candidates
to share their full story, youknow, to get beyond just the
resume, so that way we can havemore information to see if
you're a good match at ourorganization or elsewhere.
You know, so we're using thistool that, you know, to allow
(18:48):
you to put your best footforward in a way that a uh a
sterile application or resumecan't, right?
So that's you know, that'sthat's how I would convey it to
uh to a candidate.
Now, on the employer side, Ithink you're right.
You know, we you you think of itas like the initial phone
screen, you know, is really whatit's it's kind of doing.
But these things do have thecapability to essentially spin
(19:11):
up assessments, you know, typequestions, you know, because
they can be behavioral questionsand then they, you know, they
can be hard skill typequestions.
So, you know, what we considerto be like, oh, you reach this
stage and now this assessmentgets triggered.
I do wonder if this is kind of amerging of two stages in the
workflow, and it just to helpspeed up the process and get to
(19:33):
point B a bit quicker.
And I do wonder too, if you havean assessment that you like, you
know, how how can you marrythose things so that assessment
gets completed and more of thisconversational AI experience
versus, hey, go through and youknow, type out the information
and check the box and you know,et cetera.
Because, you know, if you canget all that done in one fell
swoop, then maybe that's themaybe that's the answer uh
(19:55):
there.
And but then you also want tothink about consistency.
So do you, you know, youprobably don't want AI just
making up ad hoc assessments byyou know by the position and you
know, whatever the hiringmanager had to say, you know,
per se, you know, you I don'twant to necessarily get into it,
but you might be opening a canof worms with compliance and
yeah, and just following astandardized procedure there.
(20:16):
But but yeah, I think you'reright.
I think I think there could be aconvergence of what we're
calling AI interviewing withactually we're talking about
assessments.
SPEAKER_02 (20:24):
Oh, and I also think like, you know, when we think
about where we're findingbudget, you know, sure, AI
interviews like it's taken, youknow, we're saving headcount in
theory, right?
But like, boy, there's a lot ofdollars in assessments too.
And, you know, everyone's alwaystrying to figure out where the
money's gonna come from.
And I wouldn't gloss over thefact that like, boy, there's
gonna be some you know definiteoverlap opportunities with these
(20:47):
AI interview tools, you know,and assessments and where, you
know, uh everything's in thespirit of giving the better can
the right candidates a chance toyou know better align themselves
with opportunities that they'reapplying to, right?
Well, you know, I want I do wantto talk a little bit about a few
things, you know, in themarketplace.
I but I before we get there, I'mgonna ask one more question
though, Mark.
(21:07):
So when we talk about all thesetools at HR Tech, you know, I
think you you brought up aninteresting point.
And I'm gonna paraphrase it andI'm gonna let you explain it.
But when we think aboutdifferentiators, you know, you
see, we see a lot of dataproviders.
Talk about the data providers atHR Tech and what you're seeing
(21:29):
with overlap or maybe you know,sharing um or you know, where
people are getting their datafrom to build some of these
tools.
You know, and I know that's kindof a leading question, but um,
you know, give me um give meyour give me your thoughts, you
know, when it talks to wherepeople are getting their data
from and how we should bethinking about that as TA
practitioners.
SPEAKER_01 (21:49):
Sure.
Okay, so I'm gonna put this in afew different categories.
And one I'm gonna call thecandidate sourcing slash profile
search uh tools.
And you know, for the sake ofthis, I'm gonna avoid naming any
specific providers or vendors.
Um, but you know, I can describethe category.
Like these would be tools thatrecruiters log into.
(22:11):
Uh they're trying to search forcandidates.
There's there's gonna be youknow a certain amount of
matching features, and thesetools are now adding um agentic
AI.
So if your recruiters don't wantto use it, well, we'll have an
AI use it and search for thecandidates and message them and
get them to apply, et cetera.
These are basically a set oftools that are like um we'll
(22:34):
call them LinkedIn competitors,or hey, you know, you're
spending a lot of money over onLinkedIn, you know, here's a you
maybe you could find the samecandidates and others, you know,
in this tool.
I guess the breakdown like forthe data question, well, where
does that data come from inthese tools?
And the answer is most of themare LinkedIn profiles.
(22:55):
Um like they are.
Like that's that's like here's aperson that does this job.
Now that is supplemented withdata behind the scenes.
And there's probably about ahandful, and really a couple
that power the data behind thescenes.
And this is where they're gonnabring in the phone number and
the personal email address andmaybe some other context bits
(23:16):
that they've left on you knowother websites.
This is where you'll get likeGitHub also thrown in there for
the tech positions.
That's why they always demo aJava developer because you got
the hard skills and you getstuff you know over uh from
GitHub.
Now, these tools can be supervaluable and they all have their
own way and workflows and userinterfaces that I think are are
really slick, but like theycan't they can they can't invent
(23:38):
candidates.
They don't have proprietorproprietary databases where
candidates have uploaded theirinformation.
They're scraping.
And you know, like it or not,the biggest source of that
scrape data is LinkedIn, andthen it's enriched with these
other data sources.
So I guess that's that's one,but like, you know, they can put
whatever hundred hundreds ofmillions, a billion candidates
(23:59):
on their on their pitch decksand these sorts of things.
It's really the same.
It's the same candidate pools,enriched with similar data sets.
What's going to make thosesolutions different is the user
interface, the differentcommunication tools, and like
you know, doesn't plug in withyour existing workflow.
So that's one, but I'm wonderingif you were thinking of other
types of data providers.
SPEAKER_02 (24:21):
Oh no, I think that's I think that's exactly
it.
Like, you know, everyone talksabout you know the billions of
different data sources thatthey're pulling things from.
And I think, you know, if we'rebeing honest, it's you know,
everyone's fishing from the sameocean, right?
And you know, I think uh I thinkcunliness is one piece, but I I
I think that there's a lot ofoverlap that we don't really
talk about in this space.
SPEAKER_01 (24:42):
Yeah.
There is another category dataprovider I do want to highlight.
And these this is what I wouldcall going back to the context,
one of the foundational layersof data that people talk about
but don't seem to execute on alot, or at least they they
understand the concept, butthey're not really understand
what that means.
(25:02):
And it has to do with skillstaxonomy.
So going through the exercise ofreally understanding your
organization and the roleswithin it, and this, not not
just the job title and the jobdescriptions, but what are the
actual skills needed to completethis work?
And where does your organizationwant to go and what skills are
needed for that?
(25:23):
Well, you have your big HCMslike a workday or SAP, and
there's there's areas there forskill data, uh, but it's not
populated like out of the box.
Like that's something you got togo do.
And you can kind of likebrainstorm and think and start
typing in skills there, butthere are data providers that
provide this framework of skillstaxonomy, which so essentially
(25:44):
like think about like reallycompleting your work, your
internal workforce data withskills, and then that informing,
i.e., context, of okay, talentmobility, who do we need to go
get from outside?
You know, what are the skillsnecessary?
And now that's gonna facilitatebetter matching and queries like
(26:05):
on those whatever technology youhave layered on top of that.
So I would add that in thecontext bucket, and these
skills, skill type dataproviders can provide that
enrichment so everything elseperforms better.
SPEAKER_02 (26:19):
Yeah, I think we're gonna see quite a shift in um
skills demand over the next youknow year to decade.
You know, we talk about uh, youknow, the example I forever give
as a, you know, when I went toIU, it was you got to get an
informatics degree.
That's what my mom said.
No one knew what it was, butlike, hey, it was had to do with
computers, maybe, you know, andand and obviously I didn't get
(26:40):
an informatics degree, and herewe are.
Um but hey, 10 years later, whatwas the number one degree people
were looking to hire for?
People with informatics degrees.
You know, then like, hey, lastyear it was prompt engineers.
You know, I think you know, theinteresting piece you know that
I'm following is you know, Ithink we saw people putting a
microphone in front of a lot offolks over at HR Tech saying,
hey, what skills do you thinkare gonna be most in demand in
(27:02):
the next, you know, next fiveyears?
And my answer is probablycontrarian, and it's uh reading
and writing, critical thing,being able to critically think,
being able to speak.
I that is those are the skillsthat are gonna be in demand.
You know, everyone can you knowdrop an article into Chat GPT
and say, hey, turn this into aLinkedIn post.
(27:23):
And like uh, you know, the oldadage is hey, I I know what it
is when I see it.
You know, hey, I I I know whatit is.
Um, you know, human wrote it,you know, versus if it was
written by you know ChatGPT andjust popped out.
And I think you know, we'regonna see a pretty big increase
in demand for critical thinking,you know, reading, or well,
maybe reading is not the rightone, but writing and speaking.
(27:46):
Anyway, that's a that's a rabbithole we don't need to go down
today.
Um, probably.
All right.
So coming out, you know, comingout of HRTAC, you know, a lot of
pressure for uh from clientsfrom executive leadership
saying, hey, I got this greatreport from Gardner.
It says we need to do somethingwith AI.
And I would say it's a prettyoverwhelming landscape.
(28:08):
You know, how do you think abouthelping, how do you think about
evaluating maybe AI solutionsfirst?
And you know, if you're a ifyou're a buyer, um, you know,
what are some of the questionsthat you know you ask that kind
of cuts through the marketingspeak, Mark?
You know, helping a how howwould you help a buyer thinking
about you know workflow problemsrather than you know technology
(28:29):
features or benefits?
SPEAKER_01 (28:31):
Yeah, I guess first first I would say don't pay
attention to the Joneses.
I do think there's an element ofthat is part of the marketing
rush is to make everyone feellike they're not participating
in some sort of party.
And if you don't do this, you'reyou're gonna get left behind.
I you know, like yes, you needto be evaluating at all times
(28:55):
how you can um improve yourprocess and be efficient and
learn about new tools andleverage points, you know, to
apply to your business.
So I'm not I'm not saying likejust put your head in the sand
and pretend like this isn't oneof the most amazing times in
technology innovation because itis.
But what I'm what I am saying isis like, well, I heard so-and-so
is using this you know, piece oftech or that piece of tech.
(29:17):
You know, we need to do thattoo, or you get pressure from
your executive team, like, weneed to use AI.
And what's absent of a lot ofthese conversations, and we get
inquiries like this all thetime, is it it's like they're
vaguely in an area, you know,they've been dropped in this
territory, but they don't knowwhere to go.
(29:39):
Like they don't know is it is itwest, is it north, is it east,
you know, is it through you knowthis door behind that tree?
So um and then but the reason isis they haven't they haven't
slowed down and just thought yougo back to the critical
thinking.
What is the problem?
What problem are you trying tosolve?
And so I guess now I'll I'll getinto like what's the framework
(30:03):
that I've been using and uh I Ithink will be really helpful of
of helping TA leaders thinkthrough this, but also helping
partners, you know, vendorsolutions clarify, you know,
their pitch and thinking.
But um I can't take credit forthis, and if we have time, I'll
tell you the story where it camefrom.
But there's this is called thefive questions.
Um I've added a six because Ithink it's important in this day
(30:26):
and age.
SPEAKER_02 (30:27):
It's not to your framework now, Mark.
See, it's six.
It's six questions.
So look at that.
So now it's the Thomas Tinomethod.
There we go.
SPEAKER_01 (30:34):
Oh, great, perfect.
Um, no, I can't take credit forthis.
Um, but uh or you know, I willsay I use it and I have refined
it over the years.
So the first thing is what isthe problem?
And then I would add it, I'd adda kind of an addendum to that.
And what is the impact of thatproblem?
Because if you're in a business,every business has a lot of
(30:54):
problems.
But you have to quantify thatproblem, and and that way you
can prioritize which problemsyou're trying to solve.
Number two is why is what I'mdoing today not working?
What is it about your currentsituation?
Maybe it's your current techstack, maybe it's your current
process, maybe it's the peopleyou have or the roles and
responsibilities you've divviedup, but like why is what I'm
(31:17):
doing today not working?
And that's where you get to theroot cause.
So really it's like the firstone is what's my problem?
Like, what are the symptoms andhow bad are they?
Two is why am I experiencingthose symptoms?
And if you can't get clear onone and two, do not pass go.
Because then you're gonna end uptalking with a bunch of
solutions and they're gonna putall sorts of ideas in your head
of what your problem may or maynot be.
(31:38):
And if you are, you know, end upbuying one that's not actually
mapped to the root cause or theproblem, you're gonna burn a lot
of capital, a lot of time, and awhole year business cycle before
you even realize that.
So, like that is the criticalpoint is the one and two.
What's my problem and why iswhat I'm doing today not
working?
Okay.
From there, now you can go andlook for solutions.
(32:02):
And um, you can make sure thatif you're talking to a solution,
that they match your problemsituation, they match your one
and two.
But three, what will you help medo differently?
And this is where you get todifferentiators.
A lot of solutions.
Okay, you say you can solve myproblem.
What are you gonna help me dodifferently than I'm doing
today?
(32:22):
And how does that compare toother solutions that are in the
market?
Number four, why should Ibelieve you if you're talking to
a solution?
And you know, that can sound alittle crass maybe, but really
what you're asking is like,what's the believability here?
Are you somebody who juststarted and I'm customer number
one?
Have you been in business for awhile?
(32:44):
Where have you had success?
Do you have case studies,testimonials?
Do you have reviews that aren'tjust paid for?
Like, can I talk to somebody?
Like you have a referral.
So that's why, why should Ibelieve you?
That's your trust andcredibility if you're a solution
provider, and that's your BSdetector if you are a buyer of a
solution.
Number five, what are the costs,ROI, and roll-up planning?
(33:07):
So, what's the pricing modelhere?
You know, is it per user, permodule, is it subscription,
annual, you know, et cetera.
What's the ROI?
This goes back to problem.
The question number one what isthe impact of this problem
having on my business?
If you can't connect the ROI ofthat solution to your original
problem statement and theimpact, like that that step is
(33:30):
often missed, but that's how youget stuff done internally.
That's how you're gonna sell itto the board or to your your
CHRO or who's ever making thefinal decision on that.
You have to be able to show theROI as it relates to your
problem.
And then rollout plan.
Is this like going live by theend of the week?
Is this the three, four months?
Do I need to bring in a projectmanager?
Do you provide a projectmanager?
(33:51):
What happens after I'm live?
What sort of ongoing support,training, customer success, and
those sorts of things?
And those things can mitigateyour risk of like, hey, it was
the right solution, wasn't stoodupright.
It was the right solution, butman, that support, you know,
it's just not where we needed,or we didn't have the ongoing
training.
All right.
So that leads to number six iswhat are my risks and how can I
(34:14):
mitigate them?
And some of that is getting therollout plan right.
But the other ones, this isgonna have to do with data
security, uh, compliance.
And you know, in the age of AI,like, you know, we need to have
a conversation about hey, whatare you opening me up to in
terms of you know bias, emerginglaw and compliance and those
sorts of things?
(34:36):
So those are I would call nowthe six questions.
But that's how I talk topartners to evaluate them, to
you know, create this idea of ashort list of who we feel like
we can trust to introducecustomers to.
And on the flip side, when we'retalking with customers, this is
how we can help clarify theirthinking about you know what
they're actually trying tosolve.
SPEAKER_02 (34:57):
Yeah.
Well, I think uh you and I wentthrough those same uh five
question trainings uh for a wella number a number of years, I'd
say.
And I think that you really hasjust helped frame you know how
we approach a lot of our problemsolving too, which has been
super fun.
So um, okay, well, I know thatwe're past time.
I don't think we're gonna getinto our marketplace, Mark, but
(35:20):
you know, I'm gonna say, youknow, I got two questions to
close with.
One, looking forward, so youknow, which what's maybe your
most contrarian prediction abouttalent acquisition technology?
You know, what is what'severyone think will happen that
something that you might bemight might strongly disagree
with?
SPEAKER_01 (35:39):
Um so I would say there's there's a lot of fear,
uncertainty.
You know, AI is gonna take allof our jobs.
And um particularly it's likethe like the idea that there's
gonna be this superintelligenceor AGI that could become
malevolent and want to turn usall into paperclips.
And if we don't, you know, if wedon't stop this now or get
(36:01):
alignment, you know, we're alldoomed.
I I have strong reasons forbelieving, you know, that
stuff's not true.
But really, it it boils down toof just having a fundamental
fundamental understanding ofwhat it means to be human and
what humans uniquely do, andwhat AI or any robots or you
(36:21):
know computer programs, whatthey are, and what they're meant
to do.
And so I'm not scared of that,and that's probably a separate
podcast of digging in the exactreasons why.
But because I'm not scared ofthat, I also view that as the
the quintessential opportunityhere.
There will be tasks that AI willdo, and they will do them
faster, better, cheaper, and atscale that you just can't do as
(36:44):
a human.
And that's a good thing.
There are going to be thingsthat are uniquely human, and
that is the only place whereemployers, candidates,
businesses of all shapes andsizes, and just being a person
in this world, that's where youdifferentiate yourself.
And I I feel like there's goingto be a counter movement, or,
(37:07):
you know, all the everythingwill be AI'd, or you know,
there's going to be agents doingall this stuff.
And really where you're going tostand out is um doing the things
that only humans can do, i.e.,critical thinking, like you
alluded to before, storytelling,uh, making uh making things that
are compelling and providing ahuman connection.
(37:28):
And I think that's where you'llsee people win.
SPEAKER_02 (37:31):
Yeah, well, I guess great.
Well, yeah, last question, youknow, I think you bring some
pretty uh, I think you have avery unique lens, and I know
hey, whatever everyone's readingor listening to kind of drives
how they learn.
So what are you reading orlistening to these days, Mark?
And you know, maybe like wheredo you go when you're trying to
learn or stay ahead of our ourlandscape or this AI landscape
(37:52):
in general?
SPEAKER_01 (37:53):
Yeah.
Well, given the nature of what Ido, I'm plugged into the
industry.
So I'm always talking withindustry peers and you know
folks at these differentproviders.
So that's certainly a source ofinformation that you know it's
not everyone has time to do thatbecause it's not their job.
I would say more broadly is Ireally, I really love taking
ideas from everywhere.
And so not specifically, youknow, industry or you know,
(38:15):
directly, but like, you know,these are you call them your
social media follows, but I Ipride myself on finding
independent thinkers who Iadmire intellectually, and then
I love it when they disagreewith each other on whatever
topic it is, because that givesme an opportunity to like weigh
two really good arguments andthen decide between those.
(38:36):
And so I guess I don't want togive you a list of people to
follow or anything like that,but I I would urge others, you
know, like you know, if you findyourself in an echo chamber
where everyone seems to beagreeing with them themselves,
step outside of that becausethere's always smart people on
both sides of an argument, andgetting good arguments from two
sides helps you find a better,you know, the more correct
(38:56):
answer ultimately.
And a book I'm reading, and Irecommend it to everybody, but
it's you know, it takes a lot toget your brain wrapped around.
It's called The Beginning ofInfinity by David Deutsch.
And you might want to start withsome podcast interviews.
His last name is spelled uhD-E-U-T-S-C-H, but gives you a
real kind of what I was talkingbefore of giving the foundation
(39:18):
of understanding what it meansto be human, what is knowledge,
uh, how do we grow knowledge andyou know why we shouldn't be so
worried about the robots takingover.
SPEAKER_02 (39:28):
Yeah, so just light, light reading, you know, for
your for your for your afternooncoffee, or maybe the perfect
book if uh you know you ifyou're if you can't sleep.
No, David Deutsch is now on myTwitter feed every day, uh since
you and I have been having thoseconversations.
So fantastic recommendation.
And again, I go back to like youknow, the energy at HR Tech is
you know, it it is good that wehave people coming in from
(39:50):
outside of the TA space andbuilding products.
And you know, I think sure,adding a new uh widget or
feature to you know to an ATS orHRS is something that you know
these large vendors want to talkabout.
But like, you know, I thinkwe're gonna have some really
good ideas come from people whoare not, you know, in the weeds
every day, too.
And you know, I love that youknow, we are all trying to
(40:12):
leave, you know, to learn fromoutside of our industry, period.
All right.
Well, I think that's a goodplace to put a pin in this one,
Mark.
Well, I think our six questionshas to turn into a webinar at
some point too, because youknow, I I think we both get
energized talking about problemsolving and and our approach to
it.
So maybe we'll add that one intothe old ticker also.
Well, till next time, we'll linkeverything in the show notes and
(40:35):
you'll know where to find us.
But yeah, thanks, Mark, asalways, for this and the
continual journey we're on withnow with Tellivity.
So onward.
SPEAKER_01 (40:43):
Thanks for having me and looking forward to it.
And I'll see you in the nextone.
All right, thanks for tuning in.
SPEAKER_02 (40:49):
As always, head on over to changestate.io or shoot
us a note on all the socialmedia.
We'd love to hear from you, andwe'll check you guys next week.
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