January 28, 2025 • 28 mins
Hariharan Kolam, Co-Founder and CEO at Findem joins host James Mackey to discuss how the advancements in AI are reshaping the hiring landscape.
He shares how Findem's solutions revolutionize recruitment, moving beyond traditional resumes to create a more efficient talent-sourcing process.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Hi, this is James Mackey, your host, and today
we're joined by Hari Kolam, thefounder and CEO of Findum.
Hari, I'm glad you could joinus today.
Speaker 2 (00:08):
Thank you.
Thank you for having me, James.
Speaker 1 (00:11):
Yeah, I'm really excited.
I've heard a lot about Findumand I'm looking forward to
learning more and then goingthrough your product and telling
people about what you're doing.
It's really exciting and, justto start us off today, I would
just love to learn a little bitmore about you.
Where are you, where are youjoining us from today?
Speaker 2 (00:27):
So I'm right in the middle of the Silicon Valley
from Redwood City, California.
Speaker 1 (00:31):
Nice, ok, cool, cool.
And you've been working onFindum for what like the better
part of five, six years, is thatright?
Speaker 2 (00:38):
Yeah, it's close to five and a half years now, right
, I mean, we started the companyjust before the pandemic Saw
through the pandemic, sawthrough a crazy market, saw
through the market of the lastcouple of years.
So, yeah, it's an interestingtime to build a talent
acquisition startup and we'velearned a lot through the
process.
Speaker 1 (00:55):
It sounds like a great time.
It sounds like you probably hada lot of fun that first year
Starting a company in the middleof COVID and talent acquisition
.
I don't envy you, but you founda way to make it work.
Speaker 2 (01:07):
Yeah, the company that Pandem has become possibly
is representative of the timesthat we've endured, so it's a
very fun time to actually buildright in the middle of the AI
wave that we are a part of.
Speaker 1 (01:17):
Yeah, for sure.
I couldn't agree more.
It's been a challenging time inbusiness, but there's also it's
been a time for an incredibleopportunity on the market,
particularly within recruitingtechnology, and I'd say, within
the last 12 months, we're seeingso much happening.
We're seeing a lot of companiesmake bet on platform plays,
creating all-in-one solutions.
We're also seeing othercompanies make bets on AI, and
(01:39):
it's interesting to see thedifferent use cases and how
folks are incorporating AI intotheir products.
So you know, that's all like.
I'm really excited to dive intoall of that with you.
But yeah, I mean, before we dothat, I mean I would love to
just get learn a little bit moreabout why you started Findem.
I mean, how did you tell us alittle bit about your founding
story?
Speaker 2 (01:58):
Sure.
So this is my third startup insequence and second one that I'm
co-founding.
One of the common themes inpretty much all the companies
that I've co-founded before,including Findem, is this
deep-rooted data and adeep-rooted infrastructure
problem that we're trying tosolve.
So I consider myself a big datadata warehousing distributed
(02:18):
systems engineer by training.
As an entrepreneur, I getattracted to problems where the
complexity lies in solvingcomplex data problems, so I
never built anything for talentor HR before FindM.
Most of my career has gone intobuilding and solving data
problems for CISO or CIO right.
I mean.
This is different, but thecomplexity of the problem lies
(02:40):
in defining how you think aboutthe talent acquisition
challenges.
One of the observations that ledfor me to essentially get
excited about the talentacquisition problem,
particularly the search problem,is the belief that there's a
better way to represent aprofessional right.
I mean, for the past century,one of the core invariants of
talent workflow has been aresume.
A resume is only as good as theperson writing it.
(03:03):
It's neither complete norconsistent, nor verified, and
resume as a data set has beenthe primary source for all
talent workflows.
If I'm building a pipeline, I'msearching for information
written down by a person.
If I'm analyzing the workforce,I'm aggregating information
based on a data set of a resume.
The problem lies because resumeis a very bleak source for
(03:23):
capturing the impact of aprofessional's career Because
it's a set of keywords that Ican choose to write and, if I'm
lucky, it possibly matches thekeywords that the recruiter
chooses to find me right.
Many cases when we make hiringdecisions, you make it based on
what impact a person can createin my company, and that usually
relies on the impact one hascreated in can create in my
(03:44):
company, and that usuallydepends relies on the impact one
has created in the past intheir career, and those are not
searchable because the peopledon't write things down that
they don't believe is important.
Our intuition when startingFindM was if I were to digitize
a professional's career, I wouldnot look at the user-dependent
information.
But I'm going to essentiallymarry their profile with the
(04:07):
company's information, which Ithink is when they worked in a
particular company, and sort itby time.
We call it the 3D profiles,which is person, company and
time.
That indexes the career in avery digitized way that allows
us to build automated workflowson top.
It became a fascinating problembecause now a four kilobytes of
a text file became a 50megabytes of enriched 3D data,
(04:27):
which falls into the era ofreally what automation grounds
up.
So it became a problem that Ithink we got excited about, and
we knew how to solve it.
Speaker 1 (04:35):
Well, yeah, I love it .
I love it and just fastforwarding to today and I would
love to learn a little bit moreabout your platform, and I would
love to learn a little bit moreabout your platform.
I am looking at your websitetoday.
Copilot for sourcing, inboundmanagement, candidate
rediscovery.
What would you say is the coreproduct offering that, as of
(04:57):
today, your customers when theycome to find them?
What is the core use case thatyou're seeing in 2024 or 2020,
as we're leading into Q1 2025?
Speaker 2 (05:08):
Yeah, there are two problems that we essentially
solve for talent acquisitionprofessionals.
One is building a pipeline andanalyzing the workforce.
There's a search part andthere's an inside part.
I mentioned three-dimensionalprofile, which means the data
asset that we create about aprofessional is very unique and
big.
It is automation-ready.
It is automation ready.
It is AI ready.
Also, ai on top of a resume isincomplete because it's going to
(05:29):
give you garbage in and garbageout by redefining the resume.
The data set is big.
So the solution that we offeris to leverage data and AI and
you mentioned co-pilot forsourcing to build robust talent
pipelines.
When I say talent pipeline, arecruiter typically does three
activities to build a pipeline.
They hunt for new talent, whichmeans they source, which means
they go outside and find newpeople that they don't know.
(05:51):
They farm existing talent,which means they go inside their
network, be it their applicanttracking system or referrals
within the company, or possiblypeople that are alumni or people
that I think you've beennurturing in the CRM.
I mean you go and farm them oryou nurture them for tomorrow,
which is build a pipelineproactively for tomorrow.
All these three elements aredifferent workflows and
different tools stored indifferent places, right.
(06:13):
So what we built here is tounify all of that.
It's a single pane of glassthat actually looks at talent
across all available channelsand provides a single point view
for talent teams to buildpipeline.
And similarly, 3d Profile isbuilt on a large-scale BI
platform.
It allows rich analytics andinsights to further analyzing
(06:33):
the workforce a lot moreefficiently.
So we go to the market withthese two major offerings.
One is a consolidation of allpipeline building efforts.
The other is a consolidation ofall data and insights effort.
Speaker 1 (06:44):
Okay, got it.
And so for the inbound pipelineor the inbound management, so
in candidate rediscovery.
So essentially, you're usingdata to pull from the CRM the
most relevant talent and surface, that, and then is that
accurate.
Speaker 2 (07:04):
Rediscovery essentially is looking at your
applicant tracking system.
That and then.
Is that accurate?
Rediscovery essentially islooking at your applicant
tracking system and your networkto find candidates that are a
match, that may have applied nowor applied in the past.
It goes and enriches theinformation and finds them
through attributes.
Speaker 1 (07:17):
And then is it how, from a sourcing perspective,
what does it are people doingagain like they're doing the
outreach through your, yourproduct as well?
So they're finding, they'redoing the rediscovery process,
they're finding the folks thathave already applied in the past
and they're doing the sourcingthrough Findham.
Speaker 2 (07:35):
They do the whole soup to nut, right From find to
engage to inbound scanning, allof those to Findham.
It's a centralized place to doit.
Again, James, I'm not surewhether it is useful to show and
tell the story, but I think ifit is useful and if it's a
precedent, I can actually dothat as well.
Speaker 1 (07:49):
Yeah, for sure.
I mean, if you want to do that,I think that'd be awesome.
Speaker 2 (07:52):
Yeah, I think I show a couple of things so that way
you actually can visualize whatwe mean just for everybody
tuning in, like in the episodelink.
Speaker 1 (08:02):
We also we do post the.
We post the video on YouTube soyou can actually go on and you
can watch the video to see theshare screen that Hari is doing
right now.
Speaker 2 (08:14):
Thank you, James.
So here's an example of aprofile on, say, I looked at
your profile, right, James?
Speaker 1 (08:20):
Right.
Speaker 2 (08:21):
And on Find them, and this is it actually shows you
information that you that right,james Right and on Find, I mean
, this is it actually shows youinformation that is indexed on a
search engine based on theinformation that you put either
on your LinkedIn profiles andwhatnot, right?
So this is only as good as aperson searching in a title or
English.
When you look at it from a 3Dprofile, you actually can look
at timeline of the company, yourtenure and the spectrum of
(08:42):
where the company was, sothereby you actually get a
comprehensive view of yourcareer.
Right, so you can correlatethat with the financing round,
so you actually know what anindividual essentially has gone
through.
And these battle scars areextremely hard to capture in
english because people don'tjust write.
People write what they want towrite and but search is based on
the, the impact that you'veendured, not based on the
english that you wrote, right?
So how do you apply?
This is here's an exampleworkflow Supposing I'm actually
(09:05):
building a pipeline for, say, anaccount executive, right?
So?
And this is linked to my lever,ats and this is my job
description, so we find the waythe automation works is in a
single click you can leveragethe information about the job
and leverage the 3D profile ofthe candidates and go ahead and
build a search in a veryautomated way where it's going
(09:27):
to transform and translate thejob description into a
searchable set of attributes.
An attribute works in 3D and itactually goes omni-channel it's
not just one channel as asearch.
If I were to look at it, itautomatically went ahead and
converted a search for me andshows me talent across my entire
pipeline.
I look at it.
It automatically went ahead andconverted a search for me and
shows me talent across my entirepipeline.
(09:47):
If I look at my inboundapplicants, it goes into my ATS
and finds talent that matchesall of these attributes.
Yeah, if you look at pastapplicants, it shows you here.
If I'm looking at externalcandidates, it shows you here.
So the warm, hot, warm and coldrelationships are all in a
single pane of glass.
Search becomes an almostsimplistic exercise.
Okay, everyone of thesecandidates is essentially
(10:10):
AI-powered, so the goal here issingle pane of glass
omni-channel search,attribute-based search.
Speaker 1 (10:15):
Got it and I'm curious too, as you're looking
into 2025, where do you see yourproduct heading, your platform
heading, Particularly as we'redialing into AI use cases, are
you seeing requests fromcustomers in terms of product
roadmap in one direction oranother, Like how do you think
about building AI into yourproduct roadmap into 2025?
Speaker 2 (10:40):
Yeah, ai is a very important topic and it's also a
topic where there's a lot ofaspirations as well as a lot of
fear, right, I mean, we hearthere's obvious places where we
implemented AI in 2023,messaging or possibly writing
candidate notes on hiringmanager notes and all those cool
things which generate AI isessentially functional.
We believe and this is somethingthat I think is going to be an
(11:02):
important pillar as we startbuilding out the platform for
2025, is the amount of clicksthat are dead right now.
In terms of building thepipeline, there's a significant
automatable part of the jobthat's going to be AI-enabled,
right.
So, if you think about arecruiter's lifecycle, there is
an IQ part of their job which issearching, building a pipeline
(11:23):
and generating that slate ofcandidates.
There's an EQ part of the jobwhich is taking the candidates
and then running them through aprocess and then closing them
right.
We believe that the IQ part,predominantly, is going to be
automated through AI-enabledworkflows.
Right.
Eq part, through the right setof data set, is going to empower
the recruiters to actually goahead and have meaningful
conversations with the hiringmanager.
For us, that's the vision onwhat does a talent team and a
(11:46):
talent tech stack look like inthe AI-first first era, and
which parts are automatable,which parts are not, and that's
a very important driving factoron how we think about a product
strategy.
Speaker 1 (12:01):
I totally agree.
It's interesting and we saw,you know, 2023, a lot of focus
on, I think initially kind ofco-pilot.
We saw content.
It's interesting the differentprofiles.
I mean I started a productcompany called June and June's
focus is essentially knockingout pre-screening, voice AI
interviews.
So essentially, like allinbound applicants, right as
(12:24):
soon as they apply, June givesthem a call and, you know, walks
through what we consider theknockout questions, where you
know, where recruiters areessentially in the first five to
10 minutes, they'redisqualifying candidates, which
takes a ton of hours and a tonof human horsepower to
accomplish.
And so from an automation top offunnel whether it's sort of
(12:45):
like what FINDM is doing right,where you're doing resurfacing,
sourcing, end-on-management, Imean there's just where it's
like you get into these likehigh volume aspects that require
a lot of time and headcount todo.
I think that that's like reallywhere the most we're going to
see the most disruption withintalent acquisition, right.
I mean, because then you getlike into down funnel motions.
(13:06):
The reality is like the Canadapipelines just aren't quite as
big.
So it's not that it wouldn't bevaluable down funnel towards,
like as you're gettingapproaching, like final rounds
and stuff like that.
It's just that it's not.
You know, there's there'sthere's a greater need at more
of the the top of funnel rightFor a lot of the automation and
AI.
So I think that those are theuse cases that are really going
(13:29):
to be the most disruptive andwhere companies are essentially
going to grow the most.
Yeah, you got products likeyours, you know, start products
like popping up like June, likemine, and then you have an
interview intelligence right.
So you got like Bright Hire andPillar companies like that,
which are essentially doing theco-pilot on Zooms and from there
like essentially like packagingdata and then helping write
(13:52):
custom questions and thenrecording everybody's answers,
seeing if they actually answeredeverything that they should
have, and then they're actuallytaking almost like a Gong-based
approach where they're rankinghiring managers on the quality
of the interviews.
So I mean that's a prettyinteresting use case as well.
Speaker 2 (14:11):
It indeed is right.
I think the AI transformationacross every function, like you
just described, it's going tolook and feel differently and I
think it's going to elevate therole of what a recruiter will
look like for tomorrow, right?
So it's very well said.
Speaker 1 (14:23):
Yeah, I mean I think just like the whole, like AI
kind of talent operations top ofthe level workflows.
You know, we also just had theCEO of Sense on the show and
that was a really interestingconversation where he was
talking about kind of theautomation layers that they're
thinking through as well.
And then you got even companieslike I recommend also for
(14:44):
people to check out the episodewith Nikos, the CEO of Workable,
and they're I mean they'rebasically implementing AI and
automation into like every stepof their applicant tracking
system, which is pretty cool too.
But yeah, I mean I'm curious,like, with some of this
automation that you're seeingand like the AI you're
incorporating, like whatindustries are you seeing the
most demand from working withinthe tech sector?
(15:07):
Or do you see a lot of demandfrom industries where it's like
higher volume recruiting,potentially like healthcare,
retail?
You know manufacturing lightindustrial, I mean, do you see a
lot of need for automationthere?
Or, like, where are you seeingthe most demand right now?
Speaker 2 (15:26):
Well, I think we categorize the market into two
different buckets.
Right, there's a high precisionsearching and there's a high
volume.
I mean, in high precisionsearching, the precision around
who you're looking for becomessuper important to build a
pipeline.
Tech, of course, essentiallyfeatures quite a lot there.
I mean healthcare essentiallyis an interesting one where the
other part of it, which is whereworkflow wins, where you're
(15:46):
looking at automating a bunch oftasks to actually get the
outcome because it is a conveyorbelt approach, so different
kinds of things, the precisionone on executive search, folks
that are doing mid-careerprofessionals.
On the tech side there's a lotof pull.
I mean I think the marketrebound essentially is real
there, right, healthcareconstructions, financial
services, right, I mean it Ithink is slow burn, but I think
it is actually the significantamount of nuances around
(16:10):
combining either precision or aworkflow element to carve out a
solution.
So we actually have seen inbroad strokes, right, I mean we
have a partnership with Paychexwhich I think is tackling a
different kind of a companywhich is doing a different kind
of a high volume stuff.
The challenge therepredominantly is going to be on
the workflow side, right.
So different pockets ofindustries and companies.
We've seen different elementsof the platform essentially
(16:30):
shine a lot more.
Speaker 1 (16:31):
Yeah, I hear you on that.
Well, yeah, it's definitely areally, really exciting time to
be in AI and talent acquisitionin general.
Curious to get your thoughtsjust from like on the future of
AI, like future use cases, overthe next 18 months.
So are there any other areasthat really interest you?
Maybe it's down the pipeproduct strategy for Findum, or
maybe it's like, okay, it's notthe right product build out for
(16:55):
your team, but that you're justkind of excited to see evolve in
the industry.
Speaker 2 (17:00):
I think the whole chatter around agent is a very
important advancement.
I think it's starting out in avery simplistic way, but I think
it is something that I thinkI'm very excited to look out for
, because it's an important toolthat actually talks about
contextualizing automation,right as a thing.
Right.
So in this world ofidentification, I mean, how does
many of the tasks?
Because, if you think abouttalent acquisition in the
(17:21):
industry that we live in, about80% of the tasks are fairly
menial and automatable, right?
So those are things that Ithink are not even exciting for
most of the recruiters.
Those are necessarily evil froma workflow perspective, right?
So for me, I'm particularlyexcited about the next wave of
action modeling that happens,which is so.
You actually had a largelanguage model, and how do you
(17:42):
get into this mode of takingactions based on leveraging AI
in a framework of an agent?
It's something that I think isan exciting next big thing for
2025 in front of us.
Speaker 1 (17:56):
Yeah, for sure, for sure.
And, by the way, I saw on thewebsite something I know you had
also mentioned, like executivesearch, and I was wondering is
there a services element toFINDM or is it 100% product
platform-based?
Speaker 2 (18:10):
It's 100% product platform-based.
Findem is very well-suited forexec search because the nature
of the search is highlyprecision right.
For example, if I say, hey,find me the CFO in Georgia who's
working in a PEVC company, sawthrough a company from early
stage on exit and currentlyworking in a manufacturing
sector near impossible search.
How do you think you could dothat in a one-dimensional resume
search way?
Right?
Finder is geared towardsfinding that needle in the
(18:30):
haystack because 3D profilescapture the attributes.
So we found a lot of pull fromthe exec search side.
Speaker 1 (18:37):
Okay, got it.
Yeah, that makes a ton of sense.
Well, what else can you tell usabout Findum?
I mean, I would love to learneven more about the product
Future.
You know.
I think like maybe onedirection we can go into is I
was kind of interested to learnbecause I initially did think
that more on the like when itcomes to recruiting automation.
I don't know why, but I assumelike my mind kind of went to
(18:59):
more like the higher volumebucket that you were discussing,
right Like you know companiesthat are like potentially
construction or health care.
But when I was speaking withthe CEO of Sense and they're
kind of like echoing what you'resaying too, you know, I think
around they said about 30% oftheir 20 to 30% of their
business is like knowledgeworkers, like within technology.
So that's interesting.
Do you also?
Do you see demand in staffingand recruiting?
Speaker 2 (19:23):
So we don't sell into staffing and recruiting, but
there's always demand, right.
I mean, I think one of theworkflow will win there, because
I think it's purely aboutoptimizing the time, because
bottom line here in the staffingindustry essentially is people
time.
Speaker 1 (19:37):
Yeah, yeah, because I think that's another
interesting point is likestaffing and recruiting
companies being early adoptersfor a lot of the more disruptive
recruiting automation.
Speaker 2 (19:49):
Yeah 100%, because it actually ties into the bottom
line quite directly Right?
So it does, and I think forthat reason the AI adoption in
staffing and agency has beenpredominantly high, and I think
we've seen that as well.
Speaker 1 (20:02):
Yeah, for sure.
I mean that's for June.
We're looking at where we'reseeing demand for AI
interviewing and we're seeingdemand in the staffing and
recruiting segment.
I mean other SaaS companies too, but there's definitely that
use case because it can impacttop line bottom line, you know,
(20:24):
services companies, thinnermargins, right, and one of the
challenges of scaling a staffingbusiness is, just because
you're scaling top line, revenuegrowth doesn't necessarily mean
that you're making more money,right, because, like the
overhead, it's so overheadintensive, right, so it's not so
appealing, as is the marginRight.
Because, like the overhead, it'sso overhead intensive, right,
so it's not so appealing, as isthe margin, right.
So, yeah, I mean, I think it'slike there's.
(20:46):
It's interesting to see, likeyou know, where the where the
demand is kind of coming frominitially and and yeah, staffing
and recruiting.
You know, based on a lot of theCEOs I've spoken to, they're
they're saying that they'regetting a fair amount of demand
from staffing and recruiting andthen they're also kind of
considering the staffing andrecruiting early adopters, sort
of like ahead of the market,right.
Speaker 2 (21:08):
That's very true.
When you think about staffing,definitely right, I mean.
When you think about evenbigger enterprises, right, I
mean, one of the beautifulthings.
It's an interesting dichotomy.
When you think about anyrecruiter and you tell them that
here's the requisition, I needto go close the role, the very
first thing that they would sayis I need to go source right.
Most of the most of themessentially believe that they
need to go and build newrelationship all the time, which
(21:28):
is go and talk to customers.
Candidates outside 90 of thehighest for most enterprises are
people that they already knowright.
So they either applied now orapplied previously, or possibly
are sitting in their crmsomewhere.
Or people that I think could bereferred by an employee within
the company, or folks that Ithink have nurtured in the past
right.
So hunting essentially is themost laborious way to
essentially go and get acandidate.
(21:49):
Imagine this that you're takinga completely cold network and
then trying to convince them totake a meeting right, While
completely overlooking your warmand hot leads within your
network because they're notdiscoverable.
So in reality, when you thinkabout productivity I mean the
aspect of combining in-networkand out-network is an important
manifestation and I think whenwe think through it from an
option and productivityperspective, it's an important
(22:11):
beachhead because you'respending less time doing
possibly getting better yield,than doing it purely going out
and coming in, which is huntingto farm, but that could be
farmed in nature.
Speaker 1 (22:20):
Yeah, definitely.
Do you have any thoughts on AIand compliance for hiring?
I don't know if you've spent alot of time just going through
what, from a regulationperspective, is likely going to
happen, but curious to get yourtake.
If you know we don't have todive into this, I'm curious.
I mean, is that somethingyou've given a lot of thought to
(22:42):
or have any thoughts around?
Speaker 2 (22:45):
So AI even the New York AI bias audit that, I think
, is a mandatory checkbox forevery single enterprise contract
that we need to deal with.
Along with the InfoSec, the AIbias questionnaire is an
important part of the salescycle.
So we do essentially haveinteresting experiences because,
as we see, the industryessentially go mature I mean,
(23:06):
the perspective on what AI cando or cannot do essentially is
evolving right.
So one of the conscious thingsthat we believe in, particularly
in recruiting, which I think ismanifesting in a way where the
laws essentially are evolving aswell, is decisioning, like,
who's doing the decisioning forwhat right?
I mean, is it an assisteddecisioning or is it a
displacement decision?
I mean, are we essentiallyusing AI to make a decision on
(23:27):
behalf of a recruiter orassisting the recruiter to make
better decisions?
Right?
We firmly believe that,considering the state of affairs
with AI I mean considering thestakes at hand I mean assistance
is exactly the way toessentially go, which means that
AI, automated decision-making,is something that I think is a
big no in the design principlein FindUp.
I mean it's pretty much all ofour workplaces are cognizant of
(23:49):
that.
I truly believe that thecompliance part of AI
essentially is going to evolveto make sure that the
decisioning essentially isappropriately moderated,
protected and verified, and Ithink that is something that
we're already seeing in theearly tea leaves of the whole
industry.
Speaker 1 (24:04):
So when you said on enterprise contracts, you had
mentioned that something istypically baked into those.
What was that again?
Speaker 2 (24:11):
So when you go through an enterprise contract
with any mid-size to large-sizeenterprise, we used to
essentially do InfoSecrequirements as part of the
contracting, because you used togo and say I need to integrate
my email, I need to integrate myapplicant tracking system,
right.
So AI as a next step is we'reseeing it more often than not
now in pretty much all of ourengagements.
Speaker 1 (24:33):
Yeah, yeah, it's interesting.
I mean, do you see, like, howrisk adverse are you seeing a
lot of?
Have you?
Has that been a prohibitor tomove forward with some
enterprise customers wherethey're just basically saying,
hey, we want to kick it untilnext year when we have a better
pulse on where the regulation isgoing to fall?
Speaker 2 (24:49):
I mean, so I think that people are conscious about
AI decisioning.
So one of the things that we'vemade sure with Findem as a
platform is all the searching isBI first, so we don't
essentially let AI make adecision there.
Ai decisioning essentially isan overlay on top of the results
to assist the recruiters withthe right data at the right time
not on matching.
(25:09):
Matching essentially is BI first, which actually eases the nerve
.
I think there are a lot ofanxiety around automated
decision making from AI, which Ithink is what the regulations
is also protecting, which iswhat the company is also
concerned about.
Speaker 1 (25:22):
Yeah about.
Yeah, that makes a lot of sense.
It'll be interesting to see howthat continues to evolve over
the next year or two, and it'sinteresting to see how companies
are making bets on recruitingtech companies and whether
they're looking at AI assistantversus how much of the decision
process are they taking over.
But, ari, anyways, this hasbeen a ton of fun.
(25:43):
I really appreciate you comingon the show with me today
sharing your expertise, tellingus about Findum.
I know our audience is going tolearn a lot from everything
that you've shared with us today.
We really appreciate yoursupport and your contribution to
the show.
Speaker 2 (25:57):
I appreciate it and thanks for hosting me here,
James.
It was a pleasure talking toyou as well.
Speaker 1 (26:01):
Yeah, absolutely, and for everybody tuning in.
Thank you so much and we'lltalk to you real soon.
Take care.
Hi, this is James Mackey, your host, and today
we're joined by Hari Kolam, thefounder and CEO of Findum.
Hari, I'm glad you could joinus today.
Speaker 2 (00:08):
Thank you.
Thank you for having me, James.
Speaker 1 (00:11):
Yeah, I'm really excited.
I've heard a lot about Findumand I'm looking forward to
learning more and then goingthrough your product and telling
people about what you're doing.
It's really exciting and, justto start us off today, I would
just love to learn a little bitmore about you.
Where are you, where are youjoining us from today?
Speaker 2 (00:27):
So I'm right in the middle of the Silicon Valley
from Redwood City, California.
Speaker 1 (00:31):
Nice, ok, cool, cool.
And you've been working onFindum for what like the better
part of five, six years, is thatright?
Speaker 2 (00:38):
Yeah, it's close to five and a half years now, right
, I mean, we started the companyjust before the pandemic Saw
through the pandemic, sawthrough a crazy market, saw
through the market of the lastcouple of years.
So, yeah, it's an interestingtime to build a talent
acquisition startup and we'velearned a lot through the
process.
Speaker 1 (00:55):
It sounds like a great time.
It sounds like you probably hada lot of fun that first year
Starting a company in the middleof COVID and talent acquisition
.
I don't envy you, but you founda way to make it work.
Speaker 2 (01:07):
Yeah, the company that Pandem has become possibly
is representative of the timesthat we've endured, so it's a
very fun time to actually buildright in the middle of the AI
wave that we are a part of.
Speaker 1 (01:17):
Yeah, for sure.
I couldn't agree more.
It's been a challenging time inbusiness, but there's also it's
been a time for an incredibleopportunity on the market,
particularly within recruitingtechnology, and I'd say, within
the last 12 months, we're seeingso much happening.
We're seeing a lot of companiesmake bet on platform plays,
creating all-in-one solutions.
We're also seeing othercompanies make bets on AI, and
(01:39):
it's interesting to see thedifferent use cases and how
folks are incorporating AI intotheir products.
So you know, that's all like.
I'm really excited to dive intoall of that with you.
But yeah, I mean, before we dothat, I mean I would love to
just get learn a little bit moreabout why you started Findem.
I mean, how did you tell us alittle bit about your founding
story?
Speaker 2 (01:58):
Sure.
So this is my third startup insequence and second one that I'm
co-founding.
One of the common themes inpretty much all the companies
that I've co-founded before,including Findem, is this
deep-rooted data and adeep-rooted infrastructure
problem that we're trying tosolve.
So I consider myself a big datadata warehousing distributed
(02:18):
systems engineer by training.
As an entrepreneur, I getattracted to problems where the
complexity lies in solvingcomplex data problems, so I
never built anything for talentor HR before FindM.
Most of my career has gone intobuilding and solving data
problems for CISO or CIO right.
I mean.
This is different, but thecomplexity of the problem lies
(02:40):
in defining how you think aboutthe talent acquisition
challenges.
One of the observations that ledfor me to essentially get
excited about the talentacquisition problem,
particularly the search problem,is the belief that there's a
better way to represent aprofessional right.
I mean, for the past century,one of the core invariants of
talent workflow has been aresume.
A resume is only as good as theperson writing it.
(03:03):
It's neither complete norconsistent, nor verified, and
resume as a data set has beenthe primary source for all
talent workflows.
If I'm building a pipeline, I'msearching for information
written down by a person.
If I'm analyzing the workforce,I'm aggregating information
based on a data set of a resume.
The problem lies because resumeis a very bleak source for
(03:23):
capturing the impact of aprofessional's career Because
it's a set of keywords that Ican choose to write and, if I'm
lucky, it possibly matches thekeywords that the recruiter
chooses to find me right.
Many cases when we make hiringdecisions, you make it based on
what impact a person can createin my company, and that usually
relies on the impact one hascreated in can create in my
(03:44):
company, and that usuallydepends relies on the impact one
has created in the past intheir career, and those are not
searchable because the peopledon't write things down that
they don't believe is important.
Our intuition when startingFindM was if I were to digitize
a professional's career, I wouldnot look at the user-dependent
information.
But I'm going to essentiallymarry their profile with the
(04:07):
company's information, which Ithink is when they worked in a
particular company, and sort itby time.
We call it the 3D profiles,which is person, company and
time.
That indexes the career in avery digitized way that allows
us to build automated workflowson top.
It became a fascinating problembecause now a four kilobytes of
a text file became a 50megabytes of enriched 3D data,
(04:27):
which falls into the era ofreally what automation grounds
up.
So it became a problem that Ithink we got excited about, and
we knew how to solve it.
Speaker 1 (04:35):
Well, yeah, I love it .
I love it and just fastforwarding to today and I would
love to learn a little bit moreabout your platform, and I would
love to learn a little bit moreabout your platform.
I am looking at your websitetoday.
Copilot for sourcing, inboundmanagement, candidate
rediscovery.
What would you say is the coreproduct offering that, as of
(04:57):
today, your customers when theycome to find them?
What is the core use case thatyou're seeing in 2024 or 2020,
as we're leading into Q1 2025?
Speaker 2 (05:08):
Yeah, there are two problems that we essentially
solve for talent acquisitionprofessionals.
One is building a pipeline andanalyzing the workforce.
There's a search part andthere's an inside part.
I mentioned three-dimensionalprofile, which means the data
asset that we create about aprofessional is very unique and
big.
It is automation-ready.
It is automation ready.
It is AI ready.
Also, ai on top of a resume isincomplete because it's going to
(05:29):
give you garbage in and garbageout by redefining the resume.
The data set is big.
So the solution that we offeris to leverage data and AI and
you mentioned co-pilot forsourcing to build robust talent
pipelines.
When I say talent pipeline, arecruiter typically does three
activities to build a pipeline.
They hunt for new talent, whichmeans they source, which means
they go outside and find newpeople that they don't know.
(05:51):
They farm existing talent,which means they go inside their
network, be it their applicanttracking system or referrals
within the company, or possiblypeople that are alumni or people
that I think you've beennurturing in the CRM.
I mean you go and farm them oryou nurture them for tomorrow,
which is build a pipelineproactively for tomorrow.
All these three elements aredifferent workflows and
different tools stored indifferent places, right.
(06:13):
So what we built here is tounify all of that.
It's a single pane of glassthat actually looks at talent
across all available channelsand provides a single point view
for talent teams to buildpipeline.
And similarly, 3d Profile isbuilt on a large-scale BI
platform.
It allows rich analytics andinsights to further analyzing
(06:33):
the workforce a lot moreefficiently.
So we go to the market withthese two major offerings.
One is a consolidation of allpipeline building efforts.
The other is a consolidation ofall data and insights effort.
Speaker 1 (06:44):
Okay, got it.
And so for the inbound pipelineor the inbound management, so
in candidate rediscovery.
So essentially, you're usingdata to pull from the CRM the
most relevant talent and surface, that, and then is that
accurate.
Speaker 2 (07:04):
Rediscovery essentially is looking at your
applicant tracking system.
That and then.
Is that accurate?
Rediscovery essentially islooking at your applicant
tracking system and your networkto find candidates that are a
match, that may have applied nowor applied in the past.
It goes and enriches theinformation and finds them
through attributes.
Speaker 1 (07:17):
And then is it how, from a sourcing perspective,
what does it are people doingagain like they're doing the
outreach through your, yourproduct as well?
So they're finding, they'redoing the rediscovery process,
they're finding the folks thathave already applied in the past
and they're doing the sourcingthrough Findham.
Speaker 2 (07:35):
They do the whole soup to nut, right From find to
engage to inbound scanning, allof those to Findham.
It's a centralized place to doit.
Again, James, I'm not surewhether it is useful to show and
tell the story, but I think ifit is useful and if it's a
precedent, I can actually dothat as well.
Speaker 1 (07:49):
Yeah, for sure.
I mean, if you want to do that,I think that'd be awesome.
Speaker 2 (07:52):
Yeah, I think I show a couple of things so that way
you actually can visualize whatwe mean just for everybody
tuning in, like in the episodelink.
Speaker 1 (08:02):
We also we do post the.
We post the video on YouTube soyou can actually go on and you
can watch the video to see theshare screen that Hari is doing
right now.
Speaker 2 (08:14):
Thank you, James.
So here's an example of aprofile on, say, I looked at
your profile, right, James?
Speaker 1 (08:20):
Right.
Speaker 2 (08:21):
And on Find them, and this is it actually shows you
information that you that right,james Right and on Find, I mean
, this is it actually shows youinformation that is indexed on a
search engine based on theinformation that you put either
on your LinkedIn profiles andwhatnot, right?
So this is only as good as aperson searching in a title or
English.
When you look at it from a 3Dprofile, you actually can look
at timeline of the company, yourtenure and the spectrum of
(08:42):
where the company was, sothereby you actually get a
comprehensive view of yourcareer.
Right, so you can correlatethat with the financing round,
so you actually know what anindividual essentially has gone
through.
And these battle scars areextremely hard to capture in
english because people don'tjust write.
People write what they want towrite and but search is based on
the, the impact that you'veendured, not based on the
english that you wrote, right?
So how do you apply?
This is here's an exampleworkflow Supposing I'm actually
(09:05):
building a pipeline for, say, anaccount executive, right?
So?
And this is linked to my lever,ats and this is my job
description, so we find the waythe automation works is in a
single click you can leveragethe information about the job
and leverage the 3D profile ofthe candidates and go ahead and
build a search in a veryautomated way where it's going
(09:27):
to transform and translate thejob description into a
searchable set of attributes.
An attribute works in 3D and itactually goes omni-channel it's
not just one channel as asearch.
If I were to look at it, itautomatically went ahead and
converted a search for me andshows me talent across my entire
pipeline.
I look at it.
It automatically went ahead andconverted a search for me and
shows me talent across my entirepipeline.
(09:47):
If I look at my inboundapplicants, it goes into my ATS
and finds talent that matchesall of these attributes.
Yeah, if you look at pastapplicants, it shows you here.
If I'm looking at externalcandidates, it shows you here.
So the warm, hot, warm and coldrelationships are all in a
single pane of glass.
Search becomes an almostsimplistic exercise.
Okay, everyone of thesecandidates is essentially
(10:10):
AI-powered, so the goal here issingle pane of glass
omni-channel search,attribute-based search.
Speaker 1 (10:15):
Got it and I'm curious too, as you're looking
into 2025, where do you see yourproduct heading, your platform
heading, Particularly as we'redialing into AI use cases, are
you seeing requests fromcustomers in terms of product
roadmap in one direction oranother, Like how do you think
about building AI into yourproduct roadmap into 2025?
Speaker 2 (10:40):
Yeah, ai is a very important topic and it's also a
topic where there's a lot ofaspirations as well as a lot of
fear, right, I mean, we hearthere's obvious places where we
implemented AI in 2023,messaging or possibly writing
candidate notes on hiringmanager notes and all those cool
things which generate AI isessentially functional.
We believe and this is somethingthat I think is going to be an
(11:02):
important pillar as we startbuilding out the platform for
2025, is the amount of clicksthat are dead right now.
In terms of building thepipeline, there's a significant
automatable part of the jobthat's going to be AI-enabled,
right.
So, if you think about arecruiter's lifecycle, there is
an IQ part of their job which issearching, building a pipeline
(11:23):
and generating that slate ofcandidates.
There's an EQ part of the jobwhich is taking the candidates
and then running them through aprocess and then closing them
right.
We believe that the IQ part,predominantly, is going to be
automated through AI-enabledworkflows.
Right.
Eq part, through the right setof data set, is going to empower
the recruiters to actually goahead and have meaningful
conversations with the hiringmanager.
For us, that's the vision onwhat does a talent team and a
(11:46):
talent tech stack look like inthe AI-first first era, and
which parts are automatable,which parts are not, and that's
a very important driving factoron how we think about a product
strategy.
Speaker 1 (12:01):
I totally agree.
It's interesting and we saw,you know, 2023, a lot of focus
on, I think initially kind ofco-pilot.
We saw content.
It's interesting the differentprofiles.
I mean I started a productcompany called June and June's
focus is essentially knockingout pre-screening, voice AI
interviews.
So essentially, like allinbound applicants, right as
(12:24):
soon as they apply, June givesthem a call and, you know, walks
through what we consider theknockout questions, where you
know, where recruiters areessentially in the first five to
10 minutes, they'redisqualifying candidates, which
takes a ton of hours and a tonof human horsepower to
accomplish.
And so from an automation top offunnel whether it's sort of
(12:45):
like what FINDM is doing right,where you're doing resurfacing,
sourcing, end-on-management, Imean there's just where it's
like you get into these likehigh volume aspects that require
a lot of time and headcount todo.
I think that that's like reallywhere the most we're going to
see the most disruption withintalent acquisition, right.
I mean, because then you getlike into down funnel motions.
(13:06):
The reality is like the Canadapipelines just aren't quite as
big.
So it's not that it wouldn't bevaluable down funnel towards,
like as you're gettingapproaching, like final rounds
and stuff like that.
It's just that it's not.
You know, there's there'sthere's a greater need at more
of the the top of funnel rightFor a lot of the automation and
AI.
So I think that those are theuse cases that are really going
(13:29):
to be the most disruptive andwhere companies are essentially
going to grow the most.
Yeah, you got products likeyours, you know, start products
like popping up like June, likemine, and then you have an
interview intelligence right.
So you got like Bright Hire andPillar companies like that,
which are essentially doing theco-pilot on Zooms and from there
like essentially like packagingdata and then helping write
(13:52):
custom questions and thenrecording everybody's answers,
seeing if they actually answeredeverything that they should
have, and then they're actuallytaking almost like a Gong-based
approach where they're rankinghiring managers on the quality
of the interviews.
So I mean that's a prettyinteresting use case as well.
Speaker 2 (14:11):
It indeed is right.
I think the AI transformationacross every function, like you
just described, it's going tolook and feel differently and I
think it's going to elevate therole of what a recruiter will
look like for tomorrow, right?
So it's very well said.
Speaker 1 (14:23):
Yeah, I mean I think just like the whole, like AI
kind of talent operations top ofthe level workflows.
You know, we also just had theCEO of Sense on the show and
that was a really interestingconversation where he was
talking about kind of theautomation layers that they're
thinking through as well.
And then you got even companieslike I recommend also for
(14:44):
people to check out the episodewith Nikos, the CEO of Workable,
and they're I mean they'rebasically implementing AI and
automation into like every stepof their applicant tracking
system, which is pretty cool too.
But yeah, I mean I'm curious,like, with some of this
automation that you're seeingand like the AI you're
incorporating, like whatindustries are you seeing the
most demand from working withinthe tech sector?
(15:07):
Or do you see a lot of demandfrom industries where it's like
higher volume recruiting,potentially like healthcare,
retail?
You know manufacturing lightindustrial, I mean, do you see a
lot of need for automationthere?
Or, like, where are you seeingthe most demand right now?
Speaker 2 (15:26):
Well, I think we categorize the market into two
different buckets.
Right, there's a high precisionsearching and there's a high
volume.
I mean, in high precisionsearching, the precision around
who you're looking for becomessuper important to build a
pipeline.
Tech, of course, essentiallyfeatures quite a lot there.
I mean healthcare essentiallyis an interesting one where the
other part of it, which is whereworkflow wins, where you're
(15:46):
looking at automating a bunch oftasks to actually get the
outcome because it is a conveyorbelt approach, so different
kinds of things, the precisionone on executive search, folks
that are doing mid-careerprofessionals.
On the tech side there's a lotof pull.
I mean I think the marketrebound essentially is real
there, right, healthcareconstructions, financial
services, right, I mean it Ithink is slow burn, but I think
it is actually the significantamount of nuances around
(16:10):
combining either precision or aworkflow element to carve out a
solution.
So we actually have seen inbroad strokes, right, I mean we
have a partnership with Paychexwhich I think is tackling a
different kind of a companywhich is doing a different kind
of a high volume stuff.
The challenge therepredominantly is going to be on
the workflow side, right.
So different pockets ofindustries and companies.
We've seen different elementsof the platform essentially
(16:30):
shine a lot more.
Speaker 1 (16:31):
Yeah, I hear you on that.
Well, yeah, it's definitely areally, really exciting time to
be in AI and talent acquisitionin general.
Curious to get your thoughtsjust from like on the future of
AI, like future use cases, overthe next 18 months.
So are there any other areasthat really interest you?
Maybe it's down the pipeproduct strategy for Findum, or
maybe it's like, okay, it's notthe right product build out for
(16:55):
your team, but that you're justkind of excited to see evolve in
the industry.
Speaker 2 (17:00):
I think the whole chatter around agent is a very
important advancement.
I think it's starting out in avery simplistic way, but I think
it is something that I thinkI'm very excited to look out for
, because it's an important toolthat actually talks about
contextualizing automation,right as a thing.
Right.
So in this world ofidentification, I mean, how does
many of the tasks?
Because, if you think abouttalent acquisition in the
(17:21):
industry that we live in, about80% of the tasks are fairly
menial and automatable, right?
So those are things that Ithink are not even exciting for
most of the recruiters.
Those are necessarily evil froma workflow perspective, right?
So for me, I'm particularlyexcited about the next wave of
action modeling that happens,which is so.
You actually had a largelanguage model, and how do you
(17:42):
get into this mode of takingactions based on leveraging AI
in a framework of an agent?
It's something that I think isan exciting next big thing for
2025 in front of us.
Speaker 1 (17:56):
Yeah, for sure, for sure.
And, by the way, I saw on thewebsite something I know you had
also mentioned, like executivesearch, and I was wondering is
there a services element toFINDM or is it 100% product
platform-based?
Speaker 2 (18:10):
It's 100% product platform-based.
Findem is very well-suited forexec search because the nature
of the search is highlyprecision right.
For example, if I say, hey,find me the CFO in Georgia who's
working in a PEVC company, sawthrough a company from early
stage on exit and currentlyworking in a manufacturing
sector near impossible search.
How do you think you could dothat in a one-dimensional resume
search way?
Right?
Finder is geared towardsfinding that needle in the
(18:30):
haystack because 3D profilescapture the attributes.
So we found a lot of pull fromthe exec search side.
Speaker 1 (18:37):
Okay, got it.
Yeah, that makes a ton of sense.
Well, what else can you tell usabout Findum?
I mean, I would love to learneven more about the product
Future.
You know.
I think like maybe onedirection we can go into is I
was kind of interested to learnbecause I initially did think
that more on the like when itcomes to recruiting automation.
I don't know why, but I assumelike my mind kind of went to
(18:59):
more like the higher volumebucket that you were discussing,
right Like you know companiesthat are like potentially
construction or health care.
But when I was speaking withthe CEO of Sense and they're
kind of like echoing what you'resaying too, you know, I think
around they said about 30% oftheir 20 to 30% of their
business is like knowledgeworkers, like within technology.
So that's interesting.
Do you also?
Do you see demand in staffingand recruiting?
Speaker 2 (19:23):
So we don't sell into staffing and recruiting, but
there's always demand, right.
I mean, I think one of theworkflow will win there, because
I think it's purely aboutoptimizing the time, because
bottom line here in the staffingindustry essentially is people
time.
Speaker 1 (19:37):
Yeah, yeah, because I think that's another
interesting point is likestaffing and recruiting
companies being early adoptersfor a lot of the more disruptive
recruiting automation.
Speaker 2 (19:49):
Yeah 100%, because it actually ties into the bottom
line quite directly Right?
So it does, and I think forthat reason the AI adoption in
staffing and agency has beenpredominantly high, and I think
we've seen that as well.
Speaker 1 (20:02):
Yeah, for sure.
I mean that's for June.
We're looking at where we'reseeing demand for AI
interviewing and we're seeingdemand in the staffing and
recruiting segment.
I mean other SaaS companies too, but there's definitely that
use case because it can impacttop line bottom line, you know,
(20:24):
services companies, thinnermargins, right, and one of the
challenges of scaling a staffingbusiness is, just because
you're scaling top line, revenuegrowth doesn't necessarily mean
that you're making more money,right, because, like the
overhead, it's so overheadintensive, right, so it's not so
appealing, as is the marginRight.
Because, like the overhead, it'sso overhead intensive, right,
so it's not so appealing, as isthe margin, right.
So, yeah, I mean, I think it'slike there's.
(20:46):
It's interesting to see, likeyou know, where the where the
demand is kind of coming frominitially and and yeah, staffing
and recruiting.
You know, based on a lot of theCEOs I've spoken to, they're
they're saying that they'regetting a fair amount of demand
from staffing and recruiting andthen they're also kind of
considering the staffing andrecruiting early adopters, sort
of like ahead of the market,right.
Speaker 2 (21:08):
That's very true.
When you think about staffing,definitely right, I mean.
When you think about evenbigger enterprises, right, I
mean, one of the beautifulthings.
It's an interesting dichotomy.
When you think about anyrecruiter and you tell them that
here's the requisition, I needto go close the role, the very
first thing that they would sayis I need to go source right.
Most of the most of themessentially believe that they
need to go and build newrelationship all the time, which
(21:28):
is go and talk to customers.
Candidates outside 90 of thehighest for most enterprises are
people that they already knowright.
So they either applied now orapplied previously, or possibly
are sitting in their crmsomewhere.
Or people that I think could bereferred by an employee within
the company, or folks that Ithink have nurtured in the past
right.
So hunting essentially is themost laborious way to
essentially go and get acandidate.
(21:49):
Imagine this that you're takinga completely cold network and
then trying to convince them totake a meeting right, While
completely overlooking your warmand hot leads within your
network because they're notdiscoverable.
So in reality, when you thinkabout productivity I mean the
aspect of combining in-networkand out-network is an important
manifestation and I think whenwe think through it from an
option and productivityperspective, it's an important
(22:11):
beachhead because you'respending less time doing
possibly getting better yield,than doing it purely going out
and coming in, which is huntingto farm, but that could be
farmed in nature.
Speaker 1 (22:20):
Yeah, definitely.
Do you have any thoughts on AIand compliance for hiring?
I don't know if you've spent alot of time just going through
what, from a regulationperspective, is likely going to
happen, but curious to get yourtake.
If you know we don't have todive into this, I'm curious.
I mean, is that somethingyou've given a lot of thought to
(22:42):
or have any thoughts around?
Speaker 2 (22:45):
So AI even the New York AI bias audit that, I think
, is a mandatory checkbox forevery single enterprise contract
that we need to deal with.
Along with the InfoSec, the AIbias questionnaire is an
important part of the salescycle.
So we do essentially haveinteresting experiences because,
as we see, the industryessentially go mature I mean,
(23:06):
the perspective on what AI cando or cannot do essentially is
evolving right.
So one of the conscious thingsthat we believe in, particularly
in recruiting, which I think ismanifesting in a way where the
laws essentially are evolving aswell, is decisioning, like,
who's doing the decisioning forwhat right?
I mean, is it an assisteddecisioning or is it a
displacement decision?
I mean, are we essentiallyusing AI to make a decision on
(23:27):
behalf of a recruiter orassisting the recruiter to make
better decisions?
Right?
We firmly believe that,considering the state of affairs
with AI I mean considering thestakes at hand I mean assistance
is exactly the way toessentially go, which means that
AI, automated decision-making,is something that I think is a
big no in the design principlein FindUp.
I mean it's pretty much all ofour workplaces are cognizant of
(23:49):
that.
I truly believe that thecompliance part of AI
essentially is going to evolveto make sure that the
decisioning essentially isappropriately moderated,
protected and verified, and Ithink that is something that
we're already seeing in theearly tea leaves of the whole
industry.
Speaker 1 (24:04):
So when you said on enterprise contracts, you had
mentioned that something istypically baked into those.
What was that again?
Speaker 2 (24:11):
So when you go through an enterprise contract
with any mid-size to large-sizeenterprise, we used to
essentially do InfoSecrequirements as part of the
contracting, because you used togo and say I need to integrate
my email, I need to integrate myapplicant tracking system,
right.
So AI as a next step is we'reseeing it more often than not
now in pretty much all of ourengagements.
Speaker 1 (24:33):
Yeah, yeah, it's interesting.
I mean, do you see, like, howrisk adverse are you seeing a
lot of?
Have you?
Has that been a prohibitor tomove forward with some
enterprise customers wherethey're just basically saying,
hey, we want to kick it untilnext year when we have a better
pulse on where the regulation isgoing to fall?
Speaker 2 (24:49):
I mean, so I think that people are conscious about
AI decisioning.
So one of the things that we'vemade sure with Findem as a
platform is all the searching isBI first, so we don't
essentially let AI make adecision there.
Ai decisioning essentially isan overlay on top of the results
to assist the recruiters withthe right data at the right time
not on matching.
(25:09):
Matching essentially is BI first, which actually eases the nerve
.
I think there are a lot ofanxiety around automated
decision making from AI, which Ithink is what the regulations
is also protecting, which iswhat the company is also
concerned about.
Speaker 1 (25:22):
Yeah about.
Yeah, that makes a lot of sense.
It'll be interesting to see howthat continues to evolve over
the next year or two, and it'sinteresting to see how companies
are making bets on recruitingtech companies and whether
they're looking at AI assistantversus how much of the decision
process are they taking over.
But, ari, anyways, this hasbeen a ton of fun.
(25:43):
I really appreciate you comingon the show with me today
sharing your expertise, tellingus about Findum.
I know our audience is going tolearn a lot from everything
that you've shared with us today.
We really appreciate yoursupport and your contribution to
the show.
Speaker 2 (25:57):
I appreciate it and thanks for hosting me here,
James.
It was a pleasure talking toyou as well.
Speaker 1 (26:01):
Yeah, absolutely, and for everybody tuning in.
Thank you so much and we'lltalk to you real soon.
Take care.
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