November 6, 2025 • 35 mins
AI won’t replace 90% of what a strong people analytics team delivers. But what exactly is in that 90%—and how long will it stay out of reach? Roxanne Laczo (Head of People Analytics at Cloudflare), Cole Napper (VP of Research, Innovation & Talent Insights at Lightcast), and Noelle London (Founder and CEO of Illoominus) join David for a grounded roundtable on what AI can and can’t do in HR today.
This isn’t a breathless tour of shiny tools. It’s a candid look at the real dynamics reshaping the people analytics function: the transactional work being automated, the strategic work that still demands human context, and the business acumen gap that could soon define who stays relevant—and who doesn’t.
Related Links:
- Join the People Managing People community forum
- Subscribe to the newsletter to get our latest articles and podcasts
- Connect with Noelle, Cole, and Roxanne on LinkedIn
- Check out Illoominus, Lightcast, and Cloudflare, Inc.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
David Rice (00:00):
AI won't replace 90% of what a strong people
analytics team provides.
What's in that 90%that's uniquely human?
Roxanne Laczo (00:08):
What's in there is what's inside
our head, our brain.
There's stakeholderinfluence in storytelling.
I think maybe at some pointthat 90% might change, but
AI will not be able to everreplace fully and completely.
David Rice (00:21):
Are we at risk of creating sort of like
a two-tiered system whereAI becomes the good enough
solution for most organizations?
Noelle London (00:28):
Traditionally, people analytics is a resource
that enterprise companies get.
It's not a resource thatpeople have the budgets
to necessarily do.
What we're seeing isorganizations hire for
these types of capabilitiesearlier than even we saw two,
two and a half years ago.
That makes it really exciting.
David Rice (00:49):
Is people analytics position to become the sort
of power center of HR andtherefore AI transformation?
Cole Napper (00:56):
I'm gonna say no.
And the reason why isnot an issue of skill.
It's an issue of will.
David Rice (01:01):
Looking ahead 12 to 18 months, what specific
skills should they bedeveloping to stay relevant?
And conversely, what skillsshould you stop investing
time in because you think AIwill eventually handle that?
Welcome to The People ManagingPeople Podcast — the show
where we help leaders keepwork human in the era of AI.
(01:21):
I'm David Rice, I'm your host.
On today's episode, I'mdelighted to be joined by three
brilliant minds who work in thepeople analytics space for a
bit of a round table discussion.
First up, we have Roxanne Laczo.
She is the Head of PeopleAnalytics at Cloudflare.
Welcome, Roxanne!
Roxanne Laczo (01:39):
Hello, good to be here!
David Rice (01:40):
And we also have Cole Napper, who you might
remember from his previousappearance on the podcast.
He is the VP of Research,Innovation & Talent
Insights at Lightcast.
Welcome, Cole!
Cole Napper (01:50):
Thanks for having me, David.
David Rice (01:51):
And finally, we have Noelle London.
She is the Founderand CEO of Illoominus.
Welcome, Noelle!
Noelle Lo (01:57):
Thanks for having me.
Excited to be here.
David Rice (01:59):
Awesome.
All right, today we're gonna bechatting a bit about the current
state of people analytics,where we see it going in the
future and how we redefine thefunction, what it means for HR.
So I wanna start with this.
When we were talking beforethis, we discussed that AI
won't replace 90% of what astrong people analytics team
provides, and Roxanne gaveus this sort of hot take.
(02:21):
So I wanna start there.
Roxanne, what's in that90% that's uniquely human?
And do you think thatpercentage will hold as
AI capabilities advance?
Roxanne Laczo (02:32):
Yeah, absolutely.
So I think what's inthere is what's inside
our head, our brain.
There's strategic contextof organization and the
business that AI can'tnecessarily pick up on.
There's institutionalknowledge and relationships
that you have in organization.
Where you're gonna getaccess to information,
data highlights that you'renot gonna get necessarily.
(02:52):
AI tool cannotnecessarily scrape.
There's stakeholder influencein storytelling, which is
something that's really hard torecreate in any AI tool unless
you have that really strongcontext that I refer to above.
So I think the last thingis also being able to ask
the right questions giventhe relationships you have,
what you know about theorganization, what you know
(03:12):
about the business problems.
I think maybe at some pointthat 90% might change, but
I think in the short term,really what's gonna change is
that it's the value of whatwe provide as individuals with
our relationship building andour context, that AI will not
be able to ever replace fullyand completely at the level at
which I think everybody on thiscall would like it to operate.
David Rice (03:33):
That's interesting.
Cole, I'm interested to get youropinion on this because you're
very in the weeds on skills.
You're seeing a lot ofwhat's happening around
transformation and automation.
What do you think will sortof stick around for the
people analytics function?
Cole Napper (03:49):
Going back to your question from earlier,
you said 90% of a strong peopleanalytics team won't go away.
I think if you're nota strong team, I think
90% of it might go away.
I kind of put this intotwo categories of are you
doing transactional workor are you doing strategic
or transformational work?
90% of their strategictransformational work
(04:09):
will not go away.
Everything whatRoxanne said is true.
I think 90% of thetransactional work will go away.
We recently did some research atLightcast that I shared with you
guys before we did this podcast.
It plotted all of the HRtechnology or all HR skills on
how valuable they were, how muchthey've grown, but also how much
(04:29):
AI is going to disrupt them.
And people Analytics wassimultaneously the most valuable
skill in hr, but also one ofthe top ones that it was most
likely to be disrupted by AI.
And so I found that to be kindof a fascinating paradox of
what's been going on is a lotof the transactional components
of people analytics, I thinkwill go over the next few years.
David Rice (04:50):
Noelle, from your perspective, you know,
'cause you're always thinkingabout how people put together
their text tax, right?
And how AI plugs into all that.
What do you see as sort of whereit's kind of like pushing, I
don't wanna say pushing humansout of the process, right?
But a little bit like wheredoes it have a big impact
on sort of like how peopleinteract with the technology
(05:13):
that they currently have?
Noelle London (05:14):
Yeah.
Well, I mean, I'll prefaceit with saying, and I think
we'll talk about this alittle bit later in the
conversation, is that whenwe say people analytics, it
means really different thingsat different sized companies
and different levels ofmaturity within companies.
So when we're saying peopleanalytics, it's not exactly the
same thing in the exact sameneed at different organizations.
(05:36):
When we are thinkingabout people analytics
and we're thinking aboutorganizations that likely
may not have a Roxanne.
I think that Roxanne is anincredibly valuable resource
to her organization, andI think, you know, maybe
90% is a little bit high onsaying you can't replace 90%.
I also don't even think aboutit as necessarily replacing, I
(06:00):
think the word replace is morelike this is about a co-pilot
and a co-pilot that can help youon a lot of those things that.
It's frankly work that'swearing people down.
And so some of that workwhere, you know, when we talk
to organizations that arepotentially a little bit earlier
in their journey or smalleraround headcount, there's
(06:21):
things that they're doinglike maintaining integrations,
like going through data healthand cleanup, that those are
things that are kind of keepingthem in this reactive place.
Where they're doing regularreporting, but maybe
it's backward looking.
That stuff can be really timeintensive if you don't have a
fully built out team to do it.
And so those are someopportunities to take that
(06:44):
work off the plate, have aco-pilot to help you out so
that you can spend more time,like Roxanne said, with that
context that's in your head.
I do think there's still somethings, you know, we're still AI
obviously it's changing for uslike every single week at this
moment, but some of those thingsare still really hard to manage,
(07:05):
which I'm not gonna lie about.
Cole and I actually hada conversation about this
earlier this week where,think about those edge cases.
So many edge cases are coming.
Some of the things that we seeis, think about something like
transfers in internal mobility.
We're seeing that organizationsare managing that differently
within their systems.
(07:25):
And so those are the kindsof things where, yeah, edge
cases, we've still really gottabe paying attention to those.
And I think that context inthat organizational insight
of how do we take all of this?
The AI's helping us withas our co-pilot in people
analytics so that we canspend more time thinking about
what to go and do about itand less time on that work.
(07:48):
That again, we saywears you down.
David Rice (07:50):
Well that's interesting 'cause like
talking about, you know,a lot of companies don't
have the Roxanne, right.
I'm curious, are we at riskof creating sort of like
a two-tiered system wherelike AI becomes good enough,
the good enough solutionfor most organizations?
Like really only well-resourcedcompanies get that
true strategic insight,that have that context.
Noelle London (08:12):
Yeah, I mean, this is the one that
I think I get really excitedabout just because I think
that traditionally whatwe've seen is that people
analytics is a resource thatenterprise companies get.
It's not a resource thatpeople don't have the budgets
to necessarily do this atorganizations that let's say
are 12,000 or less employees.
(08:35):
I know you know Roxanne,that may be an exception with
where you are right now, butI think that's what we're
seeing is that we're seeingorganizations higher for these
types of capabilities androles earlier than even we saw
two, two and a half years ago.
And so more organizationsare asking, Hey, my
organization's changing.
I need to be able to think aboutthe future of my workforce.
(08:58):
I need that data to help me tomake those kinds of decisions.
So we're seeing it come earlierwithin organizations and to me
that makes it really exciting.
Yes, there's the largerorganizations that
have 30 people on apeople analytics team.
You know, that's a verydifferent people analytics
(09:19):
means something very differentthere than it does at a smaller,
more nimble HR organization.
To me, it's not necessarilycreating a two tier system.
I actually think what'sreally exciting about it and
the opportunity that we canunlock is when we can bring
that data together to shareinsights across companies.
(09:40):
That's the reallybig opportunity is.
The benchmarking, the learningfrom peers on what's working
within their organizations.
'cause we don't necessarilyhave playbooks for some of
these big changes that arehappening in hr. I think that's
a really big opportunity,is the network effect of
learning across organizationsthat are mid-market.
(10:01):
And to me that's an opportunityto almost leapfrog these
organizations that aregetting up to speed quickly
in people analytics to takethem to the next level.
Roxanne Laczo (10:11):
Getting back to what Noelle said earlier
around is like, how are wedefining people analytics here?
And like we could have awhole other podcast on what
people analytics means.
I have a real strong point ofview on that, but I guarantee
you most of the companiessaying they're doing it aren't
doing how I would define it.
Right.
And then I would also say, onthe other hand, many companies
who don't have a peopleanalytics team have people, what
I doing, what I would define aspeople analytics, do you have
(10:33):
talent, do you have assessment?
And you can't do any of thosethings without analytics.
So I think it's like.
It's organizational readiness,it's context dependent, it's
side the organization, it'ssavviness of the business.
I think to say that there'sa risk with a two tier is
maybe not quite right, butthe risk we have is not
about being able to do thingsand delivering insights.
(10:53):
It's really around thecapability of your business
partners to be able to useAI to make decisions, right?
That doesn't have tobe driven by people.
Analytics, like 0% has to bedriven by people analytics.
Do you have the capability andinvestment within your entire
people or HR team that you'reenabling people, training
people on how to use differenttypes of tools, systems that
(11:14):
have AI embedded in them tostart to gather all these
insights and decisions andare we like putting people
analytics out of a job?
Not necessarily, but it'sreally around, this is
a capability problem.
It's not an investmentproblem, in my opinion.
Cole Napper (11:27):
Going back to the two tier question.
I actually think the historyof people analytics is more
two-tiered than the future.
I mean, you go back, I talkabout this in my book, people
Analytics that should be outby the time this is released,
but there were companiesthat had a hundred, 200,
300 person people analyticsteams not that long ago.
And largely they'rebeing dismantled.
(11:49):
I just don't see a futurewhere you need a 50 person plus
people analytics team, and Ithink that's actually the most
egalitarian version of thefuture of people analytics.
And what AI is doingis decreasing the
barriers to entry.
So the moving from havingzero people or zero technology
focused on doing this toone is so much easier than
(12:09):
it was perhaps five yearsago, even a year ago.
And I think that's betterfor the future of hr and
that's better for thefuture of people analytics.
David Rice (12:18):
Yeah, I mean it's interesting like with this
particular function, 'causeI look at like sort of the
trends around a lot of otherroles within the organization
and there is from leadership,I think on a lot of other
roles, a tendency to leaninto like good enough, right?
You think about, I don'tknow, marketing or something.
Right?
You know, there is sort ofthat brazenness to be like,
(12:38):
oh, this is good enough.
I don't know if it'll followsuit with something like
this just because, I mean,correct me if I'm wrong, but
it feels like a lot of peoplewithin the org kind of don't
understand it to begin with.
So when you go to then applythat even using AI, I'm
not sure how easy it is to.
Understand or trustwhat you get back maybe,
am I wrong about that?
(12:59):
Do you still need somebodyin between you and the AI?
I guess if you're a leader?
Cole Napper (13:04):
This is an area where I have
very strong opinions.
I think that it's been atravesty for how long HR has
had leaders that weren't datanative or didn't understand
how to use data to makedecisions, and I think AI is
actually gonna force that issue.
And so I think it's no longer.
You could say acceptable,but I would say it is going
(13:26):
to be existential for theHR functions themselves.
I mean, you've already seenthe Moderna example of them
merging HR and it essentiallyto say, I, it is taking over
HR because it can use data tomake decisions and HR can't.
That's kind of the subtext ofwhat I saw in that announcement,
and I think, I hope that'snot a trend that continues
and it won't continue.
(13:47):
If HR steps up to the plate,if HR can be the leaders
that they think they can be.
I just think it's no longeracceptable to be a non-data
literate HR leader, and I thinkthat's one of the reasons why
it's actually exciting to bea people analytics leader at
this moment because I thinkit's one of the functions in
HR that's going to prepare youto be the chief people officer
(14:07):
of the CHRO of the future.
Roxanne Laczo (14:09):
I'm gonna tag onto that and I'm gonna say
that we're gonna pick on HRteams here for a few minutes.
The problem is not howdo we AI something?
It's like, do youunderstand the business?
Are you actually also a businessleader versus just kind of
what we would put into a boxof our traditional HR leader.
And this is like the strategicHR business partner problem
that I've literally beenlistening to for 20 years now.
(14:31):
Like if you actually can't speakthe language of the business,
that's problem number one.
And that's kind of whatI think Cole is alluding
to in another way.
So it's like we needto stop thinking about
AI as being the how.
We can't do anything withAI until we know really
like what is the problemthat we're trying to solve?
Then we're just, we'rethrowing like crap at crap.
Right?
And what's the outcome?
(14:51):
Like more crap.
So it's like it'sa no win situation.
Noelle London (14:54):
I'm gonna jump in and maybe defend HR a little
bit just because I think that,you know, in my day to day I
to work with HR leaders thatI think really want to have
the data, so I'm gonna givethem the benefit of the doubt.
I think that typically what I'vebeen seeing is that these are
HR leaders where they're partof the organization has been
(15:17):
chronically underinvested in.
When you think about everycompany's got a customer
data platform, if youdon't have a customer data
platform in 2025, like whatare you operating off of?
That type of investment alwayswent to sales and marketing
because we were thinkingabout revenue and sure, sales
(15:38):
and marketing can have whatthey want, but hr, I'm going
through hrs budget and I'mgonna start cutting things
and you're not gonna getthe employee data platform.
Well, also, we haven't reallyhad that as much until now.
I think that there's some piecesof, there's a lot of catch up
that HR has to do in terms ofsystems and data foundation
(16:01):
to be able to fully takeadvantage of AI, in my opinion.
Like, yeah it's really excitingright now because you think
about, you know, every HRtech tool has something in AI.
Think about like in even lastyear, we had customers that
used three people in threeweeks to go through their
employee engagement surveyand they were spending for
(16:25):
a couple hundred people,they're spending like 30k on
an employee engagement tool.
You don't need todo that anymore.
In the past, we would've thoughtabout my critical HR tech stack.
If I'm the CFO and I'm havingthat conversation with hr,
you get an HRIS system, youget a talent acquisition
system, you get engagement,and if you're lucky, you
(16:45):
get a performance system.
Now, what we can say, and I'mbullish on is that now I think
that the people analytics ofhow do we actually connect
those different systems?
I think that has to becomecritical tech stack and I
think that we're starting tosee the realization of now
that I can AI apply some partsof the other work, does that
(17:08):
leave room for us to startthinking strategically of how
to piece the puzzle together?
David Rice (17:13):
This is interesting.
Now I kind of wanna pivota little bit 'cause I think
there's like, I think there'sa gap between like what
boards expect from AI andhr and then like what is
actually possible right now.
So I'm curious, like inyou all's opinion, how do
you bridge that disconnectbetween executive pressure
to, you know, sort of do AI?
I mean you've got all theseCEOs out there, like we're an
AI driven organization, right?
(17:34):
And they wanted an hr, butthe reality of, to your point
Noelle, what HR systems andteams at present have the
pieces in place to deliver maynot actually live up to that.
Noelle London (17:45):
I mean, I think it's a lot of what I was
just mentioning, but I thinkthat it's, there's a lot of
things you can talk about,use cases within your existing
HR tech stack that you'vestarted to take advantage
of some of those tools.
I think while you're beingasked right now, how are you
including AI and hr? Now'syour time to make the business
(18:07):
case for people analytics.
Now's the time to say, youknow, we can do the like
onesie, twosies on, and Ithink Cole had a term for
this earlier this week, but wecan do the small scale stuff
if you really want us to bestrategic to the business.
If we're all focused onprofitability this year.
(18:28):
My number one cost as anorganization is my people.
Well then we reallywanna be understanding
what's working within theorganization and what's not.
So to me it's, we cando the onesie twosie.
Sure.
You want AI and hr,you're pressuring us like
make that business case.
Now's the time to make that ask.
Roxanne Laczo (18:47):
Well, I also just think there's a lot of
enablement that has to happen.
So you can't justsay, Hey, do AI.
'cause guess what, likeAI typically costs money.
It takes time to learn it.
So I've seen in differentorganizations or conversations
I have is like there's thisbig expectation, but a lot
of people, especially peoplewho aren't used to that, they
don't even know where to start.
So you can't tell abunch of like recruiting
(19:08):
coordinators to go start touse AI when maybe they never
even been exposed to it.
So there's really anenablement piece that I
think we're missing here.
From organizations whereit's like, number one,
you actually have toinvest in enabling people.
You actually have toinvest in tools, right?
So you can't say at HR,we're cutting your budget,
but go get a bunch of AItools to do all this work.
So I think again, itkind of gets back to the
conversation around we wantHR to be more strategic
(19:31):
business focused partners.
What are the things that aregonna enable us to do that
investment in AI, investmentin technology, investment
in people analytics,investment in upskilling?
Cole Napper (19:40):
Yeah.
I'll add onto a few things thatboth Noelle and Roxanne said,
and from the board perspective,and again, this is different for
different companies, but I thinka general theme you're seeing
across the economy is boards aresaying to CEOs, we want you to
A, make AI investments, whichcost money like Roxanne said.
B, we want you tokeep opex flat.
(20:03):
So what happens is, C, youcut headcount spin, and
then because the math shouldadd up that you add AI that
should increase productivity.
So you should stillsee productivity growth
as an organization.
But what's ending uphappening is because I
think we've all seen thesestatistics, like 95% of AI
(20:23):
pilots are failing right now.
That you're actually, whatyou're doing is you're squeezing
your current employees.
Even more to see thoseproductivity games.
So people are workinglonger hours, you're
seeing more disengagement.
A lot of the things that Ithink Roxanne was pointing out
about capability gaps as well,and I think that manifests
itself in this really kindof weird squeeze that we're
seeing as a society where theproductivity numbers actually
(20:46):
are going up, but it's notbecause of AI, it's because
more juice is being squeezedoutta the lemon at the moment.
David Rice (20:52):
I would agree with that.
I've heard a few differentsessions recently, like I,
I think there's also someeducation that needs to happen
with like all leadershipteams, quite frankly,
where we talk about likethe difference between AI,
machine learning automation.
Like these aren't necessarily,they may be similar, but
they're not exactly thesame thing all the time.
(21:12):
Right.
I know like when we weretalking before this call, you
distinguish between like microAI solutions, so using something
like a GPT for a daily taskand then a macro solution where
vendors embed into AI workflows.
So like where for an HRleader listening to this,
right, where should they sortof be placed in their bets
and like learning the most?
Cole Napper (21:31):
I've kind of got a few thoughts on this.
So going back to that kind ofboard or C-suite perspective
versus like what HR istelling their HR employees
and the board in the C-suiteis saying, we wanna see.
Productivity gains andkind of a fundamental
transformation of hr, andthat's that macro AI adoption.
So we want entire workflows,entire processes to be
(21:54):
AI native or AI embedded.
So that you see, again,just a fundamental shift in
the way that work is done.
HR is telling HR employees,go try out ChatGPT or Gemini
or whatever and see if you canwrite a job description better.
Or you know, some of theHR technologies might have
an AI chat bot within them.
Try to see if you can pilotthat chat bot to do something.
(22:17):
And there's a fundamentaldisconnect between those
two things because theexpectation and the reality.
There's too big of adivergence there if you're
looking at a, from a microlens or a macro lens.
The last thing I would saythough is I think that's
V one of AI adoption inHR and what V two looks
like is very different.
(22:37):
So right now I'd say 95%of the spend on AI adoption
from V one has been on.
Let's get a new AI wrapper.
Let's put some AI on top ofour existing infrastructure,
maybe a little bit aroundthe edges about capability,
ability, or data.
I think, and everybody I thinkis seeing that's not working at
(22:58):
scale the way we hoped it would.
And so I think V two isgonna look vastly different.
I always say if you had ahundred incremental dollars
to spend on AI, I'd spend95% of it on better data
and better infrastructure,and 5% on a better wrapper.
And I think that's where thedirection is going to go.
That's why, I mean, I getreally excited working at a
(23:19):
company like Livecast becausewe have this type of data and
that's what's exciting for mein terms of like the research
I mentioned earlier about likepeople analytics and its value
with our research call Beyondthe Buzz, which again, I guess
you can put in the show notes,but I just think it's, we're
in a transformational phase andwe haven't figured it out yet.
Noelle London (23:36):
I really like that Cole.
You know, I think right nowto me of thinking about the
journey and the maturity ofhow we get more AI and HR to.
Take away some of that workthat is wearing people down
is, I think right now, likequick wins with some of those
AI tools and like the AI piecesthat are within your systems.
(23:58):
Get some of those quickwins that are available,
but that's not adoption,that's not where it stops.
It's all about.
Get some of those quick wins,but be investing in exactly
what you're saying of getyour data foundation in order.
Get your data foundationclean and trustworthy.
Start connecting it becausethat's where we're really gonna
(24:18):
unlock is when we can start.
Mapping out the entire employeejourney, pooling those systems
together to help us withsome of those things like
internal mobility, workforceplanning, which right now
can feel really difficultto do with current state.
Roxanne Laczo (24:31):
I'm just gonna add in that I get the
concept of like encouragingpeople to use ChatGPT or
Gemini to make tasks quicker.
But I wanna get people awayfrom thinking that they're
doing some amazing thingwith AI by doing that.
That is not a strong usecase for AI in my opinion.
I'm like, that is you learninghow to be a little bit more
productive using a toolthat's available to you.
So we, I have a colleaguefriend of mine, we talked
(24:53):
a lot about AI slop.
Like we're not gonna countthe AI slop when we think
about value add activities.
That's like, greatthat you're doing that.
But the and value add activitiesare the bigger picture things
that we can measure more simply.
Things that we know atscale are really gonna
make a lot of sense.
So I'm not saying stop doingthose things 'cause those things
are really important, but thatby the way, should just be what
(25:14):
you're doing in your normal job.
That's not like I used AIto solve a huge problem.
That's like a productivitygain that everybody
should be thinkingabout doing in some way.
David Rice (25:22):
Yeah.
I think in a lot of waysit's a speed gain, right?
Like it allows you to dothat faster so that you can
then actually have a littlebit of time left over to
try to do this other thing.
And it's like, yeah, everyrole, I don't even care
what industry you're in.
This is part of it now.
And it's just like, that's.
The term that is just so hotright now is table stakes.
(25:43):
You know, nobody saidthat like two years ago.
It was very rare that youto hear that as a term.
And now every, I feel likeit's in every LinkedIn post.
I wanna go back a sec, becausewe had talked about how like
Cole, you had mentioned.
People, analytics leader, sortof being in a good position to
become the CHRO of the future.
We're kind of suggestingthat a proactive people
analytics leader herecould take on that role of
(26:04):
leading AI transformation.
I've just recently done a pieceon, you know, hiring your chief
first chief AI officer in IRU.
Essentially that as youlook for a chief AI officer,
you should look at maybe HRpeople instead of somebody
in a technical role, becausepeople tend to be sort of the
big bottleneck a lot of timesaround these transformations.
So I'm curious, you know,like is people analytics
(26:26):
positioned to become the sortof power center of HR and
therefore AI transformation?
Cole Napper (26:33):
I'm gonna take the other side of my
own argument and say no.
And the reason why isnot an issue of skill.
It's issue of will.
Almost every people analyticsleader I talk to doesn't
want it, aren't interestedin it, and frankly, I think
there are some parts of thechief people officer job that
people analytics is probablynot that well equipped for.
Things like more on theemployee relations, dealing
(26:54):
with organizational politics.
The organizational therapy youhave to do at the C-suite to
be a good chief people officer.
It's not a fun jobin a lot of ways.
Let's say I flew too closeto the sun at a prior
organization, so I have alittle bit of experience there.
The thing is, I think the chiefpeople officer job would have to
change for that to be the case.
And I don't thinkthat's not possible.
(27:14):
I've actually been sittingon an idea of writing about
like a future operating modelfor HR for a few years now.
I just haven't had the, frankly,the will either to publish it.
'cause I feel like I'm get alot of friendly fire from it.
I think there is a future whereit is possible, and I just don't
think it is the current state.
Roxanne Laczo (27:30):
I just think there's so many past becoming
a chief people officer thatit's a yes and there's 50
other ways to get there.
Right.
So might, you know,being an AI powerhouse
enable you to get there?
Yeah.
But I'm gonna go backto what I said earlier.
It's like, do youunderstand the business?
'cause if you're a peopleanalytics leader and you have
no connection to the businessand you don't understand
the business, you shouldn'tbe a chief people officer.
(27:51):
So I think that's somethingalso you see really good.
A lot of companies bringin people from outside of
hr, heads of engineering,different backgrounds
and do really amazingthings with people teams.
So I think like that'sone pathway to get there.
But I agree with Cole.
There's some people out thereI know in kind of our type
of roles who aspire to dothat, but for the most case.
(28:11):
I don't see that as somethingpeople saying like, my next
job's gonna be the CHRO.
Noelle London (28:15):
Just to take that a little bit differently.
I mean, I think one thing thatI've been thinking a lot about
lately is people analyticsis like the perfect AI and
HR are, if you will, of, it'snot necessarily somebody that
comes from people analytics.
Both understands the data, butthen is really strong in the
(28:37):
interpretation of the data.
And so having somebodystand into that role.
Within our HR department,this is how our
strategy fits together.
This is how we go andinterpret the outputs
that are coming from AI.
I think in many ways this personis really well positioned to
help the HR organization around,you know, what's our strategy?
(28:59):
How do the pieces fit together?
The other thing, I mean, notnecessarily advocating either
way, but a couple of thingsthat I think this person is
really well positioned to dofor the HR organization is,
pardon my French, but it'slike HR teams have really
been siloed for so long.
When you think about an HRteam, you've got a recruiter
(29:23):
that's running to meet quotaand bring down time to hire.
You've got somebody inemployee relations who has
a very different persona.
You've got somebody in employeeexperience who has a very
different persona that it kindof can feel like you've got this
team where you've got peoplerunning in different directions.
With the people analyticsperson that's able to bring
(29:44):
together the data to showthis is the employee journey.
This is, we are all, you know,responsible for KPIs within that
employee journey, but this ishow the function works together.
I think that's potentially aninteresting strength that this
person can bring to the role.
And then the other thing is.
(30:05):
Somebody that's within peopleanalytics, knows their numbers
in and out, and so you lookat those numbers, you're able
to look at, you know, makedata-driven decisions, help to
build business cases for why itis strategic to the business to
make some of these decisions andinvestments that we're making.
So not necessarily sayingif somebody doesn't have
the will, like no, theyshouldn't be in that role.
(30:26):
There are a couple of thingsthat historically maybe
have been stumbling blocksfor the department that
this person would bringreally great value to.
David Rice (30:37):
And he said, pardon my French there.
I thought I was gonna getto use my bleep button.
Noelle London (30:41):
Siloed.
David Rice (30:43):
Well, we're kind of coming up on time here,
but I wanted to leave us withthis, you know, because I
think one thing that comesoutta this conversation for
me is that a lot of companiesshould be rethinking the HR
and people analytics, you know,what they actually are and
do within the business today.
And I'm curious then in youall's opinion as they do
that, looking ahead 12 to 18months, for folks that are in
(31:04):
the people analytics space,what specific skill should
they be developing to stayrelevant 18 months from now?
And conversely, what skillsshould you stop investing
time in because you think AIwill eventually handle that?
Roxanne Laczo (31:18):
I'm gonna get on my soapbox.
I think it's the third timeand say business acumen, right?
If you can't speak thelanguage of the business.
It's a problem.
So it's something I thinkprobably, I mean, myself I'm
still doing that as well, right?
So it's not easy to do, butyou really need to learn how
to partner and partner withleaders in a way that allow
you to be more embedded withwhat's going on in the business.
(31:39):
It's really hard for you to doyour job without doing that.
So I think that's one thing.
And I do think just generalliteracy around AI ethics.
Governance, rules, laws,committees, all that stuff
that's coming out is gonnabe really important as we
continue to move down that path.
And then I also thinkthere's still a piece in
a lot of people, analyticsteams and these are the
(32:00):
teams that I wouldn't evencall people analytics.
These are the basicreporting teams.
You have to be ableto build towards
storytelling and influence.
So there's a really strongconsulting component and
bigger companies have teams,have people, analytics
consultants more or lesstied to different businesses.
I think that's areally good model.
It's something we're workingtowards as we continue to
grow and evolve on my team.
But I think those are threereally things that are important
(32:22):
and I think like thingsthat you should no longer
think about investing in is.
Manual transactional, repetitivedata cleaning activities that
belongs to the people thatown the source data systems.
That doesn't belong topeople analytics teams.
And one thing that AI can enableyou is creating dashboards,
writing code, producingproducts, having deliverables.
You don't have to spend hoursand hours of time coding and
(32:44):
producing those where you havetools probably already available
to you in your organizationthat can enable you to get
those out the door real quickly.
Noelle London (32:51):
I couldn't agree more.
Roxanne, I think embeddingyourself in the business,
creating really strongstakeholder relationships
so that you are invaluableso that you're coming in
Roxanne, you can't see it,but you are pointing at
your head in question oneof like it's all up there
and you've got a lot of it.
And so as much as you canbuild your knowledge of the
(33:12):
business, you can build a lotof that knowledge in your head
that makes you more invaluable.
Don't sleep on what'shappening in terms of AI.
Stay up to speed,educate yourself, stay
smart on those things.
To me, that's what'smost critical.
Cole Napper (33:29):
I just violently agree with what
Roxanne and Noelle has said.
I feel like I can only kindof echo it in the sense
that Roxanne's point of allbusiness acumen is just.
It's so important.
I talk and write about thisstuff all the time, and I
think a lot of people cometo it and they're expecting
me to be talking about likeExcel formulas or like how
to write code or something.
(33:49):
And it's almost alwaysabout like how to create
differential value for yourbusiness through humans,
not because humans are theproblem or something like that.
And this is why peopleanalytics is interesting.
It's why it's fun,it's why it's exciting.
And I think.
Again, spending yourincremental time, that's
where I would be spending it.
If you're just trying tocreate, you know, the best new
(34:12):
widget, go and buy something.
Go and use AI, go do something.
Because again, thetransactional space is
getting completely disrupted.
Understand your data.
That's key.
Investing in the rightdata infrastructure key,
but being the person whoruns a report, I think the
half-life on that is short.
David Rice (34:30):
Absolutely.
Well, this has been afascinating conversation.
I love talking with all of you.
Thanks for joining us today.
I really appreciatey'all coming on the show.
Cole Napper (34:37):
Thanks for having me.
Roxanne Laczo (34:39):
Thank you, David.
Noelle London (34:39):
Thank you.
David Rice (34:40):
Alright.
Well listeners, until next time,work on that business acumen.
Sign up for the People ManagingPeople newsletter, as always.
AI won't replace 90% of what a strong people
analytics team provides.
What's in that 90%that's uniquely human?
Roxanne Laczo (00:08):
What's in there is what's inside
our head, our brain.
There's stakeholderinfluence in storytelling.
I think maybe at some pointthat 90% might change, but
AI will not be able to everreplace fully and completely.
David Rice (00:21):
Are we at risk of creating sort of like
a two-tiered system whereAI becomes the good enough
solution for most organizations?
Noelle London (00:28):
Traditionally, people analytics is a resource
that enterprise companies get.
It's not a resource thatpeople have the budgets
to necessarily do.
What we're seeing isorganizations hire for
these types of capabilitiesearlier than even we saw two,
two and a half years ago.
That makes it really exciting.
David Rice (00:49):
Is people analytics position to become the sort
of power center of HR andtherefore AI transformation?
Cole Napper (00:56):
I'm gonna say no.
And the reason why isnot an issue of skill.
It's an issue of will.
David Rice (01:01):
Looking ahead 12 to 18 months, what specific
skills should they bedeveloping to stay relevant?
And conversely, what skillsshould you stop investing
time in because you think AIwill eventually handle that?
Welcome to The People ManagingPeople Podcast — the show
where we help leaders keepwork human in the era of AI.
(01:21):
I'm David Rice, I'm your host.
On today's episode, I'mdelighted to be joined by three
brilliant minds who work in thepeople analytics space for a
bit of a round table discussion.
First up, we have Roxanne Laczo.
She is the Head of PeopleAnalytics at Cloudflare.
Welcome, Roxanne!
Roxanne Laczo (01:39):
Hello, good to be here!
David Rice (01:40):
And we also have Cole Napper, who you might
remember from his previousappearance on the podcast.
He is the VP of Research,Innovation & Talent
Insights at Lightcast.
Welcome, Cole!
Cole Napper (01:50):
Thanks for having me, David.
David Rice (01:51):
And finally, we have Noelle London.
She is the Founderand CEO of Illoominus.
Welcome, Noelle!
Noelle Lo (01:57):
Thanks for having me.
Excited to be here.
David Rice (01:59):
Awesome.
All right, today we're gonna bechatting a bit about the current
state of people analytics,where we see it going in the
future and how we redefine thefunction, what it means for HR.
So I wanna start with this.
When we were talking beforethis, we discussed that AI
won't replace 90% of what astrong people analytics team
provides, and Roxanne gaveus this sort of hot take.
(02:21):
So I wanna start there.
Roxanne, what's in that90% that's uniquely human?
And do you think thatpercentage will hold as
AI capabilities advance?
Roxanne Laczo (02:32):
Yeah, absolutely.
So I think what's inthere is what's inside
our head, our brain.
There's strategic contextof organization and the
business that AI can'tnecessarily pick up on.
There's institutionalknowledge and relationships
that you have in organization.
Where you're gonna getaccess to information,
data highlights that you'renot gonna get necessarily.
(02:52):
AI tool cannotnecessarily scrape.
There's stakeholder influencein storytelling, which is
something that's really hard torecreate in any AI tool unless
you have that really strongcontext that I refer to above.
So I think the last thingis also being able to ask
the right questions giventhe relationships you have,
what you know about theorganization, what you know
(03:12):
about the business problems.
I think maybe at some pointthat 90% might change, but
I think in the short term,really what's gonna change is
that it's the value of whatwe provide as individuals with
our relationship building andour context, that AI will not
be able to ever replace fullyand completely at the level at
which I think everybody on thiscall would like it to operate.
David Rice (03:33):
That's interesting.
Cole, I'm interested to get youropinion on this because you're
very in the weeds on skills.
You're seeing a lot ofwhat's happening around
transformation and automation.
What do you think will sortof stick around for the
people analytics function?
Cole Napper (03:49):
Going back to your question from earlier,
you said 90% of a strong peopleanalytics team won't go away.
I think if you're nota strong team, I think
90% of it might go away.
I kind of put this intotwo categories of are you
doing transactional workor are you doing strategic
or transformational work?
90% of their strategictransformational work
(04:09):
will not go away.
Everything whatRoxanne said is true.
I think 90% of thetransactional work will go away.
We recently did some research atLightcast that I shared with you
guys before we did this podcast.
It plotted all of the HRtechnology or all HR skills on
how valuable they were, how muchthey've grown, but also how much
(04:29):
AI is going to disrupt them.
And people Analytics wassimultaneously the most valuable
skill in hr, but also one ofthe top ones that it was most
likely to be disrupted by AI.
And so I found that to be kindof a fascinating paradox of
what's been going on is a lotof the transactional components
of people analytics, I thinkwill go over the next few years.
David Rice (04:50):
Noelle, from your perspective, you know,
'cause you're always thinkingabout how people put together
their text tax, right?
And how AI plugs into all that.
What do you see as sort of whereit's kind of like pushing, I
don't wanna say pushing humansout of the process, right?
But a little bit like wheredoes it have a big impact
on sort of like how peopleinteract with the technology
(05:13):
that they currently have?
Noelle London (05:14):
Yeah.
Well, I mean, I'll prefaceit with saying, and I think
we'll talk about this alittle bit later in the
conversation, is that whenwe say people analytics, it
means really different thingsat different sized companies
and different levels ofmaturity within companies.
So when we're saying peopleanalytics, it's not exactly the
same thing in the exact sameneed at different organizations.
(05:36):
When we are thinkingabout people analytics
and we're thinking aboutorganizations that likely
may not have a Roxanne.
I think that Roxanne is anincredibly valuable resource
to her organization, andI think, you know, maybe
90% is a little bit high onsaying you can't replace 90%.
I also don't even think aboutit as necessarily replacing, I
(06:00):
think the word replace is morelike this is about a co-pilot
and a co-pilot that can help youon a lot of those things that.
It's frankly work that'swearing people down.
And so some of that workwhere, you know, when we talk
to organizations that arepotentially a little bit earlier
in their journey or smalleraround headcount, there's
(06:21):
things that they're doinglike maintaining integrations,
like going through data healthand cleanup, that those are
things that are kind of keepingthem in this reactive place.
Where they're doing regularreporting, but maybe
it's backward looking.
That stuff can be really timeintensive if you don't have a
fully built out team to do it.
And so those are someopportunities to take that
(06:44):
work off the plate, have aco-pilot to help you out so
that you can spend more time,like Roxanne said, with that
context that's in your head.
I do think there's still somethings, you know, we're still AI
obviously it's changing for uslike every single week at this
moment, but some of those thingsare still really hard to manage,
(07:05):
which I'm not gonna lie about.
Cole and I actually hada conversation about this
earlier this week where,think about those edge cases.
So many edge cases are coming.
Some of the things that we seeis, think about something like
transfers in internal mobility.
We're seeing that organizationsare managing that differently
within their systems.
(07:25):
And so those are the kindsof things where, yeah, edge
cases, we've still really gottabe paying attention to those.
And I think that context inthat organizational insight
of how do we take all of this?
The AI's helping us withas our co-pilot in people
analytics so that we canspend more time thinking about
what to go and do about itand less time on that work.
(07:48):
That again, we saywears you down.
David Rice (07:50):
Well that's interesting 'cause like
talking about, you know,a lot of companies don't
have the Roxanne, right.
I'm curious, are we at riskof creating sort of like
a two-tiered system wherelike AI becomes good enough,
the good enough solutionfor most organizations?
Like really only well-resourcedcompanies get that
true strategic insight,that have that context.
Noelle London (08:12):
Yeah, I mean, this is the one that
I think I get really excitedabout just because I think
that traditionally whatwe've seen is that people
analytics is a resource thatenterprise companies get.
It's not a resource thatpeople don't have the budgets
to necessarily do this atorganizations that let's say
are 12,000 or less employees.
(08:35):
I know you know Roxanne,that may be an exception with
where you are right now, butI think that's what we're
seeing is that we're seeingorganizations higher for these
types of capabilities androles earlier than even we saw
two, two and a half years ago.
And so more organizationsare asking, Hey, my
organization's changing.
I need to be able to think aboutthe future of my workforce.
(08:58):
I need that data to help me tomake those kinds of decisions.
So we're seeing it come earlierwithin organizations and to me
that makes it really exciting.
Yes, there's the largerorganizations that
have 30 people on apeople analytics team.
You know, that's a verydifferent people analytics
(09:19):
means something very differentthere than it does at a smaller,
more nimble HR organization.
To me, it's not necessarilycreating a two tier system.
I actually think what'sreally exciting about it and
the opportunity that we canunlock is when we can bring
that data together to shareinsights across companies.
(09:40):
That's the reallybig opportunity is.
The benchmarking, the learningfrom peers on what's working
within their organizations.
'cause we don't necessarilyhave playbooks for some of
these big changes that arehappening in hr. I think that's
a really big opportunity,is the network effect of
learning across organizationsthat are mid-market.
(10:01):
And to me that's an opportunityto almost leapfrog these
organizations that aregetting up to speed quickly
in people analytics to takethem to the next level.
Roxanne Laczo (10:11):
Getting back to what Noelle said earlier
around is like, how are wedefining people analytics here?
And like we could have awhole other podcast on what
people analytics means.
I have a real strong point ofview on that, but I guarantee
you most of the companiessaying they're doing it aren't
doing how I would define it.
Right.
And then I would also say, onthe other hand, many companies
who don't have a peopleanalytics team have people, what
I doing, what I would define aspeople analytics, do you have
(10:33):
talent, do you have assessment?
And you can't do any of thosethings without analytics.
So I think it's like.
It's organizational readiness,it's context dependent, it's
side the organization, it'ssavviness of the business.
I think to say that there'sa risk with a two tier is
maybe not quite right, butthe risk we have is not
about being able to do thingsand delivering insights.
(10:53):
It's really around thecapability of your business
partners to be able to useAI to make decisions, right?
That doesn't have tobe driven by people.
Analytics, like 0% has to bedriven by people analytics.
Do you have the capability andinvestment within your entire
people or HR team that you'reenabling people, training
people on how to use differenttypes of tools, systems that
(11:14):
have AI embedded in them tostart to gather all these
insights and decisions andare we like putting people
analytics out of a job?
Not necessarily, but it'sreally around, this is
a capability problem.
It's not an investmentproblem, in my opinion.
Cole Napper (11:27):
Going back to the two tier question.
I actually think the historyof people analytics is more
two-tiered than the future.
I mean, you go back, I talkabout this in my book, people
Analytics that should be outby the time this is released,
but there were companiesthat had a hundred, 200,
300 person people analyticsteams not that long ago.
And largely they'rebeing dismantled.
(11:49):
I just don't see a futurewhere you need a 50 person plus
people analytics team, and Ithink that's actually the most
egalitarian version of thefuture of people analytics.
And what AI is doingis decreasing the
barriers to entry.
So the moving from havingzero people or zero technology
focused on doing this toone is so much easier than
(12:09):
it was perhaps five yearsago, even a year ago.
And I think that's betterfor the future of hr and
that's better for thefuture of people analytics.
David Rice (12:18):
Yeah, I mean it's interesting like with this
particular function, 'causeI look at like sort of the
trends around a lot of otherroles within the organization
and there is from leadership,I think on a lot of other
roles, a tendency to leaninto like good enough, right?
You think about, I don'tknow, marketing or something.
Right?
You know, there is sort ofthat brazenness to be like,
(12:38):
oh, this is good enough.
I don't know if it'll followsuit with something like
this just because, I mean,correct me if I'm wrong, but
it feels like a lot of peoplewithin the org kind of don't
understand it to begin with.
So when you go to then applythat even using AI, I'm
not sure how easy it is to.
Understand or trustwhat you get back maybe,
am I wrong about that?
(12:59):
Do you still need somebodyin between you and the AI?
I guess if you're a leader?
Cole Napper (13:04):
This is an area where I have
very strong opinions.
I think that it's been atravesty for how long HR has
had leaders that weren't datanative or didn't understand
how to use data to makedecisions, and I think AI is
actually gonna force that issue.
And so I think it's no longer.
You could say acceptable,but I would say it is going
(13:26):
to be existential for theHR functions themselves.
I mean, you've already seenthe Moderna example of them
merging HR and it essentiallyto say, I, it is taking over
HR because it can use data tomake decisions and HR can't.
That's kind of the subtext ofwhat I saw in that announcement,
and I think, I hope that'snot a trend that continues
and it won't continue.
(13:47):
If HR steps up to the plate,if HR can be the leaders
that they think they can be.
I just think it's no longeracceptable to be a non-data
literate HR leader, and I thinkthat's one of the reasons why
it's actually exciting to bea people analytics leader at
this moment because I thinkit's one of the functions in
HR that's going to prepare youto be the chief people officer
(14:07):
of the CHRO of the future.
Roxanne Laczo (14:09):
I'm gonna tag onto that and I'm gonna say
that we're gonna pick on HRteams here for a few minutes.
The problem is not howdo we AI something?
It's like, do youunderstand the business?
Are you actually also a businessleader versus just kind of
what we would put into a boxof our traditional HR leader.
And this is like the strategicHR business partner problem
that I've literally beenlistening to for 20 years now.
(14:31):
Like if you actually can't speakthe language of the business,
that's problem number one.
And that's kind of whatI think Cole is alluding
to in another way.
So it's like we needto stop thinking about
AI as being the how.
We can't do anything withAI until we know really
like what is the problemthat we're trying to solve?
Then we're just, we'rethrowing like crap at crap.
Right?
And what's the outcome?
(14:51):
Like more crap.
So it's like it'sa no win situation.
Noelle London (14:54):
I'm gonna jump in and maybe defend HR a little
bit just because I think that,you know, in my day to day I
to work with HR leaders thatI think really want to have
the data, so I'm gonna givethem the benefit of the doubt.
I think that typically what I'vebeen seeing is that these are
HR leaders where they're partof the organization has been
(15:17):
chronically underinvested in.
When you think about everycompany's got a customer
data platform, if youdon't have a customer data
platform in 2025, like whatare you operating off of?
That type of investment alwayswent to sales and marketing
because we were thinkingabout revenue and sure, sales
(15:38):
and marketing can have whatthey want, but hr, I'm going
through hrs budget and I'mgonna start cutting things
and you're not gonna getthe employee data platform.
Well, also, we haven't reallyhad that as much until now.
I think that there's some piecesof, there's a lot of catch up
that HR has to do in terms ofsystems and data foundation
(16:01):
to be able to fully takeadvantage of AI, in my opinion.
Like, yeah it's really excitingright now because you think
about, you know, every HRtech tool has something in AI.
Think about like in even lastyear, we had customers that
used three people in threeweeks to go through their
employee engagement surveyand they were spending for
(16:25):
a couple hundred people,they're spending like 30k on
an employee engagement tool.
You don't need todo that anymore.
In the past, we would've thoughtabout my critical HR tech stack.
If I'm the CFO and I'm havingthat conversation with hr,
you get an HRIS system, youget a talent acquisition
system, you get engagement,and if you're lucky, you
(16:45):
get a performance system.
Now, what we can say, and I'mbullish on is that now I think
that the people analytics ofhow do we actually connect
those different systems?
I think that has to becomecritical tech stack and I
think that we're starting tosee the realization of now
that I can AI apply some partsof the other work, does that
(17:08):
leave room for us to startthinking strategically of how
to piece the puzzle together?
David Rice (17:13):
This is interesting.
Now I kind of wanna pivota little bit 'cause I think
there's like, I think there'sa gap between like what
boards expect from AI andhr and then like what is
actually possible right now.
So I'm curious, like inyou all's opinion, how do
you bridge that disconnectbetween executive pressure
to, you know, sort of do AI?
I mean you've got all theseCEOs out there, like we're an
AI driven organization, right?
(17:34):
And they wanted an hr, butthe reality of, to your point
Noelle, what HR systems andteams at present have the
pieces in place to deliver maynot actually live up to that.
Noelle London (17:45):
I mean, I think it's a lot of what I was
just mentioning, but I thinkthat it's, there's a lot of
things you can talk about,use cases within your existing
HR tech stack that you'vestarted to take advantage
of some of those tools.
I think while you're beingasked right now, how are you
including AI and hr? Now'syour time to make the business
(18:07):
case for people analytics.
Now's the time to say, youknow, we can do the like
onesie, twosies on, and Ithink Cole had a term for
this earlier this week, but wecan do the small scale stuff
if you really want us to bestrategic to the business.
If we're all focused onprofitability this year.
(18:28):
My number one cost as anorganization is my people.
Well then we reallywanna be understanding
what's working within theorganization and what's not.
So to me it's, we cando the onesie twosie.
Sure.
You want AI and hr,you're pressuring us like
make that business case.
Now's the time to make that ask.
Roxanne Laczo (18:47):
Well, I also just think there's a lot of
enablement that has to happen.
So you can't justsay, Hey, do AI.
'cause guess what, likeAI typically costs money.
It takes time to learn it.
So I've seen in differentorganizations or conversations
I have is like there's thisbig expectation, but a lot
of people, especially peoplewho aren't used to that, they
don't even know where to start.
So you can't tell abunch of like recruiting
(19:08):
coordinators to go start touse AI when maybe they never
even been exposed to it.
So there's really anenablement piece that I
think we're missing here.
From organizations whereit's like, number one,
you actually have toinvest in enabling people.
You actually have toinvest in tools, right?
So you can't say at HR,we're cutting your budget,
but go get a bunch of AItools to do all this work.
So I think again, itkind of gets back to the
conversation around we wantHR to be more strategic
(19:31):
business focused partners.
What are the things that aregonna enable us to do that
investment in AI, investmentin technology, investment
in people analytics,investment in upskilling?
Cole Napper (19:40):
Yeah.
I'll add onto a few things thatboth Noelle and Roxanne said,
and from the board perspective,and again, this is different for
different companies, but I thinka general theme you're seeing
across the economy is boards aresaying to CEOs, we want you to
A, make AI investments, whichcost money like Roxanne said.
B, we want you tokeep opex flat.
(20:03):
So what happens is, C, youcut headcount spin, and
then because the math shouldadd up that you add AI that
should increase productivity.
So you should stillsee productivity growth
as an organization.
But what's ending uphappening is because I
think we've all seen thesestatistics, like 95% of AI
(20:23):
pilots are failing right now.
That you're actually, whatyou're doing is you're squeezing
your current employees.
Even more to see thoseproductivity games.
So people are workinglonger hours, you're
seeing more disengagement.
A lot of the things that Ithink Roxanne was pointing out
about capability gaps as well,and I think that manifests
itself in this really kindof weird squeeze that we're
seeing as a society where theproductivity numbers actually
(20:46):
are going up, but it's notbecause of AI, it's because
more juice is being squeezedoutta the lemon at the moment.
David Rice (20:52):
I would agree with that.
I've heard a few differentsessions recently, like I,
I think there's also someeducation that needs to happen
with like all leadershipteams, quite frankly,
where we talk about likethe difference between AI,
machine learning automation.
Like these aren't necessarily,they may be similar, but
they're not exactly thesame thing all the time.
(21:12):
Right.
I know like when we weretalking before this call, you
distinguish between like microAI solutions, so using something
like a GPT for a daily taskand then a macro solution where
vendors embed into AI workflows.
So like where for an HRleader listening to this,
right, where should they sortof be placed in their bets
and like learning the most?
Cole Napper (21:31):
I've kind of got a few thoughts on this.
So going back to that kind ofboard or C-suite perspective
versus like what HR istelling their HR employees
and the board in the C-suiteis saying, we wanna see.
Productivity gains andkind of a fundamental
transformation of hr, andthat's that macro AI adoption.
So we want entire workflows,entire processes to be
(21:54):
AI native or AI embedded.
So that you see, again,just a fundamental shift in
the way that work is done.
HR is telling HR employees,go try out ChatGPT or Gemini
or whatever and see if you canwrite a job description better.
Or you know, some of theHR technologies might have
an AI chat bot within them.
Try to see if you can pilotthat chat bot to do something.
(22:17):
And there's a fundamentaldisconnect between those
two things because theexpectation and the reality.
There's too big of adivergence there if you're
looking at a, from a microlens or a macro lens.
The last thing I would saythough is I think that's
V one of AI adoption inHR and what V two looks
like is very different.
(22:37):
So right now I'd say 95%of the spend on AI adoption
from V one has been on.
Let's get a new AI wrapper.
Let's put some AI on top ofour existing infrastructure,
maybe a little bit aroundthe edges about capability,
ability, or data.
I think, and everybody I thinkis seeing that's not working at
(22:58):
scale the way we hoped it would.
And so I think V two isgonna look vastly different.
I always say if you had ahundred incremental dollars
to spend on AI, I'd spend95% of it on better data
and better infrastructure,and 5% on a better wrapper.
And I think that's where thedirection is going to go.
That's why, I mean, I getreally excited working at a
(23:19):
company like Livecast becausewe have this type of data and
that's what's exciting for mein terms of like the research
I mentioned earlier about likepeople analytics and its value
with our research call Beyondthe Buzz, which again, I guess
you can put in the show notes,but I just think it's, we're
in a transformational phase andwe haven't figured it out yet.
Noelle London (23:36):
I really like that Cole.
You know, I think right nowto me of thinking about the
journey and the maturity ofhow we get more AI and HR to.
Take away some of that workthat is wearing people down
is, I think right now, likequick wins with some of those
AI tools and like the AI piecesthat are within your systems.
(23:58):
Get some of those quickwins that are available,
but that's not adoption,that's not where it stops.
It's all about.
Get some of those quick wins,but be investing in exactly
what you're saying of getyour data foundation in order.
Get your data foundationclean and trustworthy.
Start connecting it becausethat's where we're really gonna
(24:18):
unlock is when we can start.
Mapping out the entire employeejourney, pooling those systems
together to help us withsome of those things like
internal mobility, workforceplanning, which right now
can feel really difficultto do with current state.
Roxanne Laczo (24:31):
I'm just gonna add in that I get the
concept of like encouragingpeople to use ChatGPT or
Gemini to make tasks quicker.
But I wanna get people awayfrom thinking that they're
doing some amazing thingwith AI by doing that.
That is not a strong usecase for AI in my opinion.
I'm like, that is you learninghow to be a little bit more
productive using a toolthat's available to you.
So we, I have a colleaguefriend of mine, we talked
(24:53):
a lot about AI slop.
Like we're not gonna countthe AI slop when we think
about value add activities.
That's like, greatthat you're doing that.
But the and value add activitiesare the bigger picture things
that we can measure more simply.
Things that we know atscale are really gonna
make a lot of sense.
So I'm not saying stop doingthose things 'cause those things
are really important, but thatby the way, should just be what
(25:14):
you're doing in your normal job.
That's not like I used AIto solve a huge problem.
That's like a productivitygain that everybody
should be thinkingabout doing in some way.
David Rice (25:22):
Yeah.
I think in a lot of waysit's a speed gain, right?
Like it allows you to dothat faster so that you can
then actually have a littlebit of time left over to
try to do this other thing.
And it's like, yeah, everyrole, I don't even care
what industry you're in.
This is part of it now.
And it's just like, that's.
The term that is just so hotright now is table stakes.
(25:43):
You know, nobody saidthat like two years ago.
It was very rare that youto hear that as a term.
And now every, I feel likeit's in every LinkedIn post.
I wanna go back a sec, becausewe had talked about how like
Cole, you had mentioned.
People, analytics leader, sortof being in a good position to
become the CHRO of the future.
We're kind of suggestingthat a proactive people
analytics leader herecould take on that role of
(26:04):
leading AI transformation.
I've just recently done a pieceon, you know, hiring your chief
first chief AI officer in IRU.
Essentially that as youlook for a chief AI officer,
you should look at maybe HRpeople instead of somebody
in a technical role, becausepeople tend to be sort of the
big bottleneck a lot of timesaround these transformations.
So I'm curious, you know,like is people analytics
(26:26):
positioned to become the sortof power center of HR and
therefore AI transformation?
Cole Napper (26:33):
I'm gonna take the other side of my
own argument and say no.
And the reason why isnot an issue of skill.
It's issue of will.
Almost every people analyticsleader I talk to doesn't
want it, aren't interestedin it, and frankly, I think
there are some parts of thechief people officer job that
people analytics is probablynot that well equipped for.
Things like more on theemployee relations, dealing
(26:54):
with organizational politics.
The organizational therapy youhave to do at the C-suite to
be a good chief people officer.
It's not a fun jobin a lot of ways.
Let's say I flew too closeto the sun at a prior
organization, so I have alittle bit of experience there.
The thing is, I think the chiefpeople officer job would have to
change for that to be the case.
And I don't thinkthat's not possible.
(27:14):
I've actually been sittingon an idea of writing about
like a future operating modelfor HR for a few years now.
I just haven't had the, frankly,the will either to publish it.
'cause I feel like I'm get alot of friendly fire from it.
I think there is a future whereit is possible, and I just don't
think it is the current state.
Roxanne Laczo (27:30):
I just think there's so many past becoming
a chief people officer thatit's a yes and there's 50
other ways to get there.
Right.
So might, you know,being an AI powerhouse
enable you to get there?
Yeah.
But I'm gonna go backto what I said earlier.
It's like, do youunderstand the business?
'cause if you're a peopleanalytics leader and you have
no connection to the businessand you don't understand
the business, you shouldn'tbe a chief people officer.
(27:51):
So I think that's somethingalso you see really good.
A lot of companies bringin people from outside of
hr, heads of engineering,different backgrounds
and do really amazingthings with people teams.
So I think like that'sone pathway to get there.
But I agree with Cole.
There's some people out thereI know in kind of our type
of roles who aspire to dothat, but for the most case.
(28:11):
I don't see that as somethingpeople saying like, my next
job's gonna be the CHRO.
Noelle London (28:15):
Just to take that a little bit differently.
I mean, I think one thing thatI've been thinking a lot about
lately is people analyticsis like the perfect AI and
HR are, if you will, of, it'snot necessarily somebody that
comes from people analytics.
Both understands the data, butthen is really strong in the
(28:37):
interpretation of the data.
And so having somebodystand into that role.
Within our HR department,this is how our
strategy fits together.
This is how we go andinterpret the outputs
that are coming from AI.
I think in many ways this personis really well positioned to
help the HR organization around,you know, what's our strategy?
(28:59):
How do the pieces fit together?
The other thing, I mean, notnecessarily advocating either
way, but a couple of thingsthat I think this person is
really well positioned to dofor the HR organization is,
pardon my French, but it'slike HR teams have really
been siloed for so long.
When you think about an HRteam, you've got a recruiter
(29:23):
that's running to meet quotaand bring down time to hire.
You've got somebody inemployee relations who has
a very different persona.
You've got somebody in employeeexperience who has a very
different persona that it kindof can feel like you've got this
team where you've got peoplerunning in different directions.
With the people analyticsperson that's able to bring
(29:44):
together the data to showthis is the employee journey.
This is, we are all, you know,responsible for KPIs within that
employee journey, but this ishow the function works together.
I think that's potentially aninteresting strength that this
person can bring to the role.
And then the other thing is.
(30:05):
Somebody that's within peopleanalytics, knows their numbers
in and out, and so you lookat those numbers, you're able
to look at, you know, makedata-driven decisions, help to
build business cases for why itis strategic to the business to
make some of these decisions andinvestments that we're making.
So not necessarily sayingif somebody doesn't have
the will, like no, theyshouldn't be in that role.
(30:26):
There are a couple of thingsthat historically maybe
have been stumbling blocksfor the department that
this person would bringreally great value to.
David Rice (30:37):
And he said, pardon my French there.
I thought I was gonna getto use my bleep button.
Noelle London (30:41):
Siloed.
David Rice (30:43):
Well, we're kind of coming up on time here,
but I wanted to leave us withthis, you know, because I
think one thing that comesoutta this conversation for
me is that a lot of companiesshould be rethinking the HR
and people analytics, you know,what they actually are and
do within the business today.
And I'm curious then in youall's opinion as they do
that, looking ahead 12 to 18months, for folks that are in
(31:04):
the people analytics space,what specific skill should
they be developing to stayrelevant 18 months from now?
And conversely, what skillsshould you stop investing
time in because you think AIwill eventually handle that?
Roxanne Laczo (31:18):
I'm gonna get on my soapbox.
I think it's the third timeand say business acumen, right?
If you can't speak thelanguage of the business.
It's a problem.
So it's something I thinkprobably, I mean, myself I'm
still doing that as well, right?
So it's not easy to do, butyou really need to learn how
to partner and partner withleaders in a way that allow
you to be more embedded withwhat's going on in the business.
(31:39):
It's really hard for you to doyour job without doing that.
So I think that's one thing.
And I do think just generalliteracy around AI ethics.
Governance, rules, laws,committees, all that stuff
that's coming out is gonnabe really important as we
continue to move down that path.
And then I also thinkthere's still a piece in
a lot of people, analyticsteams and these are the
(32:00):
teams that I wouldn't evencall people analytics.
These are the basicreporting teams.
You have to be ableto build towards
storytelling and influence.
So there's a really strongconsulting component and
bigger companies have teams,have people, analytics
consultants more or lesstied to different businesses.
I think that's areally good model.
It's something we're workingtowards as we continue to
grow and evolve on my team.
But I think those are threereally things that are important
(32:22):
and I think like thingsthat you should no longer
think about investing in is.
Manual transactional, repetitivedata cleaning activities that
belongs to the people thatown the source data systems.
That doesn't belong topeople analytics teams.
And one thing that AI can enableyou is creating dashboards,
writing code, producingproducts, having deliverables.
You don't have to spend hoursand hours of time coding and
(32:44):
producing those where you havetools probably already available
to you in your organizationthat can enable you to get
those out the door real quickly.
Noelle London (32:51):
I couldn't agree more.
Roxanne, I think embeddingyourself in the business,
creating really strongstakeholder relationships
so that you are invaluableso that you're coming in
Roxanne, you can't see it,but you are pointing at
your head in question oneof like it's all up there
and you've got a lot of it.
And so as much as you canbuild your knowledge of the
(33:12):
business, you can build a lotof that knowledge in your head
that makes you more invaluable.
Don't sleep on what'shappening in terms of AI.
Stay up to speed,educate yourself, stay
smart on those things.
To me, that's what'smost critical.
Cole Napper (33:29):
I just violently agree with what
Roxanne and Noelle has said.
I feel like I can only kindof echo it in the sense
that Roxanne's point of allbusiness acumen is just.
It's so important.
I talk and write about thisstuff all the time, and I
think a lot of people cometo it and they're expecting
me to be talking about likeExcel formulas or like how
to write code or something.
(33:49):
And it's almost alwaysabout like how to create
differential value for yourbusiness through humans,
not because humans are theproblem or something like that.
And this is why peopleanalytics is interesting.
It's why it's fun,it's why it's exciting.
And I think.
Again, spending yourincremental time, that's
where I would be spending it.
If you're just trying tocreate, you know, the best new
(34:12):
widget, go and buy something.
Go and use AI, go do something.
Because again, thetransactional space is
getting completely disrupted.
Understand your data.
That's key.
Investing in the rightdata infrastructure key,
but being the person whoruns a report, I think the
half-life on that is short.
David Rice (34:30):
Absolutely.
Well, this has been afascinating conversation.
I love talking with all of you.
Thanks for joining us today.
I really appreciatey'all coming on the show.
Cole Napper (34:37):
Thanks for having me.
Roxanne Laczo (34:39):
Thank you, David.
Noelle London (34:39):
Thank you.
David Rice (34:40):
Alright.
Well listeners, until next time,work on that business acumen.
Sign up for the People ManagingPeople newsletter, as always.
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