Spreadsheets, Stories, and Strategy: What Moves HR Forward with AI

Spandanna: 00:00:00

I think there's an opportunity here to say: "I'm gonna leverage AI to

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redefine the work that I do and still make

myself a valuable asset to this company

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and be very much part of the strategy."

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I think the takeaway is if you're

sticking to your job description, I

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think it's a lost cause on the AI race.

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Thomas Kunjappu: They keep

telling us that it's all over.

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For HR, the age of AI is upon

us, and that means HR should

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be prepared to be decimated.

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We reject that message.

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The future of HR won't be handed to us.

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Instead, it'll be defined by those

ready to experiment, adopt, and adapt.

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Future Proof HR invites these builders to

share what they're trying, how it's going,

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what they've learned, and what's next.

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We are committed to arming HR

with the AI insights to not

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just survive, but to thrive.

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Thomas: Hello and welcome to the Future

Proof HR Podcast, where we explore how

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forward-thinking HR leaders are preparing

for disruption and redefining what it

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means to lead people in a changing world.

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I'm your host, Thomas

Kanjappu, CEO of Cleary.

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Today's guest is Spandana

Suddapalli, Head Of People at Vidmob.

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A people leader whose HR experience

spans global tech, scaling startups

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and deep work with C-Suites on

organizational transformation.

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Now, Spandana believes the future of HR

lies in a redefined partnership between

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human insight and automated efficiency.

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That tension is something I'm

excited to explore together.

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As AI reshapes administrative

tasks and org structures alike,

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she sees a heightened need for

strategic business partnership.

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And a need to rethink performance

and building emotional fluency as a

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competitive edge for organizations.

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So a lot to talk about, Spandana.

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Welcome to the podcast.

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Thank you.

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Thanks for having me.

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I'm pretty excited to be here.

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Thank you for being here

and to your plans as well.

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So tell me about your personal journey

a little bit about how you kind of

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come into this function, the kinds of

experiences you've had and what kind of

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sparked your interest in HR to begin with.

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Spandanna: Boy.

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How much time do you have?

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I actually was a bio major, if you can't

tell by the plants, back in college.

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But I thought my journey

was going to be in academia.

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I wanted to become a professor

and that's the trajectory I

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was kind of going towards.

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But I had a little bit of a gap

where I needed a job before I

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could go back to grad school,

and that's how I found Priceline.

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And they really just wanted me to come

in and analyze their exit surveys.

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And I had a background in

statistics, so I thought, why not?

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I can just use my degree to use.

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And then I found the world

of corporate culture and I

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never knew anything about it.

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And it was really, really fascinating and

I think Priceline was a very interesting

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place to be at a very interesting time

because I felt like 2012- now I'm dating

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myself - was around when HR was also

going through a huge tech transformation.

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We were looking at a lot more

HR tech coming into play and the

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people there were really adept at

compliance and talking to people.

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However, it was not very

data-driven strategic function.

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And so my five and a half years there,

it was like fully transformed into

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a data-driven strategic function.

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And that really kind

of hit the bug for me.

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I was like, enough with the academia,

I love this corporate culture.

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I'm going to stay in technology.

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And one thing that I also got really

interested in is the fact that Priceline,

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which was owned by Booking Holdings, had

different tech companies that they owned.

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So I got to do different projects across

the board with Kayak and OpenTable

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and Booking and all of these really

cool companies who are doing their

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own HR, but in very different ways.

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And so it was like a crash

course into HR, essentially.

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Thomas: So that analytical mindset clearly

served you well as you got into the role.

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Sure.

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Now we're at the cusp of

this big transformation.

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Maybe back then it was about

SaaS and analytics, and now

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we're talking all things AI.

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So, it seems like a lot of HR

projects with AI, some people are

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talking about they're doomed to fail.

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Yeah.

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Why is that, or are they doomed to fail?

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Spandanna: Well, I think there's a

lot of noise around it as we do with

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any sort of new innovation, right?

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I feel like I read a quote the other day.

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It's like, AI may not be able to

take your job, but someone who

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knows how to use it definitely will.

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And I think, especially for

people functions, we do have

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to be a little careful with it.

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I know, you know, Meta recently

got in trouble for using

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AI for interview scans but

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I don't throw in the towel

and say it's doomed to fail.

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I think it's the way we use it

and use it powerfully enough

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to make it that much better.

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I don't fundamentally believe

that AI is going to fail us all.

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Thomas: So tell me about some of those

guardrails, frameworks, how to go

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about it successfully, what experiences

do you have and thoughts for others

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about doing it the right way, then?

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Spandanna: Yeah.

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One of the things I - and this might

be a very generic example - but you

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know, since COVID, we've gotten very

much used to sort of this virtual world.

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And so, when you interview people,

it's always over Zoom or Google Meets

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and people kind of got used to that.

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And so when we were doing interviews,

even though we have a hybrid

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culture, so we are expected to be

in the office three times a day.

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We kept doing our interviews virtually.

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And we kept going through the whole

full cycle recruiting process, and then

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we would make offers and then we would

bring people in and realize that this

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is not the person we wanted or they're

not performing well enough, et cetera.

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So, there needs to be a bit

of a mixture of human touch.

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With using technology, I think leveraging

this virtual world is really great, or

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even AI screens is really great, but

there needs to be some human intervention.

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So I purposefully reached out to all of

the leadership and said, from now on,

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we're not sending any offers out without

an in-person interview, and I'd rather

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spend the money in having people fly over

to the office - to New York - and meet

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with whoever it needs to be the hiring

manager or the CEO or the leadership.

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And then, only we'll make an offer.

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And that has quite honestly saved us a lot

of money because we weren't exiting people

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three months later or six months later.

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Thomas: Fascinating.

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I guess now we're not talking about

necessarily AI revolution but certainly

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the shortcuts that have come with the

virtual COVID era where everything's

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been shifted to remote and you've

got completely into efficiency mode.

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And so, you've gotten the funnel

all the way down to remote.

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Yeah.

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And actually were you guys fully

remote and then you kind of came back

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towards hybrid more recently, but kept

the interview process fully remote?

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Spandanna: Yeah, so that's the thing.

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It's like when you change processes - and

again, I fully admit that the pandemic

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was a confusing time for all of us.

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Like we were just figuring out

how to do so many things in a not

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- Some of us got, got a

lot of plants, right?

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Yeah.

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Yeah, exactly.

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It was a great time for plants.

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To be a plant parent, rather.

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But I think it does apply to the AI

screening as well, in sort of like a

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contrast to that, just to bring it home.

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Even when we use AI tools to screen

resumes and stuff, we do test

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it against several other things.

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Is it like selectively only giving

us DEI candidates, is it only

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giving us candidates who are a

certain age and over or under?

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So we do have those guardrails because

you know, AI is only as good as the

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amount of times you use it, right?

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You gotta keep feeding the prompts and so

it only gets better the more you use it.

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We are not at a stage where I

feel comfortable opening it up to

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having AI interview screen someone.

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I still take that very personally,

like I still need it to be some

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recruiter or hiring manager

initially talking to the person.

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But we do use it for resume screenings and

stuff, but I do check those guardrails.

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Is it doing what it's

supposed to be doing?

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Thomas: So thank you for taking

that metaphor from the COVID

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era and digital to the AI world.

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So, in the previous example

about interview process, you

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felt like you went too far with

remote too deep into the process.

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Yeah.

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And I imagine the idea that got you to

change that back and approach leadership

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to change the interview process was

because of results of hiring and three or

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six month retention and manager feedback.

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So you kind of, unfortunately,

felt some pain that got you

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kind of reversing course.

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Yeah.

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In the world of AI, if I take the

equivalent of that, it would be, we got

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sued by someone who was a candidate.

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And so now we've realized

we need to come back.

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So you can't quite have that, right?

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You wanna be a little bit cautious.

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So you have a great example about a

guardrail, around effectively in decision

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making and in interview screening

and any kind of human interaction.

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Your ethos sticks is to make

sure everything is human-driven.

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Spandanna: 100%.

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And I think we do get a lot of feedback,

especially given the market where it is.

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It's very hard to find a job now.

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And I hear that with

every candidate I talk to.

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So, the human element of this

couldn't be more powerful right now.

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I've had candidates where I'm just

happy to be talking to someone.

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I don't even care that I get this job.

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Not that they don't care, but it's

like I'm happy that there is a human

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on the other side and they care about

what I'm saying and want to listen to

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my experience or my career journey.

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AI stuff is a lot of the things

that I use it daily is on around

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policy announcements, right?

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I write something, but then I'm

like: "This is sounding too legalese.

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Can I just like kind of, you

know, judge it up a little bit

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and make people excited about it?"

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So that's what I use it for.

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And similarly for resumes,

it's great for screening.

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So things like, I need someone

in New York and the resume

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clearly says LA, California.

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The person could be great

but I cannot hire them.

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And those screenings are really

helpful but the other humanistic

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aspects, you do have to be very careful

and I fundamentally believe that.

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Thomas: So, let me just stick

on this topic one more, just to

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push you a little bit further.

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Yeah.

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Because you're right in a weak labor

market and one that is inundated,

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especially in the recruiting funnel

with just hurdles of robots coming in

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your way, that's the average experience.

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Yeah, with the idea of efficiency for

the employer, and given that there's

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a lot of power for employers and a

push from a lot of CFOs and leadership

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to just focus on efficiency, cut down

on cost efficiency for everything,

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especially back office, including HR.

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Yeah.

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It's a recipe for much more of that.

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You are specifically, and as with

your leadership team, have resisted

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that and saying no, there's some

lines that we wanna draw with

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our recruiting process anyway.

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Yeah.

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Right where we want to spend, I mean,

think of it spending more money, right?

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By being thoughtful and spending

more time with human experiences.

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Yeah.

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How does that happen, right?

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Like what is the case that you can

make to a, I don't know, a naive CFO

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that says what everyone else is doing.

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"Let's cut costs."

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"Why are we doing all this?"

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Spandanna: Yeah.

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Well, that tension is always there.

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I think if you as the head of people,

don't experience tension with your

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head of finance, then one of you

is not doing the job right, right?.

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Like it's always- don't get me wrong,

I really like our CFO and we do have

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healthy debates around what makes sense.

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What I do appreciate though, is

it's really important to do your own

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research ahead of time whenever you're

trying to make a point or trying to

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get approval on something, right?

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I look at recruitment costs

and a lot of the time we rarely

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outsource our recruiting.

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We are doing it in-house.

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and I try to calculate the amount of time

and money we would've spent in recruiting

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someone and how much that would cost, and

are we willing to do that three months

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in a time where we now have to exit them,

potentially have to give them severance

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because the job market is bad and you

feel bad for doing this and whatever.

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I've had an instance where someone

signed their offer letter, showed up

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to a work event before their start date

just so they can like, you know, meet

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people, et cetera and I got feedback

from literally everybody on the team

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just calling, including people who ended

up interviewing them, saying: "This

232

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person was just not right at the event.

233

00:14:24,215 --> 00:14:28,985

I feel very uncomfortable about them

coming in and starting to work",

234

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et cetera, and that was like, this

person already signed the offer.

235

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And that's a very hard conversation to

have because they've already put in their

236

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resignation, they're about to come in

and they're trying to start this new job.

237

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Very excited about it.

238

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And now I have to make that call saying:

"Sorry, I think we're going to rescind

239

00:14:49,610 --> 00:14:55,550

the offer because they were concerning

pieces of feedback on your behavior."

240

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We probably spent, I don't know, x

amount of thousands of dollars in

241

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interviewing this person that I could

have spent in flying someone over to

242

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meet us in person and make that decision.

243

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So the simplest answer to that

is, the way I handle our CEOs is

244

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to show up with a spreadsheet.

245

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I always have a spreadsheet.

246

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Like it doesn't even matter if it's

a conceptual conversation, like

247

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it's just good for the business.

248

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I always put numbers to it.

249

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And it may not always tell a good

story, but I think that's the

250

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language they really understand.

251

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Thomas: Interesting.

252

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So I had two takeaways from that.

253

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Yes, the data, but also you did

tell a story here, which is putting

254

00:15:42,645 --> 00:15:46,575

a real emphasis on what happened

with a very specific instance.

255

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Yeah.

256

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But you're really making a broader case,

but which really just gets in the minds of

257

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the team that you're looking to convince.

258

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So I think that is clearly a part of

the arsenal here to make the case.

259

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But the other thing that sticks out to me

is just how thankless the HR job can be.

260

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That is a tough conversation to have.

261

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And people aren't saying: "Great.

262

00:16:05,820 --> 00:16:06,900

Here's an award for doing that."

263

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No.

264

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And yet, that's the kind of tough

stuff that needs to happen behind

265

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the scenes to enable the workforce.

266

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So, let's switch gears a little

bit and talk about the workplace

267

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more broadly 'cause HR has its

place within this transformation

268

00:16:20,680 --> 00:16:22,060

that is happening across the board.

269

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So let's talk about performance, maybe.

270

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So how should we rethink

performance from all aspects

271

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within an AI integrated workplace?

272

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Spandanna: Oh boy.

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There's so many avenues we

can go with performance.

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I don't even know which one to touch.

275

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There's this idea of: "Is

AI gonna take over my job?"

276

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to "How am I going to be

evaluated against bots and agents

277

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versus human intervention?"

278

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So, I think more broadly,

279

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I do hear this narrative of the landscape

itself changing in the workforce.

280

00:17:01,045 --> 00:17:05,935

And again, performance is such

a huge component of what we

281

00:17:05,935 --> 00:17:12,135

do because it hits culture, it

hits aptitude, it hits skillset.

282

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So it's just like, how do we

assess this and how do we make

283

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sure the right people are staying?

284

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And also, the new question you have to

answer is: "Do I need a full headcount

285

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for this thing that needs to get done?"

286

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And that is like a very massive

conversation a lot of heads of

287

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HR are talking about right now.

288

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And headcount planning has

never been the same after that.

289

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I do it every year where

we're reevaluating.

290

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"Okay, there's a business strategy change.

291

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Do we need X number of people doing

this thing that we are no longer going

292

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to be provisioning for our clients?"

293

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And I think that's both an

innovation-wise, really exciting.

294

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But from a people perspective, it

almost feels diabolical, right?

295

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You never want to tell someone: "Hey.

296

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We realized that this job doesn't need

a full-time person to be doing this."

297

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And that's a tough conversation to have

because people are then going to be like,

298

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what do you mean you hired seven people

to do this job and now you're telling all

299

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of them that you don't need them anymore?

300

00:18:25,505 --> 00:18:32,495

And that has implications to culture

and it has implications to how people

301

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perform because they all of a sudden

everybody's on edge about their own jobs.

302

00:18:40,275 --> 00:18:44,395

Contrary to, I'm a big believer

in AI and I think it's going

303

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to transform the way we work.

304

00:18:47,545 --> 00:18:49,255

That's not everybody's belief.

305

00:18:50,185 --> 00:18:55,605

And we recently did a pulse survey and

we did ask people, because AI adoption

306

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is part of our strategy and I do want

people to start using it a bit more.

307

00:19:00,195 --> 00:19:04,515

And our question was: "How

likely are you to adopt to AI

308

00:19:04,515 --> 00:19:07,095

tools in your day-to-day job?"

309

00:19:07,095 --> 00:19:11,485

And how that's going to change your

role being more effective, et cetera.

310

00:19:12,445 --> 00:19:18,145

Only 70% said yes, that they

are very actively using AI tools

311

00:19:18,145 --> 00:19:19,795

or would like to use AI tools.

312

00:19:20,215 --> 00:19:25,465

So there's still like 30%, which

isn't a small number, who are

313

00:19:25,465 --> 00:19:28,535

like very much AI-resistant.

314

00:19:29,725 --> 00:19:31,465

Thomas: And there's an

industry context here, right?

315

00:19:31,470 --> 00:19:34,835

Because depending on the industry

that you're in, It should be, I

316

00:19:34,835 --> 00:19:37,955

would imagine a lot lower than

that in many other industries.

317

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Spandanna: 100%.

318

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And I'm in a tech industry and

like, I'm not having any different

319

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conversations than many other heads

of HR are having in their role.

320

00:19:47,905 --> 00:19:53,825

But as gen AI became more mainstream,

we actively added it to our own

321

00:19:53,825 --> 00:19:58,505

strategy a couple years ago, just around

the same time most companies were.

322

00:19:59,555 --> 00:20:03,035

I cannot tell you how many exit

interviews I've had where people

323

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were like, I just don't like AI.

324

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I gotta get out of here.

325

00:20:06,395 --> 00:20:09,035

Like that was sort of

like the theme of it.

326

00:20:11,390 --> 00:20:14,450

Those are like kind of

going back to performance.

327

00:20:16,820 --> 00:20:21,590

Assessing performance has never

been more challenging than now

328

00:20:22,040 --> 00:20:29,180

because the role itself, the job

itself is changing constantly.

329

00:20:29,180 --> 00:20:31,760

My job changes constantly.

330

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My team's job changes constantly.

331

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It's like everywhere.

332

00:20:35,300 --> 00:20:38,270

Before it was pretty straightforward

because you hand someone a

333

00:20:38,270 --> 00:20:41,420

job description, you say, here

are your responsibilities.

334

00:20:41,810 --> 00:20:47,980

This is what we're measuring you against,

and you get a score based off of that.

335

00:20:48,040 --> 00:20:52,170

Maybe you do upward reviews,

downward reviews, 360, whatever.

336

00:20:53,190 --> 00:20:58,300

Now, the constant struggle I get is like:

"My job wasn't the same three months ago,

337

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so what are you actually assessing me on?"

338

00:21:02,567 --> 00:21:04,697

This has been a fantastic

conversation so far.

339

00:21:04,937 --> 00:21:07,727

If you haven't already done so,

make sure to join our community.

340

00:21:07,967 --> 00:21:10,637

We are building a network of the

most forward-thinking, HR and

341

00:21:10,637 --> 00:21:13,817

people, operational professionals

who are defining the future.

342

00:21:14,297 --> 00:21:16,907

I will personally be sharing

news and ideas around how we

343

00:21:16,907 --> 00:21:18,917

can all thrive in the age of ai.

344

00:21:19,277 --> 00:21:22,262

You can find it at go cleary.com/cleary

345

00:21:22,692 --> 00:21:23,302

community.

346

00:21:23,927 --> 00:21:24,767

Now back to the show.

347

00:21:27,011 --> 00:21:29,821

Thomas: Ah, so you talk about both

the performance review right there,

348

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but earlier you're also talking

about just headcount planning.

349

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Yeah.

350

00:21:33,616 --> 00:21:37,946

It almost feels like anything that

has a halflife where you're trying to

351

00:21:38,006 --> 00:21:41,816

look more than a few weeks out, a few

months out, a couple quarters out.

352

00:21:41,996 --> 00:21:42,056

Yeah.

353

00:21:42,086 --> 00:21:45,586

Any process that is in that kind

of cyclical level, it needs to be

354

00:21:45,586 --> 00:21:47,326

rethought or it's riskier, right?

355

00:21:47,356 --> 00:21:47,446

Yeah.

356

00:21:47,456 --> 00:21:49,496

Companies used to have

five-year strategic plans.

357

00:21:49,496 --> 00:21:52,436

I feel like that's a little less

in vogue for most industries.

358

00:21:52,466 --> 00:21:52,556

Yeah.

359

00:21:52,616 --> 00:21:55,056

But that was a standard, at

least a couple of decades ago.

360

00:21:55,096 --> 00:21:59,046

And in some ways, right, quarterly

KPIs or OKRs have kind of replaced

361

00:21:59,046 --> 00:22:00,666

the five-year strategic plans.

362

00:22:00,936 --> 00:22:00,996

Yeah.

363

00:22:01,046 --> 00:22:01,361

But then,

364

00:22:04,111 --> 00:22:07,891

there's that dynamic nature,

and you're also hitting on the

365

00:22:07,991 --> 00:22:09,821

psychological impact here, right?

366

00:22:09,821 --> 00:22:15,211

So, I think I always hear about agility

and ability to learn or adaptability.

367

00:22:15,581 --> 00:22:20,136

Those are just things that are

gonna be more valued but based on

368

00:22:20,136 --> 00:22:23,136

what you're saying, maybe, things

are accelerating too fast, right?

369

00:22:23,136 --> 00:22:27,211

And for a big part of the working

population, it's just too much.

370

00:22:27,211 --> 00:22:29,491

And of course, in an exit

interview, that's the one place

371

00:22:29,491 --> 00:22:32,971

where you can be pretty, nakedly

open with your thinking, right?

372

00:22:33,001 --> 00:22:34,951

As close to it as you kind of

see in the corporate world.

373

00:22:35,381 --> 00:22:37,726

What does that mean for workplaces, then?

374

00:22:37,726 --> 00:22:41,986

Is it that there's a static percent

of the population that is not gonna

375

00:22:41,986 --> 00:22:46,786

be fit for or gonna be interested

in the kinds of jobs of the future?

376

00:22:46,786 --> 00:22:48,906

The ones where the people

using AI are gonna replace the

377

00:22:48,906 --> 00:22:51,116

people who are doing it before?

378

00:22:51,166 --> 00:22:55,506

Spandanna: I can only speak from a people

perspective because we are in the people

379

00:22:55,506 --> 00:22:59,346

business and that's how I view things.

380

00:22:59,346 --> 00:23:01,946

Like is customer success gonna go away?

381

00:23:02,006 --> 00:23:03,236

Maybe, right?

382

00:23:03,296 --> 00:23:09,036

Like, if it's just a means of prompt

that we need to give people and you

383

00:23:09,036 --> 00:23:11,526

just have bots doing it, perhaps.

384

00:23:14,016 --> 00:23:15,366

Maybe I'll tell a story.

385

00:23:15,396 --> 00:23:20,681

Very, very, very early on in my career, I

had a boss who's still a mentor of mine,

386

00:23:21,731 --> 00:23:26,171

and my go-to was always spreadsheets

and I was like, I'm going to do

387

00:23:26,171 --> 00:23:30,761

compensation, I'm gonna do operations,

and this is really comfortable, whatever.

388

00:23:31,361 --> 00:23:35,381

But his thinking was, well, if you're

going to be head of HR one day, you

389

00:23:35,381 --> 00:23:37,481

are gonna have tough conversations.

390

00:23:37,691 --> 00:23:40,556

And that was not my forte.

391

00:23:40,646 --> 00:23:42,416

It's not my comfortable space.

392

00:23:43,286 --> 00:23:52,286

And this guy had a talent for

firing people and they would send-

393

00:23:52,286 --> 00:23:53,606

Never quite described that way.

394

00:23:53,611 --> 00:23:53,731

No.

395

00:23:53,801 --> 00:23:54,291

Tell me more.

396

00:23:54,391 --> 00:23:59,926

No, but he had people send him

fruit baskets after he fired them.

397

00:24:00,691 --> 00:24:04,441

Because they were like, wow, this is

the best thing that's happened to me.

398

00:24:04,711 --> 00:24:08,561

Now I can go retire or I can

work on this hobby that I've

399

00:24:08,861 --> 00:24:10,391

put off, or whatever it was.

400

00:24:10,811 --> 00:24:14,381

So, he was really good at it and

he was like, you have to do this.

401

00:24:14,381 --> 00:24:18,311

So, he kind of gave me

pointers on how to do it.

402

00:24:18,311 --> 00:24:20,141

And there was one thing that I took away.

403

00:24:20,141 --> 00:24:23,901

It was like, yes, you're

delivering really tough news.

404

00:24:25,431 --> 00:24:30,326

But that's that much more important

to remember how you're delivering it.

405

00:24:30,926 --> 00:24:36,986

And so, I take that in

stride in everything I do.

406

00:24:37,196 --> 00:24:40,376

I'm not always delivering good news.

407

00:24:40,466 --> 00:24:42,176

Actually, most of the time I'm not.

408

00:24:42,491 --> 00:24:47,226

But there's a way to deliver

that in a way people don't feel

409

00:24:48,591 --> 00:24:51,651

just completely led down by it.

410

00:24:51,831 --> 00:24:52,251

Right?

411

00:24:52,311 --> 00:24:57,321

And that is the difference,

I think, the people in people

412

00:24:57,321 --> 00:25:01,071

space need to really focus on.

413

00:25:01,551 --> 00:25:06,921

This is not going to be, at least for a

long time, I don't think is going to be

414

00:25:06,921 --> 00:25:12,726

replaced by AI, which is why I think when

we first met, I told that story of I was

415

00:25:12,786 --> 00:25:17,436

in a board meeting and one of the board

members said: "Spandana is the only one

416

00:25:17,436 --> 00:25:19,416

who's going to have a job in the future."

417

00:25:19,956 --> 00:25:24,286

Because it has to deal with

managing both bots and people.

418

00:25:24,286 --> 00:25:26,586

And that's the reality of the world.

419

00:25:27,036 --> 00:25:31,496

And so again, I think, which is why

I'm very thoughtful, going back to the

420

00:25:31,496 --> 00:25:36,866

recruiting question, is like I take

every interview that I do very seriously.

421

00:25:36,866 --> 00:25:42,426

And my team also make sure that

we respond to every application.

422

00:25:42,426 --> 00:25:46,746

We make sure that we are letting them

know where they are in the process and

423

00:25:46,746 --> 00:25:48,756

try to be as transparent as possible.

424

00:25:49,996 --> 00:25:52,336

So the human element of it is, I think,

425

00:25:54,586 --> 00:25:58,526

the biggest thing we have to

leverage, as people in the people

426

00:25:58,526 --> 00:26:04,316

space and not be spooked by what's

coming down the pike with AI.

427

00:26:06,251 --> 00:26:09,371

Thomas: So how do you enable that in

the organization to not be spooked, to

428

00:26:09,371 --> 00:26:14,391

know that change is coming, it's ever

increasing, roles are gonna change.

429

00:26:14,421 --> 00:26:19,591

Yes, it's gonna be hard for

you or us or anyone to look

430

00:26:19,591 --> 00:26:20,881

at performance the same way.

431

00:26:21,421 --> 00:26:25,891

How can you coach that or

enable in an organization.

432

00:26:25,921 --> 00:26:27,871

Spandanna: I don't think

it's a new formula.

433

00:26:27,871 --> 00:26:32,301

It's a formula that's been used

and driven and proven with.

434

00:26:32,301 --> 00:26:34,521

I come from a startup culture.

435

00:26:34,611 --> 00:26:41,121

Some days, you are watching sunrises from

your desk because that's just the reality

436

00:26:41,121 --> 00:26:43,441

of being in a fast-paced environment.

437

00:26:44,581 --> 00:26:49,621

The people I find who are very, very

successful in those environments

438

00:26:49,621 --> 00:26:54,361

are people who are redefining their

work and redefining their roles.

439

00:26:54,991 --> 00:27:04,371

And I encourage, I push myself definitely

to be adaptable and try to come up

440

00:27:04,371 --> 00:27:06,651

with ways where we can be adaptable.

441

00:27:06,741 --> 00:27:09,111

And I encourage my team to do that, too.

442

00:27:09,651 --> 00:27:14,551

A lot of the times the question is:

"Hey, I used to do this and I feel

443

00:27:14,551 --> 00:27:17,191

like AI is going to take over my job."

444

00:27:18,361 --> 00:27:19,441

Thomas: We've all heard that before.

445

00:27:19,501 --> 00:27:19,861

Yeah.

446

00:27:21,001 --> 00:27:22,801

Spandanna: I dunno if I agree with that.

447

00:27:23,041 --> 00:27:28,171

I think there's an opportunity here to

say: "I'm gonna leverage AI to redefine

448

00:27:28,171 --> 00:27:35,881

the work that I do and still make myself

a valuable asset to this company and

449

00:27:35,881 --> 00:27:39,221

be very much part of the strategy."

450

00:27:39,321 --> 00:27:42,771

I think the takeaway is if you're

sticking to your job description, I

451

00:27:42,771 --> 00:27:45,621

think it's a lost cause on the AI race.

452

00:27:47,106 --> 00:27:48,856

Thomas: And also it's on you, right?

453

00:27:48,856 --> 00:27:48,916

Yeah.

454

00:27:48,916 --> 00:27:53,826

It's not on leadership,

your management, HR.

455

00:27:53,886 --> 00:27:55,956

I mean, to some degree, they need

to be thinking about this at the

456

00:27:55,956 --> 00:27:59,646

organization level, but it's on

you specifically to understand and

457

00:27:59,646 --> 00:28:03,966

redefine well what is higher value

for you to be working on, right?

458

00:28:03,966 --> 00:28:04,196

Yeah.

459

00:28:04,756 --> 00:28:06,286

Spandanna: And that's no way for others.

460

00:28:06,286 --> 00:28:07,126

Exactly.

461

00:28:07,176 --> 00:28:11,766

The leadership does a lot where it's

like, you know, as things are moving-

462

00:28:11,866 --> 00:28:18,616

at least we do- where we try to give

people tools around AI and we try to

463

00:28:19,356 --> 00:28:24,276

encourage them to use them, put in

policies for adoption and also kind of

464

00:28:24,276 --> 00:28:26,286

warn them around guardrails and stuff.

465

00:28:26,286 --> 00:28:27,756

So around compliance and things.

466

00:28:28,386 --> 00:28:29,856

So that already exists.

467

00:28:29,856 --> 00:28:34,296

I don't know if you've ever seen this

very famous President Obama clip where he

468

00:28:34,296 --> 00:28:39,066

was like: "Oh, you go into these like big

places and you're sitting with like hedge

469

00:28:39,066 --> 00:28:41,676

funders and Harvard grads and whatever.

470

00:28:42,291 --> 00:28:45,081

And once you're there, you're

like, oh, they're not all that.

471

00:28:46,881 --> 00:28:48,411

You're actually not that smart."

472

00:28:48,411 --> 00:28:52,641

So I think people need to be

a little bit more pragmatic.

473

00:28:52,641 --> 00:28:56,511

I think waiting for leadership

to do something is not actually

474

00:28:56,511 --> 00:28:57,831

gonna help your career.

475

00:28:57,921 --> 00:29:03,351

My advice to you would be to think of

something maybe even leadership hasn't

476

00:29:03,351 --> 00:29:08,121

thought about and you, you present it to

them and they're all already like, whoa,

477

00:29:08,271 --> 00:29:10,411

this person is so impressive, you know?

478

00:29:12,771 --> 00:29:16,971

Thomas: So let's talk about

that with the HR function a

479

00:29:16,971 --> 00:29:18,311

little bit more deeply, right?

480

00:29:18,311 --> 00:29:21,151

That concept of that: "Hey,

all roles are changing.

481

00:29:21,151 --> 00:29:22,471

There's gonna be a shift in that."

482

00:29:22,591 --> 00:29:27,781

Let's talk about like how HR roles

may shift and how would you go about

483

00:29:27,781 --> 00:29:30,391

redefining and pick your role, right?

484

00:29:30,731 --> 00:29:35,131

So how do you think about the

various sub-functions or the

485

00:29:35,131 --> 00:29:38,021

leadership roles within HR evolving.

486

00:29:38,081 --> 00:29:43,151

And do you have any ideas or examples

about how we might put that nugget into

487

00:29:43,151 --> 00:29:48,721

practice, which is how can you yourself

think about: "Okay, ways to evolve my

488

00:29:48,721 --> 00:29:51,261

own role based on new possibilities?"

489

00:29:51,561 --> 00:29:56,021

Spandanna: Yeah, super futuristic, I think

we will end up in a world where we are

490

00:29:56,021 --> 00:30:00,941

also managing bots as we are with humans.

491

00:30:00,941 --> 00:30:04,751

So that is what is coming.

492

00:30:04,751 --> 00:30:06,491

I know that that's sitting there.

493

00:30:06,831 --> 00:30:13,551

But right now, a few things that I do is,

you mentioned that we're going so fast.

494

00:30:13,551 --> 00:30:15,231

We're moving too fast, right?

495

00:30:15,321 --> 00:30:21,366

And in that world, it's really important

to have right policies in place.

496

00:30:21,366 --> 00:30:24,576

So that is a big thing that

I do, where it's like you

497

00:30:24,576 --> 00:30:27,336

can't just run with using AI.

498

00:30:27,336 --> 00:30:31,116

You have to have certain guardrails,

you have to have checks and balances.

499

00:30:31,116 --> 00:30:33,636

So I definitely do that.

500

00:30:35,166 --> 00:30:40,806

We have to monitor or be cognizant of

the type of AI tools people are using.

501

00:30:40,896 --> 00:30:45,741

So it's like, is it open

AI versus Claude AI?

502

00:30:45,741 --> 00:30:48,861

And there's like, you know, there

are different iterations of it.

503

00:30:49,281 --> 00:30:55,731

There's that - to be very, very tactical-

there's also privacy issues, right?

504

00:30:55,821 --> 00:30:58,661

Like, are we divulging company secrets?

505

00:30:58,661 --> 00:31:00,401

We have to be very careful about that.

506

00:31:00,791 --> 00:31:07,991

So those are the aspects of it that

I definitely get into in terms of

507

00:31:07,991 --> 00:31:09,911

the current working environment.

508

00:31:10,481 --> 00:31:16,031

The other thing I have heavily encouraged

though, within the people team is to

509

00:31:17,051 --> 00:31:22,361

carve out time to see what developments

are happening on the HR space.

510

00:31:22,871 --> 00:31:29,481

Just so we are ahead of how other

companies are using AI and potentially

511

00:31:29,481 --> 00:31:30,861

being able to leverage that.

512

00:31:32,976 --> 00:31:34,896

Thomas: Carve out time to learn yourself.

513

00:31:35,036 --> 00:31:40,316

Yeah, those are some very practical

things that I guess as an HR leader.

514

00:31:40,646 --> 00:31:47,181

Yeah, you need to have the skillset

going forward to create and help craft a

515

00:31:47,181 --> 00:31:50,191

company-wide AI policy around governance.

516

00:31:50,191 --> 00:31:52,171

Maybe a training and onboarding, probably.

517

00:31:52,321 --> 00:31:55,591

For most companies is

valid, at this stage.

518

00:31:55,691 --> 00:32:00,751

And that includes the policies

around knowing about data privacy,

519

00:32:00,751 --> 00:32:03,571

which is always was a thing, but

it's just even more, I guess,

520

00:32:03,571 --> 00:32:05,401

arguably top of mind with AI tools.

521

00:32:05,401 --> 00:32:08,881

But as long as we have so many tools

that we can log into that still holds

522

00:32:09,431 --> 00:32:11,261

but it just seems to escape faster.

523

00:32:11,426 --> 00:32:15,701

Spandanna: Well there's also like

the fondest example I've heard is,

524

00:32:15,751 --> 00:32:18,601

I think this woman from the UK.

525

00:32:18,631 --> 00:32:19,681

It was a small company.

526

00:32:19,681 --> 00:32:21,901

It was like, you know,

60 people or something.

527

00:32:21,991 --> 00:32:28,396

She had everybody write a paragraph

of what their day to day looks like,

528

00:32:28,396 --> 00:32:30,046

a day in the life of looks like.

529

00:32:30,256 --> 00:32:31,186

And they all did.

530

00:32:31,216 --> 00:32:34,996

And they sent it to her and

she put it in a AI tool.

531

00:32:35,386 --> 00:32:40,246

And then she asked it to build

an employee life cycle for each

532

00:32:40,246 --> 00:32:41,446

of the roles that they had.

533

00:32:41,746 --> 00:32:42,586

And it did.

534

00:32:42,646 --> 00:32:46,606

And she like sent it over and she was

like, here are the five things you can

535

00:32:46,606 --> 00:32:49,876

do to improve your career or do whatever.

536

00:32:50,236 --> 00:32:54,886

So it's just like, there are a

lot of fun things happening in

537

00:32:54,886 --> 00:32:58,756

this space, and I do think we're

only just scratching the surface.

538

00:33:00,556 --> 00:33:01,986

Thomas: That's really interesting.

539

00:33:01,986 --> 00:33:02,196

Yeah.

540

00:33:02,196 --> 00:33:05,006

So think about how you can

crowdsource different things.

541

00:33:05,086 --> 00:33:06,646

I mean, everyone has their survey data.

542

00:33:06,646 --> 00:33:09,586

There's probably ways to slice

that stuff into creating outputs

543

00:33:09,586 --> 00:33:14,431

that you, otherwise, maybe wouldn't

imagine using that as an input for.

544

00:33:14,431 --> 00:33:17,551

So you can play with inputs and

outputs as kind of a takeaway there.

545

00:33:17,551 --> 00:33:18,331

That's pretty interesting.

546

00:33:18,901 --> 00:33:21,211

Let me ask you about the futuristic

thing that you mentioned.

547

00:33:21,211 --> 00:33:23,421

So, yeah, so what does that mean?

548

00:33:23,421 --> 00:33:28,631

So, what does that mean as bots, an

agentic AI becomes part of, I dunno, does

549

00:33:28,631 --> 00:33:29,831

it become part of headcount planning?

550

00:33:29,881 --> 00:33:32,551

These tools are increasingly just

part of the, you know, they're

551

00:33:32,551 --> 00:33:33,871

teammates in some ways, right?

552

00:33:33,871 --> 00:33:37,351

But yeah, arguably it's just what

software has always been, which is,

553

00:33:37,651 --> 00:33:39,601

here's a function in the B2B space, right?

554

00:33:39,751 --> 00:33:40,591

Here's a function.

555

00:33:41,101 --> 00:33:43,831

Here are people are working on

certain outcomes and here's some

556

00:33:43,831 --> 00:33:45,601

software to get them there faster.

557

00:33:45,931 --> 00:33:48,841

And now this is like just, maybe

there's a greater percentage

558

00:33:48,841 --> 00:33:50,251

software, fewer people, I don't know.

559

00:33:50,251 --> 00:33:55,211

But, how does that changed, especially

if anything, the HR lens, right?

560

00:33:55,211 --> 00:33:59,431

As you go towards headcount planning

or enabling various functions.

561

00:34:01,951 --> 00:34:05,226

Spandanna: That's a tough question

to answer because I don't think

562

00:34:05,226 --> 00:34:09,036

we're there yet, but I know we're

getting there very, very quickly.

563

00:34:09,246 --> 00:34:14,136

it's hard to imagine that it wouldn't have

an effect on literally everything, right?

564

00:34:14,166 --> 00:34:17,196

Like how we do performance.

565

00:34:17,196 --> 00:34:20,826

How do we think about headcount planning?

566

00:34:20,976 --> 00:34:29,315

My thinking is in an agentic

world, the role of HR is going

567

00:34:29,315 --> 00:34:32,166

to be a lot more technical.

568

00:34:32,406 --> 00:34:36,516

Like if you do not have any

technical chops, I highly

569

00:34:36,516 --> 00:34:38,315

recommend that you do it now.

570

00:34:38,786 --> 00:34:44,666

Just to kind of be able to explain why

things are going well or not going well.

571

00:34:44,755 --> 00:34:46,766

and I think that's really important.

572

00:34:47,456 --> 00:34:54,926

And then the second piece of it is there's

always going to be that tension of,

573

00:34:56,156 --> 00:34:58,856

you know, AI is able to do this today.

574

00:34:59,006 --> 00:35:01,076

Is AI able to do something else?

575

00:35:01,076 --> 00:35:08,096

To the extent where there are no people

working and is it just agents, right?

576

00:35:08,426 --> 00:35:11,301

Hopefully it doesn't happen

in my lifetime, but if it

577

00:35:11,301 --> 00:35:14,751

does, then there's so many

578

00:35:17,731 --> 00:35:23,796

iterations of working that we haven't

even scratched the surface on.

579

00:35:23,796 --> 00:35:28,296

Like when I was in grad school, we talked

about- are you familiar with DAOs, the

580

00:35:28,296 --> 00:35:31,256

Decentralized Autonomous Organizations?

581

00:35:32,876 --> 00:35:36,951

Thomas: I remember that's kind of

gone up and down along with the rest

582

00:35:36,951 --> 00:35:38,561

of the crypto world in a few years.

583

00:35:38,561 --> 00:35:38,771

Yeah.

584

00:35:38,776 --> 00:35:39,046

Spandanna: Yeah.

585

00:35:39,191 --> 00:35:43,871

But it was like only two years ago

that I had a professor who like firmly

586

00:35:43,871 --> 00:35:47,981

believed in DAOs, and that's what she

thought we were all heading towards.

587

00:35:47,981 --> 00:35:51,891

Like, there's not gonna be

no more corporate entities.

588

00:35:51,891 --> 00:35:55,811

No more like Delaware

Incorporated or whatever it is.

589

00:35:55,811 --> 00:35:58,931

We're all just going to be

communities coming together.

590

00:35:59,441 --> 00:36:03,941

I can see a world where it's all

agentic and people are just kind of like

591

00:36:04,121 --> 00:36:07,001

contributing to projects, et cetera.

592

00:36:08,141 --> 00:36:08,291

But I

593

00:36:10,446 --> 00:36:10,751

don't know.

594

00:36:10,751 --> 00:36:16,101

It's a very hard question to answer

now with like how the agentic and

595

00:36:16,241 --> 00:36:22,171

human mixture is going to affect

our workforce and the way we work.

596

00:36:24,751 --> 00:36:28,201

Thomas: When you bring up DAOs, for

me, it almost made me realize maybe

597

00:36:28,471 --> 00:36:35,021

we are currently in the peak cycle

of that hype cycle with AI, just

598

00:36:35,021 --> 00:36:38,501

like you were when you were taking

that course with where DAOs were.

599

00:36:38,921 --> 00:36:44,281

And it's just actually this future

with like AGI and organizations that

600

00:36:44,281 --> 00:36:48,001

are self, not just organizing, but

just like working with agents is just

601

00:36:48,271 --> 00:36:51,111

asymptotically 5 to 10 years away.

602

00:36:51,111 --> 00:36:55,101

And there's like so much more that we to

build towards actually get that right.

603

00:36:55,101 --> 00:36:58,821

But there's that, in fact, there are many

other problems to solve in the near term

604

00:36:58,821 --> 00:37:00,831

or even in the near term generations.

605

00:37:00,891 --> 00:37:01,041

Yeah.

606

00:37:01,101 --> 00:37:01,641

But who knows?

607

00:37:02,161 --> 00:37:05,091

But it's always interesting to

think about how that could turn out.

608

00:37:05,431 --> 00:37:09,541

So let me ask you then,

just as we close out here.

609

00:37:09,571 --> 00:37:09,661

Yeah.

610

00:37:10,061 --> 00:37:13,081

Just around building

an AI strategy, right?

611

00:37:13,081 --> 00:37:14,731

As like advice for peers.

612

00:37:15,181 --> 00:37:18,751

I think you were telling me, you went

to like a bootcamp and you learned a

613

00:37:18,751 --> 00:37:22,081

bunch of stuff and then you launched

an AI policy within a week, right?.

614

00:37:22,081 --> 00:37:24,541

And that's something you mentioned

as like a tactical thing that

615

00:37:24,541 --> 00:37:25,651

people should be able to do.

616

00:37:25,651 --> 00:37:32,721

So, if I'm an uninitiated CHRO or people

ops person or HRBP, whatever the case

617

00:37:32,721 --> 00:37:34,491

may be, and I want to like do that.

618

00:37:34,491 --> 00:37:35,061

What did you do?

619

00:37:35,061 --> 00:37:38,031

How did that go from like soup to

nuts to launch something like that?

620

00:37:38,091 --> 00:37:38,301

Spandanna: Yeah.

621

00:37:38,621 --> 00:37:46,316

Well, the truth of the matter is whether

you have a fully functioning AI policy

622

00:37:46,316 --> 00:37:48,686

or not, your people are using AI.

623

00:37:48,856 --> 00:37:52,101

And so, where I started was

with our head of compliance

624

00:37:52,428 --> 00:37:58,008

and security to see which AI tools were

the most famous amongst our people.

625

00:37:58,848 --> 00:38:03,088

And then, we also got to see,

because we use Google Suite,

626

00:38:03,198 --> 00:38:05,298

we do use Gemini quite a lot.

627

00:38:05,368 --> 00:38:10,078

And so, we also got to see what sort of

things that they're really using it for.

628

00:38:10,488 --> 00:38:15,708

And then my second stop was with legal to

talk through some of the risks and what

629

00:38:15,708 --> 00:38:23,503

are the guardrails and the things that

you can do or cannot do within the policy.

630

00:38:23,503 --> 00:38:24,433

So we did that.

631

00:38:24,903 --> 00:38:27,903

And then we also encouraged AI adoption.

632

00:38:27,903 --> 00:38:33,243

So we've had Gemini come in and do

demos on how to use them, particularly

633

00:38:33,243 --> 00:38:35,103

for our customer success people.

634

00:38:35,583 --> 00:38:41,313

And it really took off like everybody,

and again, we're a hundred people

635

00:38:41,313 --> 00:38:47,093

company so it's not massive, but

people took off and came up with very

636

00:38:47,093 --> 00:38:50,093

creative ways of servicing our clients.

637

00:38:50,093 --> 00:38:51,823

And none of them were the same.

638

00:38:51,873 --> 00:38:56,283

I can use AI for this and I can

use AI for that, but it was very

639

00:38:56,283 --> 00:38:57,963

much, they all have the same job.

640

00:38:58,003 --> 00:38:59,563

That was very exciting to see.

641

00:38:59,568 --> 00:39:05,473

But it was all within, if you do

have a policy in place, then it makes

642

00:39:05,473 --> 00:39:09,013

things that much easier because you've

already communicated those guardrails

643

00:39:09,013 --> 00:39:11,473

and you've mentioned lawsuits earlier.

644

00:39:11,473 --> 00:39:12,823

It also avoids those.

645

00:39:14,843 --> 00:39:17,063

Thomas: So thank you

for a bit of a roadmap.

646

00:39:17,063 --> 00:39:19,023

And It's not a multi-year

project either, right?

647

00:39:19,023 --> 00:39:22,603

You can get it up and off the ground

and making an impact and getting

648

00:39:22,603 --> 00:39:26,243

some alignment, reducing the risk

exposure and getting some alignment

649

00:39:26,243 --> 00:39:29,933

across compliance, legal, the

employee base, functional leaders and

650

00:39:29,933 --> 00:39:31,823

even just tooling standardization.

651

00:39:31,823 --> 00:39:31,973

Yeah.

652

00:39:32,033 --> 00:39:33,928

Could be a part of the wins.

653

00:39:33,978 --> 00:39:35,508

One last question.

654

00:39:35,748 --> 00:39:39,138

If I could just ask if you're

talking to your peers because

655

00:39:39,138 --> 00:39:40,518

you're moving at a pace.

656

00:39:40,518 --> 00:39:43,388

I can feel that In terms of AI

adoption, both at your function as

657

00:39:43,388 --> 00:39:44,858

well as the organization, overall.

658

00:39:44,918 --> 00:39:45,068

Yeah.

659

00:39:45,238 --> 00:39:51,008

And what would you say to your

peers and HR leaders who feel a

660

00:39:51,008 --> 00:39:56,688

bit paralyzed by the pace of change

that's happening around them in other

661

00:39:56,688 --> 00:39:58,938

functions or in other companies?

662

00:39:58,938 --> 00:40:03,528

Like they're not sure exactly how to

start, where to go, what to do, like

663

00:40:03,528 --> 00:40:04,698

what would be your message to them?

664

00:40:04,998 --> 00:40:05,418

Spandanna: Yeah.

665

00:40:05,418 --> 00:40:09,078

I would say, look, I fully get that.

666

00:40:09,078 --> 00:40:11,928

It could be dizzying seeing all of this.

667

00:40:11,928 --> 00:40:17,328

And particularly if you're in a head

of HR role, you're talking payroll,

668

00:40:17,328 --> 00:40:21,038

to the future of HR on any given day.

669

00:40:21,398 --> 00:40:23,968

And so you're constantly

context switching.

670

00:40:24,358 --> 00:40:29,938

My recommendation would be to not try

to solve for all of it at the same time.

671

00:40:29,998 --> 00:40:36,088

And the good thing about HR - and we

talked about this before - HR tech is

672

00:40:36,088 --> 00:40:38,608

like the last to innovate on anything.

673

00:40:40,138 --> 00:40:44,128

And so, the good news is even

if it feels like we're in a rat

674

00:40:44,128 --> 00:40:49,378

race, chances are this particular

function isn't going to innovate as

675

00:40:49,378 --> 00:40:51,118

fast as you think it's going to..

676

00:40:51,568 --> 00:40:55,048

So I would say take a couple of items.

677

00:40:55,048 --> 00:40:58,768

It could be learning and development,

or it could be performance management.

678

00:40:58,798 --> 00:41:01,168

It could be engagement.

679

00:41:02,053 --> 00:41:06,523

Take one of those and see how

- actually, one of the easiest things

680

00:41:06,523 --> 00:41:08,113

people can do is compensation.

681

00:41:08,893 --> 00:41:11,083

AI has changed the game.

682

00:41:11,143 --> 00:41:17,473

Like before, I used to run 128

column spreadsheets of like

683

00:41:17,833 --> 00:41:20,473

comp, merit cycles, et cetera.

684

00:41:20,533 --> 00:41:23,863

Now, I think I forgot a lot

of Excel formulas now because

685

00:41:23,863 --> 00:41:26,713

I don't use them fast enough.

686

00:41:26,713 --> 00:41:32,393

So definitely look at what the low

hanging fruit are, where you can

687

00:41:32,393 --> 00:41:37,853

incorporate it slowly, and definitely

don't try to do all of it at once.

688

00:41:39,738 --> 00:41:42,618

Thomas: Thank you for sharing that and

for the broader conversations [inaudible].

689

00:41:42,618 --> 00:41:46,863

There's so much we talked about specific

use cases that you're doing within

690

00:41:46,863 --> 00:41:51,023

your function as well as a lot of

the change management and development

691

00:41:51,023 --> 00:41:54,923

opportunities and challenges that

you've faced as an HR leader that many

692

00:41:54,923 --> 00:41:56,883

of us out there are going through.

693

00:41:56,883 --> 00:41:59,528

So I think there's some insight

and some experiences that

694

00:41:59,528 --> 00:42:00,818

we can now all learn from.

695

00:42:01,208 --> 00:42:05,818

And I also appreciate the future,

you're looking at all the shifts that

696

00:42:05,818 --> 00:42:10,488

can happen but also appreciating the

guardrails that you wanna put in place so

697

00:42:10,488 --> 00:42:14,898

that you know you're actually mindfully

moving fast and slow at the same time.

698

00:42:14,898 --> 00:42:16,908

From what I can tell, like

that's maybe one way to put it.

699

00:42:16,908 --> 00:42:18,648

And that you have guardrails in place.

700

00:42:18,648 --> 00:42:21,108

You're thinking about which

areas to drop it into.

701

00:42:21,378 --> 00:42:21,438

Yeah.

702

00:42:21,438 --> 00:42:27,483

But also explicitly human and non-AI

in certain types of engagements, right?

703

00:42:27,483 --> 00:42:30,273

Or certain types of moments in

the employee journey on purpose.

704

00:42:30,333 --> 00:42:30,393

Yeah.

705

00:42:30,733 --> 00:42:35,923

So I think that's really thoughtful

and a good place for many people to go.

706

00:42:36,283 --> 00:42:40,493

So with all that said, thank you

once again, for the conversation.

707

00:42:40,763 --> 00:42:44,643

For everyone out there listening,

thank you for your attention and

708

00:42:44,643 --> 00:42:46,113

hope you found some value in this.

709

00:42:46,513 --> 00:42:50,863

About how you might be able to

future proof your organization and

710

00:42:50,923 --> 00:42:55,513

your HR function so that we can

all thrive within the age of AI.

711

00:42:56,023 --> 00:42:58,933

So with that said, we'll

see you all on the next one.

712

00:42:59,023 --> 00:42:59,383

Thank you.

713

00:42:59,653 --> 00:43:00,523

Thank you.

714

00:43:02,333 --> 00:43:05,393

Thanks for joining us on this

episode of Future Proof HR.

715

00:43:05,633 --> 00:43:08,243

If you like the discussion, make

sure you leave us a five star

716

00:43:08,243 --> 00:43:11,123

review on the platform you're

listening to or watching us on.

717

00:43:11,543 --> 00:43:14,483

Or share this with a friend or colleague

who may find value in the message.

718

00:43:14,783 --> 00:43:19,733

See you next time as we keep our pulse on

how we can all thrive in the age on AI.

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