In this episode of the Future Proof HR podcast, our co-host and executive producer Jim Kanichirayil sits down with Kirsten Faurot, Chief People Officer at Bombora, to talk about what it really takes to build AI adoption inside an organization without creating fear, confusion, or resistance. Drawing from Bombora’s experience as a tech-forward B2B marketing data company, Kirsten shares how her team approaches AI governance, tool selection, manager enablement, and employee communication in a way that keeps people engaged while still moving the business forward.

Together, they unpack the gap between the scary headlines around AI and what healthy adoption actually looks like in practice. Kirsten explains why leaders need to create clear guardrails, define use cases by team, and give employees space to talk honestly about what AI change means for their jobs. The conversation also covers how visible internal success stories can accelerate adoption, why managers play such a critical role in helping hesitant employees lean in, and how learning stipends, internal training, and practical experimentation can make AI feel more useful and less threatening.

From DevOps and recruiting to sales and marketing, this episode offers a practical look at how HR leaders can help teams use AI to reduce manual work, improve efficiency, and create more room for higher-value work. It is a grounded conversation about trust, communication, adoption, and what it means to lead AI transformation without losing the human side of work.

Topics Discussed

• How Bombora approaches AI adoption with guardrails, approved tools, and clear standards
• Why fear around AI and job loss has to be addressed directly, not ignored
• How leaders can frame AI as support for better work, not just efficiency pressure
• Why showcasing employee wins in public helps drive broader adoption
• How Bombora uses manager coaching and monthly check-ins to move slow adopters forward
• What DevOps, recruiting, marketing, sales, and SDR teams can learn from real AI use cases
• How learning stipends and internal sharing can help build an AI-ready culture
• Why executive communication matters as much as the strategy itself

Additional Resources

• Cleary’s AI-powered HR Chatbot
• Future Proof HR Community
• Connect with Kirsten Faurot on LinkedIn

Note: This episode was recorded in Dec 2025

All you have to do is turn on the news or look anywhere, right?

2

And what are the stories telling

us, oh, AI is coming for my job.

3

So I think there is that reality, right?

4

That they might have seen friends

who jobs are changing drastically.

5

So as excited as they are, there also

is that worry of it gonna get so good

6

that maybe I won't be necessary anymore?

7

Jim Kanichirayil: AI is gonna come

and completely take all of our jobs.

8

Anyone who spent time on LinkedIn

or reading tech magazines has

9

probably seen a headline like that.

10

And in some instances that

can certainly be true.

11

We've already seen the impact of

that across a lot of organizations.

12

Certainly if you're in marketing

or sales, you've seen rumblings and

13

heard rumblings about how AI is gonna

fundamentally transform those functions.

14

I'm sure all of us have experience with

reading about some out of touch CEO.

15

Who has passed down an edict that says

that we're gonna be AI first, and their

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first move after making that announcement

is laying a bunch of people off.

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So it's a very real concern that

exists in the world of work.

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But the question becomes is that view of

the future that catches all the headlines,

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really what an AI embedded culture is

gonna look like in the world of work.

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What if I told you there's a way that you

could embed AI into your organization,

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get a high degree of employee adoption and

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do some really interesting things

that actually spotlight how employees

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are utilizing AI to make their jobs

more interesting and more rewarding.

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That's the story that we're gonna

tell today, and the person that's

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gonna be joining us to share

that story is Kirsten Faurot.

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Kirsten leads all Human

Resources activities at Bombora.

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She's the Chief People Officer and she's

responsible for leading organizational

28

development, and collaborating with

teams to help Bombora continue to

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grow rapidly in revenue and staff.

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She's got over 20 years of experience

building and cultivating HR

31

systems that function effectively

for employees at all levels.

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She's focused particularly on

driving organizational initiatives

33

and employee development.

34

Establishing positive, engaging, and

productive cultures and directing talent

35

acquisition and performance management?

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Kirsten holds a Master's of science

and organizational Psychology from

37

Baruch College and a bachelor's

degree in International Relations

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from American University.

39

She's also certified in the Hogan

Personality Assessment and the

40

Myers-Briggs, type instrument assessment.

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Kirsten, welcome to the show.

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Kirsten Faurot: Thank you.

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Jim Kanichirayil: it's gonna

be a fun conversation and we're

44

gonna get into the weeds with,

all sorts of stuff as far as, AI.

45

And we're gonna bust a myth

actually today, or maybe several.

46

So looking forward to that conversation.

47

But I think before we get started with

48

what we're here to talk about.

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I think it's important

for you to set the stage.

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So why don't you get us and the

listeners up to speed on your company

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landscape and the lay of the land.

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Kirsten Faurot: So I work as the

Chief People Officer for Bombora.

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Bombora is a B2B marketing data company.

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We've been around for 10 years.

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thanks to our industry leading

proprietary data, we call it intent data.

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and, what that means essentially

is that we provide our clients with

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valuable insights that they can

use to figure out who's in market.

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For whatever they produce or make.

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We help them figure out how to reach

them, how to prioritize that reach,

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how to personalize it, and then of

course, how to measure the results.

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So it's extremely

valuable for our clients.

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Jim Kanichirayil: I'm pretty excited

to have this conversation is that

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I think you're one of the first,

sales tech leaders to be on the show.

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And when I think about sales tech

organizations, or at least that's

65

where I put Bombora, I have some

assumptions in terms of where they

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are from a tech stack perspective.

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So Give us a general overview of what

the organization's, mindset is, when it

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comes to technology, and particularly

efficiencies across the organization.

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Kirsten Faurot: I would say

overall our mindset is to be

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extremely, forward thinking.

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As a company, we're very

technologically savvy, I would say.

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What we do and the way we provide

the information to our clients, we

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really do meet them wherever they

are, whatever systems they're using,

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whatever platforms, wherever they are.

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Do we need to give them information

right into their Salesforce instance?

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We're gonna be able to meet

them wherever they are.

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So as a result, we actually

have an extremely broad.

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set of platforms that we work

with, set of systems we work with.

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so it really does Kanichirayil

Kanichirayil Kanichirayil run the gamut.

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to me what's been interesting is, of

course, AI innovations can be found in

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all of those places, and so we've been

able to really work those in, into all

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the different areas of our company.

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Jim Kanichirayil: Yeah, I wanna

dig in a little bit deeper and look

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internally, one of the things that you

mentioned is that as an organization.

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you tend to be pretty forward thinking

from a technology perspective and

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tech savvy as well from an internal.

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I guess governance or rules

of the road perspective.

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I think one of the challenges that

comes up is when you're dealing

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with an organization that tends

to be that way, you might have

90

employees that are going all sorts of

different directions and tinkering.

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So share with us a little bit about how

you set up the guardrails internally to

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define for everybody what coloring inside

the lines looks like versus going rogue.

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Kirsten Faurot: So we have, a lot of

data privacy regulations that we are

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extremely, Mindful about, to us that's

the most important thing that we do.

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That we make sure that we're using the

data the way it's supposed to be used.

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We had to make some very strict

guardrails and some very strict guidelines

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around, which LLMs are you using?

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What data can you upload to those?

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So before we really started allowing

everybody and encouraging everyone to use

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ai, it used to be very clear who could

and could not use it, and what programs

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that they could and could not use.

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It was about towards the end of last year,

early this year when we really started

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to realize, okay, we have to expand this.

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And so then, our head of privacy, our CTO

and our head of data science, got together

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and really analyzed what are the systems

that we wanna make sure everyone can use.

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what kind of licenses do we

need to get for those, right?

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Do we need to get an enterprise license

so that our data does not actually go

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into their LLM, that kind of thing.

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and so now we have a very specific

standard, very specific set of tools

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that people are allowed to use.

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So the engineering team might be using.

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Claude, Whereas some of our other

groups are maybe using chat,

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GPT, the enterprise version.

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So it really depends on the group.

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but it's very clear to people you can't

just go out and start using Gemini on

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your own and throwing stuff in there.

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That is definitely not okay.

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there's very clear information

that people can use.

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Jim Kanichirayil: So when you zoom out

and think about how those decisions.

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Are made and what you

should be thinking about.

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Is there a framework or a checklist of

questions that you came up with that

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you're like, Hey, we need to consider

this, or have we thought about that?

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As you're working through which

platforms are appropriate for

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which groups in which organization?

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Kirsten Faurot: So I think the biggest

and most important question people

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really need to ask themselves is what

are they trying to achieve, right?

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Am I trying to achieve faster software

engineer writing, coding, writing?

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Am I trying to achieve

better marketing materials?

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Am I trying to achieve, better

recruiting efforts, right?

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To be able to source better candidates.

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I think that's the first thing

people really have to decide what it

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is that they're trying to achieve.

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And then the next step would

be doing an analysis of what

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are the systems out there?

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And really digging into what

if I feed them information?

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What of my information of my

company's private information

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do I need to safeguard?

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Right.

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Is it if it's employee information?

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That is, of course, I hope

everybody realize an absolute no.

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That should not go out to any LLM if

it's coding, what coding is appropriate.

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Is there coding that for, in our

instance, some of the coding might.

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from places that has data of our clients.

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So we wanna make sure that

code is never put into an LLM.

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So it's just really figuring out

what is the use case and then what

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is the most appropriate system.

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And then lastly, I would say, what are

the guidelines you wanna keep in mind?

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What has to be secure?

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Jim Kanichirayil: that's

a good set of criteria.

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And it's interesting when you said

analysis, I was thinking, you would go

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the direction of like business process or

actually how work is done to, potentially

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identify those repetitive areas or

things that could be easily automated,

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I would probably throw that in there as

things that you need to consider too.

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when you ask the question, what

are we trying to accomplish?

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I think going through the effort

of identifying how could this given

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process or workflow be better is

a good starting point for people

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who want to dip their toe in.

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And I think that sets

the stage a little bit.

159

One of the things that I'm wondering is

with an organization that is wired like

160

yours, I wouldn't anticipate any sort

of headwinds when it comes to rolling

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out any sort of major AI initiative.

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Was that what you experienced when,

you started getting rolling as

163

an organization on the AI front?

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Kirsten Faurot: Yeah, it's people, right?

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People

166

interesting and fun to work with,

and that's why I love what I do.

167

At the same time, everybody is very aware.

168

all you have to do is turn on

the news or look anywhere, right?

169

And what are the stories telling

us, oh, AI is coming for my job.

170

So I think there is that reality, right?

171

That they might have seen friends

who jobs are changing drastically.

172

So as excited as they are, there also

is that worry of it gonna get so good

173

that maybe I won't be necessary anymore?

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And to be honest, my biggest hope

and my biggest, all my efforts when

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we've been rolling these things

out, is to really get people to be

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honest about what they're thinking.

177

it's much easier to deal with somebody

who's telling you what's really going on

178

in their head and help them to overcome

Any kind of resistance, whatever that

179

might be, then for them to just quietly

worry about it, and perhaps not embrace

180

it as much as they, they can and should.

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Jim Kanichirayil: digging a little

deeper, when I think about your

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context, you're, a sales tech company.

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And when I think about the sales

organization, your typical,

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predictable revenue model of building

a sales organization, you gotta.

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Bunch of SDRs.

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Then you have account executives

and sales engineers and so on.

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If I'm thinking about how is AI going

to change the sales function, if

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I'm an SDR, the marketing that's out

there says AI is gonna replace SDRs.

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We know that the reality of it, based

on our LinkedIn dms is quite different.

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It seems like there's a

long way away from there.

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One of the things that I'm curious

about, and I'm just using sales

192

as an example, for those people

that had those sort of concerns,

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Hey, my job's gonna be wiped out.

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How did you enter those

conversations and what did you do

195

to navigate those conversations?

196

to give a view of the future that

keeps the team engaged or the person

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engaged, versus checking out and

looking at other opportunities.

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Kirsten Faurot: So we definitely

were very intentional about this.

199

there were a couple different

things we were doing.

200

One was the get go.

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have our monthly, we call

them all hands meetings.

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Probably a lot of companies do this.

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You have everybody come in, the

company they attend remotely.

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Like in our case where many people are not

near an office, spend an hour and a half.

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We go through what's

happening in the company.

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We hear about new hires,

promotions, all the great stuff.

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we usually have a client

come in and talk as well.

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But what we started to do was

we said, alright, we're gonna

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devote part of this monthly.

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Very valuable, very expensive time

because of course, every employee

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that works for us is there.

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we're gonna use that to

showcase some successes.

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And that was super intentional.

214

One, it allowed those people who were

getting out there and trying these

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new things to get a pat on the back.

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Hey.

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I'm doing such a great job.

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I'm being chosen to share what

I've learned and what I'm doing

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and to show the results of my

work to the whole company, right?

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So it showed that person, yay, great job.

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It also shows everybody

else, this is what you get.

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This is the reward you get, right?

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You're gonna be seen as more valuable.

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and we've done that consistently every

month since we've really leaned into this.

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And it's at the point now where,

honest, we often have so many

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people saying, I wanna talk about

this, I wanna talk about that.

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We don't have enough time to spend

the entire meeting doing that.

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So now we have a Slack channel

that's devoted to this.

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We have what we call.

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AI alerts, and that's, an email

that can go out about some new

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things that are being done.

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And whenever anything like that happens,

all the leadership team chimes in.

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This is great, this is amazing.

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So they're getting all that

good positive feedback.

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that being said, there's obviously gonna

still be people who are less open perhaps,

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or maybe their amount of resilience

that they have is a little lower.

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And so for those people, what I try to do

is we meet with our managers once a month

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and we really try to talk to them about

who's doing well, who's using it, who's

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not using it as much, how, and then we

try to coach them, how are you gonna talk

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to them to help them better embrace it?

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Because.

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No matter what I try to tell everybody

who says to me AI is, it's a little scary.

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I say, listen, you gotta lean

in, Not, it's not gonna go away.

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And the people who can embrace it are

gonna learn how to use it and how to

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use it efficiently and effectively.

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And then the good news is you get

to do stuff that maybe is the least

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important part of your job or the

least exciting part of your job.

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All that manual wrote work,

somebody else does it for you.

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You still have to do the work

of overseeing it, but you

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don't have to do it yourself.

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And that frees you up to have a

lot of time to do other things.

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Jim Kanichirayil: Your best feedback

comes from your customer base,

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and in this case, you're trying

to roll out a new AI initiative.

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Your customers are your employees, and

by tapping employees to build in public

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with what they're working on and celebrate

those successes, I think that's a really

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strong practice in getting buy-in.

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When you're thinking about those all

hands and those showcase, success

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showcases, was there anything in

particular that your employees did that.

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Struck you as, oh, I never thought about

that as a use case, or This is a big win.

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Kirsten Faurot: We had one where,

and this is a little technical, but

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we are an organization that's very

heavily technologically focused.

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A huge part of our company, almost

half is engineering and product.

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And so anyway, we had one of our DevOps.

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Engineers, he's a junior, not

maybe a mid-level engineer.

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He had this idea of how do I get AI to

test the code that I've written right?

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So I don't have to test it all myself.

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'cause that's a huge

amount of time, right?

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They have to write the code,

then they have to test it.

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So he tinkered on this for a while

and he showed it to us last month.

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And was floored.

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Everyone was like.

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How on earth it saved so much time.

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And that's just one example.

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There's so many examples, but to

me that was a great one because I

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feel like that's a great example of.

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He doesn't have to do the code

review and spend as much time,

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he still has to check things.

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I don't ever wanna say that AI takes

over and people that think that, right?

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If somebody pulled something off

AI and then just gave it to their

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boss and said it's done, that would

be really bad look because that's

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not how AI should be used at all.

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But doing it this way where you say, okay,

it takes me four hours to write some code,

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and then it takes me another two hours or.

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It's an hour and a half to check it.

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If I can shorten that time to 15 minutes

where the work is done and I can move

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on and do some more coding, just if

you multiply that by the number of

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hours over a year, that's huge savings.

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Jim Kanichirayil: Yeah.

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Kirsten Faurot: efficient.

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Jim Kanichirayil: Yeah.

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you're getting into a space

where my old IT recruiter, hat

292

is getting put on, especially

when you're talking about DevOps.

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Now that example that you talk about.

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Where you're leveraging AI to reduce

the amount of time that it takes

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to go ahead and do code reviews,

that's a massive business impact.

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If I'm a QA in that organization and

I hear that I'm gonna be freaking

297

out, like an SDR would be hearing

about, oh, we're gonna replace our

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entire SDR function with AI agents.

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I'm gonna have that same feeling.

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So did you encounter that?

301

And if you did.

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What was that conversation like to get

people to think differently about what

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the future of their work looks like?

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Kirsten Faurot: Yeah.

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So what's interesting is in our,

the way we structure our engineering

306

teams, we don't have a really

massive QA group of engineers, right?

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There's a few, and those

people are much more senior,

308

And so they're looking at it

at a very different level.

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but I'm trying to think of some

other examples from our company where

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Jim Kanichirayil: these are all the

directions that we can go, we can

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look at just stuff that we've seen on

LinkedIn, SDR teams getting wiped out.

312

The QA is another example.

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Marketing teams getting wiped out because

314

So pick anything in that range and

apply it to the conversation and I

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think we still end up in the same space.

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Kirsten Faurot: another example

I think that would be really

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interesting is recruiting.

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because recruiting, used to take so

long, like a lot of times what we

319

have to do for the roles that we're

looking for, to looking to fill,

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we really have to go after people.

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It's, we'll get people applying, to

find the talent that we really need

322

because we need such specific skill

sets that we have to search for them.

323

So now, in any case, our recruiter

got a little worried because I kept

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saying to her, Hey, I need you to

try, this new AI tool, that new

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AI tool, this looks really cool.

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I just went to a, a meetup

and people were talking about

327

using this and they could find.

328

People, in all different

places, not just in LinkedIn.

329

And I have to tell you,

she did get really nervous.

330

But what I did was I said, look, you

have to try this and you have to lean in

331

because it's, we're gonna start using it.

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Like we have to start using this.

333

This is such a huge time

saver and let's see.

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What ended up happening was she filled

jobs so much more quickly and we had

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jobs that were open for three months that

she started filling in five, six weeks.

336

So it halved the time it takes and

then of course, what does that happen?

337

What happens then is she has

happy customers because her

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internal customers are the hiring

managers that are like, thank you.

339

I really needed that job filled.

340

I didn't wanna have to wait three months.

341

For me, that's one way to help them

understand, by really being there

342

and help and walking them through it

and helping them see the benefits,

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We're a company that's not massive.

344

we're 160 people, so I feel like we

can invest the time in our people

345

and really help bring them along

because we can see where they're.

346

Maybe struggling to adapt.

347

it's worth it.

348

Like we feel like if, wherever we can,

we wanna retain that institutional

349

knowledge and we wanna repurpose

their skill sets if we have to.

350

if a job completely went away.

351

I would really work hard to

try to figure out, is there

352

another place for this person?

353

Is there something else

they could be doing?

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Because we wanna keep growing our revenue,

we wanna keep growing our client base.

355

We're not looking to just

keep be exactly the same.

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Thomas Kunjappu: This has been

a fantastic conversation so far.

357

If you haven't already done so,

make sure to join our community.

358

We are building a network of the

most forward-thinking, HR and

359

people, operational professionals

who are defining the future.

360

I will personally be sharing

news and ideas around how we

361

can all thrive in the age of ai.

362

You can find it at go cleary.com/cleary

363

community.

364

Now back to the show.

365

Jim Kanichirayil: Yeah, and the recruiting

use case makes a lot of sense, especially

366

if you're talking about sourcing as

part of the candidate lifecycle that.

367

That's one of the biggest areas where

I would much rather spend my time

368

interviewing solid candidates and

aligning them to roles that I have

369

open than just going through the

blocking and tackling of sourcing.

370

That was a big pain.

371

Kirsten Faurot: Absolutely.

372

Jim Kanichirayil: The broad theme of what

we're talking about is, trying to bust

373

this myth that AI is coming for our job.

374

And part of that exercise involves

reimagining what your job is

375

gonna look like going forward.

376

And we'll continue to talk about

that in a second, but I wanna zoom

377

back out, in terms of why this

myth exists in the first place.

378

And I put the blame

oftentimes on executives that.

379

Talk about shoot the moon things and

lay out edicts about, oh, we're gonna

380

eliminate this role and that role and

go AI first and go all in that way.

381

When you think about, executive

mindset within the organization.

382

Sometimes that executive mindset

when it's overindexed towards

383

efficiency can make things worse.

384

So where was the executive group's

mindset when it came to AI and

385

how it should be utilized for,

achieving strategic initiatives?

386

Kirsten Faurot: Listen,

that's their job, right?

387

Their job is to make sure the

business is super successful.

388

and my job is to help them understand

what are the impacts on what I

389

believe is the most important resource

that we have, which is our people.

390

It's the most expensive

resource for most companies.

391

So that is what I've always.

392

Focused on with my CEO, we have very

long, not long talks, but we have

393

very direct talks about these things.

394

is it important to think about

how you're saying what you're

395

gonna say before the all hands?

396

When we talk about all hands, we usually

spend a good 20 minutes going through

397

how he's gonna present whatever he

is gonna present, Because I want him

398

to make sure he's saying in a way.

399

a way where people can really hear it

and hear the right thing, not hear fear.

400

I should start looking for another job,

but hear, okay, this is really cool.

401

I'm gonna be able to learn

some new things, right?

402

I'm gonna be able to use

our learning stipend.

403

I'm gonna be able to do, try new things.

404

I'm gonna be able to try new ways

of approaching my job, and I'm

405

gonna be celebrated for that.

406

so that's probably what I would say.

407

What I would think, just spend some

time with your CEOs trying to help them

408

understand how what they say is important,

but also how they say it is important.

409

Because the goal is always not to just say

things, but to get people to hear them.

410

Jim Kanichirayil: Yeah,

411

Kirsten Faurot: what I would say.

412

Jim Kanichirayil: I wanna go back

to some thing that we were talking

413

about earlier and it's tied in with

showcasing success, but also some

414

of the communication strategies

that you built to drive adoption.

415

One of the things that you talked about

was how you're empowering managers

416

to have different conversations

with line level employees.

417

Tell us a little bit more about what that

involved and what that actually looks

418

like when people are trying to execute.

419

What were those conversations?

420

centered around how did you coach people?

421

How do you develop people to give them

a view of what the future looks like?

422

Kirsten Faurot: So essentially we have

a pretty robust management, training,

423

management focused, program, right?

424

So in the HR team, there's just

three of us, including me, but we

425

make sure we meet with each manager,

each people manager, once a month.

426

between the three of us, we can

cover all the people managers.

427

So that's one way that we have a

set of questions that we always ask

428

them, how are your employees doing?

429

How are they using ai?

430

What's new?

431

What are they doing differently?

432

what benefits have they

seen from it, right?

433

We have monthly training

sessions, which we record, right?

434

If they can't make it, we record

those so they can catch those later.

435

and then, we will be starting in January.

436

tracking.

437

explicitly and more openly how people's

jobs have gotten more efficient.

438

So we're trying to establish that as a

way where managers can have, those, the

439

overall KPIs, the overall OKRs, right?

440

And then we have our department goals,

and then we have our individual goals.

441

And the individual goals for each

employee is gonna be, how am I using

442

AI to make my job more efficient?

443

And so that's a way for the

manager to have something very.

444

Concrete to talk about.

445

the other thing that we have, which we

really lean into with our employees is

446

we have a, a learning stipend that they

can use mostly in any way they want.

447

It has to be job related, we've been

really, Focused on sharing how people are

448

using it to learn more about AI as well.

449

we have a resource page on our intranet

where we have lots of listings of

450

people who have taken different classes,

people who have read certain books,

451

and they, oftentimes will say, this was

really helpful, this was really useful.

452

And people know, oh, that's a, a

software engineer working in, with data.

453

So I wanna.

454

Look at what that person is learning,

or that's somebody in marketing and

455

they're using it to create new decks

or new, new marketing materials for

456

our salespeople, that kind of thing.

457

Jim Kanichirayil: So one of the

things that I liked about what you

458

just described is how you're tracking

efficiency across various functions.

459

Part of the conversations that

you have with the leadership

460

tier, frontline leaders and

what they should be looking at.

461

it's easy enough for me to figure

out what that looks like, let's say

462

from, definitely from a developer

perspective and a product perspective.

463

But how would you apply that,

efficiency metric or benchmark that

464

in an organization in, in, let's say

marketing or, even sales, how would you.

465

what guidelines did you build

for those functions to, to

466

track that efficiency piece?

467

Kirsten Faurot: Yeah, for marketing it

actually wasn't that difficult because,

468

how much material are you producing?

469

how quickly are you putting

together sales enablement pieces?

470

, Things like that.

471

That was all fairly easy to track

because it all became quicker.

472

So that was something I think the CMO

found was not as hard to put in place.

473

She also has a team that was

for the most part, like super

474

excited about all of this.

475

It's a little trickier

with sales, I will say.

476

So we have a...

477

even though we're a sales organization,

we are not a sales heavy organization

478

in the same way that a traditional SaaS

company would be because we really do

479

sell our products in many different ways.

480

so actually have a

relatively small sales team.

481

But I, okay, so one way, there's

one group that sells particularly

482

to advertising agencies.

483

And so what they've been able to do is

they use AI to figure out better audiences

484

for these agencies that they would

recommend for their different clients.

485

So that's one way that they've

been using AI and they've been

486

tracking that, and that's been.

487

Pretty helpful for them.

488

don't have a great example for SDRs

other than helped us with writing,

489

the intro emails that they try to send

out on LinkedIn and things like that.

490

But we do have a pretty small SDR

team, so I will say that's one of

491

the pieces that's, for me, it's

a focus for more for next year.

492

Jim Kanichirayil: It's clear that you

have a fair degree of adoption across

493

the entire organization, but regardless

of what the initiative is, you're never

494

gonna get a hundred percent commitment

from the entire employee landscape.

495

How are you handling the

people that are slow to adopt?

496

What's the conversation and the thought

process there to move them along?

497

Kirsten Faurot: Yeah.

498

So this I think is an ongoing thing

that we're doing with all our employees.

499

We really wanna support them.

500

We wanna help them succeed.

501

So as we work with them and it becomes

perhaps apparent that they're struggling,

502

we're gonna be leaning in and figuring

out what can we do to help them get there?

503

How can we help them understand

how this can benefit them?

504

How can we help them?

505

See how much this is gonna help them

be more productive and honestly happier

506

because they're not gonna have to work

on maybe some of the things that in the

507

past they did that were not as gratifying.

508

Jim Kanichirayil: Yeah, I think

the emphasis on moving to more

509

high value work and supporting,

people that are lagging in.

510

Ways to get them upskilled or at

least rethink where they're at.

511

It's a good policy.

512

One of the things that you mentioned

earlier that caught my attention was

513

that within the organization there's

a fairly strong learning orientation

514

built in, and I would expect that from

a development and product led, company.

515

how do you anticipate leveraging.

516

or leaning more into those stipends

or those reimbursements as a way

517

to get more of these people further

along in their adoption, how does

518

that fit into your overall strategy?

519

Kirsten Faurot: one of the things

that, one of the people on my

520

team is gonna be doing is really.

521

As we've celebrated ai, she's gonna

start celebrating in a much more visual

522

and upfront way who's used the learning

stipend and how they're using it.

523

So that will start to become a

corner piece of a cornerstone

524

of our, monthly all hands.

525

and in addition, our CEO thinks this.

526

Such a good idea that he himself will

be writing each of those people who do

527

something like that, a personal email.

528

And he is really wonderful about that.

529

He spends a lot of time trying

to really craft something that

530

is very specific to that person.

531

and it's extremely valuable.

532

Honestly, it's way more

valuable than giving somebody

533

a, some kind of monetary gift.

534

Jim Kanichirayil: Yeah, I think,

especially in a tech forward organization,

535

tapping into what a lot of people, I

don't know, a developer who doesn't

536

put the opportunity to learn new

things high on their list when they're

537

trying to figure out what projects

to take on, what companies to work

538

Kirsten Faurot: Yep.

539

Jim Kanichirayil: One of the things that

stands out about the conversation that

540

we've had so far is it seems like the

organization is by and large, positive

541

in terms of its outlook, in terms of

how they use ai, how it's adopting it.

542

you're still in the process

of getting more adoption and

543

having it be more embedded.

544

So this is a work in progress

when you look back from today.

545

And you look at where you started from,

what were some of those key lessons

546

that you learned in that process so

far that you think is important for

547

other leaders who are building their AI

initiatives that they need to have on

548

their radar so they don't make a misstep?

549

Kirsten Faurot: I think one of the

things really is what we spoke about

550

at the beginning, that you really

need to figure out ways to make

551

sure that the AI that they're using

makes sense that people aren't just.

552

It has to be organized in a way where

people can feel like they're using

553

their time successfully, effectively.

554

So I think first, making sure that

there's a real solid strategy for each

555

team, not just overall for the company,

but you, so that they're using tools

556

that make sense for the work they do.

557

So to me, that's number one.

558

Number two, I think is.

559

Really helping them realize that we're

doing what we said we were gonna do.

560

And what I mean by that is we are

making AI a key of our company, right?

561

It's the AI is our thought partner.

562

It's another employee almost for us.

563

It's somebody that you can rely

on to help you get your work done.

564

And we support that.

565

That idea by continually showcasing

those successes and rewarding

566

people for those successes.

567

to me, those are two

really important learnings.

568

And then I think the other thing is.

569

To allow the, to allow people to

be real and to let them say, Hey,

570

I'm, this makes me a little nervous.

571

They shouldn't be afraid

to say that out loud.

572

it shouldn't be that everybody

feels like, oh, I have to smile

573

and say, yes, I believe in this.

574

I believe in this.

575

Even though inside I'm wondering

does this really make sense?

576

How is this gonna impact me?

577

Because at the end of the day,

I think as HR leaders, we know.

578

People are always focused

on what it means for them.

579

So you have to meet them where they are,

and you have to help them understand

580

how this is gonna benefit them.

581

And if you can make that clear to them

that this is not about replacing your job,

582

it's about making your job more effective.

583

It's about helping you have.

584

A thought partner to work with.

585

Somebody to lean on somebody.

586

Maybe not somebody, a system to

lean on gonna help make your life

587

easier and your work life easier.

588

That's something where we all win.

589

And I think that to me is probably

the most important part of it, of what

590

we're trying to achieve at Bombora.

591

Jim Kanichirayil: Solid stuff.

592

Kirsten, I know that people are gonna

want to continue the conversation.

593

So for those folks who want to chat

more about what you're doing in terms

594

of AI adoption, what's the best way

for them to get in touch with you?

595

Kirsten Faurot: Yeah, so

message me on LinkedIn, right?

596

I'm sure you're gonna share my

name on the show notes, and you'll

597

have a link to my LinkedIn profile.

598

Just message me and I'd be

happy to talk with anybody.

599

Jim Kanichirayil: So again, I appreciate

you hanging out with us, Kirsten, and,

600

and sharing with us your experience.

601

When I am inventorying all the things

that we talked about in this conversation.

602

there's one particular thing that stands

out as something that, Any organization

603

that is looking to gain adoption on, in on

any initiative should think about, which

604

is building that initiative in the open

and more specifically using your frontline

605

people as evangelists for adoption.

606

I think one of the reasons why, Your

initiative is going as well as you have

607

is that you've embedded those success

stories in open forum on a regular basis,

608

and you're having frontline people share

their experiences to build a view or a

609

vision for what the future looks like.

610

And I think that's super important in

building that group of evangelists from

611

your employee groups versus having it be

a top down executive driven initiative.

612

And it's just like anything else.

613

If I say something.

614

I'm more likely to believe it than

if you tell me the same thing.

615

So in that same respect, whenever you

can facilitate or enable building in

616

the open and having your employees

be the evangelist for the initiative,

617

you're gonna have a lot of success in

getting traction on that initiative.

618

So thanks for hanging out with us.

619

For those of you who've been

listening to this conversation, we

620

appreciate you hanging out as well.

621

If you liked the discussion, make sure you

leave us a review on your favorite podcast

622

player and then tune in next time where

we'll have another leader hanging out

623

with us and sharing with us the stories

of how AI is helping them future-proof HR.