In this episode of the Future Proof HR podcast, Thomas Kunjappu sits down with Ken Moses, Chief People Officer at Cascade Environmental, whose career spans both high-tech and heavy industry. Known for his experimental mindset and willingness to challenge convention, Ken has turned curiosity into a leadership philosophy; one that’s redefining what HR can look like in an AI-driven world.

From testing recruiting bots and using AI to analyze engagement surveys to reimagining how internal communications and recognition programs are built, Ken shows how innovation in HR doesn’t have to start with grand strategy; it can begin with a single “what if.”

Ken shares how he and his team have turned everyday HR challenges into experimentation grounds for smarter, more efficient, and more human-centered practices. He breaks down how to test ideas safely, maintain authenticity while using AI, and bring your team along for the ride.

Topics Discussed:

• How curiosity drives innovation across every HR function.
• Using AI to turn time-consuming HR work into faster, higher-impact processes.
• The “augmented, not automated” mindset that keeps HR human.
• Building trust and authenticity when introducing AI-powered tools.
• Finding the ROI in experimentation and scaling what works.
• Why data literacy, creativity, and courage are becoming HR’s must-have skills.
• How HR leaders can carve out time to think differently without waiting for permission.

If you’re thinking about how to future-proof your HR function through curiosity, creativity, and practical experimentation, this conversation is a roadmap for doing more than keeping up; it’s about building what’s next.

Additional Resources:

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

Every day technology is improving.

2

There's a lot more tools

to help HR coming out.

3

That's why we went with the talent

acquisition tool, which we're looking

4

at a AI coaching model, which takes

personality data from the employee,

5

whether it's the disk or they can

6

take a survey online and it will,

based on their personality style,

7

assign them a virtual coach

8

who will fit that style.

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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

11

be prepared to be decimated.

12

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,

16

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,

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where we explore how

forward-thinking HR leaders

21

are preparing for disruption

and redefining what it means

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to lead people in a changing world.

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I'm your host, Thomas

Kunjappu, CEO of Cleary.

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Now, today's guest is Ken Moses,

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the Chief People Officer

at Cascade Environmental.

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With a rich career spanning software

and environmental services in global

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leadership of HR, Ken brings a

uniquely experimental mindset to

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today's conversation on what's

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happening with AI.

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From policy drafting to manager insights,

recruiting bots, internet transformations.

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Ken is living the innovation

HR leaders often talk about,

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but rarely operationalize.

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So I'm excited to have this conversation.

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Ken, welcome to the podcast.

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Ken: Thanks, Thomas.

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It's great to be here.

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And I'm looking forward

to sharing some insights

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and maybe some stories

with your base out there.

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Thomas: Absolutely.

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So let's start with,

what's that sign I see?

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So drinking wine at lunch is not a crime.

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What's that about?

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Is that true?

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I don't know.

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It's funny.

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The sign I've had since probably 2006,

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2007, I got it in a restaurant in Phoenix.

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I was working for a

software company at the time

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and was doing an acquisition.

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And one of

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the perks that the

acquired company had was

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every other Friday, they would

provide the employees with beer.

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They'd have a beer cart,

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literally a shopping cart full

with beer, and they would go

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up and down the aisle, giving every point

beers, and they'd do a big cheers then.

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That was also

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during the time when tech

would have Friday beer busts

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and kegs and things like that.

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So when we acquired the company, I was

doing the integration, and the first

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question I got is, are we going to

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lose our beer cart?

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And I chuckled.

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I said, over a course of time,

we'll probably have to phase

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it out because of liability.

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However, on the first one,

post-acquisition, I want

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to push the beer cart.

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And they looked at me like I was crazy.

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And they said, you're HR.

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You're not supposed to do that.

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I said, I'm a business-focused person.

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What a better way to meet

employees and have fun and learn

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your business and your culture

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by me doing the beer cart.

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So when the GM and I went to lunch.

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They had this sticker.

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I said, perfect we got this

sticker now fast forward

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to Covid and the pandemic.

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We're essential business and

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day drinking became acceptable

we had vendors who were

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sending bottles of tequila.

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We did a tequila tasting, wine

tasting, chocolate and wine pairing.

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So I still hold true.

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And in fact, I hate to admit it,

but I have a wine lunch today.

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We'll be taking off after lunch today.

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We're recording on a Friday.

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So you're staying true to your word there.

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And that's really interesting because you

bring it to this posture about what HR is.

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It actually drew my attention

exactly for that reason.

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That's often not what, at least

you're espousing publicly, that

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HR leaders may have all types

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of personalities and opinions and

thoughts privately, but this is not that.

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So speaking of that, so your career

has spanned a few different industries.

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And can you tell me a little bit about

how that has shaped your approach

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towards HR with this experimental

approach we've been talking about?

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Ken: Sure.

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Yeah.

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Half my career has been

in high tech doing HR.

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Half of it's been in blue collar

industries, whether it's drilling,

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concrete cutting or air freight.

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My approach to HR is industry agnostic.

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You need to understand the business.

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Compensation is compensation.

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Hiring is hiring.

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Training employees and developing

employees are, it doesn't

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matter what industry you're in.

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So as long as you approach it as a

business and learn to understand the

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business and then also learn what the

business perception and needs from HR are.

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You can't go into a company with

an HR playbook and say, we're doing

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this without gaining alignment,

without understanding the business

110

and gaining support.

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You're going to fail if you do

that and not get credibility.

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Thomas: Okay.

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So then tell me more about

the experimenter mindset.

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Ken: Oh, sure.

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Experimenter or inquisitive, I guess

that would be another way to do it.

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With technology, I've always been

very interested in technology.

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Back in college, I'll age myself,

back in the late 80s, when computers

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were on during their infancy.

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Apple had just put out their first Mac.

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K-pro and HP and Toshiba

were the big players.

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I was at UC Santa Barbara finishing up

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my undergrad, and I organized and put

on a computer fair to teach and show

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people the value of microcomputing.

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To this day, the school still does it.

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To this day, the city took over

and actually the city had up until

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I guess last probably pre-COVID

would have a computer fair.

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I've always had that mindset and

have never been afraid of technology.

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Fast forward now, fast forward to a few

years ago, when this word AI came out.

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And a lot of people, that was

and still is the buzzword, right?

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So you're getting on calls

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with vendors who are touting AI is going

to change HR's and the business's life.

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And when you ask how, at that

time, they couldn't give answers.

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Certainly, you had agentic AI,

which has still been prevalent

134

in the call center industry.

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But really nothing at that time for HR.

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So I got curious.

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I took an hour to think.

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I said, where could we be?

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What could help us with AI?

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What tasks do we currently do

that we can get more horsepower

141

and more assistance with AI?

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And sat in the team.

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We did some brainstorming.

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The first thing that came up were

job descriptions, low hanging fruit.

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We had a project where we were ensuring

all our roles had job descriptions.

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We had maybe 20 roles that didn't.

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So I held a staff meeting, showed my team.

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We did a job description

together, and they were just

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wowed on the capability of AI.

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And I guess my approach to AI is it's not,

I don't think it's yet truly automated.

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I think it's what I call it augmented.

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It still requires some human

interaction, a human touch to it.

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From there, we did policies,

internal communications.

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It allows you to put words onto paper,

and then you can edit with the voice.

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Fast forward to our engagement survey.

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For the past three years, we've

done employee surveys and engagement

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surveys, and I would laboriously

go through the surveys, try to

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determine what the trends are,

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try to look at themes,

observations, statistical analysis.

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And I thought, okay, what if we use AI?

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What if I put data into AI?

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And I have to be careful because if

you're using chat, it's public data.

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So whatever you put out there

could be available to others.

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We have chat and we also have

co-pilot, which is more internal.

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So I put the data in there and I

started experimenting with different

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prompts and came up with a prompt that

would give me exactly what I needed.

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I wanted to write a report for

each of our people leaders,

168

which showed them their data,

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showed them their top scores

and bottom scores, and gave the

170

analysis and observations of what

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it means and how to improve it.

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Prior to that, I was doing it

manually, and it took me to do,

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let's say, 100 reports.

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It took me four to five months.

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With AI, I was able to get

that down to about six weeks.

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AI provided the structure and the words.

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I clearly had to go in and edit.

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AI is not foolproof.

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They still have what

they call hallucinations.

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They make up stuff sometimes.

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But, that got us down that path.

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And I was doing advising for the

company we use for the survey.

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And I said, I've been talking to them

about including AI in their model.

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So rather than having to go out and do

it, by a push of a button, have AI analyze

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your data, build in some prompt questions,

and in their latest release, they're able

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to put AI into it, which is phenomenal.

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Recently, we've taken it to the new level.

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We've taken a look at

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how we can improve our staffing process.

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You can imagine in a semi-skilled

industry, you're doing a lot of hiring.

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So

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when we looked at our hiring

process, it was how can we

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improve the efficiency of our

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two employees in talent acquisition?

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So they can do bigger and

better things for the company.

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So we partnered, I was at a conference

in Miami, or it was Lauderdale,

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and met a guy who owned a company.

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He was from the Bay Area,

had an AI tool, which would

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actually contact the candidate,

200

interview the candidate

based on questions we said.

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It was like having a

conversation with an employee.

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It was the voice and the cadence

and modulation was perfect.

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It adapted to what the

candidate was asking.

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They'd ask about benefits.

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They would have a question on that.

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If the candidate spoke Spanish, it

would, on the fly, convert itself

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back into a Spanish-speaking AI tool.

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Now, we decided as a company, we were

going to be transparent and let the

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candidate know this is an AI bot.

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It's been successful.

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There's discussions

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on candidate from an EEOC standpoint.

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Is AI discrimination proof?

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And we're not letting AI and the tool

make our decisions on candidates.

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That's where I say it's augmented.

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We're coming

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in and evaluating what the bot put

together for us and making a decision.

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Thomas: So you've gone through so

many different examples, which really

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speaks to the practical experience

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that you've had, right?

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Trying to bring this concept technology

into the day-to-day practices

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of a real live HR function.

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I'd like to go through

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each of them one by one.

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I counted four.

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Let's go through job description,

internal communications, analysis in

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terms of, I guess it's about performance

reviews, as well as arguably the

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whole entire interview

process to some degree.

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And then go through it

with a couple of lenses,

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just what was the idea or the spark for

that to go into that particular use case?

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What would be the outcome if you

were successful using AI for it?

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And then what governance or

data or privacy, if anything,

233

issues are there related to it?

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And I think it's interesting.

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We can do it in rapid fire, but I

think it's interesting go through

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that real quick for a bunch of these

use cases, because these are often

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some of internal blockers,

I think, for a lot of HR

238

professionals about how you actually

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go from an idea or reading an

article to getting to something

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successful out there, right?

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So job description, how did you, in

your particular case, how did that

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come up as an initial thing to try.

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And then how do you know

that you're successful?

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What is AI actually helping you do there?

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And then, are there any privacy

or governance implications?

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Ken: Look, throughout my career, I've

always taken a look at the HR function.

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And my team knows that we're overhead.

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With or without us, the company

will still make its product.

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We'll still pay for employees.

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We'll still provide services.

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So whatever we do needs to provide value.

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And at the same time, I've always

taken the position that we need to

253

take a look at the less value-add, the

time-sucking work, let's call it, and

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find ways to become more efficient.

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Whether it's outsourcing, like

we've done with leave-of-absence

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administration, or rely on tools — AI.

257

And for those that have written

job descriptions before, I cut

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my teeth in compensation.

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So I've written a lot of job descriptions.

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They're dry.

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They're not always fun.

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So I said, why not try out AI and

see what it can do for, I did my job

263

description first, Chief People Officer.

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And it gave me some great starting point.

265

So from there, I was

able to add the prompts.

266

All right, HR in the environmental

industry, responsible for talent

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management, talent acquisition, competent

benefits, the whole suite of HR.

268

And came up with a

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document, shared it with my team

and said, wow, this is pretty good.

270

And that's when I held the meeting

271

and showed them how we can do it.

272

So it was definitely done for

productivity savings, efficiency.

273

And again, sometimes the hardest

thing to do is put pen to paper.

274

So this provided at

275

least an outline, if not a

complete document that we could

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share with the incumbents to say,

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all right, does this meet

80% of what you're doing?

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Does it meet more or less?

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Where do we need to tweak it?

280

From a privacy standpoint,

pretty not much.

281

We put in our overall revenue,

but you can get that data.

282

So it was really low impact.

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The company's

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name wasn't included

any of the AI searches.

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Thomas: This is really about writing and

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it's a type of task they're putting in

that category of time sucking in some

287

sense, but it also means something.

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You can't just put slop out there.

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Like each bullet means something, both for

compensation as well as in terms of talent

290

attraction and for the rest

of the funnel down because

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people will be asking questions

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about it, searching for things as well.

293

So it does matter.

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The outcome does matter.

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Oh, absolutely.

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It's clearly an efficiency

doing that much faster.

297

And specifically is a writing task

where you're solving the blank

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canvas problem where you have a bit

of a starting point and because of

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that you can be much faster and of

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course, there's levels to that

game you could integrate that

301

into your overall headcount

302

planning process, spit something

out at 80 percent automatically

303

with more advanced prompts, but

that's a great place to start.

304

And interesting analysis on the

privacy point, because this is

305

theoretically, I think, a great

place to start because you could

306

have a free tool.

307

They could train on your data for

what you're putting in, and it's

308

actually okay because you're not going

to put in any private information.

309

And the whole point is a job

310

description at the end of the

day is an artifact that is

311

going to be public facing.

312

So pretty low impact from that

standpoint, however, and it

313

happens with some frequency and

there's probably layers of automation

314

and improvements of efficiency

315

you can get to.

316

But in some ways, the other thing

is it's a light bulb, right?

317

So let's talk about internal comms as

318

this like second use case.

319

Are you talking about sending

out executive communications or

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here's a policy update, here's an

employee handbook or open enrollment

321

communications that are happening

322

on a rolling basis?

323

Just tell me again about

how did the idea come about?

324

Hey, maybe AI for this.

325

What's the potential outcome?

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And then how did that go?

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Ken: So it started off simple.

328

For the holidays, we have a message from

our CEO, which goes out to all employees.

329

We're all busy.

330

It was write two-paragraph email

331

to employees thanking them for the work

they do for Labor Day, hypothetically.

332

And again,

333

this is why I'm always going to

refer to augmented HR because it puts

334

pen to paper and then I can tweak.

335

We can get a quote from our CEO.

336

We can tweak some of the words to

make it sound like it's from his tone.

337

Fast forward to some

338

companies with some technology

now that you can create an avatar

339

that looks 90 to 95% human and

real of yourself with your voice.

340

And it will do something.

341

It'll put out a message on it.

342

That's to me, the next gen

of internal communications.

343

Thomas: In terms of the efficiency

gain, blank canvas problem,

344

it's a writing challenge.

345

Maybe it's a bit more

sensitive information,

346

maybe not depending on the actual comms,

347

but if you're sharing, for example,

internal like growth projections

348

before you're ready to share external,

349

there might be some instances

350

where you want to be careful about that.

351

But actually, it's not about data

privacy, but there's a different

352

element of governance that comes to

mind, especially with video avatars

353

and even just an AI written essay

that you attribute to the CEO, right?

354

Which is actually about trust and

what anything even means anymore.

355

So how do you think about that?

356

Like in this

357

particular case, right?

358

Like writing, you're

attributing it, HR ghostwriting

359

on behalf of execs has

been going on forever.

360

But what is the balance there?

361

Maybe this goes to your

362

augmentation conversation.

363

I think it does because

it's more than trust.

364

I think it's authenticity and the

community, you don't want to lose the

365

CEO or anyone's authenticity by putting

366

something that doesn't sound

like it comes from them.

367

It does, again, you're right,

that's the augmented part.

368

We go through the communication.

369

And again, I'm using

AI for its creativity.

370

This has been a fantastic

conversation so far.

371

If you haven't already done so,

make sure to join our community.

372

We are building a network of the

most forward-thinking, HR and

373

people, operational professionals

who are defining the future.

374

I will personally be sharing

news and ideas around how we

375

can all thrive in the age of ai.

376

You can find it at go cleary.com/cleary

377

community.

378

Now back to the show.

379

Ken: I will attest I haven't always

been the most creative person.

380

I can be just a little bit,

but this helps with creativity.

381

People will say, the other side of the

aisle would say, you're being lazy.

382

And I said, no, I'm being efficient.

383

So it's a fun conversation to have.

384

But then, I've written

policies through AI.

385

The latest one,

386

we needed a new recognition program.

387

We wanted to provide a recognition

program for our overhead employees.

388

And I put it into an AI box, draft me a

recognition program for overhead employees

389

who work for an environmental industry.

390

This is for above and beyond.

391

And it came out with a program.

392

Now, did we use what AI generated?

393

No.

394

It provided at least a starting

point for us where we were able to

395

then put our company spin on it.

396

And we just recently had the

employees name the program just

397

to generate some excitement and

some commitment to the program

398

and had a contest for that.

399

So that's rolling out within

the next couple of days.

400

Thomas: That's interesting.

401

It's a different use case, but the insight

to me at the abstract layer is actually,

402

it's not about writing in this case,

403

but it's about thinking, right?

404

So the internal comms and

job description examples

405

are about solving the

blank canvas problem.

406

We need to write something.

407

But in this case, it's helping

you just get started, right?

408

In some ways, arguably just

replaces the Google search

409

and searching for, okay,

410

how do I implement a recognition program?

411

But I think it's more than that

412

because you can go back and

forth a couple of times,

413

get very specific,

414

because there might not be something

about your industry, like you said, and

415

a specific type of thing that you're

416

looking for.

417

And you might have a

couple iterations of that.

418

So it's like you're talking to someone

and bouncing ideas back and forth a

419

little bit to get started, get oriented.

420

So I found that

421

personally also be very useful,

especially you pair with voice actually

422

which has been pretty interesting

lately but let's go into some of your

423

more interesting honestly use cases.

424

A hundred personalized performance

reports for a hundred different

425

managers or employees, right?

426

Now, that is, I think, an example

of something that would not

427

have been possible without AI.

428

There's no bandwidth.

429

There's no HR team with

that kind of budget.

430

Maybe there's some companies with

the training for managers who are

431

taking performance review data and

going through a rigorous process.

432

But also that is not happening

anywhere near at that level, right?

433

Ken: No, but what it also

allowed me to do is expand.

434

So prior to using AI, we only

put together the reports for

435

each of our business units.

436

Call it 37 different reports.

437

So by having some

horsepower next to me, i.e.

438

AI, I was able to provide more meaningful

data down to the manager level to

439

help them as leaders, understand

their workforce and provide them

440

with suggestions on areas to focus in

on, which then became part of their

441

performance review scores and goals.

442

Thomas: So tell me about the spark,

443

because this is a process that

happens where every company has

444

some level, some type of, if not

performance review process, at least

445

some compensation review process.

446

So there's something there.

447

And then you were doing it

a certain way on some cycle

448

and that there was an idea to

do it completely different way.

449

Like how did that even come about

450

and how did you pair the

concept of AI with that?

451

Ken: We wanted to be able to provide the

452

data to the manager level and knowing

how many people managers we had and

453

knowing that in previous years I

454

wrote all the different reports.

455

I took a look at how much time it

took to do the 37 reports and how much

456

time it would take to do 100 reports.

457

So again, curiosity,

what could AI do for me?

458

So I was talking with the owner of

the company that we do the survey

459

with and we were just bouncing

off ideas and i started playing

460

with prompts and came up with

461

Thomas: And just to be clear so you have

one vendor that you're using for your

462

surveys but then when you're saying you're

playing with prompts you're doing like

463

i don't know Gemini, Copilot, ChatGPT.

464

General AI.

465

I'm assuming but going to the governance,

I assume in this case, we're talking about

466

performance data.

467

It was a corporate account, a logged

in, cannot train on this data.

468

Ken: We did it with our internal.

469

Thomas: Got it.

470

Okay.

471

But this is interesting

because it's now the concept

472

of how far you can go with one of those.

473

Like generic consumer or

prosumer grade AI tool.

474

But yeah, continue.

475

Ken: And it took some

refinement to get to the prompt.

476

I asked things like trends, observations,

strengths, development needs, areas of

477

improvement, create an action plan to

accomplish, and went through refinement

478

and got to something which I like.

479

One of the frustrating parts of

using an AI tool is you may have

480

the greatest prompt and you love the

direction, you love the formatting,

481

but after doing a number

of reports, it changes.

482

And that's frustrating because

then you have to try to go back and

483

either recreate what you had before,

try to refer it back to one of

484

the other reports that it created.

485

So that was challenging at times.

486

That's probabilistic when we're used to

487

deterministic outcomes with a lot

of software, with if-then logic,

488

this is a little bit different

489

and it leads to those frustrations.

490

That's why I love that metaphor

491

of you're working with a very smart

intern, but with its own little quirks

492

that you're not gonna get quite right.

493

And you're right to point out

some of the pitfalls, right?

494

These are some of the

reasons why people give up

495

or don't even get started, right?

496

But I think it's actually very valuable

for you to have gone through that, right?

497

That process and seeing

some of the ins and outs.

498

And I'd love to hear the

second half of that story.

499

Was that, is that feedback that

you give back to your, to the

500

vendor that you're working with,

501

which in a way, because some of those

problems can be solved with a more

502

purposeful tool focused on that over time.

503

Yeah.

504

Over time, he's refining his tool

because some of the vision we had on

505

it is rather than having to type in a

prompt's automatically in there and we'll

506

generate the report for you.

507

That's the next generation of that.

508

But I think that's the fun stuff with AI.

509

Every day technology is improving.

510

There's a lot more tools

to help HR coming out.

511

That's why we went with the talent

acquisition tool, which we're looking

512

at a AI coaching model, which takes

personality data from the employee,

513

whether it's the disk or they can

514

take a survey online and it will,

based on their personality style,

515

assign them a virtual coach

516

who will fit that style.

517

Thomas: Hang on.

518

Let's not jump to too many

use cases, but I love it.

519

There's so

520

many different things here.

521

But going back to the analysis

of performance reviews.

522

So a couple observations about this.

523

First of all, around the ideation, if

you have a task that's coming up on

524

some cyclical basis, weekly, monthly,

quarterly, maybe annually, where

525

you're thinking, oh, my goodness,

okay, it was hard enough to do 30%.

526

Now I have to do a hundred.

527

This is going to be terrible.

528

I'm doing the same thing to some

529

degree over and over again.

530

Stop and think about how

you can leverage AI, right?

531

It's like a spark of

insight kind of thing.

532

But another observation, I want

to see if you agree with this

533

is that often there's talk about how

AI is going to take away a lot of jobs.

534

And especially in,

535

you describe it as overhead, right?

536

If HR function or so many

537

other functions where it's

really just in a, I don't know,

538

arguably in a purely capitalistic

539

model, you can just make that

spend go to zero and affect

540

nothing else, then that'd be great.

541

But you could say that about every

function, arguably some degree, but

542

putting that aside, given that there's

this fear and maybe rational concern

543

about jobs going away, tasks which make

544

up a lot of today's jobs going

away, that's very visible.

545

But what's not as visible is what new

546

tasks can come into play for those people.

547

And I think this is actually

an example of that, right?

548

So there's actually maybe much more

demand for HR services than today's

549

model and cost structure allows for.

550

But in this example, because

you're able to leverage AI,

551

you're able to provide a deeper, better

service that should theoretically make a

552

bigger impact on what you're

driving all along, which is

553

employee performance and manager

554

performance.

555

But that's just not something that

would have been possible, right?

556

That's just not.

557

So now the job is doing much more.

558

In this

559

case, it's multiplying the same

thing, but also it's a different

560

product in the sense that you're

561

going deeper and enabling

managers for every single

562

employee in a much deeper way.

563

Do i think AI will replace

HR in its entirety?

564

No.

565

I mean 30 years from now?

566

Who knows?

567

It could be like the Jetsons.

568

But I do think that it will allow

your department to, it can do a lot

569

of the heavy lifting for you so you

can focus on more valuable tasks.

570

It will allow, I think it

takes HR into, at least

571

for me, the big data space.

572

Where I would like to take

AI next is more predictive.

573

Take a look at

574

my hiring trends, my turnover

trends, compare that to company

575

revenue or EBITDA or areas.

576

And where are my risks?

577

Do I have manager risks?

578

Do I have turnover risks in certain areas?

579

Do I have people leaving?

580

Why are people leaving?

581

That, to me, is the exciting part of AI.

582

If HR is here to drive company

performance, which I believe

583

that's our primary role through

people, programs, and strategies.

584

What a better way to support

that through that data.

585

Okay, let's go into that and

the future of the function,

586

even though we could go deeper

587

on so many of these other uses

you brought up around staffing

588

and coaching, recognition

589

but suffice it to say, you could take

that same model and think about how it can

590

really apply for any kind of HR program.

591

But taking your observation about the

shifts you expect, in demand for the

592

role as well as skill sets that may

need to be evolving in response to that.

593

What do you think that future function and

future proof HR professional looks like?

594

Ken: It's definitely somebody who is

computer literate and technology savvy.

595

I believe it's a person who is just

open-minded to the realm and the

596

what-if possibilities out there.

597

I truly believe that

every HR professional,

598

I know we're always busy and we

have a lot on our plates, but if

599

we can take an hour, a half hour,

600

whether it's two, three times a week,

a day, just to get away from our

601

emails, get away from the project,

and just think about what if,

602

the possibilities that will help

drive innovation and change.

603

Thomas: So let's speak to that a

little bit more, because what do you

604

say to the HR leaders that say, what

the organization needs for me is it's

605

already 10x more than what we can

deliver with our current resourcing.

606

And so I'm just trying

to tread water here.

607

And innovation is not something

the org is looking for from us.

608

What they're really looking for is getting

people hired, making sure that they

609

stay, making sure that we're compliant.

610

I-9s are checked, and we don't get sued.

611

And I'm just doing my best

to stay afloat with that.

612

Look, role's evolved.

613

Way back, when I started HR, or

was personnel at the time, was

614

record-keeping, policy, compliance.

615

But the role's evolved.

616

It's evolved to being an integral part

of the business and the leader not just

617

have a seat at the table, but help set

the table, help create the strategy.

618

I think when it comes to HR headcount,

I've been in the same position too.

619

I need more headcount.

620

For everything we need to do, we're

just not capable of delivering without

621

bending the tensile strength of the team.

622

So what do you do?

623

And that's where I would suggest

624

any leader to look at AI is, how

can I make myself and my department

625

more efficient by the use of AI?

626

What tasks can I give to AI

so I can focus on other more

627

strategic, important initiatives?

628

It does take, I think, a

forward-thinking leadership team.

629

A leadership team who embraces AI, you

can then put AI in the culture and start

630

training your employee workforce on AI.

631

I think different industries will

adopt it quicker than others.

632

So my last question then about this is,

we've talked about a lot of different

633

experiments you've done personally.

634

And then it's gone in towards a

team and things that you've full-on

635

implemented at the organization

all the way into changing your,

636

at least for some subset of roles,

637

the way you do interviewing

with leveraging AI.

638

So some of that takes

alignment, of course,

639

beyond the HR department and

including and up to experimental

640

or full-on budget, right?

641

So how do those conversations happen,

642

especially in relation to

what you mentioned before,

643

which is it's as more

so than ever, overhead.

644

Ken: Budget's always a

consideration, right?

645

Especially when you're looking at things

like the staffing and the training.

646

Again, you have to balance that

with your role in the organization

647

to improve performance of the

organization and increase the

648

skill sets of your employees.

649

The way we were able to do it was

taking a look at how many people we

650

interview in the year, how many people

apply for the job, how much time we're

651

spending doing the interviews, and

do a cost-benefit analysis on that,

652

the cost of the tool, the benefits

of the tool outweigh the small cost.

653

And we just go through

our budgeting process.

654

And include it as part of our IT spend.

655

What we did with, because this

tool came mid-year, is we shifted

656

some dollars that we were using

for something else into this tool.

657

And what's nice, it's a

month-to-month contract,

658

subscription-based is always great.

659

It's just not typically a commitment

unless you're getting a year and

660

you want better price points.

661

But if it doesn't work, we'll move on.

662

Flexibility is key.

663

And getting an ROI case and which also

goes back to understanding the business

664

even beyond the function but it is

possible even when there's pressure.

665

Absolutely.

666

Thomas: I love that so let's say just to

667

close out, I'm curious Ken.

668

Let's say we're talking in

two to three years from now.

669

What do you think looks like is table

stakes for HR professionals and HR

670

leaders, which seems like an emergent

thing today, whether it's a skill,

671

an activity, what comes to mind?

672

Ken: Wow.

673

I'm going to stick with the big data.

674

HR is going to be asked to equate

people data into improving company

675

performance whether it's trends,

risks, areas of opportunities.

676

I think that's going to

become more prevalent.

677

Thomas: So then I guess even, so software

678

and digital literacy, it was one skill

set you mentioned earlier, but this

679

is almost more general than that.

680

It's like data analysis,

analytics, numeracy is the skill

681

set maybe in response to that

or an increasing amount of that

682

Ken: Put creativity in there, innovation,

that type of mindset, curiosity.

683

Thomas: Love it.

684

You clearly have a curious

mindset and it's amazing.

685

I have to cut off the number

of use cases that we're like,

686

we just don't have enough time.

687

So we'll have to come back and

discuss more as you come out with

688

them over time and have more results.

689

But this has been a

fascinating conversation.

690

So practical.

691

Thank you for talking through

how you thought about and very

692

tactically implemented some of

693

these use cases at your organization.

694

And I think it'll provide some light

for others to reflect in about what

695

could be possible in their orgs as well.

696

And with all that said, I want

to say thank you once again,

697

Ken, to everyone out there.

698

Good luck as you are future-proofing

your organizations and your HR function.

699

Signing off, I'll see you on the next one.

700

Bye now.

701

Thanks for joining us on this

episode of Future Proof HR.

702

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703

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704

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705

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how we can all thrive in the age on AI.