In this episode of the Future Proof HR podcast, Thomas Kunjappu sits down with Jessica DeLorenzo, Chief Human Resources Officer at Kimball Electronics, to explore how HR leaders can guide AI adoption as a business transformation, not just a technology rollout. Drawing from her non-traditional path into HR and nearly a decade inside a global, low-margin manufacturing organization, Jessica shares how HR can move from reactive service delivery to predictive, value-adding leadership.

Jessica explains why she pushes back on the idea of having an “AI strategy” in isolation, and instead frames AI as an enabler of broader business strategy, from margin expansion and decision-making to workforce capability and meaningful work. She walks through how Kimball approaches AI adoption thoughtfully and selectively, balancing experimentation with governance, cost discipline, and real-world operational complexity.

This episode offers a grounded look at what it takes to lead AI change inside a complex, regulated manufacturing environment, where processes are imperfect, audits are real, and transformation must deliver value without eroding trust.

Topics Discussed:

1. Moving HR from service delivery to predictive, strategic leadership
2. Why AI strategy should be treated as a business strategy
3. Using AI as a teammate, not a replacement
4. Protecting the human in the loop during AI adoption
5. Lowering fear and resistance through play and experimentation
6. Building AI capability responsibly in low-margin organizations
7. Selective licensing, early adopters, and managing AI ROI
8. Safeguarding high-potential talent during experimentation
9. HR’s evolving role as a predictor, advisor, and change partner

If you’re an HR leader, people strategist, or executive navigating AI adoption inside a complex organization, this episode offers practical insight into how HR can lead transformation while keeping humans, trust, and long-term value at the center.

Additional Resources:

1. Cleary’s AI-powered HR Chatbot
2. Future Proof HR Community
3. Connect with Jessica DeLorenzo on LinkedIn

The human in the loop, I don't think it's ever going to go away.

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I think the human in the loop is just

going to have heightened expectations

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around strategy, connecting the dots,

critical thinking, curiosity, creativity.

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I think those things are going to be what

sets apart the future skill sets that

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are going to really change organizations.

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Thomas Kunjappu: They keep

telling us that it's all over.

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For HR, the age of AI is upon

us, and that means HR should

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be prepared to be decimated.

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We reject that message.

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

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Instead, it'll be defined by those

ready to experiment, adopt, and adapt.

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Future Proof HR invites these builders to

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

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

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We are committed to arming HR

with the AI insights to not

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just survive, but to thrive.

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Hello and welcome to the Future Proof

HR Podcast, where we explore how

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forward-thinking leaders in HR and

across the board are preparing for

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disruption and redefining what it means

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 Jessica

DeLorenzo, the Chief Human Resources

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Officer at Kimball Electronics.

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Human-centered and purpose-driven,

Jessica leads global HR to build

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capabilities, sustain a vital talent

pipeline, and create meaningful

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careers across a multi-generational,

globally diverse workforce.

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From executive searches and leadership

development to serving as an internal

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counselor to the C-suite, she's known for

turning values into operational habits,

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flexibility, partnership, and respect

while enabling the business to execute.

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Jessica, welcome to the podcast.

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Jessica DeLorenzo: Thanks.

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Happy to be here.

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Thomas Kunjappu: Before we get into

Kimball Electronics, I'd love to learn a

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little bit about your overall career path.

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I know you've been in Higher Ed and

now into a public company leadership.

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Can you tell us a little bit

about what shaped your path?

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Jessica DeLorenzo: Yeah.

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After college, I started my

career in Higher Education

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on the administration side.

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Just grew up with teaching, learning,

leadership development, human

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development sort of skill set.

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Spent almost a decade in higher education.

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And at the time I hit a plateau, Kimball

Electronics was spinning off of Kimball

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International as its own public company.

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So it was building out the infrastructure.

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And part of that was the HR team.

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And I interviewed for a role, was invited

to join the organization and had no

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idea that I had boarded a rocket ship,

both personally and professionally.

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The company's growth,

my own personal growth.

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So I did that role for a couple

of years and was tapped on the

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shoulder to be the successor for the

vice president of HR at that time.

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And here we are today, been at

the company for almost 10 years

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and it's been quite a ride.

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Thomas Kunjappu: That's so

interesting to hear about.

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It's rarer for folks to be in a role for

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a decade.

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Did I hear that right?

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So actually your transition

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into HR was at Kimball.

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It was.

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You were in administration

in higher education.

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Jessica DeLorenzo: Yeah, absolutely.

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Yeah.

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So it was very much a sort of

experiment, I would say, in transferable

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skills going from higher education

to a public global manufacturer.

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So yeah, I have a very non-traditional

path to this seat, but I think

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it's proof that it can be done in

just the evolution of HR in general.

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It's much less

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transactional and it's much more

consultative and human-centered and

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behaviors-driven and motivation.

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So if you have those skill sets, you

really can be a trusted counselor

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and guider for the organization.

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Thomas Kunjappu: Tell me a little

bit about, yeah, exactly where,

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like some of the tensions that the

function is facing at this time, right?

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One phrase that we hear often is

in HR, let's do more with less.

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There's pressure to make

more things happen, but...

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there's like less budget.

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What do you think of that?

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Do you hear that?

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How do you manage that as an HR leader?

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Jessica DeLorenzo: Yeah,

that's particularly true.

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I think for us here at

Kimball Electronics,

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we're a really low margin

business, doing more with less.

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I won't say it's a mantra, but it is a

theme and a pressure for the organization.

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And doing more with

less really to us means

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continuous improvement and

working smarter, not harder.

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So for us, one of the biggest

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tensions when that comes to my mind in

the terms of doing more with less is the

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evolution we're going through with AI.

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And for us, it's really important

to consider what language we use.

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Being a global company, language

is really important to us.

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So when we're navigating these

tensions, it's really important

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for us to focus on the end.

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Yes, the AI evolution is here.

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So

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the and or the tension is, it's

changed and it's really difficult.

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And there are certain

human emotions about that.

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And there's opportunity and there's

hope to do things a little bit better,

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especially for our business, using

language around AI and that tension that

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there is margin expansion opportunity

here to do more with less because

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of the tools that we have access to.

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And there's ways to really build

out some meaningful work for people.

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So it's not necessarily

replacement, it's and what can I do?

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How can AI be a teammate to

me to make me more than I am?

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Thomas Kunjappu: I love that perspective.

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Do you have examples, either

both in the HR function,

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where you've expanded the and gotten more

things happening, or more broadly at the

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organization in any function, which of

course in HR, we're a lever point, right?

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We're helping leverage through

whether it's being a counselor

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or through L&D efforts to enable

that in the broader organization.

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Jessica DeLorenzo: Yeah.

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So the examples

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that come to my mind is we've been

driving digital, whatever that means,

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in the manufacturing environment.

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And the business students have gotten

really good about dashboarding and now

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layering in AI behind the dashboards

to give them certain trending.

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So it's really interesting to see AI has

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given you access to more

information with trending and

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you're using it to make decisions.

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So from the HR perspective, my concern

is, and what training do you need

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to increase your cognitive thinking

and your strategic thinking to be

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able to have stronger insights based

on the data that it's giving you?

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So it's protecting the human in the loop.

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So we're using a lot of

different technologies and

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predictive analytics, machine learning.

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And we need to make sure people

have the skills to use it, to

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make decisions appropriately using

the information that they have.

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Thomas Kunjappu: So how do

you deal with the change

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management that comes with this?

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I think there's fear often, you

already alluded to that, right?

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With with an employee base in really

any kind of industry at this point.

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And there's a big change

management effort.

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So you're talking about training

being like one aspect of it.

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But yeah, how do you think about

guarding against fear, that fearful

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element when you think about AI

from an employee's perspective?

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Jessica DeLorenzo: I really believe in

the power of play as a way to lower the

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barrier of entry into the world of AI.

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So our change management philosophy

is really about acknowledging

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the human and the emotion and

acknowledge and validating that.

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And to us, it's that equally with the why.

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The why can feel very like business

jargon to the individual who still

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fears their job being replaced.

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So if you can acknowledge the human

aspect of it, invalidate their feelings

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before you go into the business case

for it, I think that can be a really

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powerful way to lowering defenses,

calming the situation, getting people

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a little bit more open to exploring

and playing around with the technology.

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Thomas Kunjappu: I love that play.

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It just activates this different

part of our brain and energies,

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both how we just show up for

ourselves and also with each other.

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So you said something in our prep

call that think that resonated for me.

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I wanted to throw it back to you and

have you expand on it a little bit.

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And having an AI strategy isn't

necessarily a goal in and of itself.

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It's more of a business strategy

where AI is enabling it.

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Tell me about the difference

that you see there.

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Jessica DeLorenzo: So at Kimball,

we have been really diligent and I

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think proactive in that we have a AI

policy, AI use policy as part of our

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information security management system.

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But it really makes me cringe a little bit

when people say, what's our AI strategy?

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Because I think it's, we're

missing an opportunity if we don't

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consider the larger ecosystem

in terms of business strategy.

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And AI is a tool along the

way of our business strategy,

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but it can be transformative.

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How does AI change our business model?

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How do we go to market?

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Who our customers are?

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How we serve our customers?

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What features do we sell?

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What services do we provide?

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So just by saying we need to deploy an

AI strategy is really maybe doing the

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company a disservice by not looking

at how can we use it as a tool to

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increase profit for our stock owners?

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How do we create more

meaningful work in jobs?

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How are we more responsible citizens?

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How are we better partners to

our suppliers and our customers?

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Because we have data that we can

aggregate and have a conversation with.

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And it's

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that sort of consideration of the

whole ecosystem of the company

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that drives more towards, it's

got to be a business strategy.

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It's if we just use AI as a

tool for efficiency, we're not

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going to get as far as we could.

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So really stepping it up and asking

a question of what are some real

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possibilities to transform the business

model, I think is a hard question.

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but I think it's the one we need to be

asking ourselves to stretch our thinking.

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Thomas Kunjappu: Yeah, it's interesting.

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You threw in the concept of an AI

strategy, which if you think about

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it that way, myopically, you tend

to lead to efficiency gains, right?

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So we're doing a bunch of different things

and let's have AI or a human in the loop

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AI somehow make that more efficient,

which is one part of the equation.

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But the other side that tends to

ignore, if you just think about it as

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a starting point is creativity around

margin expansion or new business

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ideas, which can be enabled by AI.

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But often on the HR seat, though,

I will say, I feel that's the hot

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topic in boards and in leadership

teams, we need an AI strategy.

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And let's and part of that is let's make

sure we have an acceptable use policy

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and like an IT policy towards that.

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But I hear you about that.

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You need that.

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But that's just a part of the picture.

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Yeah.

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So going back to your concept

of play, I was really interested

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and wanted to dig in a bit.

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But you personally built,

let me put it this way.

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I think you had an aha

moment as you were going on.

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You try to do something

like off on your own.

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I would say the HR team in

general, it's leverage for

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the whole entire organization.

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And the chief human resource officer is

leveraged for that levered organization.

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And yet you went in and played

around with some AI and I think

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maybe came to some realizations.

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Could you tell us a little bit

about your personal journey?

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Jessica DeLorenzo: Yeah, it started with

some curiosity, just what is this thing?

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a lot of the trends around make

your action figure, I think was

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one of the first things that I did.

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If you remember that is I'm an

HR professional and you give

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the prompt is a few other pieces

of information around you.

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Now make me an action figure.

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And it gives you an image of you in a

box and like you upload your picture and

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all this, it makes you a little action

figure, which was just really fun to do.

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So I then shared that with my team because

there's so much research that shows

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that if you see your manager or a leader

in the organization using AI, you're

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like three times more likely to use it.

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It was important for me to showcase my

failures and my successes and my little

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pieces of joy along the way with my team.

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And it was so interesting because

I'm known for wearing like these

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red heeled shoes and my AI figure

Rain did not have my red shoes on.

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And my team was so quick to point it

out, like, where are your red shoes?

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And it was a really, that was a bit of an

aha because it was like, it can go so far.

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And then you add the human sort of

the emotion and the relationship

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and the context around it makes

it just a little bit better.

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So that was one of the

first things that I did.

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And we've, I've taken a few AI courses

and I've done, I've read a couple books.

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And I finally took a pretty structured

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class as part of my MBA program.

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And it was like exactly what I needed.

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It was like four days immersive.

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Here's rapid fire.

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Here's then this tool.

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So you just build a tool library.

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And out of that, I was

like, I can build a tool.

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Agent.

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Like I now know what an agent

is, so I can build it too.

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So I got into our company platform

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and just had a conversation with

the tool in the agent creator.

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And we now have this little agent that I

built that we're testing and rolling out.

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And it was just as simple

as act as my HR generalist.

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And here's our guiding principles.

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Here's our employee handbook.

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Here's our benefits guide.

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Now create an agent where you

can query these things and answer

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questions as customer service

for employee self-service.

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So it's an agent that I can ask

questions to and we're testing it

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and it's really fun, but I literally

vibe coded it in 10 minutes.

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And here it is.

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And we, so then we rolled it out to

our HR teams and the next sort of

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A step in the journey was

having them then play with it.

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So the task was, here's this agent that

I built in 10 minutes, you can too.

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By the way, here's how you do it.

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But let's create a logo.

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Let's create a mascot for this agent.

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So then they just went wild

in terms of the creativity.

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And we had a contest of who's

the mascot and we voted.

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And now there's a little, there's

a little Kimby the HR helper

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is the name of this agent.

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It unlocked some fun and creativity and

lowered again, a barrier of entry for

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the other HR teams, members to get in

and just play around, find a little joy.

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My journey has been experiment.

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What is this thing all the way to

some real formal education, building

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a library, building understanding,

reading my own education.

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And then now just back to our loop to

let's play around and figure stuff out

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and build it, iterate a little bit.

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Then come back.

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So it's like a circle.

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It's a virtuous circle, I guess I

would say, in terms of a journey.

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It's not over.

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It'll continue.

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Thomas Kunjappu: I love that.

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So we're talking a little bit

about how you're enabling all of

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this throughout the organization.

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There's policies, there's tools, and guess

this encouragement to play and try things.

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And is that what you just described,

we've been talking about the HR team.

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Is that been like a general ethos,

like across, across Kimball, or are

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there, I imagine adoption varies quite

widely, right across the organization.

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So how do you think about this enablement

of, and from an L&D perspective or

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upskilling and reskilling perspective for

the entire organization with regard to AI?

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Jessica DeLorenzo: Yeah, so we were

really intentional about partnering

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with our AI technology supplier.

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They have free courses and they were

more than willing to host webinars that

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we would record and then put into our

HRIS system to really start to build a

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library of Kimball-specific resources

for people, at least for awareness.

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Everybody had to go through the

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training of the AI policy.

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There's a piece of awareness.

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Here's the training and the webinar

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that's available to you.

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There's some awareness.

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In turning into sort of the engagement

and enablement step, we didn't just

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give everybody the same level license

because we're a low margin business.

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So we can't, we being stewards

of cost and expense, we wanted

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to manage the level of licenses.

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So they were given out selectively to

groups we knew were early adopters.

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Like our customer facing organization

really uses it for a lot of market

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research and business intelligence.

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Our manufacturing, our engineering

teams use it a lot for, again,

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industry Ford Auto initiatives and

how do we increase quality, reduce

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scrap and all of those things.

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So early adopters got licenses.

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The executive team got early licenses,

again, top down, the executive team got

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early licenses again, top down, setting

the example, fail hard, fail fast.

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Let's have some fun.

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But so that was really good and

started sharing some early successes.

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And then more people, as last week when

we were talking about this HR, this agent

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that I built, found out 75% of the HR

team didn't have the license to be able

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to access it, which to me was a win.

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Okay.

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Now you all, now we know, let's get them

at licenses so then they can play around.

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But this enablement as was being

really selective about who got the

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license to be able to do more because

there was already an engaged audience.

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It was a captive audience that we then

could build some scaffolding around and

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they could be our champions

in the organization.

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Now, getting a license is very easy.

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You ask for one and you get

one, but it's just creating the

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awareness that you can do more.

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And when you run into a problem,

you just ask and it's automatically

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given to you, basically.

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That's how we've been able to manage

the cost of it with the awareness and

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the enablement so that we have people

who are really going to use it and use

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it well to then become champions and

get that interest and that curiosity

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so more people are asking so that

they can start to experiment and just

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continue to build on that momentum.

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Thomas Kunjappu: Yeah, I like

that way of thinking, maybe

329

because I'm personally frugal,

330

but I like to make sure that you're, I

don't know, I'm proving to myself that I'm

331

running every day for a month before I go

in and get the expensive running shoes.

332

You've earned the fact that you've

built the habits and there's

333

some value for that in this way.

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And I think this is a smart way to go

about it because there've been some

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news reports about how there isn't ROI

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for AI in many organizations.

337

And I think what's happened is the big

guys have been pushing enterprise-wide

338

licenses because that's a big rev

stream, of course, for the technology

339

companies and go wall to wall.

340

And then people will figure it out.

341

But, of course, it's hard to enable and

get the right knowledge and the time.

342

And actually, there's a bunch of steps

behind just having the technology.

343

And so this is a smart

way to get that pull.

344

And that's a great example of

these folks on the HR team saying,

345

hey, I want to participate in this

project, but I don't have access.

346

So now you've proven that,

okay, there's a reason.

347

that you need access.

348

So it's like a light speed bump

to move you in that direction.

349

So that once you get to this concept

of slowly enabling across the board,

350

are you also sharing or have you

created like any kind of councils or

351

cross-functional collaborations around

these kind of efforts or rituals or

352

practices where you're sharing what's

happening across the organization?

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Jessica DeLorenzo: Yeah.

354

So we have an established council

structure where at Kimball, essentially

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every functional area has a council

and that manager from the business

356

unit in the global environment

is a member of that council.

357

And the council structure is meant

to be a forum for best practices,

358

sharing, standardization initiatives,

solving real business problems.

359

So one of our councils is,

we have a digital council.

360

So the digital council is building

out a sort of functionality of their

361

group to be innovation hunters.

362

So they're the ones who are council

members that go out to the business and

363

bring back really strong use cases or

really cool or innovative ways that people

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are using the technologies out in the

business and bringing it to the forefront.

365

And they've already found that the

business in this location is actually

366

doing the same thing as the business

in this location, but their tool

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is just a little bit different.

368

How come they weren't

just doing the same thing?

369

The council structure is really

helpful for us in identifying those

370

best ways of using the technology.

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So that's been something that's

been a structure that's worked

372

well at Kimble that we've

373

leveraged to continue the enablement

of AI around the organization.

374

For HR specifically, what we've done is I

have pretty regular meetings where I bring

375

the global HR team together,

the HR managers at least, and

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they've requested as part of this

meeting, can we have some time to

377

showcase AI wins or best practices?

378

And I said, done.

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Agenda updated.

380

I said, but what I need from you is

a commitment that you will come to

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this meeting prepared with an idea.

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I'm not just going to

add it to the agenda.

383

We need to have some ownership and some

accountability to bring that to the forum.

384

So judging by their, I think,

excitement and enthusiasm, I

385

think they will, but that's a

386

little, that's a little TBD.

387

But yeah, those are ways I think by

just little small tweaks or small

388

behavior changes of the already

established rituals of the

389

organization are going to continue

to be really strong ways drive

390

the adoption around the company.

391

We also have each functional unit, each

business unit has continuous improvement

392

goals by fiscal year, by dollar amount.

393

There's going to be a new sort of

category of AI enabled by savings.

394

So we were talking about something.

395

I don't remember what the project

was, but one of the HR teams

396

was using AI for something.

397

They saved a ton of money,

found some efficiencies.

398

And I asked them, are you

going to count that as some?

399

AI-enabled savings for your CI goal,

and it hadn't even crossed their mind.

400

So it's just one of those things about

we just need to become natural in

401

terms of how we're thinking about as a

teammate and an enabler of the business.

402

This has been a fantastic

conversation so far.

403

If you haven't already done so,

make sure to join our community.

404

We are building a network of the

most forward-thinking, HR and

405

people, operational professionals

who are defining the future.

406

I will personally be sharing

news and ideas around how we

407

can all thrive in the age of ai.

408

You can find it at go cleary.com/cleary

409

community.

410

Now back to the show.

411

Thomas Kunjappu: Yeah.

412

So there's, thank you for going

through all of these different

413

ways that you're reinforcing how

this is becoming central to the

414

way that teams are operating.

415

Now, because of this experimental and

not an experimental, but it's starting

416

small, and you're slowly expanding the

scope, both in terms of licenses projects,

417

revenue and compensation based incentives,

418

eventually, you're slowly moving out way.

419

One thing that often happens is

your high talent folks, your or high

420

potential folks are the ones who get

sucked in early on all these projects

421

that could fail, could, and maybe

necessarily could have a high failure

422

rate, but that's, it's a point of

experimentation and that could potentially

423

lead to, I don't know, burnout, right?

424

Or I've heard that kind of like concern.

425

How do you think about that?

426

So that maybe it's like, is it the

same folks who end up, how do you

427

safeguard against disillusionment,

428

let's say from high potential folks who

are always in experimentation mode and

429

not necessarily getting

to results all the time?

430

Jessica DeLorenzo: I think one of

the risks of just, as we would say,

431

throw AI at it or throw AI at

it, throw a high potential at it.

432

There's a little risk

there, like you pointed out.

433

I think about some, let's figure out the

process and insert AI into the pain points

434

of the process.

435

That means that you have

to map out your process.

436

And there's a process on paper.

437

And there's a process that

happens not on paper, right?

438

And once you start really

understanding the puts and takes

439

and the connections between,

440

it is messy.

441

It is really messy, especially

in a company like ours where

442

there are so many audits from

443

regulators, from customers.

444

We have to have extensive documentation

of a process and what we do and

445

when and how, and what are the

inputs and what are the outputs.

446

And you put that on a wall and then you

add all the things that happen outside

447

of that process to make the process work.

448

It's really messy.

449

And as I think about AI and high

potentials, that AI does not work

450

in a linear way like humans do.

451

First of all, by putting a high potential

in the room with that on the wall,

452

they're already going to

be completely overwhelmed.

453

How am I in the world going to fix

454

this mess of a process?

455

And you're then going to expect

me to insert AI into that.

456

It's just, you're setting them

up for failure right off the bat.

457

There's just no practical way to do that

458

on time or on budget

or within expectations.

459

And if they fail, then that's really

damaging to the high potentials

460

confidence, to the reputation of the

organization, to the disillusionment.

461

We guys are really not that great anyways.

462

We have this sophisticated process.

463

We can't even use it.

464

So it it can just be really harmful and

really damaging if we're not careful.

465

One of the guardrails that I think

really works beyond the safeguards,

466

the technical, practical safeguards

in place that IT teams do behind

467

the scenes to protect everything.

468

I think a guardrail for high potentials

is teaching them good prompting.

469

good prompting.

470

And I think what I mean by that

is don't expect AI to say, to give

471

them the process and then have

them spit out an outcome for you.

472

That's just not how it works.

473

AI is not linear like a human being.

474

So by developing the ability and high

potential talent to sort of investor

475

embody this leadership principle

around the end in mind, I think can

476

be a safeguard because then you're

teaching the high potential person how

477

to use AI to get to the desired state.

478

What is the end in mind?

479

And using them as a teammate.

480

So act as my business strategist.

481

Here's what I'm working on.

482

Here are the inputs.

483

Give me a solution.

484

Or here's what I think good looks

like within a few parameters.

485

And then go and let it go.

486

Don't give it the steps of the

process because you're just really

487

limiting, I think, the creativity

and the value of AI by doing that.

488

So for high potentials to protect them,

I think, from the disillusionment is to

489

protect them, I think, of the messiness

of the linear thinking of humans,

490

specifically when we're talking about

AI, but I also think building in them the

491

leadership capability around visioning

and being able to describe and articulate

492

what good looks like and visioning that

because then you can work backwards.

493

So it works with AI, but it also works

outside of AI as you're influencing others

494

and you're leading teams and getting

on new projects and new perspectives.

495

I think that's our high potential talent

really could benefit in that way, with

496

the technology, but even outside of

the technology with this skill set

497

and this mindset you're building.

498

Thomas Kunjappu: So I think there's

a lot of depth to this here.

499

So I want to really

make sure I get it here.

500

Are you saying that if someone is

embedded in a role and is excellent

501

at it, and it's a complex, messy,

let's say it's a manufacturing process

502

that someone's really good at, and

they're being challenged to improve

503

it, either for efficiency or whatever

the outcome is, the starting point

504

is very messy, very complicated.

505

And what's on paper usually doesn't

even accomplish a good job at

506

capturing the entire picture.

507

A better starting point than that is

to zoom out even further and think

508

about the outcome and almost reimagine

the entire process from scratch

509

and expect different, completely

different new process potentially on

510

the tail end of a successful project

511

with a lot of if else guard rails

and trying to push that in with AI.

512

Is that what I'm hearing?

513

Jessica DeLorenzo: Yeah.

514

I think it's going to be a real

interesting evolution because I would

515

advocate that when you prompt it with

the end in mind and a really good

516

outcome, like outcomes-based prompting,

the process doesn't even matter because

517

AI is doing the process to act as the

teammate and enhance the high potentials

518

work product, their performance, right?

519

But I think where that falls short is

in an organization like Kimball where

520

you have to have a process, right?

521

So you've got to be selective, I think, of

where you can use it and where you can't.

522

You can't expect just to wrap AI

around the entire supply chain process.

523

It's just not feasible if you try

to do that disillusioning because

524

I don't even know where to start.

525

But I think if you can pick out even

different pieces of the process and then

526

use that as a what's the end in mind.

527

I think you can get some pretty

creative ways in that how I get here

528

can be a million different ways.

529

I think that's the way to guard real

high potentials is break it down, but

530

then within that breakdown, have them be

able to pan out to see what's possible.

531

Thomas Kunjappu: It's about

picking the right projects, right?

532

So just being smart about which

kinds of processes AI can really

533

be a partner in helping improve it.

534

But then once you pick that, and

that might be you want to reduce the

535

complexity, the variance a little bit,

or break it down into just like this

536

type of machine for this type of market,

537

or this type of process for this type

of employee, whatever the thing is,

538

you break it down to something

that's manageable and reduce the

539

variance a little bit and then

you can go but then once you get

540

into that and you understand that

541

process pretty well you want to zoom back

out and think about how can you what is

542

a goal in this particular sub area right

because it goes back to the comment I

543

made earlier about these studies that are

coming up about lack of ROI from projects.

544

And part of it's also just thinking

you can apply it everywhere, right?

545

You have to be smart about it.

546

And I guess there's a difference between

experimentation versus going live.

547

Actually, I'm just trying to connect the

dots to like what we talked about earlier,

548

Jessica, about the agent that you quickly

built, as an experiment for the HR team.

549

There's probably a journey to go from

that to something where there is like

550

something you could, I don't know,

introduce in terms of continuous

551

improvement, goal realization.

552

Right.

553

Jessica DeLorenzo: Yeah, absolutely.

554

And it's, it'll be a journey.

555

And how do you balance that with

feeling hard and feeling fast?

556

It's everything within me to

say, let's just put Kimby out

557

there and see what happens.

558

But you're, but I realized

that's even realized that can

559

even create some disillusionment

560

because if it's not ready, people

are like, this is terrible.

561

Like, why would I ever use this?

562

And so it doesn't solve

anything for the HR team.

563

So I think that's the balance and

the tension in my own experiences.

564

Is when is it ready?

565

When is it ready?

566

What processes are ready?

567

What work is ready for us to put AI in it?

568

There's some pretty cool reports and AI

tools out there that you can put your,

569

if you don't know where to start,

you can put your job title in and

570

it'll give you a breakdown of, based

on assumed job activities, this is

571

the potential to insert AI into it.

572

So it even gets you a head start in terms

of where should I be thinking about it

573

and where shouldn't I be thinking about

increasing using AI in the business.

574

This little AI agent that we built

for our handbook search, companies

575

are doing this all over the place.

576

So I think it's a valid idea.

577

It's just a matter of how do

we get it good enough to not

578

destroy value when we deploy

579

it on a broader scale within the company.

580

Thomas Kunjappu: You want it to be

at least as good as what you're doing

581

right now in terms of the service

582

delivery, right?

583

That's the target.

584

And how do you know that is established?

585

And that's true for any

kind of deployment, right?

586

When you think about it.

587

So I'd love for you to imagine

the future a little bit with me.

588

Sometimes a fool's errand, but you're

on this journey, both in the HR team,

589

personally, overall at Kimball, right?

590

So what do you think any of

those could look like for the

591

organization or an organization of

the future that is future-proof?

592

As you look ahead two to

three years down the line.

593

How do you imagine processes, skills,

your orgs all evolving as this whole

594

structure that we've been talking about

starts getting deeper and more embedded

595

into many more parts of the organization?

596

Jessica DeLorenzo: Yeah, my hook

particularly for HR is that the

597

function, the teams in the near future

598

will be seen more as a predictor of

things instead of a service provider.

599

We're a support function

and we need to be optimized.

600

And I appreciate that.

601

But I think in the future with

these tools, how do we move

602

from service to predictive

603

to adding value, right?

604

So we're seen as the ones who

are in really intelligent ways,

605

advising certain things around the people

of our organization, instead of being

606

the, there's an important piece around

service, but I think that's going to

607

go from 80% of the job to 30% of the

job, because the skills of where the HR

608

teams are building is accountability.

609

So here are the tools that we've built.

610

Here are the things, the

technology available to you.

611

And you are now employee skilled in how

to interact with the technology that

612

you're less reliant on the human service

piece of the HR function, which I think

613

enables us then to be really thoughtful

in places where we're going to need

614

to be thoughtful over this evolution.

615

Change management, behavior theory,

learning theory, a lot of those

616

things in helping really be the change

agents and change partners around

617

the expertise of how the human being

works and lives and plays, right?

618

That's going to be the expertise that I

think is going to be really valuable that

619

AI is not going to be able to solve yet.

620

So those are the skill sets along

with the strategic acumen of business

621

is going to be really important.

622

I think AI is going to get pretty good

at business acumen because it learns

623

and uses its algorithm and statistics

and the model and here's what you get.

624

But the human in the loop, I don't

think it's ever going to go away.

625

I think the human in the loop is just

going to have heightened expectations

626

around strategy, connecting the dots,

critical thinking, curiosity, creativity.

627

I think those things are going to be what

sets apart the future skill sets that

628

are going to really change organizations.

629

It's pretty cool.

630

I'm really

631

excited.

632

Like I'm here along,

I'm along for the ride.

633

So

634

Thomas Kunjappu: Yeah, that's

a fascinating like description.

635

And you're saying that we're going

to move from services or where the

636

HR department is offering a service

637

to the stakeholders, right?

638

So leadership, employees, boards,

customers, and the community

639

in some kind of extended way.

640

But moving from that to actually

being more predictive and actually

641

being able to intervene so that the

predictions can be tweaked, right?

642

When you say services, isn't that

still a service that you're offering?

643

Or can you just help me

understand what you mean?

644

What's that distinction in your mind?

645

Jessica DeLorenzo: Yeah, that's true.

646

So maybe the difference

is reaction and proaction.

647

How are we reacting and responding?

648

And people are coming to us

for information versus us

649

pushing information and being at

650

the forefront, pulling the organization

along in ways instead of being

651

approached for what's this report?

652

What's our turnover report?

653

What's the handbook say about this?

654

What's the handbook?

655

What's the business process?

656

I need you to recruit for me.

657

Those are all services that we provide.

658

So I think that's a good point.

659

So I think it just changes maybe the

business model within the service.

660

It's a different type of

service to the organization.

661

It's really about, I'm not

going to do this for you.

662

I'm going to do it with you and

I'm going to do it ahead of you.

663

And I think that maybe could be,

I think maybe that could be the

664

different sort of the brand or

business model within HR in the future.

665

Thomas Kunjappu: I love that.

666

Yeah.

667

So for any one of those examples

you just went through, you

668

could probably turn it around,

669

right?

670

So it's not, hey, can you

hire this person for me?

671

Like this kind of role for me.

672

It's, hey, based on what we see in your

organizations and your turnover and what

673

kind of the strategy that we have, you're

going to need these kind of people.

674

And so this is what we're going to recruit

for you in the next organization or in the

675

next time period.

676

Hey, manager, you have these issues

coming up in your organization.

677

Here's some training for

this particular subgroup.

678

And maybe some of those reactive use

cases will still be there, but maybe that

679

will be taken over with in terms of time.

680

So people will still know.

681

want to know what the PTO policy is, but

that can be not necessarily something

682

that the team is spending a lot of

time helping deliver that service.

683

Jessica DeLorenzo: Yeah.

684

Yeah.

685

And I think it becomes maybe

686

more time for it, which is maybe

ironic and I'm contradicting myself

687

a little bit here, a little bit more

time for human interaction in the

688

prediction versus just a transaction,

689

a human transaction versus

a human interaction.

690

Maybe that's the difference.

691

Thomas Kunjappu: That's a great point.

692

Regardless of who starts it,

proactive or reactive, but it's

693

getting into an interaction versus

a, yeah, I need this from you.

694

It's like very simple and black and white.

695

So you're saying you're excited

about where this is all headed.

696

So then maybe last question is, I would

love to know if you're talking to someone

697

who is, I don't know, foolish enough

to want to get into HR and is just

698

going through college and is coming out

into the workforce for the first time.

699

What kind of advice would you have

for them, given all these changes that

700

you're seeing, both at Kimball and at

a macro level, for what it means to

701

be successful and what they should be

investing in themselves in learning?

702

Jessica DeLorenzo: I think it's

really going to come down to a couple

703

of things in terms of the human

interaction we just talked about.

704

It's going to come down to

communication, compassion,

705

curiosity, and I think creativity.

706

Oh, apparently there are four Cs.

707

But this idea of just being a really good

student of the human experience and how

708

humans are motivated and how they learn

and how they work and just the potential

709

feelings and spectrum of experiences along

the employee life cycle and just being

710

really good at navigating relationships

and communication and influence, because

711

you can, if you have the cognitive

agility, you can learn HR, you can learn

712

the policies and the procedures, you can

learn how to do this, how to do that.

713

But you've got to have this sort of

insatiable desire to serve humans and

714

people and that selfless curiosity

about what makes the other person tick

715

so that you can get them where they

need to be or get them where they want

716

to go or help them be, find a little

bit more joy in their work or find

717

more meaningful work or find work that

they need to get out of or get into.

718

I think maybe the language or the fluency

within HR for tomorrow is going to be

719

really about the human experience and

what that means in a digital evolution.

720

So that's good luck.

721

It's really exciting.

722

It's going to be fun, but I

think it really is that simple.

723

Humans are humans.

724

But we're complicated

animals, I would say.

725

Thomas Kunjappu: Yes.

726

And so that's why the demand

for HR will be there, but it's

727

going to be evolving, right?

728

In terms of what you're

going to be doing every day.

729

So I think I hear you about you want to be

curious about human beings and how you can

730

enable and get the best out

of them and for them to have

731

the highest agency and close

732

to their owning their

careers and their work.

733

But then the way you...

734

you're going to be doing that,

to your point, is going to be

735

increasingly digital, right?

736

There's going to be a lot of fluence going

to be needed and numeracy in enabling that

737

if you want to get predictive and have

those interactions versus transactions.

738

There's

739

a lot to learn, right?

740

And a lot that's being figured out.

741

So thank you so much for this

wide-ranging conversation.

742

And you, I think, exhibit some of the

things that you're espousing, right?

743

Curiosity yourself by showcase, by

building your own HR agent, right?

744

If a CHRO can do it, like everyone in

an HR ops or generalist, or you should

745

be spending some of your time messing

around with this stuff Kimball has play.

746

And that's also what we're trying to

enable probably at some level in every

747

organization across every industry.

748

And there's a lot of nuances to it,

whether it's about use policy or how

749

to roll it out, who gets into it,

how to think about it in terms of

750

prompting and not being disillusioned.

751

So thank you for going

through all of that.

752

This is all lived lessons, right?

753

The hard-fought lessons.

754

Thank you for the conversation.

755

And for everyone out there, I hope

you got some value out of this as well

756

as you're future-proofing your own

organizations and your own HR functions.

757

And with that said, thanks, Jessica.

758

And to everyone out there, good

luck and see you on the next one.

759

Thanks for joining us on this

episode of Future Proof HR.

760

If you like the discussion, make

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761

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762

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763

See you next time as we keep our pulse on

how we can all thrive in the age on AI.