Shownotes
In this episode of the Future Proof HR podcast, Thomas Kunjappu, CEO of Cleary, sits down with Amy Wang, Head of HR and Payroll Shared Services at Mercedes-Benz North America, to discuss how HR is changing as AI, shared services, and human leadership increasingly overlap.
With experience across IT, procurement, operations, and HR, Amy brings a cross-functional perspective on what it takes to build systems that are both scalable and people-centered. She explains why the future of HR depends on connecting people, processes, and technology in more intentional ways.
Together, they explore where AI can support HR workflows, where human judgment still matters most, and why better design and governance are essential for making these tools useful in practice. They also discuss how shared services is evolving beyond transactional work into a more strategic function focused on capability, data fluency, and cross-functional value.
Amy also shares why curiosity, continuous learning, and adaptability will be critical for HR leaders who want to stay relevant as work continues to change.
Topics Discussed:
- How Amy’s transition from IT leadership to HR shaped her approach to organizational change
- Why the convergence of HR, IT, and shared services is accelerating
- Where AI can support HR workflows and where human judgment must remain central
- The “human-AI-human” model for responsible AI use in HR processes
- Why poorly designed AI systems create frustration and how better governance can help
- How shared services is evolving from transactional processing to capability building
- The role of curiosity and continuous learning in staying relevant as AI reshapes work
- Why organizations must create safe environments for experimentation and innovation
- How HR can act as a translator between strategy, systems, and the employee experience
- Future opportunities for AI in areas like employee relations analysis and pattern detection
If you’re an HR leader thinking about how AI will affect HR operations, shared services, and the skills required to lead in the future, this episode offers practical insights and a thoughtful perspective on what the next shape of HR could look like.
Additional Resources:
Transcripts
It's taken me from managing change to really designing change.
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00:00:03,089 --> 00:00:06,359
So like now, HR really can
operate as like a translator.
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It helps connect people processing
technologies so that strategies
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00:00:11,039 --> 00:00:13,590
that are being revealed to employees
really become real to them.
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00:00:13,619 --> 00:00:15,719
It's become something
that's makes more sense.
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Thomas Kunjappu: They keep
telling us that it's all over.
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For HR, the age of AI is upon
us, and that means HR should
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be prepared to be decimated.
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We reject that message.
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The future of HR won't be handed to us.
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Instead, it'll be defined by those
ready to experiment, adopt, and adapt.
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Future Proof HR invites these builders to
share what they're trying, how it's going,
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what they've learned, and what's next.
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We are committed to arming HR
with the AI insights to not
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just survive, but to tHRive.
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Thomas: Hello and welcome to the Future
Proof HR podcast where we explore how
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forward thinking leaders are preparing for
disruption and redefining what it means to
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live and lead people in a changing world.
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Today's guest is Amy Wang, head
of HR and Payrolls shared services
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with Mercedes-Benz North America
with roots spanning IT leadership,
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procurement, healthcare, higher
education, and enterprise HR.
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Amy blends data technology and
human insight to build stable
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systems that enable growth.
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She serves as well on the advisory
board for Cornerstone University's
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strategic AI program and contributes
to SSNs HR Shared Services blog series.
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So a lot of hyphens here.
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I'm so excited to bring Amy onto the show.
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Welcome to the podcast.
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Amy: Thank you, Thomas.
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I'm happy to be here.
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Thomas: So there's so many things
that we could talk about, but
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I'd love to get started on.
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Talking about your CIO to HR
kind of bridge experience, so
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you don't see that very often.
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People working in IT, procurement
operations, and then also like in HR.
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So tell me how that happened.
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Amy: I started off in, it just, it
was the thing to do when I started
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it and it naturally progressed
into all the different areas of
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it from being a programmer to
working in the network data center.
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And then finally project management.
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And when I found project management,
I found that was really what really is
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the connection and the bridge between
all the different areas that I had
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been in, whether it's IT operations,
procurement, and now HR, because there's
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the people element that you really have
to focus on when you're in connecting.
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Those has really been something
that I've truly enjoyed.
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And finally being able to use.
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My degree in HR.
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It's been something that has been
really exciting for me and especially
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how I think industry has evolved.
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It's really allowed me to take all
those different aspects of my career and
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really apply it together in using it now.
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Thomas: So you're saying how
the industry has evolved.
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How has it evolved and do you see
combinations and intersections
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between these functions more now
than when you first got started?
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Amy: Oh, absolutely.
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In one of my former roles, I feel
like being in IT, it really taught
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me about things like governance
and scalability and data integrity.
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And then now being in HR, I feel
like I have a lot more mindset into
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like empathy and communications.
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And when those come together, I feel like
with how technology's changing so much.
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It's taken me from managing
change to really designing change.
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So like now, HR really can
operate as like a translator.
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It helps connect people processing
technologies so that strategies
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that are being revealed to employees
really become real to them.
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It's become something
that's makes more sense.
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Thomas: I've heard there's some
companies experimenting with this
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stuff, like having the CIO and
the CHRO's office merge together.
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Does that make sense to you or is that
like a trend for some reason you can
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see more or less of in the future?
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Amy: I think it makes perfect sense.
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I actually cited an article I read
and Moderna is actually doing that.
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They're merging both IT
and HR into one leader.
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And I think it makes
a lot of sense, right?
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Because there's so much in technology
that really touches the end user, right?
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And so being able to connect, agAIn,
people process the technology together
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for strategy, I think it makes tremendous
sense to help accelerate organizations.
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Thomas: Let's talk a little bit
about AI and how it's come in
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different ways as you have seen it.
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And maybe we can talk a little bit about
the different ways you're attacking it.
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I imagine leveraging AI in your
work, maybe in your personal life.
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You're also involved in teaching
as well as in creating content
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and blogging about the topic.
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So just tell me overall, like your
relationship with AI and how it's
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evolved over the last couple years.
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Amy: On a personal level, I think
I am experimenting more than
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probably more from my actual role.
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But in having a lot of conversations with
industry in volunteering with different
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organizations, I'm seeing it more and I
feel this is where it's super exciting.
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But I feel like also we just have to make
sure that we understand what is it that's
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specific for AI and what is it that there
should still be a human element to it.
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And that's always my primary concern,
is ensuring that we're blending the
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human element in everything we do.
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Thomas: I hear that a lot, but I would
love to get more concrete about that.
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What does that even mean?
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Why do you need a human element?
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And in what
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Amy: For example, when it comes to
professional like environments, right?
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There's things that you
don't wanna delegate to AI.
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I feel like there's still a discernment
that should be left for humans.
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For example, like when you're doing
audits or exceptions or anything that's
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involving like human context or ethics, I
think those are important things that you
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would leave specifically for humans versus
what you would go in the direction of AI.
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If you're doing audits, you don't want
to completely turn that over to a system.
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When we're talking about
audit, we're doing actually
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the human side first, right?
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Versus doing something that's specific to
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instead of using AI.
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So I feel like there's opportunities
where the human can enter the
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information and understand the
process, but then using AI to assist
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in like a second tier verifier, right?
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So maybe flag anomalies, maybe looking
at patterns or inconsistencies before
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things have to escalate, right?
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So that way I think that there's
the human-centric piece of it
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before you take it to the system.
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Thomas: Okay.
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And then to be very specific, what
kind of audit are you imagine?
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Or can we talk about a
specific audit like process?
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Amy: Maybe for example, in some
organizations I would say like payroll.
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There's a lot of pieces where an
employee maybe is badging in making
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changes, but then you want audit.
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And then you want your payroll person
to do whatever they're supposed to do.
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But then maybe the system to audit
because for example, maybe tax regulations
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change, maybe pay codes have changed,
maybe other things are changing and
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you just wanna make sure that from an
audit perspective there's compliance.
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And that would be what I would use it for.
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Thomas: Okay.
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So in this case you're talking
about like specifically for payroll?
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And like a and non-exempt employee,
when you're talk clocking in and out
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with time cards, you wanna make sure
that you are looking systematically
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at the process to ensure that
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the inputs are accurate.
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Amy: And
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Thomas: so how and how is that being done?
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Like without AI, is that just
not being done enough or is it...
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I
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Amy: think it's done by two people.
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So it's, you have
multiple people doing it.
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So if one person's doing it, you
have a second person who will verify
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with a second pair of eyes, right?
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Yeah.
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And so I actually, here's an example.
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I actually had a conversation with
an industry colleague and they were
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talking about how AI flagged what
they thought was payroll fraud.
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The data was correct, but like the
numbers didn't line up and so the human
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reviewer noticed it after actually.
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So they just use the AI,
the human reviewer noticed
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it and said, wait a second.
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I know that person returned
actually from a medical leave.
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And so that context changes the story
because the system saw a mismatch.
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But then if you have a person involved,
there's more design where you need
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to keep the human in the loop, right?
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So I think it's like you have a
human who does one part, you'll
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have AI do another part, and then
there's another part for the human.
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And there's handoffs that
need to be part of it.
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So I think it's more like having AI
work as a partner versus a replacement.
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Thomas: So in this specific
example though, you're saying
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did AI add value or not?
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What do you think?
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Amy: I think in that particular example.
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If everything worked to
normal, AI would add value.
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But this is where it's like
almost a sandwich, right?
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You have the human piece, you
have the AI piece, and then
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you have the human piece again.
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And I think that's part of the evolution
and maybe as people's processes are
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designed differently, how technology's
used differently, it'll eventually
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evolve where you don't need to have that.
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But I think in this example that I
talked about, it really has been human
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AI and then human and other examples
where you may not need a separate
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human verification again, but as things
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are being inputted to create
the validations that you're
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trying to set up for AI.
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I feel like there's still another
piece of right now that people
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still have to verify, right?
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So making sure that if someone's
entering their first piece of work and
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then AI is doing the validation or the
compliance, I think there should be spot
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checks or if things come up, I think
there still needs to be validation.
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Thomas: With a human expert, right?
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Yes.
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On top.
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On top of it.
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Amy: Yeah.
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And maybe that's a human putting
in the information to kind.
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The context, right?
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Or the prompts.
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Maybe that's who's doing the verification?
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Thomas: Yeah.
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Workflow where you have, without
AI, you have just two people
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checking something, right?
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Someone's inputting data, maybe
an employee in this case, and then
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you have one person looking it over
like a manager, and then you have HR
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looking it over, and typically you
have decreasing levels of fidelity
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and increasing surface area that each
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stakeholder then needs to like handle.
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So what happens naturally is that
the manager looks at things at some.
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There's a spot check of every 10
records or every month you just do it.
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And then HR is checking every a hundred
or every quarter or something like that.
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And I guess if I think about your
sandwich metaphor, it's like you
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could have an AI layer in there for
e either one of those checks, right?
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Where either for the manager, in
this case, you're helping them point
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their attention, like their mind
or their eyes or their time towards
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particular entries and or HR as well.
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But I think the risk you
rightfully point out is that
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like the inputs or the context
could be missing and in this
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case, there was no fraud, but
arguably no harm, no foul because
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it was something that was flagged
to HR and a human judgment was
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used to say this is not fraud.
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But the AI just didn't have
the context, but it's still
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arguably a little bit helpful.
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But on the other hand, you might
miss things if you don't have
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that human verification step.
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But then overall though, I wonder, is
this actually even helping in practice?
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Are you seeing that like
in these audit processes?
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It just, yeah, just randomly you get
like some flags, but there's so much
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shift and change that actually you're
not getting enough value from having this
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thing in the middle in the first place.
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And maybe folks who have hair unlike
me want to pull it out because they're
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just like, wait, this thing is useless.
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Or just like getting in the way.
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I know sometimes there's
frustration, right?
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Sometimes.
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With AI tools, what do
you think about that?
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Is it making enough of an advancement
in some of these use cases or?
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Does it actually end up
creating frustration?
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Amy: I think there's a lot of
frustration created, right?
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But I think the AI is as
good as the people who are
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building the context, right?
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So it's going to be as
smart as how you design it.
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So if you're buying tools and
just throwing 'em in place, right?
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And assuming it's going to close
the gap, it's not going to.
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It's really gonna be how well you
design the process, how well you
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put in all the context and all the
different flow that it could be.
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So I think in their case, now that
they know that maternity leave could be
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something that gets flagged, how will they
now maybe restructure that system to have
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it as maybe a different validation point?
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So maybe those are things
that need to happen.
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So I think it's
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something that's gonna be more of
a continuous improvement situation.
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Versus a one and done.
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You can't just code it or like
in traditional systems, you may
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be able to do some customizations
in your code or do maybe some
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configurations and you walk away.
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But I think this is more where we talk
about trAIning the AI to understand
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and recognize the different types
of situations that can come in.
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And that's only gonna be
as good as the humans.
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Who are involved in programming
it or not in traditional sense,
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but putting in the different.
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Either prompts or the different
questions in there so that
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it's getting better guidance.
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Thomas: Guardrails
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Amy: Yeah.
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All that will need to be done.
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And I think all needs to be done
a little bit differently than how
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things were done traditionally.
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And I think that's the big learning curve.
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And it really depends on how
engaged your workforce is too.
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Because I think some of these
systems could be better designed,
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but then I think there's also
fear for people that maybe then.
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There's not a place for them.
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And I don't know the full context and
how that particular example was when
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I was talking to a friend of mine,
but those are all I think things
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that we need to consider as we're
rapidly deploying new technology.
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There's so many different factors in
which can affect the success of that.
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Thomas: Absolutely.
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So let's get to that, like fear or just
like the mindset thing in one second.
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I'd love to come back to that.
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It's a huge point.
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But even before that, you're talking about
just all the edge cases and programming.
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It's almost, if the tools
doesn't work, it's your fault.
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You have to work harder on it.
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That's like what every vendor
including myself would say.
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But who ultimately is
responsible for this.
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Specifically in the HR realm.
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There's a lot of just people
going Through their day-to-day.
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Do you have time to get into this whole
world of prompting and guidance and
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guardrails and seeing this an edge case?
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And so I'm gonna try to like
work on it directly myself.
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Or is it just the vendor capabilities
are gonna increase or are we gonna
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see a whole, I dunno, crop of
consultants cropping up who are
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gonna be specializing in this stuff.
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Is there a specialized skillset
here or is it's gonna have to be a
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part of what everyone needs to be
upskilled towards to be future proof?
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Amy: I think both.
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I think there is going to be a certain
amount of upskilling that needs to happen.
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I think that people will need to evolve.
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And I think in general, like
workforce has to be evolve.
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Anytime there's new
technology that and new waves.
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I think that will require
the workforce to evolve.
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And I think those who are the most curious
at first will have the most insurance of
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job security because they really want to
learn and adapt and apply this knowledge
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and then bring other people along.
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And I think that's where the humans will
have longevity in how to partner with AI.
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Thomas: So you're already getting
at the some of the answer, I think.
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To that second concept, which is
about like roles changing within
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HR and like what you're foreseeing
and sometimes that's met with
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fear or the fear of the unknown.
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I guess a two part question.
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What's the temperature out there?
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What do people from your gauge.
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Like folks in your network.
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How do people feel about...
301
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is there fear or is people
sensing opportunity?
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What are your thoughts on how an HR
professional can stay future proof?
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Amy: I think it's both.
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I think there's definitely fear, right?
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Because things are happening
so fast and people are not
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always comfortable with change.
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But I think that the best way is
for organizations to create a safe
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place for employees to feel that
they can be part of the change.
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They create good governance so
that people aren't just not rapidly
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buying technology and throwing it in.
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Because I think if there's
good governance, will actually
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help accelerate innovation.
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Because it's better than buying
something, making mistakes, cleaning
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it up and starting over again.
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But if you actually create the
environment for people to be
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part of and to also create.
317
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Provide the right guardrails
and the right infrastructure
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for people to be successful in.
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I think you'll see a lot
more people adapt faster.
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And I also feel, this goes back
to working with your employees and
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understanding like what other skills
they have beyond their job titles.
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It's important to understand
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your employee base and
understand their skills, their
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capabilities, and their interests.
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And have those regular conversations,
not just once a year at the
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performance review, but ongoing.
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And I think understanding your employee
base, their interests and skills
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and what they wanna develop in will
actually help align with some of the
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changes we're seeing in industry and
will bring organizations along faster.
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Speaker 4: This has been a
fantastic conversation so far.
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00:18:00,255 --> 00:18:03,045
If you haven't already done so,
make sure to join our community.
332
00:18:03,285 --> 00:18:05,955
We are building a network of the
most forward-thinking, HR and
333
00:18:05,955 --> 00:18:09,135
people, operational professionals
who are defining the future.
334
00:18:09,615 --> 00:18:12,225
I will personally be sharing
news and ideas around how we
335
00:18:12,225 --> 00:18:14,235
can all tHRive in the age of AI.
336
00:18:14,595 --> 00:18:17,580
You can find it at go cleary.com/cleary
337
00:18:18,010 --> 00:18:18,620
community.
338
00:18:19,245 --> 00:18:20,085
Now back to the show.
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Thomas: So that's to me at least
an exciting kind of future.
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And that's full of
opportunities for everyone.
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But the practically, there's
a very human emotion, right?
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To fight when you have the unknown
that you're looking up against.
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So maybe speaking of the unknown, let's
talk a little bit about HR shared services
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and maybe even shared services in general.
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You've had a lot of experience
in this kind of world.
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What do you think good looks like in
HR and like payroll shared services as
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we are routing out 2025 and into 2026?
348
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Amy: So I think that good shared
services used to mean specifically more
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accurate on time and very transactional.
350
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Because lot of traditional shared
services are where you bring
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in things that you could do
repeatedly on over and over, right?
352
00:19:13,642 --> 00:19:17,122
But I think over time, I think
that automation will take away
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a lot of repetitive tasks so
that teams can actually focus
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on what employees would value.
355
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Clarity, consistency, care.
356
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So what I'm seeing is like a shift
from transactions to more capabilities.
357
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So shared services shouldn't
just be processing, right?
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It should be where talent learns how
to think more, cross-functionally
359
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manage systems, build data fluency.
360
00:19:39,282 --> 00:19:44,382
Maybe it's probably the best
training ground, honestly to develop
361
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future leaders, future roles.
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Because right now when you go into
a shared services, the traditional
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ones anyways, you're really learning
the basic parts of the organization.
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You're able to see across the
organization and maybe use that as a
365
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training ground to see where you can
accelerate those employees into other
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aspects of the company and other roles.
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And I think that's really where shared
services could really add value.
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Thomas: So you don't see that shared
services, like In terms of jobs
369
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just becoming a leaner function
overall because of automation and AI?
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Amy: I think it will be leaner in
some se extent where things that were
371
00:20:21,432 --> 00:20:28,602
truly the transactions, those types
of tasks will I think eventually, if
372
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not already be replaced by AI, right?
373
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I think you're going to see more
is where it's actually focused on
374
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providing creative the value, right?
375
00:20:39,602 --> 00:20:43,842
So what are things that you can bring
together more cross-functionally,
376
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how do you actually integrate
the systems and maybe use those
377
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capabilities to structure more broadly?
378
00:20:50,442 --> 00:20:53,562
So for organizations that use the
shared services model where they're
379
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having multiple companies or entities or
departments or teams being centralized,
380
00:20:58,572 --> 00:20:59,922
that's where you're gonna see value.
381
00:20:59,922 --> 00:21:02,262
Is the people that understand
how to centralize things.
382
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And then they can continually
involve those things that are
383
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needed for the people side.
384
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So I think it is a different
version of what we originally
385
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have noted it to be, right?
386
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It's more focused on what
are the capabilities that
387
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share services can provide.
388
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And maybe take away some
of the more task level.
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But take a look at what the data
pieces are and maybe use that to
390
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build some of the human interactions.
391
00:21:26,682 --> 00:21:29,562
Because once the technology can
clear things away, what other
392
00:21:29,562 --> 00:21:31,212
human-centered pieces are still left?
393
00:21:33,727 --> 00:21:34,147
Thomas: Interesting.
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As you're talking about, this brings my
mind back to like where we started, about
395
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a little bit about the overlaps between IT
and HR, but also I'm thinking about just
396
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processes in process management general.
397
00:21:47,407 --> 00:21:50,377
Anything back office,
including the CFO's office.
398
00:21:51,187 --> 00:21:54,757
When you put it that way, it feels like
there's a lot of convergence, right?
399
00:21:54,757 --> 00:21:58,147
And when you're just talking
about like collaboration or a
400
00:21:58,147 --> 00:22:03,067
cross-functional processes, and think
about how data flows so that you can
401
00:22:03,067 --> 00:22:05,647
just get the organization running.
402
00:22:06,697 --> 00:22:08,317
When you put it that way, it feels like
403
00:22:09,397 --> 00:22:12,577
all those functions, at least
at the shared service level,
404
00:22:12,997 --> 00:22:14,497
should be merging even more.
405
00:22:15,037 --> 00:22:15,277
Amy: Yeah.
406
00:22:15,277 --> 00:22:16,357
It feels really natural.
407
00:22:16,357 --> 00:22:21,037
And so having worked in IT for so
many years, but as an IT leader, a CIO
408
00:22:21,037 --> 00:22:24,457
for a former organization, I worked
a lot with the human aspect, right?
409
00:22:24,457 --> 00:22:29,197
Building my teams, cross training people
and making sure that from a IT shared
410
00:22:29,197 --> 00:22:31,657
services perspective, delivering value.
411
00:22:33,097 --> 00:22:38,947
Applying that now as a leader in HR, I
also have HR systems and taking that IT
412
00:22:39,457 --> 00:22:42,757
mindset and operations and those pieces.
413
00:22:42,757 --> 00:22:44,737
I think if they work really hand in hand.
414
00:22:45,247 --> 00:22:48,367
And especially with so much AI now.
415
00:22:48,367 --> 00:22:51,157
AI is not your traditional IT,
you're not really programming, right?
416
00:22:51,157 --> 00:22:52,867
So it's a different way
of looking at technology.
417
00:22:53,227 --> 00:22:57,127
But I think the concepts of working and
supporting the broader organization,
418
00:22:57,127 --> 00:23:03,277
whatever the business is having these
skills combined makes a huge difference
419
00:23:03,277 --> 00:23:04,447
and I think it makes a lot of sense.
420
00:23:04,957 --> 00:23:08,137
I feel like it's taking in the past
you have the left hand and right
421
00:23:08,137 --> 00:23:09,457
hand not talking to each other.
422
00:23:09,637 --> 00:23:12,367
Now they're working in partnership and
I think that's really kinda exciting.
423
00:23:15,427 --> 00:23:16,087
Thomas: Yeah, it is.
424
00:23:17,467 --> 00:23:22,837
How should we think about reskilling the
the workforce, not just in HR and IT?
425
00:23:23,107 --> 00:23:24,877
But just overall everyone right?
426
00:23:24,877 --> 00:23:25,957
To re-skill the workforce.
427
00:23:25,957 --> 00:23:27,967
To be able to be ready to
428
00:23:30,367 --> 00:23:35,527
blend AI technology data along with the
human element, which I think some part of
429
00:23:35,527 --> 00:23:39,667
that probably applies for probably broadly
every type of knowledge worker out there.
430
00:23:39,967 --> 00:23:45,487
But from an HR perspective, if
we want to upskill, reskill and
431
00:23:45,487 --> 00:23:49,447
ensure that people are future
proof, how can we help enable that?
432
00:23:49,792 --> 00:23:51,172
Amy: I think there's a couple ways, right?
433
00:23:51,712 --> 00:23:56,182
The first way is the individual really
has to take ownership of that, and I
434
00:23:56,182 --> 00:24:00,232
think Through the organization that they
work for really hoping to have continuous
435
00:24:00,232 --> 00:24:02,482
conversations with their leadership.
436
00:24:03,472 --> 00:24:06,892
What their goals are, maybe
the skills, learning paths.
437
00:24:06,892 --> 00:24:09,022
And I think that's important
also from a leader perspective,
438
00:24:09,022 --> 00:24:12,302
understanding what their workforce,
their team is trying to do.
439
00:24:12,332 --> 00:24:17,137
Because as AI's evolving what
work will be, you really want
440
00:24:17,137 --> 00:24:18,817
your workforce ready to go.
441
00:24:19,147 --> 00:24:22,387
And you don't wanna wait
until something gets purchased
442
00:24:22,387 --> 00:24:23,467
and say, oh, what do I have?
443
00:24:23,497 --> 00:24:24,397
What do I need to do?
444
00:24:24,637 --> 00:24:27,667
You wanna continuously understand
the people that are on your teams
445
00:24:27,667 --> 00:24:31,207
and your organization so that you
can place them in the right roles and
446
00:24:31,207 --> 00:24:34,347
not just have 'em apply, but really
understand what else is coming.
447
00:24:34,707 --> 00:24:38,577
And that way, if you have these
conversations, people feel like have
448
00:24:41,097 --> 00:24:42,537
thing important.
449
00:24:44,502 --> 00:24:48,912
I feel like people really
need to also take advantage of
450
00:24:48,912 --> 00:24:50,472
what's out there on their own.
451
00:24:50,472 --> 00:24:54,852
Really research and find opportunities
to build their own skillset right?
452
00:24:55,102 --> 00:24:58,162
And there's a lot of companies
that may have, for example, like
453
00:24:58,162 --> 00:24:59,662
a tuition assistance program.
454
00:25:00,112 --> 00:25:01,647
I would highly encourage using that.
455
00:25:03,742 --> 00:25:09,082
For example, I work with Cornerstone
University, for example, in
456
00:25:09,082 --> 00:25:10,432
their strategic AI program.
457
00:25:10,642 --> 00:25:15,832
But Cornerstone is only one of many
organizations that the Z School
458
00:25:15,832 --> 00:25:17,932
actually has partnership with.
459
00:25:18,322 --> 00:25:21,262
There's 15 different schools, and
what they do is they're partnering
460
00:25:21,262 --> 00:25:22,312
with these different schools.
461
00:25:24,022 --> 00:25:26,422
It's online and you can take
different certifications.
462
00:25:26,422 --> 00:25:29,632
And I would highly recommend
getting certified to just
463
00:25:29,632 --> 00:25:31,312
really do dig in and learn.
464
00:25:32,002 --> 00:25:37,072
For example, there's new ones coming
up that they have Through Loyola
465
00:25:37,552 --> 00:25:41,632
New Orleans School, Through Rockford
University and they're teaching
466
00:25:41,632 --> 00:25:43,732
generative AI for value creation.
467
00:25:44,452 --> 00:25:46,642
And they're also teaching
other certifications.
468
00:25:46,852 --> 00:25:51,532
Like AI agents for change or how to
become irreplaceable in the age of AI
469
00:25:51,562 --> 00:25:53,812
or even AI literacy and proficiency.
470
00:25:55,192 --> 00:25:57,802
All of those things are available to you.
471
00:25:57,802 --> 00:26:00,412
And if you don't have tuition
assistance Through your company,
472
00:26:00,862 --> 00:26:04,842
there's so much you can learn
just Through LinkedIn or YouTube.
473
00:26:05,292 --> 00:26:09,242
But I feel like you have to be
the CEO of your own career, right?
474
00:26:09,602 --> 00:26:13,322
And so you have to take charge and trying
to figure out how do I stay relevant?
475
00:26:13,862 --> 00:26:20,972
And really, I feel that curiosity is
really your own career insurance, right?
476
00:26:20,972 --> 00:26:26,102
So the more curious you are about what's
happening, you don't have to work about
477
00:26:26,132 --> 00:26:33,002
worry about being outpaced placed or out,
either replaced by AI or outpaced by AI.
478
00:26:33,452 --> 00:26:36,542
Just learn to work with it, and I
think that's really what's important.
479
00:26:36,542 --> 00:26:41,492
If you take time on your own to still
learn and to grow and ask questions, I
480
00:26:41,492 --> 00:26:42,812
think it'll make a world of difference.
481
00:26:45,452 --> 00:26:49,652
Thomas: So a lot of advice there and
a lot of like capability, like a lot
482
00:26:49,652 --> 00:26:52,922
of options out there like for folks.
483
00:26:53,082 --> 00:26:56,802
I don't know if are you working
with in the coursework that
484
00:26:56,802 --> 00:26:58,632
you're doing and helping teach.
485
00:26:59,082 --> 00:27:07,602
Is it new grads or college folks or just
mid-career folks or like looking to get
486
00:27:07,602 --> 00:27:13,737
a gain AI literacy while they're working,
but maybe it doesn't quite matter.
487
00:27:13,737 --> 00:27:17,367
But tell me a little bit from an education
standpoint, what are you focused on
488
00:27:17,697 --> 00:27:20,037
to help close this readiness gap?
489
00:27:20,427 --> 00:27:23,007
Amy: So personally, they
focus on two things, right?
490
00:27:23,337 --> 00:27:27,057
Through the programs that the Z School has
to offer through Cornerstone University.
491
00:27:27,387 --> 00:27:30,537
It's primarily professionals who
are looking to get certified in
492
00:27:30,537 --> 00:27:35,277
strategy and how to apply new
knowledge to their existing roles.
493
00:27:35,697 --> 00:27:36,717
So that's one piece.
494
00:27:36,777 --> 00:27:40,797
But they also have other programs and
depending on what level you are in terms
495
00:27:40,797 --> 00:27:46,067
of literacy of AI or career level or
maybe something you wanna do and progress.
496
00:27:46,067 --> 00:27:48,297
I think there's so many
different options you can take.
497
00:27:48,747 --> 00:27:50,577
And so I think that's a great option.
498
00:27:51,237 --> 00:27:56,277
I think the other option is
joining different communities.
499
00:27:56,307 --> 00:27:59,847
So there's a lot of different people
talking about AI and trying to learn AI.
500
00:28:00,207 --> 00:28:03,267
I would say join those communities,
find mentorship, right?
501
00:28:03,267 --> 00:28:07,407
To grow your data literacy
or your AI literacy.
502
00:28:07,707 --> 00:28:12,597
So I would use all different options and
try to use it as personal development.
503
00:28:13,137 --> 00:28:17,397
There's a lot of this is, you may be
working for a company that isn't as
504
00:28:17,397 --> 00:28:22,752
advanced or may not have those resources,
but for you to really develop, right?
505
00:28:22,752 --> 00:28:25,812
You have to take that in your own hands
and figure out what are the resources
506
00:28:25,812 --> 00:28:28,092
avAIlable to me and really leverage that.
507
00:28:28,092 --> 00:28:29,262
And that's what I would recommend.
508
00:28:29,262 --> 00:28:33,222
I would not wait for as an
employee or employer to say, this
509
00:28:33,222 --> 00:28:34,512
is what you need to learn next.
510
00:28:34,812 --> 00:28:35,472
Absolutely not.
511
00:28:35,472 --> 00:28:37,542
Go learn and ask questions.
512
00:28:37,542 --> 00:28:40,902
And if your employer supports you
Through, like I said, tuition assistance
513
00:28:40,902 --> 00:28:45,177
programs, or maybe they have a LinkedIn
learning program, or I would use those.
514
00:28:45,892 --> 00:28:48,322
Also just look at your broader
community and see if you can find
515
00:28:48,322 --> 00:28:52,672
a mentor and others to partner
with and just learn and play.
516
00:28:52,672 --> 00:28:54,292
And I think that's the
best way to go about it.
517
00:28:56,332 --> 00:29:01,252
Thomas: So I have to ask, before
we run out of time here, Amy.
518
00:29:01,732 --> 00:29:07,312
As you look ahead for the next couple
years, is there anything in particular,
519
00:29:07,312 --> 00:29:12,382
whether it's in shared services or
in any kind of like specific domain.
520
00:29:13,192 --> 00:29:14,632
Are there any particular types of
521
00:29:16,662 --> 00:29:20,262
projects that you're passionate
about, you're looking forward
522
00:29:20,262 --> 00:29:21,582
to seeing come to fruition.
523
00:29:21,882 --> 00:29:24,222
Or even just like workflows.
524
00:29:24,252 --> 00:29:29,352
'Cause we talked a lot about AI workflows
or AI being leveraged for all types
525
00:29:29,352 --> 00:29:30,642
of like workflows and shared services.
526
00:29:30,852 --> 00:29:34,902
Anything in particular you're
looking forward to getting rid of
527
00:29:34,982 --> 00:29:38,972
off of your plates as we go into
the brave new future together.
528
00:29:40,067 --> 00:29:41,987
Amy: I know that there's a lot
of the transactional stuff.
529
00:29:41,987 --> 00:29:46,157
I think there's a lot of just sometimes
there's a lot of data that comes in.
530
00:29:46,577 --> 00:29:51,497
Actually in a recent conversation I had
with a different peer in industry, one of
531
00:29:51,497 --> 00:29:56,627
their frustrations is actually sometimes
there's a lot of information that
532
00:29:56,747 --> 00:30:04,097
comes in, and specifically in employee
relations, under HR, meaning depending
533
00:30:04,097 --> 00:30:05,657
on the environment, there's often
534
00:30:06,182 --> 00:30:09,542
like a compliance line
or something like that.
535
00:30:09,542 --> 00:30:14,462
And people will send immense amounts
of information whether audio or
536
00:30:14,462 --> 00:30:17,972
video or documents that they write.
537
00:30:18,332 --> 00:30:22,132
And their employees go through
this for hours and hours.
538
00:30:22,542 --> 00:30:26,642
And so I thought it would be interesting
when we talked about it was how can you
539
00:30:26,672 --> 00:30:31,802
take all that and put it into AI and
then maybe have AI ask more questions or
540
00:30:31,802 --> 00:30:35,582
find patterns so you could understand why
are these employees having these issues?
541
00:30:35,852 --> 00:30:39,152
So if you take all the information and
it can analyze it for you, it might be
542
00:30:39,232 --> 00:30:45,257
able to find patterns to address as a
root cause versus when a person's just
543
00:30:45,257 --> 00:30:47,867
reading it and trying to figure out
what is it that I'm trying to solve.
544
00:30:48,167 --> 00:30:51,737
So I think there's exciting opportunities
to use it in those types of ways in the
545
00:30:51,737 --> 00:30:54,197
future that maybe isn't being used today.
546
00:30:56,177 --> 00:30:59,807
Thomas: That's a very
interesting use case.
547
00:30:59,927 --> 00:31:04,097
So you're imagining not just analyzing
it one time, but actually having
548
00:31:04,097 --> 00:31:08,297
something where you can ask questions
back and forth as the HR professional
549
00:31:08,297 --> 00:31:11,867
to really get some insight on what's
happening here, but then level that
550
00:31:11,867 --> 00:31:14,207
up to what's happening more broadly.
551
00:31:14,207 --> 00:31:17,482
Maybe the organization that we
can then maybe more proactively
552
00:31:18,592 --> 00:31:20,562
diagnosed employee relations issues.
553
00:31:20,562 --> 00:31:23,777
There's the absolute tail
end of multiple things.
554
00:31:23,777 --> 00:31:25,752
It's like this symptom
that you can get at.
555
00:31:25,752 --> 00:31:29,632
But I love that thought for how
that you can take that upstream.
556
00:31:31,612 --> 00:31:39,092
What else would you say is advice you
would have for or thoughts for your
557
00:31:39,212 --> 00:31:43,822
fellow professionals in HR, but how
we can all stay future proof as the
558
00:31:43,822 --> 00:31:47,752
ways that like we're working and what
we're being asked to do, as well as
559
00:31:48,082 --> 00:31:50,152
what's being demanded of
us in some ways, right?
560
00:31:50,152 --> 00:31:53,062
Whether it's from the C-suite
or from employees or other
561
00:31:53,062 --> 00:31:55,127
stakeholders is shifting.
562
00:31:56,297 --> 00:32:00,347
Amy: And I'm excited about all the
change that HRs future will be with AI.
563
00:32:00,707 --> 00:32:03,767
But I think it's important in our
role is to continue to balance
564
00:32:04,157 --> 00:32:06,527
information intelligence with humanity.
565
00:32:06,857 --> 00:32:11,447
Having AI reshaped work, I think we
still want people to define the purpose.
566
00:32:11,897 --> 00:32:16,097
And so I think AI's role is really to help
guide that shift kinda responsibly, right?
567
00:32:16,517 --> 00:32:20,417
So we're building systems that
protect trust, to empower learning
568
00:32:20,777 --> 00:32:23,747
and to keep culture and people
at its very center because that's
569
00:32:23,747 --> 00:32:24,857
something we don't wanna lose.
570
00:32:27,377 --> 00:32:29,177
Thomas: We don't wanna lose that indeed.
571
00:32:29,237 --> 00:32:31,127
And I think sometimes
572
00:32:32,207 --> 00:32:35,447
the whole function can use that reminder
because you might feel like beaten
573
00:32:35,447 --> 00:32:39,197
down and feel like, you know what,
yeah, this is all gonna just come in,
574
00:32:39,197 --> 00:32:44,227
replace everything that everyone does
and people are just not as important
575
00:32:44,327 --> 00:32:45,647
to organizations in the future.
576
00:32:45,647 --> 00:32:46,747
And maybe that's just not true.
577
00:32:48,127 --> 00:32:50,137
Thank you for this conversation, Amy.
578
00:32:50,187 --> 00:32:54,477
The concept of how shared services
is shifting and there might be some
579
00:32:54,777 --> 00:32:59,247
consolidation and it's like maybe in
the future, less about the functional
580
00:32:59,247 --> 00:33:04,167
lines, but just really how you go across
those lines together that really start
581
00:33:04,167 --> 00:33:10,577
to be where the value is and where
that those organizations can shift or
582
00:33:10,577 --> 00:33:12,227
those functions can shift and meld.
583
00:33:12,227 --> 00:33:13,907
It's a very interesting concept.
584
00:33:14,267 --> 00:33:18,697
And there's just so much that you're
doing in terms of both staying up to
585
00:33:18,697 --> 00:33:23,587
date yourself and educating others
on around all the different ways.
586
00:33:23,977 --> 00:33:26,707
And we kind talked about a couple of
different types of workflows, whether
587
00:33:26,707 --> 00:33:32,467
it's audits or we're like talking about
just like we were just now about this
588
00:33:32,467 --> 00:33:39,367
concept of proactively taking employee
relations information to change up how you
589
00:33:39,367 --> 00:33:43,947
work culturally as an HR function, which
is one of those signs I think you would
590
00:33:43,947 --> 00:33:49,622
agree of what you talked about, which is
a way to make these connections with data
591
00:33:49,622 --> 00:33:55,772
and actually uplevel what value we're
bringing to the organization overall.
592
00:33:55,922 --> 00:34:00,332
So there's some great both practical
and also conceptual insights here.
593
00:34:01,772 --> 00:34:02,672
Thank you for that.
594
00:34:02,792 --> 00:34:06,932
And for anyone who is out there
looking to future proof your own
595
00:34:06,932 --> 00:34:10,262
organizations and your own HR functions.
596
00:34:10,621 --> 00:34:14,072
I trust that you've taken at least a
couple of nuggets of insight here from
597
00:34:14,072 --> 00:34:15,812
this great conversation with Amy Wang.
598
00:34:16,532 --> 00:34:19,081
Thank you again and see
you on the next one.
599
00:34:19,866 --> 00:34:22,926
Speaker 2: Thanks for joining us
on this episode of Future Proof HR.
600
00:34:23,166 --> 00:34:25,775
If you like the discussion, make
sure you leave us a five star
601
00:34:25,775 --> 00:34:28,656
review on the platform you're
listening to or watching us on.
602
00:34:29,076 --> 00:34:32,016
Or share this with a friend or colleague
who may find value in the message.
603
00:34:32,315 --> 00:34:37,266
See you next time as we keep our pulse on
how we can all thrive in the age on AI.
