Shownotes
In this episode of the Future Proof HR podcast, Thomas Kunjappu sits down with Lorena Scott, Chief People Officer and Head of Customer Experience at Relay, to explore how AI can be used to accelerate people and CX roadmaps without losing the human moments that matter most.
Drawing on her dual mandate across People and Customer Experience, Lorena shares how Relay thinks about AI not as a replacement for people, but as a way to expand capacity, unlock time, and bring forward initiatives that would otherwise live years out on the roadmap. She explains how lessons from CX such as coaching, enablement, and quality assurance have directly informed people practices, and why unifying these functions has created a more consistent and human-centered experience across the business.
Lorena breaks down practical examples of AI in action, including AI-powered coaching to support managers and individual contributors, using AI to accelerate insights from people data, rethinking headcount planning across human and non-human capacity, and creating space for internal mobility through stretch projects. Throughout the conversation, she emphasizes the importance of showing impact through action, not promises, as the most effective way to build trust and reduce fear around AI adoption.
Topics Discussed:
- Using AI to accelerate people and CX roadmaps
- Unifying People and Customer Experience under one operating philosophy
- AI coaching for managers and individual contributors
- Expanding human impact by removing low-value work
- Rethinking headcount planning with human and non-human capacity
- Applying CX learnings to HR enablement and support
- Internal mobility and stretch opportunities in a scaled organization
- Building trust through action, not reassurance
- Preparing people teams for an AI-enabled future
If you are an HR or People leader looking to use AI to scale impact, improve enablement, and move critical initiatives forward faster without sacrificing humanity, this episode offers a thoughtful, operator-level perspective on what future-proofing really looks like.
Additional Resources:
Transcripts
I do strongly believe that if we leverage AI in the right ways,
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we can accelerate the impact that as
members of a people team we can give.
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So it's like learn from
engineers, learn from anyone who
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is an expert in building GPTs.
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Understanding how it works.
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I think there is power in becoming
an expert there to be more
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impactful on the people side.
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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
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we explore how forward thinking
people leaders are reimagining work
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and leadership in the age of AI.
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I'm your host, Thomas
Kunjapu, CEO of Cleary.
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Today's guest is Lorena Scott, Chief
People Officer and Head of Customer
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Experience at Relay, a leader who's
redefined what it means to connect
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employees and customers alike.
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Lorena's background spans
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venture investing, founding an
e-commerce business and scaling
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people in CX teams at companies like
500px, Algolia, CaseWare and Ritual.
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At Relay, she's leading both people
and culture and customer experience
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at uniting two disciplines around one
philosophy, creating environments where
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people do the best work of their lives.
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Lorena, welcome to the show.
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Lorena: Thank you very much Thomas.
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Thomas: Right off the bat, I have
to ask you about this dual mandate.
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Tell me about this combo, these
two hats that you wear between
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people and customer experience.
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Lorena: I know for most people, it feels
like it's an awkward combination or
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unique, but it is very on brand for Relay
in that we've always prided ourselves in
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providing a very human centric experience.
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And that because it is so important
to us, it really meant that having a
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unified experience, really showing up
consistently in moments that matter.
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Whether it be our external customers,
our small business owners or our
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internal customers, our relays.
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We needed one leader to really ensure
that the values and the way we did
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that and executed against that was
consistent and unique and meaningful.
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And as I look at kind of what's
the equation for success in really
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delivering incredible experiences.
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It's like how you onboard, it's how
you support and it's how you enable.
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And we leverage maybe a different.
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pieces to that formula, whether
it's people or customer experience,
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but it is very Relay-esque and
that's very special for us.
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Thomas: I know you've had
experience on different sides of
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the table and investing, founding,
obviously on the people team.
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I'm curious, what draws you to
continue to be on the people side?
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Lorena: The honest answer is I stumbled
into people about 10 years ago.
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I had a business that I started
on the e-commerce side, which
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she mentioned in the intro.
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It was an epic failure.
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It did not scale as an org.
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So I was a founder that had to
basically shut down my business.
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But I knew that I didn't want to
go back into finance and investing.
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That's not where my heart was taking me.
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And so I was fortunate enough
to have befriended a former
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investor who turned into a CEO.
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He needed someone to help very
broadly across a startup named 500px.
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And so he said, hey, can you
help me run finance and customer
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support and operations and people?
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And candidly across all of those
disciplines, I really only knew how
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to run a model or knew that well
with confidence, but he took a bet
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on me and he said, go figure it out.
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think you'll care enough.
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And we've had enough failures that you
should have learned a thing or two.
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And I found myself very much
gravitating to the people work.
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And so the short answer is I
find it really interesting.
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It's never the same.
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It's a constant journey in terms of
learning about people, about what
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matters to them, about their living
programs and experiences that are, that
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show up with a lot of heart and care.
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And I find that really rewarding
more so than living in the
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financial model these days.
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Thomas: Got it.
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So then do you think that there are any,
I mean, there are lots of differences
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between being a Chief People Officer
and a founder, but is there anything
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in mindset or any, are there any
similarities that you carried through?
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Lorena: Yeah, there are probably,
I think there are a few.
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One is just resilience, right?
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Being a founder and I was a,
I didn't have a co-founder.
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I think in that context also requires
just a lot of rolling up your sleeves,
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doing the work, having an appreciation
for what it takes to do the work and a
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lot of creativity and problem solving.
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And today there is no shortage of
work to be done as we're scaling a
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company and my kind of role of leading
people and culture in that context.
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And so it's being ruthless
about prioritizing, knowing that
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resources aren't unlimited and not
applying the same playbook that
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other organizations have used.
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So that really feels differentiated
from an employee experience.
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And so there's a lot of creativity,
a lot of problem solving, and
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you're always on and working on it.
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I think, and I love that fuels me.
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It's not for everyone, but for me, I
take a lot of joy in doing the work
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and the amount of work, I know that
sounds like little crazy, but yeah.
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Thomas: There's no shortage of that,
of the amount of work, just on the
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people side, but also obviously
on the customer experience side.
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And I think in this moment, especially
when there is a proliferation of
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AI, I'm curious, are you seeing more
adoption or possibilities in one of your
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orgs versus the other, or just in general.
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Could you talk us through a little bit
about how you're seeing at this moment,
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AI is getting into or impacting the way
that you and your team is doing work?
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Lorena: I would say we are
leveraging or thinking about AI
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equally between the two teams.
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I mentioned earlier a little bit about
the equation for success being really
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how do you onboard successfully?
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How do you support and enable?
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And so both teams have really strong
onboarding practices and rituals,
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both do a lot around enablement,
whether it's CX enablement or we call
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talent enablement on the people team.
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And we're doing a lot of support and kind
of firefighting internally or externally.
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We're trying to find
opportunities where AI can drive
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efficiencies or speed up our opportunities
to do enablement, either at scale
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or refine or improve the enablement.
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And it is balanced.
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And sometimes I'll find myself something
that has worked really well in CX
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and thinking about whether or not we
could translate that to accelerating
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some of the work we're doing on
the people side and vice versa.
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And also inspiring the sides of how
we leverage AI besides of the teams.
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Thomas: Oh, that's really interesting.
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Do any examples like come to mind?
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Because most listeners are in
the people discipline itself.
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And of course you're inspired by just
what other folks you're seeing within
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your own discipline, but also your
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being exposed a little bit to what's
going on, not at your level in terms
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of just like in-depth ownership.
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Anything come to mind, especially
in terms of inspiration that
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people leaders can take from things
that are working on the CX side.
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And by the way, also counter examples
are great too, if there's anything that
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didn't work or it's actually different.
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Lorena: I had a prep conversation.
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We talked about AI coaching.
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We've actually been using AI
to coach and QA our CX agents.
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For most, maybe the back half
of the second half of 2025.
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And we've seen improvements in confidence
levels and CSAT scores, what we call
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acceleration of one touch resolution.
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So being able to answer a ticket or a
question from a customer in one setting
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versus kind of multiple interactions.
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And so we've, we learned from that to say,
how do we accelerate just enablement or
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coaching within our own team internally,
different tools, different partnerships
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potentially, but nonetheless, just saw
the impact in ultimately like the customer
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experience by way of a CSAT score.
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And so how do we do that more quickly
on, people side, whether it's general,
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like support questions that we get
with like inquiries, like how's my
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payroll or what's the next payroll
cycle or how do my benefits work to
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things like, Hey, I'm really struggling
in this moment and giving feedback.
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How do I do it better?
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How do I do it differently?
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Thomas: Let's talk about that second
element, because that could be not
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just for the HR team, but actually more
broadly for peer to peer feedback, or
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I think crucially manager feedback.
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The AI powered coaching sounds like
it started with helping customer
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support agents in doing their work.
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Have you thought about
that for managers as well?
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Lorena: Yeah.
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So right now we're exploring.
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So the thing about Relay is that we
do have a talent enablement practice
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that focuses on kind of three points.
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And the journey one is
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our onboarding, the other
is what we call growing.
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So growing in your career.
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And the third is leadership development
path and how we support developing
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leaders and accelerating leaders at relay.
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a big part, you know, a tool that
we have in our toolkit across
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those three areas is coaching.
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But for hiring coaches that kind
of fit in the relay context and
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also in our budget, because we are
a scale up, has been challenging.
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And so coaching has really been
limited up until recently to our
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people leaders, our managers.
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And we are working with a partner
right now on AI coaching for everyone.
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So that in people leaders who have not yet
had one-to-one coaching opportunities, but
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are struggling to structure a conversation
that is both candid and caring, right?
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They are first time managers,
they've never done this before.
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They want some, like a micro moment of
coaching that an AI coach can do that for
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them to an individual contributor who is
structuring how to present or communicate
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effectively the point that they want
to make because they see an opportunity
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that needs to be addressed, but they
haven't quite been able to crystallize
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the comment in a way that is being heard.
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There's many different use cases, but
we've loaded this to have our values,
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our roadmap, our priorities, our all of
the relay intel to really refine how it
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speaks and the type of feedback it gives.
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Thomas: So it is personalized
to the organization and further,
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you're building in capacity.
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So I love these examples of AI
unlocking just new surface area that
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just would never have happened before.
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And I think before when we're talking
mentioned you're less interested in
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AI replacing roles and just, okay, let
me have AI do this instead of a person
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and more about accelerating what you
can't do otherwise or what can't today.
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How do you think about that as
a lens for investment in just
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AI projects across the board?
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Lorena: Yeah, I know when you
and I talked earlier, I like, I
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believe in the power of people.
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Part of the, why I have
the two roles that I have.
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And, but I also work in a
resource constrained environment.
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And so we can't do everything.
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And one of the really wonderful
exercises that we did in September of
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this year was to dream a little bit.
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What would it take to make
Relay a generational company?
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And we want to be known for for both on
the people and customer experience side?
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And so there were all these wonderful
and vicious dreams that we put on the
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table and then given where we're at and
the pace that we're moving, I like, we
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won't be able to do that even next year.
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It's going to be like a 2027, 2028 dream.
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And then I thought, what if we leverage
technology to accelerate or bring forward
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those plans into the 2026 roadmap?
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And so that's been really powerful
in that we've now moved forward
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things that we wanted to do because
we can leverage AI or leverage
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other technology and do it sooner.
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Do it better.
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Be continuously learning.
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That to me is we wouldn't have
been able to have coaching
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for all until I think 2027.
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And that had to mean a lot of
other things went right for us.
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And now we're going to do
it by the end of this year.
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Thomas: Wow, that's like
a two year head start.
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To enable all of this, you're
having these internal discussions
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with teams who themselves and CX
and people time people teams, they
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also have this, like many knowledge
workers, this background fear, right?
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Around what's coming with AI.
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It's coming for my jobs.
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Have you faced that?
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Maybe you haven't faced that and
different teams are different, but, and
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how do you come back and kind of shift
from this kind of fear to excitement
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for bringing in your roadmap and
being able to do more things faster.
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Lorena: I honestly have not seen
the fear and, maybe folks haven't
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expressed it to me as transparently.
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I think in general to combat that it's
not what you say, it's what you do.
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And so if the hypothesis is, or if
we're leading with, it's going to
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accelerate investment in roadmaps or
in initiatives by 12 to 18 months or
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as much as 22 or 24 months, then we
just have to show that's happening.
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it's, we're going to remove some of the
tedious tasks that were on your plate
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so that you can do more impactful work.
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We have to demonstrate that.
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And then on the CXI, for example, we want
to make you more confident in your work so
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you can show it better for our customers.
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We have to unlock that and
demonstrate that and show the
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data and how that's happening.
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And so long as you can prove
out what you're saying in action
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and that translates into the
business impact, I think that's
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where you build the credibility.
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Maybe folks are by nature a little,
should be a little nervous, but
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ultimately if you do what you say in
terms of what, how AI can be powerful
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in an org that speaks volumes.
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And I feel like that's being seen
today, at least in the Relay context.
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Thomas: It's really about showing, right?
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And that's what I'm hearing.
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That's your
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Lorena: Doing, showing it.
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Yeah.
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Thomas: So speaking of doing, I know
you're all types of internal innovations.
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One of the things that we're
talking about is this like listing
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this internal marketplace, right?
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Because let's talk a little bit about
like internal mobility, because we've
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been talking about how AI projects have
been coming into the fore at Relay.
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But on top of that, there's new skillsets
that will be, need to be developed and
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there's new potential like projects.
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Let's talk a little bit about how
you're enabling all of that, like
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creating these internal marketplaces.
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I think you call it right between
to pair talent or learning projects
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with or learning goals with potential
projects within the organization.
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Lorena: First of all, I really can't
take credit for this initiative.
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This was the creative like brainchild
of a member of the people team.
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So hopefully she'll listen to this
and know that shout out to Jenna.
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One
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of the things that we're saying is if
AI unlocks more efficiency and therefore
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gives our team members more space to
do projects or special projects and say
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that's 20 % of their time, what is the
mechanism that we're going to build to
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create a platform for people to figure
out what those stretch opportunities or
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the special projects are as we scale?
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Because informally that has happened
at Relay for the last six years.
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People, somebody in marketing will be
like, I have this problem around this
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data that I'm trying to better understand.
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And somebody from the data team will
be like, I will help you with that.
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And it's a beautiful exchange.
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It's a great partnership.
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But as our teams have gotten
larger, as Relay has gotten
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bigger and more distributed,
that doesn't happen as naturally.
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And so what can we put formally in place?
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And so this is the
marketplace idea that we have.
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It's not yet launched or built out
is how do we create a destination
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within Relay for folks to talk about
their skills, things they want to
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learn, places where they want to grow
in their career and match that with
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projects or skills needs on other teams.
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Because ultimately the way people
do progress at Relay is delivering
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on impact and taking on stretch
opportunities that allow for that impact.
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And so now we're just creating
something more formal to allow that.
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But the only way that would be possible
is that if we afforded people more
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time to take on those opportunities,
and that's where AI has been like,
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it's a partnership between like, we've
created more efficiency in the system,
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therefore there's incremental more time
to take on these stretch opportunities.
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How do we find and like, match
people to these opportunities?
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Thomas: So I think you're saying the
AI element of that is to take away
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some of the activities that you're
doing today to be able to have some
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open time to stretch into new areas.
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And on the other hand, I think
there's opportunities that will
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be enabled by AI, which will be
the stretch opportunities as well.
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And effectively in this endeavor
to create a two-sided marketplace,
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there's supply and demand.
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So we talked about a little bit of think
how AI is potentially for a lot of it.
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Producing both the
supply and demand, right?
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For new skills, proliferating
throughout the organization,
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but for on the demand side.
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Managers or leaders or project leaders
who are potentially looking for talent.
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Are there things that you're
doing systematically to encourage?
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Not necessarily the internal marketplace
itself, but just encourage either
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AI use or new project initiatives to
then call out the needs which would
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then feed into the extended path.
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Because the reason I ask this is often,
I think surprisingly, in high talent
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density environments, it's the supply
of opportunity that's considered that's
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more constraining than the actual,
the supply of opportunity versus the
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demand of people wanting to do stuff.
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So how do you ensure that there's like
a system that's generating these stretch
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opportunities consistently for people?
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Lorena: Candidly, that has not
been a problem at Relay, right?
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We have no shortage of
projects and opportunities.
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What I do worry about is people being
burnt out because they're trying to do a
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lot of things on the side of their desk
because they're curious and because they
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want to show up for their team members.
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I think what I wanted to do was make
sure that we created the space, right?
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There was a way to relieve some of the
manual tasks from folks as an example
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so that they could spend a portion
of their time picking up projects
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without breaking and being able to
really sustain the ride for as long
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as they want to be a part of Relay.
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So we haven't necessarily yet and look,
we're a 225 going to 300 person company.
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And so maybe that's a luxury of still
being small and are on the smaller side.
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But today projects and initiatives
where folks could learn and
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explore is not the constraint.
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This has been a fantastic
conversation so far.
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00:20:26,776 --> 00:20:29,566
If you haven't already done so,
make sure to join our community.
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00:20:29,806 --> 00:20:32,476
We are building a network of the
most forward-thinking, HR and
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00:20:32,476 --> 00:20:35,656
people, operational professionals
who are defining the future.
337
00:20:36,136 --> 00:20:38,746
I will personally be sharing
news and ideas around how we
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can all thrive in the age of ai.
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00:20:41,116 --> 00:20:44,101
You can find it at go cleary.com/cleary
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00:20:44,531 --> 00:20:45,141
community.
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00:20:45,766 --> 00:20:46,606
Now back to the show.
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Thomas: Maybe
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I can ask a little bit different
and maybe it is a scaling thing.
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really at scale is when it comes in.
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It's everyone for whatever
work they're doing.
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There's always more to do than there's
time for, but often what is missing is the
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connection between that and the potential
for culturally for even a manager to
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ask the question that like someone in a
completely different org would be able to
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help or head count or the team that I have
is that's the resource again, my time.
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And that's often what maybe that's
like to me, that's a supply of ideas
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problem, but really it's a cultural
issue about making sure that there's
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this, even the idea that an HR
generalist could help with a marketing
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data analytics project or vice versa.
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And because as you get a little bit
more siloed, I guess that's part of
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what you're looking to do, right?
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Make, create these connections.
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Lorena: Yes.
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And that's, that has happened
as I alluded informally.
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And when we were in just one
office, it happened naturally.
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We are now in, doing the count
one, two, three, three primary
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offices were much larger.
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And so that, hey, we run into
each other in our main office
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in Toronto, doesn't happen.
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And I also found that like the same
people who knew each other well were
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each other to do these projects together.
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When there's a lot of amazing folks,
some of them much newer to the relay
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journey, who had the appetite, the skills,
and just didn't have the connections.
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And building this marketplace means
that there's an equalizing of, if I
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have the skill, I have the bandwidth,
and there's this opportunity presented,
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I can raise my hand and do it and
learn and really drive impact.
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Thomas: Yeah.
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So the multi-location problem
starts to create it and then the
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hierarchical organization at scale
starts to reinforce it and you're
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already fighting against that early.
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But then let's talk about,
okay, the constraint then.
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besides the skillset, it's time.
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There's lots of opportunities
to bring back time for the team,
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specifically on the HR side.
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I know you're doing some interesting
stuff also with CX agents as innovators
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creating their own GPTs and stuff, but on
the HR side or on the people team side.
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Speaker: what are the big
opportunities that you see that would.
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Thomas: looking forward or even
to your point, maybe it's this
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quarter, not two years from now.
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What are the big opportunities that are
unlocked that can get people out of let's
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say some of the proverbial muck, right?
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The work muck.
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Lorena: Yeah.
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I think getting people out of
the proverbial, like, let me
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pull your vacation balances.
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Let me share more on the
benefits, which takes time.
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And it's important to people is
like the easy, low hanging fruit.
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What I'm also seeing now is
that we're able to create
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dashboards at the company level.
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And then at the team level that give
us a signal on this moment happened.
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Here's where it could learn, turn into
a good career trajectory versus an exit.
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We're basically downloading reports
and feeding it into ChatGPT and
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helping us better understand the
signal and then translating that
398
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back to managers and leaders.
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We could do that and we have
been able to do that, but this
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just accelerates time to deliver.
401
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These dashboards to deliver the
insights and the opportunity to go
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deeper because it's less about report
pulling and data amalgamation and
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more around what does this mean?
404
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How do I unpack this?
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So I think the insights are better
and we're able to action more quickly
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and then learn as a result faster.
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Thomas: I love that.
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there's, okay.
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So guess you've mentioned like
two specific opportunities, one
410
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around just, I guess the reactive
HR support work and then just.
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I would say this is really
about analysis, right?
412
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Cause I think you're talking about
particular analysis around employee
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engagement data, but there's probably
different pockets of that type of work in
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like your HR professionals world, right?
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Compensation or one-off requests for
analysis for a particular organization
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or VP, but that intermediate
step of data analysis to get to
417
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insights can be made more efficient.
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Lorena: And right now
we're resource constrained.
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So we only have one person on the
people operations team as an example.
420
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And the level of analysis and the
kind of depth of thinking would
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normally be limited by that one human.
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Thomas: So let's talk about yet another
process, which I think is interesting.
423
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Headcount planning.
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Yeah.
425
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So you said your capacity model now
includes both humans and non-humans.
426
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Tell me more.
427
00:25:45,498 --> 00:25:49,328
Lorena: That is primarily on
the CX side today as we are
428
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building out our 2026 plan.
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For example, normally past models,
capacity models, like what we went
430
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to finance to request for additional
headcount was based on the channels
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that we supported our customers and like
the growth of those channels based on
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ticket volume and kind of information
that we had in the current year.
433
00:26:10,938 --> 00:26:15,298
We have totally changed our model to
think about what are the human actions
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and what are the non-human touches that
will still deliver a great experience.
435
00:26:20,398 --> 00:26:24,518
And so I have a headcount
needs for both human agents as
436
00:26:24,518 --> 00:26:27,598
well as our non-human agents.
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And then I'm able to, as our assumptions,
for example, on efficiency and quality
438
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shift over the course of the year,
better understand what is the mix
439
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between our human and our AI agents.
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Thomas: Okay.
441
00:26:41,518 --> 00:26:46,138
So I have to ask here because this
is the kind of language that is great
442
00:26:46,138 --> 00:26:48,548
for like vendors who are trying to
like, or if you're trying to get
443
00:26:48,548 --> 00:26:51,823
clicks in the media landscape to talk
about like workforce planning with
444
00:26:51,833 --> 00:26:53,653
agents and robots replacing humans.
445
00:26:54,213 --> 00:26:57,873
But here Lauren and operator working
with your finance team and you found
446
00:26:57,873 --> 00:26:59,473
this to be like practically useful.
447
00:26:59,883 --> 00:27:01,423
How is this from your perspective?
448
00:27:01,443 --> 00:27:05,553
Any different than software,
you know, like you might ask for
449
00:27:05,553 --> 00:27:08,723
head count and software budget to
work on stuff like in the past.
450
00:27:09,113 --> 00:27:12,343
Versus now AI agents and human agents.
451
00:27:12,343 --> 00:27:16,013
Does it feel a little bit different in
the conversation that you're putting
452
00:27:16,068 --> 00:27:18,803
forward with finance than those times?
453
00:27:20,613 --> 00:27:20,868
Lorena: Yes, it
454
00:27:22,878 --> 00:27:29,278
does feel different in that I think
with software requests in terms of
455
00:27:29,278 --> 00:27:36,468
budget for tooling in the past, it's
been more about the cost savings and
456
00:27:36,713 --> 00:27:40,733
what leverage does it give us from a
financial perspective for the work?
457
00:27:40,793 --> 00:27:45,203
So reduced cost per interaction or
for using this tool is one example
458
00:27:45,213 --> 00:27:47,473
on the customer experience side.
459
00:27:50,243 --> 00:27:55,788
Rightfully or wrongfully, I know about
60 to 70 % of our CSAT score is just
460
00:27:55,808 --> 00:27:57,838
driven by timeliness of response.
461
00:27:58,228 --> 00:28:03,308
Not even the quality of the response
is if we can get to a customer in less
462
00:28:03,308 --> 00:28:09,888
than 10 minutes that they will be way
more engaged as a result, be higher C
463
00:28:09,888 --> 00:28:14,438
Score, higher NPS and higher revenue
for Relay because they'll stay with us.
464
00:28:15,078 --> 00:28:21,328
And so now I'm saying an AI agent
will be on 24/7 and will give
465
00:28:21,358 --> 00:28:23,118
responses within 60 seconds.
466
00:28:23,118 --> 00:28:27,878
That is more about revenue and
customer experience than it is about
467
00:28:28,258 --> 00:28:32,498
efficiency of like, How do I reduce
my cost to serve this customer?
468
00:28:32,908 --> 00:28:35,903
And so I think that's how it has
changed, at least how we talk about.
469
00:28:35,908 --> 00:28:39,228
What are the outcomes for the
business as a result of using
470
00:28:39,268 --> 00:28:43,638
an AI agent versus a human agent
that can only work so many hours,
471
00:28:43,638 --> 00:28:44,028
sometimes has to be retaught things.
472
00:28:44,028 --> 00:28:44,388
And so that's how
473
00:28:47,278 --> 00:28:47,938
it's shifted for us.
474
00:28:48,548 --> 00:28:48,843
Thomas: That's interesting.
475
00:28:48,958 --> 00:28:52,408
That reminds me of what the earlier
conversation we were having around
476
00:28:52,458 --> 00:28:58,998
coaching and where it's increasing
the capacity to create value, right?
477
00:28:58,998 --> 00:28:59,358
So
478
00:29:01,868 --> 00:29:03,948
let's follow the thought
experiment on both sides.
479
00:29:04,458 --> 00:29:08,288
On the coaching side, you could have
coaches at an hourly rate giving
480
00:29:08,478 --> 00:29:12,568
on-demand 24 seven coverage for
everyone in the entire organization.
481
00:29:12,918 --> 00:29:16,198
And that's one way to get at it,
but that's just like the cost like
482
00:29:16,198 --> 00:29:18,038
doesn't add up and same thing here.
483
00:29:18,108 --> 00:29:22,558
You could have as the organization
scales up, you could scale up your
484
00:29:22,658 --> 00:29:25,698
human workforce so that you have
within 10 minute touch point 24
485
00:29:25,698 --> 00:29:31,038
seven for every single interaction,
but that also doesn't scale right?
486
00:29:31,048 --> 00:29:36,608
So in this case it's actually this
other investment that you're doing
487
00:29:36,618 --> 00:29:39,998
versus headcount to actually get
to those outcomes directly, right?
488
00:29:39,998 --> 00:29:44,078
So it's contributing to the outcome
versus and actually increasing the
489
00:29:44,078 --> 00:29:47,798
capacity with software you might be or
traditional software SaaS, you might be
490
00:29:47,798 --> 00:29:52,118
asking, given the head count we have,
how can we make them more productive?
491
00:29:52,713 --> 00:29:55,208
And it's, think, did I
summarize that nuance?
492
00:29:55,208 --> 00:29:56,858
I think it's very interesting.
493
00:29:58,318 --> 00:29:58,758
Lorena: Yes.
494
00:29:58,848 --> 00:30:03,134
And the only thing I would add is that
on the human level, whether you're
495
00:30:03,134 --> 00:30:09,048
talking about CX or on the people side,
sometimes we require a lot of reeducation
496
00:30:09,048 --> 00:30:10,708
or we'll fall into bad practices.
497
00:30:10,708 --> 00:30:16,918
And so one of the benefits of the
CX AI agent is that it's constantly
498
00:30:16,918 --> 00:30:20,678
learning and it actually doesn't,
it doesn't forget the things that
499
00:30:20,678 --> 00:30:22,938
we have trained or taught it on.
500
00:30:22,998 --> 00:30:27,638
And so there is, we have to see this
play out, but there is at least a
501
00:30:28,298 --> 00:30:33,338
thinking that we have that ultimately,
the quality of the conversation could
502
00:30:33,348 --> 00:30:38,728
be even better depending on the issue
because they're constantly learning and
503
00:30:38,728 --> 00:30:42,743
getting better versus maybe forgetting
things as humans naturally do.
504
00:30:43,523 --> 00:30:43,853
Thomas: Yeah.
505
00:30:43,993 --> 00:30:47,243
This is so I have to circle back
all the way to the initial comment.
506
00:30:47,243 --> 00:30:53,843
So then, do AI agents then ultimately
replace human jobs in this world?
507
00:30:54,583 --> 00:30:58,023
As we keep shifting towards
them, adding more value.
508
00:30:58,058 --> 00:31:00,453
Like expanding the scope of
value that you can add for an
509
00:31:00,453 --> 00:31:04,243
organization and potentially in
certain vectors, doing it better.
510
00:31:06,263 --> 00:31:09,223
Lorena: I think it depends
on the job to be done.
511
00:31:09,993 --> 00:31:14,363
When I look at the types of
tickets or types of issues that
512
00:31:14,363 --> 00:31:20,913
come up for our customers, they're
not all solvable by an AI agent.
513
00:31:21,303 --> 00:31:25,483
And then when we're talking about
people's money payroll and issues,
514
00:31:26,083 --> 00:31:30,323
there are moments that really
matter where a human is required.
515
00:31:30,683 --> 00:31:34,903
And we just want to be able to divert
the resources that we have today to be
516
00:31:34,903 --> 00:31:40,203
there in those moments versus being jammed
up with things that are less impactful
517
00:31:40,883 --> 00:31:45,513
to a business, less impactful to the
relationship that we have with a customer.
518
00:31:45,683 --> 00:31:48,383
And that's the same can be
true on the people side.
519
00:31:48,673 --> 00:31:51,083
It's not that I don't want
to lean our people team.
520
00:31:51,083 --> 00:31:54,073
I just want them to be there in
the moments that really matter
521
00:31:54,273 --> 00:31:56,223
and do so in a more impactful way.
522
00:31:57,453 --> 00:31:58,443
Thomas: That is really well put.
523
00:31:59,153 --> 00:32:01,423
Let's talk a little bit about
the future then, Lorena.
524
00:32:01,443 --> 00:32:03,653
So let's say you're
looking two years ahead.
525
00:32:03,983 --> 00:32:09,753
What does a, I don't know, a people
plus CX organization look like
526
00:32:09,763 --> 00:32:11,703
in an AI enabled organization?
527
00:32:12,103 --> 00:32:13,553
You're already well down this path.
528
00:32:13,883 --> 00:32:18,028
Maybe, yeah, what does it like team
look like, especially what skillsets
529
00:32:18,038 --> 00:32:19,498
are like really like helpful?
530
00:32:19,498 --> 00:32:23,848
Do new titles emerge and or yeah, how
does this, the day to day look like?
531
00:32:25,038 --> 00:32:28,358
Lorena: Well, I'm not a very
good fortune teller or editor.
532
00:32:29,873 --> 00:32:34,348
I'm laughing because two years ago,
two and half years ago when I joined
533
00:32:34,348 --> 00:32:38,728
Relay, you would have told me how
much time I would spend in ChatGPT.
534
00:32:38,838 --> 00:32:41,578
I would have told you were
crazy, but that's my reality now.
535
00:32:41,578 --> 00:32:45,908
So I've embraced this world that we're
living in and find it really interesting.
536
00:32:45,973 --> 00:32:50,683
To answer your question, I think it has
already changed how we think about hiring.
537
00:32:51,203 --> 00:32:56,503
And from an org design perspective,
I think it is creating roles
538
00:32:56,863 --> 00:33:00,013
where having this AI expertise.
539
00:33:00,813 --> 00:33:03,043
is going to be more and more important.
540
00:33:03,043 --> 00:33:07,723
And so if I even look at like my 2026
headcount plan for both CX and people.
541
00:33:07,738 --> 00:33:12,428
I don't even know what our role that just
focused on are we using the right tools?
542
00:33:12,438 --> 00:33:14,928
Are we embedding the practices?
543
00:33:14,958 --> 00:33:19,878
Are we measuring if people are
using the Zenvesco pilot or the AI
544
00:33:19,878 --> 00:33:23,358
coaching agent, just making sure
that sort of all that is happening.
545
00:33:23,358 --> 00:33:28,718
So I think it is creating new roles where
that is a very much a core focus and
546
00:33:30,228 --> 00:33:34,448
beyond that, I'm not going to give you a
good answer Thomas, because I don't know.
547
00:33:36,378 --> 00:33:39,068
Thomas: But it's fair to call out
what was happening two years ago,
548
00:33:39,068 --> 00:33:42,098
and could I have predicted where
I am at this moment back then?
549
00:33:42,108 --> 00:33:44,788
It feels like we are
accelerating ever further.
550
00:33:44,798 --> 00:33:49,328
Maybe that caution is warranted.
551
00:33:49,658 --> 00:33:55,768
But that said, people are developing
new skills and need to stay relevant.
552
00:33:56,098 --> 00:33:58,348
And we're all about future-proofing folks.
553
00:33:59,298 --> 00:34:02,708
So if you have to give advice, and maybe
there's a simplest way to someone who is
554
00:34:02,708 --> 00:34:07,618
just coming out of college or just new
into the workplace and is interested in
555
00:34:08,158 --> 00:34:12,868
getting into the people function, what
thoughts would you have for them besides
556
00:34:13,068 --> 00:34:14,328
that you don't have a crystal ball?
557
00:34:15,652 --> 00:34:16,487
Lorena: I would say lead.
558
00:34:16,487 --> 00:34:21,568
It does feel a little counter
to what we do to be so focused
559
00:34:21,678 --> 00:34:25,748
on AI when we're committed to
delivering these incredible human
560
00:34:25,757 --> 00:34:28,138
experiences and all about the people.
561
00:34:28,618 --> 00:34:33,257
But I do strongly believe that if
we leverage AI in the right ways,
562
00:34:33,257 --> 00:34:39,128
we can accelerate the impact that as
members of a people team, we can give.
563
00:34:39,208 --> 00:34:45,478
like Learn from engineers, learn from
anyone who is an expert in building GPTs.
564
00:34:45,518 --> 00:34:46,918
Understanding how it works.
565
00:34:46,918 --> 00:34:52,733
I think there is power in becoming
an expert there to be more
566
00:34:53,043 --> 00:34:54,072
impactful on the people side.
567
00:34:54,193 --> 00:34:58,133
And I see that today on my team.
568
00:34:58,383 --> 00:35:02,223
I'm probably the least sophisticated,
the least knowledgeable.
569
00:35:02,223 --> 00:35:06,573
I'm always impressed by the GPT
that our people team are building
570
00:35:06,893 --> 00:35:09,583
from hiring to the retention side.
571
00:35:09,733 --> 00:35:13,313
so I'm like, don't be afraid of
it and ask a lot of questions.
572
00:35:13,538 --> 00:35:14,853
Experiment on the side.
573
00:35:14,863 --> 00:35:17,933
That's what I do is build and then tweak.
574
00:35:17,933 --> 00:35:21,323
And I used it for board work
with fast board session.
575
00:35:21,323 --> 00:35:22,633
And it's a lot of fun.
576
00:35:22,903 --> 00:35:23,173
Yeah.
577
00:35:24,503 --> 00:35:24,933
Thomas: Love it.
578
00:35:24,943 --> 00:35:27,903
So thank you so much for
this conversation, Larena.
579
00:35:28,303 --> 00:35:32,483
And looking ahead a little bit as well,
if you're, as we close out here, let me
580
00:35:32,483 --> 00:35:37,563
just ask you, are there any particular
projects, ideas that as you look forward
581
00:35:37,633 --> 00:35:41,543
that you're particularly passionate about
or excited about seeing how it's going
582
00:35:41,543 --> 00:35:43,653
to turn out over the next year or so?
583
00:35:45,593 --> 00:35:50,523
Lorena: I'm excited, even though I didn't
have good answers to what the future org
584
00:35:50,933 --> 00:35:55,533
design and the roles that can come out of
this new world that we're operating in.
585
00:35:55,943 --> 00:35:59,883
I'm super pumped for the AI
coaching that we talked about.
586
00:36:00,243 --> 00:36:05,603
And then looking at our vision and
our, these projects that we hoped
587
00:36:05,613 --> 00:36:09,693
to bring two years from now and
how we can bring them fast forward.
588
00:36:09,843 --> 00:36:14,683
So there's, I think to having the space
and the time to dream and then say, how
589
00:36:14,683 --> 00:36:19,283
do we leverage technology AI to bring
those forward on both the CX and people.
590
00:36:19,563 --> 00:36:22,933
It's not very specific, but that's like
the body work I'm committed to doing
591
00:36:22,933 --> 00:36:28,443
over the next two months is dreaming
and then figuring out how to harness
592
00:36:28,468 --> 00:36:31,508
how far we've come in the last couple
of years to also bring all of those
593
00:36:31,588 --> 00:36:34,538
projects forward in a scalable way.
594
00:36:34,538 --> 00:36:36,558
I like to dream and I'm
looking forward to doing that.
595
00:36:36,828 --> 00:36:37,118
Thomas: love that.
596
00:36:37,153 --> 00:36:40,908
Thank you for dreaming and sharing
some of those dreams with us
597
00:36:40,908 --> 00:36:42,408
here on the, this conversation.
598
00:36:42,768 --> 00:36:48,708
And I particularly like this idea of
basically expanding scope, accelerating
599
00:36:48,728 --> 00:36:52,988
and scaling up things that you otherwise
even maybe in your initial dream
600
00:36:52,998 --> 00:36:56,408
would think of your initial formulas
of how you might get to this outcome.
601
00:36:56,768 --> 00:37:00,438
think it's way out there, but
actually, there's opportunities
602
00:37:00,438 --> 00:37:02,023
that you can grab much faster.
603
00:37:02,023 --> 00:37:04,853
And thank you for going through some of
these and going back and forth between
604
00:37:04,993 --> 00:37:10,173
the CX and HR examples, because I do feel
like there's a lot of adoption in ⁓ many
605
00:37:10,193 --> 00:37:15,533
other functions, broadly speaking, besides
the HR and people function where there's
606
00:37:15,533 --> 00:37:18,013
so much inspiration we can take in.
607
00:37:18,013 --> 00:37:22,003
also it's important to understand about,
because if we're creating leverage in
608
00:37:22,143 --> 00:37:24,573
the workplace, it's through the people.
609
00:37:24,753 --> 00:37:27,013
And so we need to really understand
how each of these different
610
00:37:27,013 --> 00:37:29,703
functions are shifting as well.
611
00:37:29,713 --> 00:37:32,833
So thank you again for
the conversation, Lorena.
612
00:37:32,933 --> 00:37:37,513
And for everyone else who's out there
and looking to future-proof your own
613
00:37:37,523 --> 00:37:40,363
orgs and your own HR and people teams.
614
00:37:40,813 --> 00:37:41,813
I hope you took some.
615
00:37:42,113 --> 00:37:43,893
interesting nuggets out of this one.
616
00:37:44,328 --> 00:37:46,663
Best of luck and we'll be seeing you.
617
00:37:47,531 --> 00:37:50,591
Thanks for joining us on this
episode of Future Proof HR.
618
00:37:50,831 --> 00:37:53,441
If you like the discussion, make
sure you leave us a five star
619
00:37:53,441 --> 00:37:56,321
review on the platform you're
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620
00:37:56,741 --> 00:37:59,681
Or share this with a friend or colleague
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621
00:37:59,981 --> 00:38:04,931
See you next time as we keep our pulse on
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