Shownotes
In this episode of the Future Proof HR podcast, Thomas Kunjappu sits down with Amy Johnston, Head of People and Capability at Orion New Zealand Ltd., to explore what it really takes to future-proof HR inside a high-risk, highly regulated industry undergoing massive transformation. As electricity demand accelerates due to electrification, sustainability goals, and AI-driven data centers, Amy shares how HR plays a critical role in keeping people safe, workforces resilient, and organizations ready for what’s coming next.
Amy explains how Orion is using AI responsibly across People & Culture and operational teams, why HR became a “safe place to play” for AI experimentation, and how governance, privacy, and employee trust shape every deployment. She walks through real-world examples, from tier-zero automation that reduced employee service requests by 50%, to AI-assisted drone inspections that improve safety on the power network.
This episode offers a rare look at HR leadership where mistakes carry real-world consequences, and where future-proofing means balancing innovation, safety, dignity, and long-term workforce planning.
Topics Discussed:
- Why HR can be a safe starting point for AI adoption in high-risk industries
- AI governance, privacy, and employee communication in regulated environments
- Using AI to automate tier-zero HR support and reduce service demand
- Improving safety through AI-assisted inspections and operational workflows
- Workforce challenges driven by electrification and AI-related energy demand
- Retaining institutional knowledge through transition-to-retirement programs
- Treating retirees as paid alumni, coaches, and mentors
- Building diverse talent pipelines through STEM and early-career programs
- Industry-wide collaboration to solve shared workforce shortages
- What it truly means to future-proof HR when the lights have to stay on
If you’re an HR leader, people strategist, or operator working in a regulated, safety-critical, or infrastructure-heavy environment, this episode offers practical insight into how HR can lead through disruption without compromising trust, safety, or human connection.
Additional Resources:
Transcripts
If you're deploying AI on the network, you have to be a hundred
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percent sure that it's not hallucinating,
that it's making the right decisions,
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that it's not gonna hurt someone.
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So we are very much mindful of our
workers' safety and our community safety.
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We wanna make sure that we are
making the right decisions.
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So a safe, reliable network
is it's absolutely key.
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Thomas Kunjappu: They keep
telling us that it's all over.
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For HR, the age of AI is upon
us, and that means HR should
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be prepared to be decimated.
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We reject that message.
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The future of HR won't be handed to us.
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Instead, it'll be defined by those
ready to experiment, adopt, and adapt.
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Future Proof HR invites these builders to
share what they're trying, how it's going,
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what they've learned, and what's next.
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We are committed to arming HR
with the AI insights to not
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just survive, but to thrive.
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Hello and welcome to the Futureproof
HR Podcast where we explore how
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forward-thinking HR leaders are preparing
for disruption and redefining what it
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means to lead people in a changing world.
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I'm your host, Thomas
Kunjappu, CEO of Cleary.
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Today's guest is Amy Johnston,
head of people and capability
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at Orion New Zealand.
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Limited the electric.
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Electricity distribution company serving
Canterbury New Zealand, municipally
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owned via local government and
operating in a highly regulated market.
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Orion's mission is to deliver
a safe, reliable network while
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stewarding a just energy transition.
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She partners with business units to
understand the people impacts of AI
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assisted work among many other things.
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And O Orion has developed some leading
projects in this, arena and beyond just.
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Tackling the realities of
electrification and the, demands
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of it from an AI driven world.
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Amy and team are working on several
other challenges from an aging hard to
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replace technical workforce that we'll be
talking about and building new recruiting
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pipelines, and as also including with a
diverse, workforce and using AI to improve
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safety and service, for, what otherwise
could be a pretty dangerous, kind of role.
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Without further ado, we'll get into it.
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Amy, welcome to the podcast.
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Amy Johnston: Thanks so much, Thomas.
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It's an absolute pleasure to be here.
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I'm really excited for the chat today.
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and talking about a little bit more
about what we do in electricity.
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Thomas Kunjappu: Yes.
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Maybe you could just introduce us a
little bit what your organization does,
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and then I would like to take it into the
impact of the broader space with AI next.
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But could you tell us a
little bit about Orion?
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Amy Johnston: Yeah, of course.
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So at Orion, our purpose is about
powering a cleaner, brighter
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community, future with our communities.
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and what we're really focused on is, we.
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We are looking at how do we electrify.
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Orion is a municipally owned EDB, so EDB
is an electricity distribution company.
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We, own the infrastructure
and take the electricity from
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our generators to your home.
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so the power lines on the street,
the substations, that's what we do.
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Thomas Kunjappu: It's in everywhere.
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it's in the electricity, electrification.
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It's something we've been, doing, for over
a hundred years, throughout the world.
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And yet we are at a moment, with
the, the growing scope of ai where
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the demand for the work that you
do is potentially on the rise.
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I know you have some stats or ideas about
what is happening around your industry.
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Could you just tell us a little
bit about, the demand side of what
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you're seeing in electrification.
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Amy Johnston: Yeah, absolutely.
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There's really two components
to electrification.
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One is the sustainability angle.
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So we are very much looking at
how do we generate our electricity
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in a much more sustainable way.
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So hydro, wind, solar.
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And that's everything from our
hydroelectric dams, right the way
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through to putting solar on your roof.
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And those things like solar on
your roof is an example of how what
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previously was a very linear system.
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going from generation to your home is now
becoming much more circular and disrupted.
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You are generating your own power
to power your home and wanting
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to feed that back into the grid.
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So it really changes the
dynamics of the grid.
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So we are looking at distributed
energy, resources and pathways.
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And that's very much changing
the game for the industry.
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The second thing that we are really
focused within electrification
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is the use of electricity.
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And that is significantly increasing.
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So not just you at home
plugging your electric vehicle
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into, instead of buying gas.
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But also the way in which we work.
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So when you think about AI,
a data center drives or uses
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huge amounts of electricity.
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So in 2024, that was about 1.5%
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of the global production of electricity.
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that's anticipated to grow 12% every year.
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year on year and will become exponential.
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So we anticipate that in the US market
by 2035, AI data centers, quantum
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computing, all of those types of
things will utilize about 50% of the
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electricity production in the US.
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It's an absolutely astronomical challenge.
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Thomas Kunjappu: That is ridiculous.
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So we're going from something in of course
training was happening in in small pockets
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for AI researchers for many decades.
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But we had the ChatGPT
moment in late 2022.
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And then leading up to that, we've
gotten to these models that are
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being trained on petabytes of data,
which are using a lot of electricity.
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And that is only, those clusters
are only getting bigger.
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I've never heard that before.
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It's going up to 50% of potentially all
electricity use will be fundamentally
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coming from the AI revolution.
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Amy Johnston: Absolutely.
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you look at things like, Microsoft
just signed a 20 year agreement
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to turn back on Three Mile Island.
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That will essentially be a microgrid
where all of that electricity will be
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going to power a Microsoft data center.
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It's utilizing about 335 megawatts.
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The entire 335 megawatt output of
Three Mile Island for that data center.
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So it's huge.
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When you translate that into the New
Zealand environment, we have the same
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challenge, but not on the same scale.
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Our geography doesn't support data
centers in the way that it does in the US.
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So we will have some but they
won't be nearly as big as
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what they're in the US market.
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Thomas Kunjappu: But it's still gonna
make a dramatic impact for I don't know.
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The last time a new category
of machinery, right?
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Maybe it's actually electric cars, right?
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That was actually becoming a significant
portion of the actual overall grid
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demand or electric electricity demand.
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But this is just completely
upending the industry.
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And it's something I talk about
and think about AI all the time.
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But from a from a
completely different layer.
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Than you are, right?
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And especially we're thinking about
how it impacts the workers work, HR,
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and also for knowledge workers, all
the different various ways that it is
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impacting your day-to-day workflows.
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But the foundational layer
it's really important.
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The work that you're doing and the
challenges that it's posing in this
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transition for an organization like yours.
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So thank you for that context.
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Now let's talk a little bit about with
all of these challenges, coming up, since
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you've set the scene for us a little bit.
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First of all, about applications.
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So throughout Orion or
electrification as an industry,
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I imagine there are lots of use cases
that are already coming into play that
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help your employees do their work more
efficiently, safely in a better way.
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Amy Johnston: Yeah.
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At Orion, we're very much a
technology driven organization.
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It's just in our DNA having a
huge number of engineers on deck.
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So we're really interested in
technology, how does it help us work
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and how does it change our world?
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We've been, has been a really safe
place for the organization to play.
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So whether it's deploying AI agents, those
types of things, our area isn't as safety
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sensitive as some of the other areas.
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if you're deploying AI on the network,
you have to be a hundred percent
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sure that it's not hallucinating,
that it's making the right decisions,
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that it's not gonna hurt someone.
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So we are very much mindful of our
workers' safety and our community safety.
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We wanna make sure that we are
making the right decisions.
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So a safe, reliable network
is it's absolutely key.
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We've had some really cool projects, kick
off, whether that is in the P&C space
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and the use of agents, or across the
network to really help encourage safety.
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We've just had a new pilot with a
visual language model that our drone
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flies over our lines, and takes video.
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That video then runs through the AI
model and we're able to assess the
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components that are on our lines
and what condition they're in.
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So that's an early beta phases right now.
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But what it's doing is it's meaning
that we don't have to put our guys
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in a cherry picker on difficult
terrain to go up and have a look.
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So it means that we are able to better
assess our network, the quality of
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our network, and where we need to.
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To put investment to maintain the network.
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Thomas Kunjappu: That's a great example
that goes to safety and efficiency.
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In this particular use case.
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And you're combining drone
and video technology with LLMs
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to make all of that happen.
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You also mentioned on the P&C, on the
People and Cultural side, there's some
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interesting things that you've tried.
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Before we get into that, I'm
curious about a statement you made.
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Because I don't hear that often.
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From an AI perspective that
the HR or people and culture
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side is a safe place to play.
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Often there are thoughts about
we have to be careful here.
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There's either employee data or
we need to be thinking about like
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governance and making sure that stuff
that doesn't get out because it's
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really important that it's I dunno,
strategically important to the company and
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maybe it's a mindset
more than anything else.
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But I've heard that a lot.
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That, hey, this is, the HR team
is the last place where we need to
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innovate around this stuff because we
need to be careful with what we do.
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Amy Johnston: Yeah.
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I think for us, the stakes are so high
in the other parts of the business
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that getting the governance, getting
the privacy, getting all those pieces.
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We can test that in P&C.
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We can communicate it with our employees
and say, Hey, here's what we're trialing.
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Help us if you are concerned, let's
have a chat about what that means for
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your data and how we can manage that.
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And it's given the AI
team and our business and
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opportunities get comfortable.
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Inwardly focused before we go into
something that's higher stakes.
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I would say it's a mindset shift.
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but you do have to make sure that
you've got those things in place.
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Like good governance and that you
understand your privacy requirements
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and how you're going to maintain those.
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And that you communicate
it with your employees.
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We're not creating something
and landing it on them.
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We take a very consultative approach,
so we let them know it's coming.
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We'll have a chat about concerns
first, what they're thinking about
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before we actually finalize and deploy.
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Thomas Kunjappu: I love that framing
of it because, it's actually a
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unique advantage that we have, right?
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When our customers are aligned
with us in the same mission, right?
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And so it can bias some grace in some
ways and the ability to experiment
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provided that you're communicating
effectively about it, right?
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But there is an opportunity to
really work alongside your customers,
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your employees, in enabling
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whatever the project is, and whether
that involves technology or not.
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And whether that technology is AI or not.
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It's actually a relative advantage.
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It's tough to say that to
consumers, but we're experimenting
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with different technologies.
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You may or may not experience brown heads.
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Amy Johnston: Exactly.
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Thomas Kunjappu: Watch out for that.
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Amy Johnston: You know, you expect when
you flick a light switch, the lights too.
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Turn if we are doing something on the
network, that means that is unlikely
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to happen or may cause issues there.
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We're gonna face huge problems.
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Our customers are also our owners.
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So we are municipal pre-owned.
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So all of the rates payers across
Canterbury are essentially our
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owners as well, and our shareholders
as well as our customers.
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So brownout is just not something
that's on the cards for us.
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Thomas Kunjappu: Can you tell us a little
bit more about what have been some of
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these experiments that you feel like
successful or not any learnings from
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some of the things on the people and
culture side that you've done with AI?
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Amy Johnston: Yeah, so we've been on
a real journey to automate tier zero.
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And really think about how we
can do that while we're in a
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build phase for the organization.
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So we are looking at what
does the future hold?
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How are we going to retain employees
when the market does start to loosen up?
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And how are we looking
to grow in the future?
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So what is our employee experience?
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How do we retain people?
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How do we make sure that's
aligned to our long-term strategy?
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And how AI has worked in that for
us is about that automation, that
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optimization of our key tasks.
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For example, we've deployed
an agent that sits on top of
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our policies and procedures.
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It answers all frontline
questions from our employees.
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After deploying that and working
through a few hallucinations.
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We have reduced our frontline calls
into our service center by about 50%.
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Thomas Kunjappu: Oh, that's great.
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So you're doing a lot of call
over the phone support previously,
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and that's still available.
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But you're seeing the
demand for that come down.
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But is the modality from the
employee's experience perspective?
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It's gone from phone to is
it text or sending an email?
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Amy Johnston: We're a diverse.
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Our workforce is across
multiple locations.
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So that might be a drop in to the office.
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It may be a phone call,
it may be an email.
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We've been logging all of those
things as part of tier zero.
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So it's a reduction in
all of those modalities.
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So essentially the AI agent acts
like a search engine, but it's a
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closed language model that sits just
over our policies and procedures.
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So it's generating the answers
of what is our vacation policy?
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Can I take time off over Christmas?
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Are we having a shut down?
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All of those types of things
from a set pool of information.
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And that's allowed employees to get the
information right at their fingertips.
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The answer immediately, and has reduced
pressure on our coordinator and advisor.
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Thomas Kunjappu: That's amazing.
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One of the limitations of this kind of
solution is that it's based on software.
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So the expectation for a diverse
workforce to go into a piece of software
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and search for something while certain
HR teams as well as employees, their
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expectation is to knock on a door and
talk to a friendly face about something
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that they need in terms of a service.
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But it sounds like you're experiencing
those door knocks go down because
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people are naturally able to use such a
solution to get their questions answered.
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Amy Johnston: Yeah.
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The door knocks are becoming more
because they want to come and say
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Hi, which is exactly what we want.
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We are a distributed workforce anyway.
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We have been since COVID.
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For us, we are not turning
back the clock on that.
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We literally can't fit in our building.
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For us flexible working
is part of who we are.
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And so people have the ability to come
and door knock and see us, but it's about
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enabling that flexible working for not
only the employees, and so that they're
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still getting the information and the
connection that they need and want.
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But also for the P&C team, we don't
have to have someone on site every day.
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We can also have flexible working as well.
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So it's a huge benefit.
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Thomas Kunjappu: And then could I
ask about how you got a project like
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this over the finish line, right?
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So it's one thing to say, Hey,
it would be nice if we could
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work on this particular thing.
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But then who did you find were
the internal stakeholders you're
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collaborating with to get to
the ultimate outcome and value.
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Of course, your coordinators and
assistants, they were involved at some
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level, I imagine, with the project.
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But then, who else was important to go
from conception to outcome in your case?
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Amy Johnston: So we've been
very fortunate with our business
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intelligence and AI team.
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So we've been working with them for quite
some time on the types of data that we
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have within MP&C and how do we get robust
data that tells us a really great story.
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We offered ourselves up is
that a safe place to play.
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And it was really in that mindset
shift that enabled them to go,
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okay, here's somewhere that's
not gonna impact the network that
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we have the support of the team.
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The team wants to create and
play in this space so this is a
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safe place for us to do that too.
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And then it was about getting the right
governance in place to get the support of
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our senior executive team and our board.
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and so they've been really
wanting to see AI utilized.
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AI and data is going to be absolutely key.
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And not just how we manage the
assets moving forward, but how we
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manage our workforce moving forward.
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The workforce challenge that we are
facing in our industry is immense.
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And we need to be utilizing these types
of technologies just to be able to
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keep pace and supplement our workforce.
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This has been a fantastic
conversation so far.
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If you haven't already done so,
make sure to join our community.
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00:17:30,675 --> 00:17:33,345
We are building a network of the
most forward-thinking, HR and
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00:17:33,345 --> 00:17:36,525
people, operational professionals
who are defining the future.
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00:17:37,005 --> 00:17:39,615
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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00:17:41,985 --> 00:17:44,970
You can find it at go cleary.com/cleary
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00:17:45,400 --> 00:17:46,010
community.
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00:17:46,635 --> 00:17:47,475
Now back to the show.
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Thomas Kunjappu: So your People and
Culture use cases, was almost the
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sandbox obviously useful in terms of
getting some productivity for your team.
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But also started point painting the way
the path forward for many other use cases.
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I'm curious about the structure
that you had internally.
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So you mentioned there's like a BI team.
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BI/AI team.
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Did your organization invest in a
whole new AI team or did an existing
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team evolve in towards that and
then working with your use cases?
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Did they then go on to keep working with
other teams internally to work on other
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AI agentic workflows or efficiencies?
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Amy Johnston: Yeah, absolutely.
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So probably about two and a half, three
years ago, we established a business
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intelligence team as part of our wider
data, digital and technology team.
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That business intelligence
team as an asset manager.
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We have huge amounts of information.
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So it's really about how
do we process that data?
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How do we understand it.
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And how does it support our strategy?
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And just support our decision making.
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That team grew and added in a AI
function probably about a year
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and a half, year and a half ago.
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They're made up of incredible data
and computer science individuals
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and mechatronics engineers.
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They mechatronics engineer also has a
knowledge of electrical engineering.
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And is really able to progress this.
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It's a small team of three, but mighty.
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and they're absolutely phenomenal.
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In New Zealand the environment
is heavily regulated.
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So we can't, for example, as an EDB, we
cannot generate and we cannot retail.
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We're purely the lines company.
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There is 26 of us across New Zealand.
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So we're also, geographically
quite small as well.
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We've made the decision that we will
work collaboratively as an industry
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and feed this into other EDBs and
work with other electricity companies
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on how we might solve this problem
or these problems collectively.
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And that's in the best interest of
all New Zealanders and our community.
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There's no point reinventing
the wheel 26 times.
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So how do we do this together?
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And that is really key.
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But, as far as internally, we've been
a safe place for this team to play.
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And then they're now looking at what
are the use cases beyond that team.
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So what that might look for example, in
engineering, with engineering standards.
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The ability for an AI to
hallucinate that is greater, but
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our tolerance for hallucinations
in that space is much lower.
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99.9%
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is still not good enough.
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We are really looking at those
sort of things about where can we
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play and where do we need to create
a product that is exceptional.
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Thomas Kunjappu: Thank you for going
through it and I think a takeaway.
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Because the reason I ask is often, when
organizations do get to the point of
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investing in an AI strategy or having
a technology team centrally to invest
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in understanding or how to leverage
AI for various internal workflows.
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Often the people team might
be the last in line, right?
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Even if you have your hands raised,
you're not getting the time of a
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lean team that might be working on
other projects collaboratively with
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other parts of the organization.
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but I guess a takeaway I have is if
that is you and you are in a high
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00:21:16,676 --> 00:21:22,881
risk industry where the ability or
willingness to experiment on workflows
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within your product or on top of
customer data is extremely low.
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That might be a place for you to raise
your hand and get in an experiment going
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for your people and culture workflows.
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I wanted to ask about
a whole different area.
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Which is a whole other
transformation that's happening.
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As you're thinking about the
demand side, we've talked about
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for elect electrification.
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How you're meaningfully changing
your internal workflows within
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the people and culture team.
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And also in the broader organization.
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But then there's the
people dynamics itself.
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Tell me a little bit about your
employee base and this workforce
396
00:21:57,936 --> 00:22:00,726
crunch that you've been talking
about and are working through.
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Amy Johnston: Yeah.
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00:22:01,701 --> 00:22:06,951
With electrification and with that level
of increasing demand that we're seeing
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that we talked about earlier from AI data
centers, all of those types of things.
400
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That means that there's a
workforce impact to that.
401
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We've seen projections that, for example,
in electricity, in our industry, in
402
00:22:19,966 --> 00:22:26,496
the US there will need to be 30 million
new roles in our industry by 2030
403
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to deal with this level of demand.
404
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That's everything from our lines
technicians and the guys on the tools,
405
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right the way through to the back office.
406
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The engineers, right across the role.
407
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They also predict that 16 million
of those roles will actually be
408
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newly formed different roles.
409
00:22:45,551 --> 00:22:47,471
That's quite interesting in itself.
410
00:22:47,901 --> 00:22:51,681
So for us, the workforce challenge
is really about how do we ramp up,
411
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how do we get the right skill sets,
how do we ramp up our workforce?
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And how do we do that in a way that's
gonna support our long-term needs.
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So what we've seen historically,
electrical engineering is
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incredibly male dominated.
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20% of at our local university, only
20% of the entrance into the electrical
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engineering program are women.
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That's their highest percentage ever.
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We know that we can't solve the
challenge and the level and get the
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level of workforce that we need unless
we engage the entire population.
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So we need much greater
diversity in our workforce.
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Both in gender but also
in ethnic diversity.
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engaging our indigenous people in New
Zealand, Maori and Pacifica Asian.
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All sorts of we need that
diverse workforce to be able
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to solve these challenges.
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So at Orion we are really
thinking about how do we attract
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and retain a diverse workforce?
427
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How are we talent pooling them
into the organization and how
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are we retaining knowledge?
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00:23:57,881 --> 00:24:01,541
So we have some phenomenal people
in our organization who have
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00:24:01,896 --> 00:24:03,576
been here their entire careers.
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00:24:03,796 --> 00:24:05,266
40 years of tenure.
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00:24:05,606 --> 00:24:07,556
They built the substation down the street.
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They've got that knowledge in their head.
434
00:24:09,146 --> 00:24:13,496
How do we retain that knowledge, and
make sure that we can transition that
435
00:24:13,496 --> 00:24:15,356
into the up and coming population.
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00:24:15,756 --> 00:24:19,146
So it's a really unique and
interesting challenge that we
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have in our sector at the moment.
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Thomas Kunjappu: That's a
wealth of experience, right?
439
00:24:23,901 --> 00:24:27,541
I think in many organizations,
no one has spent a lifetime
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00:24:27,571 --> 00:24:28,711
working at the organization.
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00:24:29,251 --> 00:24:30,751
And so that's something to be treasured.
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00:24:31,061 --> 00:24:34,281
And something that I understand that
there's programs that you're putting
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in place to help leverage further.
444
00:24:35,861 --> 00:24:36,911
Can you tell me a little
bit more about that?
445
00:24:36,911 --> 00:24:38,001
Amy Johnston: Yeah, absolutely.
446
00:24:38,011 --> 00:24:41,286
When we had a look at our demographic
data and we realized that we have quite
447
00:24:41,286 --> 00:24:47,936
a proportion of our population at Orion
who are nearing the typical retirement
448
00:24:47,936 --> 00:24:50,306
age, which is around 67 in New Zealand.
449
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And so our concern there was that we
would have huge amounts of institutional
450
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knowledge walking out the door.
451
00:24:58,246 --> 00:24:58,466
And
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what we also found is that we
want to make sure that people feel
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00:25:02,981 --> 00:25:06,371
comfortable and safe in retiring.
454
00:25:06,411 --> 00:25:08,001
They've worked their entire careers.
455
00:25:08,031 --> 00:25:11,441
That's something to be celebrated
and we want them to have that reward
456
00:25:11,441 --> 00:25:15,191
of having their golden years to
themselves while still being engaged.
457
00:25:15,191 --> 00:25:20,181
And when we had a look at what were the
barriers to retirement but also what did
458
00:25:20,181 --> 00:25:23,021
a meaningful knowledge transfer look like.
459
00:25:23,051 --> 00:25:26,621
It wasn't just process mapping
and documenting that knowledge.
460
00:25:26,631 --> 00:25:27,591
That's part of it.
461
00:25:27,931 --> 00:25:32,491
But very much about creating an
experience that honored the Mahi
462
00:25:32,491 --> 00:25:37,711
and the work that these people
had put in and really maintain
463
00:25:38,141 --> 00:25:43,001
which is about maintaining their honor and
maintaining that knowledge that they have.
464
00:25:43,591 --> 00:25:47,376
And so we looked at a
transition to retirement plan.
465
00:25:47,866 --> 00:25:51,106
And we now have a process where
people can give us up to two
466
00:25:51,106 --> 00:25:53,086
years notice of their retirement.
467
00:25:53,656 --> 00:25:59,371
Allowing us the ability to succession
plan and really get a grasp on what
468
00:25:59,371 --> 00:26:00,811
does that transition look like?
469
00:26:01,351 --> 00:26:06,661
But also enables the retiree
to engage after retirement.
470
00:26:07,081 --> 00:26:10,021
So they can come back as
a coach and as a mentor.
471
00:26:10,121 --> 00:26:16,641
To our interns, to our power youth program
and also to our early careers employees.
472
00:26:16,861 --> 00:26:18,781
They maintain their
membership of our social club.
473
00:26:19,676 --> 00:26:22,736
So they're able to engage
in all of the Orion events.
474
00:26:22,786 --> 00:26:25,076
They can come to our Christmas
party, they can do all of those
475
00:26:25,076 --> 00:26:26,186
awesome things that we do.
476
00:26:26,636 --> 00:26:31,166
And they're able to maintain that social
connection that was absolutely important
477
00:26:31,456 --> 00:26:36,476
that social connection was upheld and
people could really see themselves as
478
00:26:36,476 --> 00:26:40,256
an alumni of Orion, not just a retiree.
479
00:26:40,746 --> 00:26:44,946
So we've really created a program that
seeks to maintain that relationship
480
00:26:44,976 --> 00:26:49,196
and allow us to still call the
guy who created the substation
481
00:26:49,196 --> 00:26:50,426
to say, Hey, what about this?
482
00:26:50,426 --> 00:26:51,426
Have we are dealing with this.
483
00:26:51,431 --> 00:26:52,311
Can we take you for coffee?
484
00:26:52,821 --> 00:26:53,681
And that's paid.
485
00:26:53,681 --> 00:26:56,431
We want to make sure that
we honor that retiree.
486
00:26:56,431 --> 00:26:58,761
They're absolutely paid for
their knowledge and their time.
487
00:26:58,856 --> 00:27:02,436
But yeah, it's a very new program
for us, but we've had thus far,
488
00:27:02,436 --> 00:27:03,966
nine people take us up on it.
489
00:27:03,976 --> 00:27:05,716
And that's in the last three months.
490
00:27:05,716 --> 00:27:07,296
So it's been a great transition.
491
00:27:08,436 --> 00:27:11,521
Thomas Kunjappu: I love that because
I like to think that this if work is
492
00:27:11,521 --> 00:27:13,201
not just about earning the paycheck.
493
00:27:13,201 --> 00:27:15,271
Although to your point, like
that's part of what you're doing
494
00:27:15,271 --> 00:27:16,866
just to say that it is valued.
495
00:27:17,106 --> 00:27:21,116
It's also the community and the ability
to feel like you have some expertise
496
00:27:21,116 --> 00:27:25,116
and that others can rely on you
for something you can still provide
497
00:27:25,116 --> 00:27:26,586
many of those things and people.
498
00:27:27,701 --> 00:27:32,436
maybe into their seventies that you
feel that need to be valued by your
499
00:27:32,436 --> 00:27:37,446
family, community, but also from your
like the organization you retired from
500
00:27:37,446 --> 00:27:38,886
especially if you spend a lifetime there.
501
00:27:38,886 --> 00:27:43,106
So I think that's a wonderful idea
which also helps solve a very real
502
00:27:43,106 --> 00:27:46,821
problem you mentioned from a workforce
perspective because there's gonna
503
00:27:46,821 --> 00:27:50,871
be so many new people we need to
train up into the system, right?
504
00:27:50,871 --> 00:27:53,661
And just all your peer organizations
throughout the country.
505
00:27:53,661 --> 00:27:57,991
And thank you for every time going to
resorting to American numbers, when
506
00:27:57,991 --> 00:27:59,041
you're talking to the millions there.
507
00:27:59,431 --> 00:28:00,841
Let's talk about that side a bit.
508
00:28:00,971 --> 00:28:03,121
So the recruiting channel,
the challenge, the funnel.
509
00:28:03,171 --> 00:28:05,811
You talk about a little bit
about the extreme upfront.
510
00:28:05,871 --> 00:28:07,311
The education pipeline.
511
00:28:07,311 --> 00:28:12,051
And you wanna basically from an education
perspective upstream from the workplace
512
00:28:12,051 --> 00:28:17,116
and recruiting just make it as easy
for everyone to get excited and learn
513
00:28:17,116 --> 00:28:20,601
about electrification and become an
electrical engineer and become part of
514
00:28:20,601 --> 00:28:22,251
this pipeline that you're recruiting into.
515
00:28:22,531 --> 00:28:25,266
But I know specifically with
electrical engineering, that's been
516
00:28:25,266 --> 00:28:29,676
a major that a lot of people have
diverted into software and potentially
517
00:28:29,676 --> 00:28:31,506
other layers of the AI field.
518
00:28:31,726 --> 00:28:36,416
I imagine this is an industry-wide
recruiting problem that crosses countries.
519
00:28:36,466 --> 00:28:40,486
I imagine that this kind of programming
and it's exciting we just talked about
520
00:28:40,486 --> 00:28:43,396
this retirement program can actually
help a little bit potentially with
521
00:28:43,396 --> 00:28:46,456
recruiting as coaching for these folks
as you're trying to recruit them in.
522
00:28:46,456 --> 00:28:50,346
But what are some of the tools or
experiments or that you're looking at
523
00:28:50,346 --> 00:28:54,616
or industry-wide that you feel like
you need to get into as an industry to
524
00:28:54,736 --> 00:28:56,626
get more of young folks into the field?
525
00:28:57,581 --> 00:28:59,471
Amy Johnston: There's been quite a
lot of work that's been done in this
526
00:28:59,471 --> 00:29:01,691
space over the last couple of years.
527
00:29:01,691 --> 00:29:04,031
Both in New Zealand but
also internationally.
528
00:29:04,041 --> 00:29:08,241
We are not in a position as an industry
where we can compete against each other.
529
00:29:08,241 --> 00:29:09,291
We need to work as a career.
530
00:29:09,996 --> 00:29:15,376
Because the problem is that large and our
need for people, people doing the right.
531
00:29:15,646 --> 00:29:18,396
Training programs and
those things is that big.
532
00:29:18,876 --> 00:29:22,836
So we've been working within New Zealand,
we've been working collectively with
533
00:29:22,886 --> 00:29:27,436
some of our industry bodies to really
say, how do we solve this problem?
534
00:29:27,466 --> 00:29:30,946
How do we look at this from
a holistic standpoint and
535
00:29:30,946 --> 00:29:34,276
really seek to engage people?
536
00:29:34,386 --> 00:29:36,786
And potentially a career in electricity.
537
00:29:37,276 --> 00:29:39,526
So the industry bodies
have been doing that work.
538
00:29:39,586 --> 00:29:43,126
Really seeking to understand
what are the barriers.
539
00:29:43,186 --> 00:29:46,906
And what's motivating people to
join particularly students and
540
00:29:46,906 --> 00:29:48,736
young people to join our industry.
541
00:29:49,256 --> 00:29:52,736
Naturally there is marketing
campaigns around that.
542
00:29:52,916 --> 00:29:55,976
There's also things associated
with the talent pipeline.
543
00:29:56,396 --> 00:30:00,416
So one of the challenges we have is
obviously in that male dominated space.
544
00:30:00,446 --> 00:30:03,846
As a male dominated industry, so
we need to increase the rates of
545
00:30:03,846 --> 00:30:07,306
females within the organizations
and within the training programs.
546
00:30:07,696 --> 00:30:13,396
So we've been really focused on STEM
programs and as an industry we have, for
547
00:30:13,396 --> 00:30:18,076
example, a program that's supported called
Wonder Project, where they take science
548
00:30:18,076 --> 00:30:20,056
projects to schools across the country.
549
00:30:20,056 --> 00:30:24,326
They're aimed at about seven years of age
and they do a cool project that's based
550
00:30:24,376 --> 00:30:28,846
on our industry to understand generation,
the grid and how power gets to your home.
551
00:30:29,376 --> 00:30:32,621
So the particular organizations
across the industry have sponsored
552
00:30:32,621 --> 00:30:34,151
those to get them into schools.
553
00:30:34,531 --> 00:30:39,631
We've worked with a organization called
GirlBoss and six of our other EDBs
554
00:30:39,631 --> 00:30:41,261
have got on board with this program.
555
00:30:41,321 --> 00:30:46,411
And we run a school holiday program for
young girls between the ages of 16 and
556
00:30:46,411 --> 00:30:48,941
21 to inspire them into electricity.
557
00:30:49,531 --> 00:30:54,006
So that's taking girls at the key
point of career decision making in
558
00:30:54,006 --> 00:30:56,046
that sort of 16 to 18 year range.
559
00:30:56,286 --> 00:30:59,166
You are really making the decisions about
what you're going to do with your life.
560
00:30:59,476 --> 00:31:05,656
And giving them real practical options,
mentors, and a real life project that
561
00:31:05,876 --> 00:31:10,291
helps to inspire them into STEM and
hopefully electrical engineering.
562
00:31:10,661 --> 00:31:14,111
What we found when we looked at it
with Alexia from Girlboss was very
563
00:31:14,111 --> 00:31:18,471
much that these young girls were
really interested in sustainability.
564
00:31:19,421 --> 00:31:22,031
But we're chasing climate science degrees.
565
00:31:22,461 --> 00:31:27,631
Now there's not too many jobs out of
climate science degrees, but within
566
00:31:27,691 --> 00:31:30,551
electricity, there is a huge...
567
00:31:31,001 --> 00:31:32,251
there's a ton of jobs.
568
00:31:32,781 --> 00:31:37,041
There's huge amount of problems to solve,
and most of them are sustainability based.
569
00:31:37,296 --> 00:31:39,456
'Cause we are looking
at how do we electrify.
570
00:31:39,886 --> 00:31:42,046
How do we create sustainable generation?
571
00:31:42,046 --> 00:31:44,386
How do we transmit in a sustainable way?
572
00:31:44,716 --> 00:31:48,906
How do we do microgrids and
distributed energy resources.
573
00:31:49,176 --> 00:31:53,406
But then also how do we work with our
communities on a just transition and
574
00:31:53,406 --> 00:31:57,016
really making sure that we're doing
this with our community not to them.
575
00:31:57,586 --> 00:32:01,396
So it's absolutely vital to
get those individuals that are
576
00:32:01,576 --> 00:32:03,196
already thinking that way at 16.
577
00:32:03,196 --> 00:32:07,346
Already excited, already motivated
about science into our field.
578
00:32:07,346 --> 00:32:10,256
So we are really looking at how
do we develop the talent pipelines
579
00:32:10,256 --> 00:32:15,996
from a really young age into the
industry and then with the end goal
580
00:32:16,056 --> 00:32:20,226
of from a P&C perspective solving
our gender pay gap and our vertical
581
00:32:20,226 --> 00:32:22,206
and occupational segregation issues.
582
00:32:22,606 --> 00:32:24,936
It's a huge pipeline.
583
00:32:25,806 --> 00:32:27,676
Thomas Kunjappu: It makes me
think I'm not sure what I'm gonna
584
00:32:27,676 --> 00:32:28,816
have for breakfast tomorrow.
585
00:32:29,266 --> 00:32:35,876
And here you're looking at industry
wide, how we can influence and market to
586
00:32:35,876 --> 00:32:42,181
and recruit the best talent across the
board to set up the industry for success.
587
00:32:42,211 --> 00:32:43,471
Decades down the line.
588
00:32:43,801 --> 00:32:45,381
I really appreciate that kind of thinking.
589
00:32:45,381 --> 00:32:50,091
And that's a very nuanced insight that
you just brought Amy, about Different
590
00:32:50,091 --> 00:32:52,491
ways to describe the same problem.
591
00:32:52,491 --> 00:32:55,701
And some, whether you're talking
about like a tough engineering
592
00:32:55,701 --> 00:32:59,451
challenge or a tough sustainability
challenge, but it's the same challenge.
593
00:32:59,721 --> 00:33:03,631
But different types of different
language and different exposure can
594
00:33:03,631 --> 00:33:05,231
speak to different types of folks.
595
00:33:05,231 --> 00:33:10,551
And you gotta do everything you
can to help us all electrify
596
00:33:10,701 --> 00:33:12,171
the right way across the board.
597
00:33:12,591 --> 00:33:12,981
Yeah.
598
00:33:14,346 --> 00:33:14,796
I love that.
599
00:33:15,366 --> 00:33:15,726
Amy Johnston: Yeah.
600
00:33:15,726 --> 00:33:18,026
And I think we need
different thinking as well.
601
00:33:18,576 --> 00:33:21,216
We need people to think outside
of the box and to attack these
602
00:33:21,216 --> 00:33:22,476
problems in a different way.
603
00:33:22,526 --> 00:33:24,686
It's how we progress,
it's how we move forward.
604
00:33:25,086 --> 00:33:27,876
And we know the importance
of that diverse thinking.
605
00:33:28,396 --> 00:33:32,326
So it's not just diversity from
a gender standpoint, it's also
606
00:33:32,326 --> 00:33:37,306
diversity of thought that's incredibly
vital to solving these challenges.
607
00:33:38,911 --> 00:33:40,921
Thomas Kunjappu: Thank you so
much for this conversation, Amy.
608
00:33:40,921 --> 00:33:43,971
It's been, really interesting
for us to dive into first of
609
00:33:43,971 --> 00:33:46,361
all the industry that you're in.
610
00:33:46,361 --> 00:33:50,051
It's been around since the Thomas
Edison days, but it's almost
611
00:33:50,051 --> 00:33:55,246
being reshaped entirely and like
the way it's all being delivered.
612
00:33:55,296 --> 00:33:59,796
The first whammy is everything about
sustainability, electrification of cars,
613
00:33:59,796 --> 00:34:05,316
as well as now the demand from AI, not to
mention a multi-generational workforce.
614
00:34:05,316 --> 00:34:07,996
There are so many
challenges to sort out to.
615
00:34:08,056 --> 00:34:12,315
And the timeline that you're
thinking on from enabling retirees
616
00:34:12,366 --> 00:34:17,196
to feel connected and being
impactful in a post-retirement era.
617
00:34:17,525 --> 00:34:21,996
As well as thinking about the next
generation to meet the demands that
618
00:34:22,366 --> 00:34:26,746
we're gonna need from this industry so
that we can make sure the lights turn on
619
00:34:26,791 --> 00:34:28,866
when you flip the switch but much more.
620
00:34:29,286 --> 00:34:33,996
But even beyond that, the amount of
demand for all the various efficiencies
621
00:34:33,996 --> 00:34:38,226
and use cases that are gonna come in
from AI over the next generation for us.
622
00:34:38,226 --> 00:34:41,356
Really, it's gonna come down to
at the most foundational level We
623
00:34:41,356 --> 00:34:43,166
need energy to make it all happen.
624
00:34:43,166 --> 00:34:46,335
So thank you for the great work
that you do in sharing that with us.
625
00:34:46,335 --> 00:34:49,025
This is a lens that I personally
haven't been thinking about.
626
00:34:49,076 --> 00:34:50,966
And I wanna thank you for that.
627
00:34:51,386 --> 00:34:55,235
And for everyone who's listening out
there and who are looking to future
628
00:34:55,235 --> 00:35:00,366
proof your own organizations and future
proofing your own HR departments,
629
00:35:00,486 --> 00:35:04,206
I think you'll have some takeaways
here while you're on that journey.
630
00:35:04,206 --> 00:35:07,986
So thanks again and I'll
see you on the next one.
631
00:35:09,044 --> 00:35:12,104
Thanks for joining us on this
episode of Future Proof HR.
632
00:35:12,344 --> 00:35:14,954
If you like the discussion, make
sure you leave us a five star
633
00:35:14,954 --> 00:35:17,834
review on the platform you're
listening to or watching us on.
634
00:35:18,254 --> 00:35:21,194
Or share this with a friend or colleague
who may find value in the message.
635
00:35:21,494 --> 00:35:26,444
See you next time as we keep our pulse on
how we can all thrive in the age on AI.
