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:

• Future Proof HR Podcast
• Future Proof HR Community
• Connect with Amy Johnston on LinkedIn

If you're deploying AI on the network, you have to be a hundred

2

percent sure that it's not hallucinating,

that it's making the right decisions,

3

that it's not gonna hurt someone.

4

So we are very much mindful of our

workers' safety and our community safety.

5

We wanna make sure that we are

making the right decisions.

6

So a safe, reliable network

is it's absolutely key.

7

Thomas Kunjappu: They keep

telling us that it's all over.

8

For HR, the age of AI is upon

us, and that means HR should

9

be prepared to be decimated.

10

We reject that message.

11

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

12

Instead, it'll be defined by those

ready to experiment, adopt, and adapt.

13

Future Proof HR invites these builders to

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

14

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

15

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

18

forward-thinking HR leaders are preparing

for disruption and redefining what it

19

means to lead people in a changing world.

20

I'm your host, Thomas

Kunjappu, CEO of Cleary.

21

Today's guest is Amy Johnston,

head of people and capability

22

at Orion New Zealand.

23

Limited the electric.

24

Electricity distribution company serving

Canterbury New Zealand, municipally

25

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

27

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.

30

And O Orion has developed some leading

projects in this, arena and beyond just.

31

Tackling the realities of

electrification and the, demands

32

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

34

replace technical workforce that we'll be

talking about and building new recruiting

35

pipelines, and as also including with a

diverse, workforce and using AI to improve

36

safety and service, for, what otherwise

could be a pretty dangerous, kind of role.

37

Without further ado, we'll get into it.

38

Amy, welcome to the podcast.

39

Amy Johnston: Thanks so much, Thomas.

40

It's an absolute pleasure to be here.

41

I'm really excited for the chat today.

42

and talking about a little bit more

about what we do in electricity.

43

Thomas Kunjappu: Yes.

44

Maybe you could just introduce us a

little bit what your organization does,

45

and then I would like to take it into the

impact of the broader space with AI next.

46

But could you tell us a

little bit about Orion?

47

Amy Johnston: Yeah, of course.

48

So at Orion, our purpose is about

powering a cleaner, brighter

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community, future with our communities.

50

and what we're really focused on is, we.

51

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

54

our generators to your home.

55

so the power lines on the street,

the substations, that's what we do.

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Thomas Kunjappu: It's in everywhere.

57

it's in the electricity, electrification.

58

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

60

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.

62

Could you just tell us a little

bit about, the demand side of what

63

you're seeing in electrification.

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Amy Johnston: Yeah, absolutely.

65

There's really two components

to electrification.

66

One is the sustainability angle.

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So we are very much looking at

how do we generate our electricity

68

in a much more sustainable way.

69

So hydro, wind, solar.

70

And that's everything from our

hydroelectric dams, right the way

71

through to putting solar on your roof.

72

And those things like solar on

your roof is an example of how what

73

previously was a very linear system.

74

going from generation to your home is now

becoming much more circular and disrupted.

75

You are generating your own power

to power your home and wanting

76

to feed that back into the grid.

77

So it really changes the

dynamics of the grid.

78

So we are looking at distributed

energy, resources and pathways.

79

And that's very much changing

the game for the industry.

80

The second thing that we are really

focused within electrification

81

is the use of electricity.

82

And that is significantly increasing.

83

So not just you at home

plugging your electric vehicle

84

into, instead of buying gas.

85

But also the way in which we work.

86

So when you think about AI,

a data center drives or uses

87

huge amounts of electricity.

88

So in 2024, that was about 1.5%

89

of the global production of electricity.

90

that's anticipated to grow 12% every year.

91

year on year and will become exponential.

92

So we anticipate that in the US market

93

computing, all of those types of

things will utilize about 50% of the

94

electricity production in the US.

95

It's an absolutely astronomical challenge.

96

Thomas Kunjappu: That is ridiculous.

97

So we're going from something in of course

training was happening in in small pockets

98

for AI researchers for many decades.

99

But we had the ChatGPT

100

And then leading up to that, we've

gotten to these models that are

101

being trained on petabytes of data,

which are using a lot of electricity.

102

And that is only, those clusters

are only getting bigger.

103

I've never heard that before.

104

It's going up to 50% of potentially all

electricity use will be fundamentally

105

coming from the AI revolution.

106

Amy Johnston: Absolutely.

107

you look at things like, Microsoft

just signed a 20 year agreement

108

to turn back on Three Mile Island.

109

That will essentially be a microgrid

where all of that electricity will be

110

going to power a Microsoft data center.

111

It's utilizing about 335 megawatts.

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The entire 335 megawatt output of

Three Mile Island for that data center.

113

So it's huge.

114

When you translate that into the New

Zealand environment, we have the same

115

challenge, but not on the same scale.

116

Our geography doesn't support data

centers in the way that it does in the US.

117

So we will have some but they

won't be nearly as big as

118

what they're in the US market.

119

Thomas Kunjappu: But it's still gonna

make a dramatic impact for I don't know.

120

The last time a new category

of machinery, right?

121

Maybe it's actually electric cars, right?

122

That was actually becoming a significant

portion of the actual overall grid

123

demand or electric electricity demand.

124

But this is just completely

upending the industry.

125

And it's something I talk about

and think about AI all the time.

126

But from a from a

completely different layer.

127

Than you are, right?

128

And especially we're thinking about

how it impacts the workers work, HR,

129

and also for knowledge workers, all

the different various ways that it is

130

impacting your day-to-day workflows.

131

But the foundational layer

it's really important.

132

The work that you're doing and the

challenges that it's posing in this

133

transition for an organization like yours.

134

So thank you for that context.

135

Now let's talk a little bit about with

all of these challenges, coming up, since

136

you've set the scene for us a little bit.

137

First of all, about applications.

138

So throughout Orion or

electrification as an industry,

139

I imagine there are lots of use cases

that are already coming into play that

140

help your employees do their work more

efficiently, safely in a better way.

141

Amy Johnston: Yeah.

142

At Orion, we're very much a

technology driven organization.

143

It's just in our DNA having a

huge number of engineers on deck.

144

So we're really interested in

technology, how does it help us work

145

and how does it change our world?

146

We've been, has been a really safe

place for the organization to play.

147

So whether it's deploying AI agents, those

types of things, our area isn't as safety

148

sensitive as some of the other areas.

149

if you're deploying AI on the network,

you have to be a hundred percent

150

sure that it's not hallucinating,

that it's making the right decisions,

151

that it's not gonna hurt someone.

152

So we are very much mindful of our

workers' safety and our community safety.

153

We wanna make sure that we are

making the right decisions.

154

So a safe, reliable network

is it's absolutely key.

155

We've had some really cool projects, kick

off, whether that is in the P&C space

156

and the use of agents, or across the

network to really help encourage safety.

157

We've just had a new pilot with a

visual language model that our drone

158

flies over our lines, and takes video.

159

That video then runs through the AI

model and we're able to assess the

160

components that are on our lines

and what condition they're in.

161

So that's an early beta phases right now.

162

But what it's doing is it's meaning

that we don't have to put our guys

163

in a cherry picker on difficult

terrain to go up and have a look.

164

So it means that we are able to better

assess our network, the quality of

165

our network, and where we need to.

166

To put investment to maintain the network.

167

Thomas Kunjappu: That's a great example

that goes to safety and efficiency.

168

In this particular use case.

169

And you're combining drone

and video technology with LLMs

170

to make all of that happen.

171

You also mentioned on the P&C, on the

People and Cultural side, there's some

172

interesting things that you've tried.

173

Before we get into that, I'm

curious about a statement you made.

174

Because I don't hear that often.

175

From an AI perspective that

the HR or people and culture

176

side is a safe place to play.

177

Often there are thoughts about

we have to be careful here.

178

There's either employee data or

we need to be thinking about like

179

governance and making sure that stuff

that doesn't get out because it's

180

really important that it's I dunno,

strategically important to the company and

181

maybe it's a mindset

more than anything else.

182

But I've heard that a lot.

183

That, hey, this is, the HR team

is the last place where we need to

184

innovate around this stuff because we

need to be careful with what we do.

185

Amy Johnston: Yeah.

186

I think for us, the stakes are so high

in the other parts of the business

187

that getting the governance, getting

the privacy, getting all those pieces.

188

We can test that in P&C.

189

We can communicate it with our employees

and say, Hey, here's what we're trialing.

190

Help us if you are concerned, let's

have a chat about what that means for

191

your data and how we can manage that.

192

And it's given the AI

team and our business and

193

opportunities get comfortable.

194

Inwardly focused before we go into

something that's higher stakes.

195

I would say it's a mindset shift.

196

but you do have to make sure that

you've got those things in place.

197

Like good governance and that you

understand your privacy requirements

198

and how you're going to maintain those.

199

And that you communicate

it with your employees.

200

We're not creating something

and landing it on them.

201

We take a very consultative approach,

so we let them know it's coming.

202

We'll have a chat about concerns

first, what they're thinking about

203

before we actually finalize and deploy.

204

Thomas Kunjappu: I love that framing

of it because, it's actually a

205

unique advantage that we have, right?

206

When our customers are aligned

with us in the same mission, right?

207

And so it can bias some grace in some

ways and the ability to experiment

208

provided that you're communicating

effectively about it, right?

209

But there is an opportunity to

really work alongside your customers,

210

your employees, in enabling

211

whatever the project is, and whether

that involves technology or not.

212

And whether that technology is AI or not.

213

It's actually a relative advantage.

214

It's tough to say that to

consumers, but we're experimenting

215

with different technologies.

216

You may or may not experience brown heads.

217

Amy Johnston: Exactly.

218

Thomas Kunjappu: Watch out for that.

219

Amy Johnston: You know, you expect when

you flick a light switch, the lights too.

220

Turn if we are doing something on the

network, that means that is unlikely

221

to happen or may cause issues there.

222

We're gonna face huge problems.

223

Our customers are also our owners.

224

So we are municipal pre-owned.

225

So all of the rates payers across

Canterbury are essentially our

226

owners as well, and our shareholders

as well as our customers.

227

So brownout is just not something

that's on the cards for us.

228

Thomas Kunjappu: Can you tell us a little

bit more about what have been some of

229

these experiments that you feel like

successful or not any learnings from

230

some of the things on the people and

culture side that you've done with AI?

231

Amy Johnston: Yeah, so we've been on

a real journey to automate tier zero.

232

And really think about how we

can do that while we're in a

233

build phase for the organization.

234

So we are looking at what

does the future hold?

235

How are we going to retain employees

when the market does start to loosen up?

236

And how are we looking

to grow in the future?

237

So what is our employee experience?

238

How do we retain people?

239

How do we make sure that's

aligned to our long-term strategy?

240

And how AI has worked in that for

us is about that automation, that

241

optimization of our key tasks.

242

For example, we've deployed

an agent that sits on top of

243

our policies and procedures.

244

It answers all frontline

questions from our employees.

245

After deploying that and working

through a few hallucinations.

246

We have reduced our frontline calls

into our service center by about 50%.

247

Thomas Kunjappu: Oh, that's great.

248

So you're doing a lot of call

over the phone support previously,

249

and that's still available.

250

But you're seeing the

demand for that come down.

251

But is the modality from the

employee's experience perspective?

252

It's gone from phone to is

it text or sending an email?

253

Amy Johnston: We're a diverse.

254

Our workforce is across

multiple locations.

255

So that might be a drop in to the office.

256

It may be a phone call,

it may be an email.

257

We've been logging all of those

things as part of tier zero.

258

So it's a reduction in

all of those modalities.

259

So essentially the AI agent acts

like a search engine, but it's a

260

closed language model that sits just

over our policies and procedures.

261

So it's generating the answers

of what is our vacation policy?

262

Can I take time off over Christmas?

263

Are we having a shut down?

264

All of those types of things

from a set pool of information.

265

And that's allowed employees to get the

information right at their fingertips.

266

The answer immediately, and has reduced

pressure on our coordinator and advisor.

267

Thomas Kunjappu: That's amazing.

268

One of the limitations of this kind of

solution is that it's based on software.

269

So the expectation for a diverse

workforce to go into a piece of software

270

and search for something while certain

HR teams as well as employees, their

271

expectation is to knock on a door and

talk to a friendly face about something

272

that they need in terms of a service.

273

But it sounds like you're experiencing

those door knocks go down because

274

people are naturally able to use such a

solution to get their questions answered.

275

Amy Johnston: Yeah.

276

The door knocks are becoming more

because they want to come and say

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Hi, which is exactly what we want.

278

We are a distributed workforce anyway.

279

We have been since COVID.

280

For us, we are not turning

back the clock on that.

281

We literally can't fit in our building.

282

For us flexible working

is part of who we are.

283

And so people have the ability to come

and door knock and see us, but it's about

284

enabling that flexible working for not

only the employees, and so that they're

285

still getting the information and the

connection that they need and want.

286

But also for the P&C team, we don't

have to have someone on site every day.

287

We can also have flexible working as well.

288

So it's a huge benefit.

289

Thomas Kunjappu: And then could I

ask about how you got a project like

290

this over the finish line, right?

291

So it's one thing to say, Hey,

it would be nice if we could

292

work on this particular thing.

293

But then who did you find were

the internal stakeholders you're

294

collaborating with to get to

the ultimate outcome and value.

295

Of course, your coordinators and

assistants, they were involved at some

296

level, I imagine, with the project.

297

But then, who else was important to go

from conception to outcome in your case?

298

Amy Johnston: So we've been

very fortunate with our business

299

intelligence and AI team.

300

So we've been working with them for quite

some time on the types of data that we

301

have within MP&C and how do we get robust

data that tells us a really great story.

302

We offered ourselves up is

that a safe place to play.

303

And it was really in that mindset

shift that enabled them to go,

304

okay, here's somewhere that's

not gonna impact the network that

305

we have the support of the team.

306

The team wants to create and

play in this space so this is a

307

safe place for us to do that too.

308

And then it was about getting the right

governance in place to get the support of

309

our senior executive team and our board.

310

and so they've been really

wanting to see AI utilized.

311

AI and data is going to be absolutely key.

312

And not just how we manage the

assets moving forward, but how we

313

manage our workforce moving forward.

314

The workforce challenge that we are

facing in our industry is immense.

315

And we need to be utilizing these types

of technologies just to be able to

316

keep pace and supplement our workforce.

317

This has been a fantastic

conversation so far.

318

If you haven't already done so,

make sure to join our community.

319

We are building a network of the

most forward-thinking, HR and

320

people, operational professionals

who are defining the future.

321

I will personally be sharing

news and ideas around how we

322

can all thrive in the age of ai.

323

You can find it at go cleary.com/cleary

324

community.

325

Now back to the show.

326

Thomas Kunjappu: So your People and

Culture use cases, was almost the

327

sandbox obviously useful in terms of

getting some productivity for your team.

328

But also started point painting the way

the path forward for many other use cases.

329

I'm curious about the structure

that you had internally.

330

So you mentioned there's like a BI team.

331

BI/AI team.

332

Did your organization invest in a

whole new AI team or did an existing

333

team evolve in towards that and

then working with your use cases?

334

Did they then go on to keep working with

other teams internally to work on other

335

AI agentic workflows or efficiencies?

336

Amy Johnston: Yeah, absolutely.

337

So probably about two and a half, three

years ago, we established a business

338

intelligence team as part of our wider

data, digital and technology team.

339

That business intelligence

team as an asset manager.

340

We have huge amounts of information.

341

So it's really about how

do we process that data?

342

How do we understand it.

343

And how does it support our strategy?

344

And just support our decision making.

345

That team grew and added in a AI

function probably about a year

346

and a half, year and a half ago.

347

They're made up of incredible data

and computer science individuals

348

and mechatronics engineers.

349

They mechatronics engineer also has a

knowledge of electrical engineering.

350

And is really able to progress this.

351

It's a small team of three, but mighty.

352

and they're absolutely phenomenal.

353

In New Zealand the environment

is heavily regulated.

354

So we can't, for example, as an EDB, we

cannot generate and we cannot retail.

355

We're purely the lines company.

356

There is 26 of us across New Zealand.

357

So we're also, geographically

quite small as well.

358

We've made the decision that we will

work collaboratively as an industry

359

and feed this into other EDBs and

work with other electricity companies

360

on how we might solve this problem

or these problems collectively.

361

And that's in the best interest of

all New Zealanders and our community.

362

There's no point reinventing

the wheel 26 times.

363

So how do we do this together?

364

And that is really key.

365

But, as far as internally, we've been

a safe place for this team to play.

366

And then they're now looking at what

are the use cases beyond that team.

367

So what that might look for example, in

engineering, with engineering standards.

368

The ability for an AI to

hallucinate that is greater, but

369

our tolerance for hallucinations

in that space is much lower.

370

99.9%

371

is still not good enough.

372

We are really looking at those

sort of things about where can we

373

play and where do we need to create

a product that is exceptional.

374

Thomas Kunjappu: Thank you for going

through it and I think a takeaway.

375

Because the reason I ask is often, when

organizations do get to the point of

376

investing in an AI strategy or having

a technology team centrally to invest

377

in understanding or how to leverage

AI for various internal workflows.

378

Often the people team might

be the last in line, right?

379

Even if you have your hands raised,

you're not getting the time of a

380

lean team that might be working on

other projects collaboratively with

381

other parts of the organization.

382

but I guess a takeaway I have is if

that is you and you are in a high

383

risk industry where the ability or

willingness to experiment on workflows

384

within your product or on top of

customer data is extremely low.

385

That might be a place for you to raise

your hand and get in an experiment going

386

for your people and culture workflows.

387

I wanted to ask about

a whole different area.

388

Which is a whole other

transformation that's happening.

389

As you're thinking about the

demand side, we've talked about

390

for elect electrification.

391

How you're meaningfully changing

your internal workflows within

392

the people and culture team.

393

And also in the broader organization.

394

But then there's the

people dynamics itself.

395

Tell me a little bit about your

employee base and this workforce

396

crunch that you've been talking

about and are working through.

397

Amy Johnston: Yeah.

398

With electrification and with that level

of increasing demand that we're seeing

399

that we talked about earlier from AI data

centers, all of those types of things.

400

That means that there's a

workforce impact to that.

401

We've seen projections that, for example,

in electricity, in our industry, in

402

the US there will need to be 30 million

403

to deal with this level of demand.

404

That's everything from our lines

technicians and the guys on the tools,

405

right the way through to the back office.

406

The engineers, right across the role.

407

They also predict that 16 million

of those roles will actually be

408

newly formed different roles.

409

That's quite interesting in itself.

410

So for us, the workforce challenge

is really about how do we ramp up,

411

how do we get the right skill sets,

how do we ramp up our workforce?

412

And how do we do that in a way that's

gonna support our long-term needs.

413

So what we've seen historically,

electrical engineering is

414

incredibly male dominated.

415

20% of at our local university, only

20% of the entrance into the electrical

416

engineering program are women.

417

That's their highest percentage ever.

418

We know that we can't solve the

challenge and the level and get the

419

level of workforce that we need unless

we engage the entire population.

420

So we need much greater

diversity in our workforce.

421

Both in gender but also

in ethnic diversity.

422

engaging our indigenous people in New

Zealand, Maori and Pacifica Asian.

423

All sorts of we need that

diverse workforce to be able

424

to solve these challenges.

425

So at Orion we are really

thinking about how do we attract

426

and retain a diverse workforce?

427

How are we talent pooling them

into the organization and how

428

are we retaining knowledge?

429

So we have some phenomenal people

in our organization who have

430

been here their entire careers.

431

40 years of tenure.

432

They built the substation down the street.

433

They've got that knowledge in their head.

434

How do we retain that knowledge, and

make sure that we can transition that

435

into the up and coming population.

436

So it's a really unique and

interesting challenge that we

437

have in our sector at the moment.

438

Thomas Kunjappu: That's a

wealth of experience, right?

439

I think in many organizations,

no one has spent a lifetime

440

working at the organization.

441

And so that's something to be treasured.

442

And something that I understand that

there's programs that you're putting

443

in place to help leverage further.

444

Can you tell me a little

bit more about that?

445

Amy Johnston: Yeah, absolutely.

446

When we had a look at our demographic

data and we realized that we have quite

447

a proportion of our population at Orion

who are nearing the typical retirement

448

age, which is around 67 in New Zealand.

449

And so our concern there was that we

would have huge amounts of institutional

450

knowledge walking out the door.

451

And

452

what we also found is that we

want to make sure that people feel

453

comfortable and safe in retiring.

454

They've worked their entire careers.

455

That's something to be celebrated

and we want them to have that reward

456

of having their golden years to

themselves while still being engaged.

457

And when we had a look at what were the

barriers to retirement but also what did

458

a meaningful knowledge transfer look like.

459

It wasn't just process mapping

and documenting that knowledge.

460

That's part of it.

461

But very much about creating an

experience that honored the Mahi

462

and the work that these people

had put in and really maintain

463

which is about maintaining their honor and

maintaining that knowledge that they have.

464

And so we looked at a

transition to retirement plan.

465

And we now have a process where

people can give us up to two

466

years notice of their retirement.

467

Allowing us the ability to succession

plan and really get a grasp on what

468

does that transition look like?

469

But also enables the retiree

to engage after retirement.

470

So they can come back as

a coach and as a mentor.

471

To our interns, to our power youth program

and also to our early careers employees.

472

They maintain their

membership of our social club.

473

So they're able to engage

in all of the Orion events.

474

They can come to our Christmas

party, they can do all of those

475

awesome things that we do.

476

And they're able to maintain that social

connection that was absolutely important

477

that social connection was upheld and

people could really see themselves as

478

an alumni of Orion, not just a retiree.

479

So we've really created a program that

seeks to maintain that relationship

480

and allow us to still call the

guy who created the substation

481

to say, Hey, what about this?

482

Have we are dealing with this.

483

Can we take you for coffee?

484

And that's paid.

485

We want to make sure that

we honor that retiree.

486

They're absolutely paid for

their knowledge and their time.

487

But yeah, it's a very new program

for us, but we've had thus far,

488

nine people take us up on it.

489

And that's in the last three months.

490

So it's been a great transition.

491

Thomas Kunjappu: I love that because

I like to think that this if work is

492

not just about earning the paycheck.

493

Although to your point, like

that's part of what you're doing

494

just to say that it is valued.

495

It's also the community and the ability

to feel like you have some expertise

496

and that others can rely on you

for something you can still provide

497

many of those things and people.

498

maybe into their seventies that you

feel that need to be valued by your

499

family, community, but also from your

like the organization you retired from

500

especially if you spend a lifetime there.

501

So I think that's a wonderful idea

which also helps solve a very real

502

problem you mentioned from a workforce

perspective because there's gonna

503

be so many new people we need to

train up into the system, right?

504

And just all your peer organizations

throughout the country.

505

And thank you for every time going to

resorting to American numbers, when

506

you're talking to the millions there.

507

Let's talk about that side a bit.

508

So the recruiting channel,

the challenge, the funnel.

509

You talk about a little bit

about the extreme upfront.

510

The education pipeline.

511

And you wanna basically from an education

perspective upstream from the workplace

512

and recruiting just make it as easy

for everyone to get excited and learn

513

about electrification and become an

electrical engineer and become part of

514

this pipeline that you're recruiting into.

515

But I know specifically with

electrical engineering, that's been

516

a major that a lot of people have

diverted into software and potentially

517

other layers of the AI field.

518

I imagine this is an industry-wide

recruiting problem that crosses countries.

519

I imagine that this kind of programming

and it's exciting we just talked about

520

this retirement program can actually

help a little bit potentially with

521

recruiting as coaching for these folks

as you're trying to recruit them in.

522

But what are some of the tools or

experiments or that you're looking at

523

or industry-wide that you feel like

you need to get into as an industry to

524

get more of young folks into the field?

525

Amy Johnston: There's been quite a

lot of work that's been done in this

526

space over the last couple of years.

527

Both in New Zealand but

also internationally.

528

We are not in a position as an industry

where we can compete against each other.

529

We need to work as a career.

530

Because the problem is that large and our

need for people, people doing the right.

531

Training programs and

those things is that big.

532

So we've been working within New Zealand,

we've been working collectively with

533

some of our industry bodies to really

say, how do we solve this problem?

534

How do we look at this from

a holistic standpoint and

535

really seek to engage people?

536

And potentially a career in electricity.

537

So the industry bodies

have been doing that work.

538

Really seeking to understand

what are the barriers.

539

And what's motivating people to

join particularly students and

540

young people to join our industry.

541

Naturally there is marketing

campaigns around that.

542

There's also things associated

with the talent pipeline.

543

So one of the challenges we have is

obviously in that male dominated space.

544

As a male dominated industry, so

we need to increase the rates of

545

females within the organizations

and within the training programs.

546

So we've been really focused on STEM

programs and as an industry we have, for

547

example, a program that's supported called

Wonder Project, where they take science

548

projects to schools across the country.

549

They're aimed at about seven years of age

and they do a cool project that's based

550

on our industry to understand generation,

the grid and how power gets to your home.

551

So the particular organizations

across the industry have sponsored

552

those to get them into schools.

553

We've worked with a organization called

GirlBoss and six of our other EDBs

554

have got on board with this program.

555

And we run a school holiday program for

young girls between the ages of 16 and

556

21 to inspire them into electricity.

557

So that's taking girls at the key

point of career decision making in

558

that sort of 16 to 18 year range.

559

You are really making the decisions about

what you're going to do with your life.

560

And giving them real practical options,

mentors, and a real life project that

561

helps to inspire them into STEM and

hopefully electrical engineering.

562

What we found when we looked at it

with Alexia from Girlboss was very

563

much that these young girls were

really interested in sustainability.

564

But we're chasing climate science degrees.

565

Now there's not too many jobs out of

climate science degrees, but within

566

electricity, there is a huge...

567

there's a ton of jobs.

568

There's huge amount of problems to solve,

and most of them are sustainability based.

569

'Cause we are looking

at how do we electrify.

570

How do we create sustainable generation?

571

How do we transmit in a sustainable way?

572

How do we do microgrids and

distributed energy resources.

573

But then also how do we work with our

communities on a just transition and

574

really making sure that we're doing

this with our community not to them.

575

So it's absolutely vital to

get those individuals that are

576

already thinking that way at 16.

577

Already excited, already motivated

about science into our field.

578

So we are really looking at how

do we develop the talent pipelines

579

from a really young age into the

industry and then with the end goal

580

of from a P&C perspective solving

our gender pay gap and our vertical

581

and occupational segregation issues.

582

It's a huge pipeline.

583

Thomas Kunjappu: It makes me

think I'm not sure what I'm gonna

584

have for breakfast tomorrow.

585

And here you're looking at industry

wide, how we can influence and market to

586

and recruit the best talent across the

board to set up the industry for success.

587

Decades down the line.

588

I really appreciate that kind of thinking.

589

And that's a very nuanced insight that

you just brought Amy, about Different

590

ways to describe the same problem.

591

And some, whether you're talking

about like a tough engineering

592

challenge or a tough sustainability

challenge, but it's the same challenge.

593

But different types of different

language and different exposure can

594

speak to different types of folks.

595

And you gotta do everything you

can to help us all electrify

596

the right way across the board.

597

Yeah.

598

I love that.

599

Amy Johnston: Yeah.

600

And I think we need

different thinking as well.

601

We need people to think outside

of the box and to attack these

602

problems in a different way.

603

It's how we progress,

it's how we move forward.

604

And we know the importance

of that diverse thinking.

605

So it's not just diversity from

a gender standpoint, it's also

606

diversity of thought that's incredibly

vital to solving these challenges.

607

Thomas Kunjappu: Thank you so

much for this conversation, Amy.

608

It's been, really interesting

for us to dive into first of

609

all the industry that you're in.

610

It's been around since the Thomas

Edison days, but it's almost

611

being reshaped entirely and like

the way it's all being delivered.

612

The first whammy is everything about

sustainability, electrification of cars,

613

as well as now the demand from AI, not to

mention a multi-generational workforce.

614

There are so many

challenges to sort out to.

615

And the timeline that you're

thinking on from enabling retirees

616

to feel connected and being

impactful in a post-retirement era.

617

As well as thinking about the next

generation to meet the demands that

618

we're gonna need from this industry so

that we can make sure the lights turn on

619

when you flip the switch but much more.

620

But even beyond that, the amount of

demand for all the various efficiencies

621

and use cases that are gonna come in

from AI over the next generation for us.

622

Really, it's gonna come down to

at the most foundational level We

623

need energy to make it all happen.

624

So thank you for the great work

that you do in sharing that with us.

625

This is a lens that I personally

haven't been thinking about.

626

And I wanna thank you for that.

627

And for everyone who's listening out

there and who are looking to future

628

proof your own organizations and future

proofing your own HR departments,

629

I think you'll have some takeaways

here while you're on that journey.

630

So thanks again and I'll

see you on the next one.

631

Thanks for joining us on this

episode of Future Proof HR.

632

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633

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634

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635

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how we can all thrive in the age on AI.