Getting Ready for Change in the Digital Disruption video transcript

Thank you. Can you all hear me? Yup? Okay, great. I'm going to set a bit of a context to start with. Has anyone seen this picture? No. One person? Yup. This is New York, and it's the Easter Parade in 1900 on Fifth Avenue. If you have a look at it closely, you'll see that it's mainly horses and carriages, right? That was the main mode of transport back then. If we flick to the next picture, 13 years later, so only 13 years later, this is the same view, the same Easter Parade. If you have a look now, can you tell me what the difference is? Yeah. It's cars. Apparently, there is a horse and a carriage, so that if you can find it, it's sort of up in the corner about halfway. But you can see in the space of 13 years, we flipped from completely using horses and carriages to using cars. Humans have a significant track record of being able to undertake large-scale changes quite effectively and quickly.

We go now and have a look at what's on the agenda at the moment, which is very much data. We hear data, data, data. A lot of people are saying, 'Well, why is it so important?' To give a context to that, if we have a look here, can anyone recognise what this is? Yeah. Of course, it's a Phoenician, what we call a cuneiform tablet. It was the way that they wrote about 4,000 years ago. It's the first style of writing apart from hieroglyphics that we know with the Egyptians. This was a clay tablet, and it had to be impressed and then it had to be copied. Can anyone tell me what the limitations of this form of communication might be? Yup, labour-intensive, absolutely. Anything else? Disintegrate, yes. It's only got a certain period of time. Yup. No, you can't catch hardly anything on it, can you? What else? Yes, distribution. Yup. Sorry, what was your ... Exactly, difficult to replicate.

This was a primary means for us right up until the last few hundred years for us to be able to communicate. Then fairly quickly, we go to the USB stick, right? Obviously, you've got the cloud, which is really replacing the USB stick and Dropbox, and those types of things now. With the USB stick in this particular example, think of Edward Snowden. Edward Snowden was an ex-CIA agent. He was a bit of a renegade, he was a bit of an amazing technologist, and one of those guys that could just almost break into anything or hack anything. What he did was he was able to hack into the US Secret Intelligence Agency, download thousands of files, pop them on a USB stick, and then release them to journalists. It was a massive issue at the time. The information spread like wildfire. We're able to find out all sorts of interesting secrets that the Five Eyes and various other agencies, including the US, were trying to keep secret. That all happened within the space of a few hours.

You can just see the context and the contrast there from where we've been in history to where we are now. If you add the cloud into that, again, it's even faster. All of this has significance because data is not necessarily an IT or a technology thing. It's very much a business thing. It's information held by business now. Regardless of how it's held, it's information that isn't necessarily an IT thing, that IT own, although IT do need to look at governance and being able to put some architecture around it. But it's what the business and what a lot of you people in HR and in the other areas you're in, you hold this information. It's absolutely vital.

We look here at the speed and the context, my team absolutely love graphs like this. I know one of my team members is always getting excited and wanting to publish similar ones, but you won't be able to read it in detail here. But every minute of the day 176,220 users make Skype calls, right? Tinder users match 6,940 times every minute of the day, and this is growing. Amazon ships 1,111 packages. Everything's happening so fast and it's growing and it's a digital disruption. It's what we call the Fourth Industrial Revolution. Some people have really no idea why it's the fourth.

It's because the first was the early days of the rail and they were able to get things from A to B in steel factories. The second was very much about getting the factories in place and getting people from the firms to come in in the industrialisation. The third was actually in the '70s and '80s when we started looking at IBM and computers, and having information stored in that way. Now, the fourth is upon us now, which is that everything is just really fast. It's getting a little bit more difficult to keep up. We're moving to the cloud. There's different ways of doing things. That's called the digital disruption. But also, if you're interested in here, this industry 4.0, that's what that means.

It's always scary at first to think, 'Wow, things are happening pretty fast.' But what I do want to show you is that it's not new. There's a bit of fear of the future, a bit of uncertainty. We see people fear-mongering a little bit and saying, 'Oh, I'm really worried about the future and what's going to happen.' Yet, this news article here was in 1928. It was concerns over unemployment. It was the front page of the New York Times. They were complaining and getting really upset about the fact that they would, A, have these new machines that would go around and they would have cement in them already made. Then these big trucks, these machines, would be able to go onto a site and just pour all this concrete, and then have it laid, and it would replace thousands of jobs.

Now, what we can see in particular, the concrete industry is what it's done is, yes, it did happen and, yes, it replaced some jobs, but those types of jobs have changed. How many of us have pretty paved concrete? How many of us have nice patterns, have different ways of laying it? That's the industrial side of it as well, which is save time and money. But we've been able to find new ways of redeploying people within that industry, and new ways of being able to look at that and go, 'We could actually make this more aesthetic and improve this, make it stronger as well.'

The traditions that were behind the old having the mixer and having to shovel the sand in and having to shovel the gravel in and putting it all together and having to lay it out, we still see there's humans that still have to go out there at the moment and make it smooth and that as well. Yes, there are machines to help them. But the end of the day, it's the human overseeing it to make sure it's correct and laid properly. That's a very simplistic version, but it was a real fear at the time.

The other one that's in this article that was a real fear was that people who opened and closed doors on trains were going to lose their jobs. The doors open automatically as we can see, they're going to lose their jobs, and there was significant concern about that. Yet, 10 years later, there was still about 4.5% unemployment. It didn't actually impact that significantly. They were able to find jobs elsewhere. They're able to improve ticketing. They were able to redeploy them to be able to check ticketing and those types of things instead of having to open and close doors.

We have seen it before, and we've seen a bit of this fear-mongering. This picture here is The New Yorker again 2017, and, again, a bit of fear-mongering here. You can see the robots are actually helping the homeless man, giving the homeless person money. Again, we're into this, 'Oh, my goodness, the future looks uncertain. What are we going to do?' But I'd argue that humans are quite resilient. You think about it through the ages, the amount of change that we've gone through in all of those industrial revolutions, the amount of change that we have to put out within our daily lives, the things that we're coping with, we've been able to get through it.

Yes, there's been some ups and downs. Yes, some people are like, 'Yes, well, I lost my job in the global financial crisis and that.' But when you often look at it, people have an amazing resilience and ability to be able to regroup and move on. As a society, and my background as a sociologist, I look at this at the cohort level and say that humans have been able to respond to this change quite positively. We are a bit scared. It is a bit uncertain. Not everyone gets through 100% all of the time. But as a community, and as a group, we managed to keep moving forward and we keep able to be able to adapt, and to be flexible, and to see the new world quite quickly. Some of these things come upon us quite quickly, but we don't realise just how quick it is. Yet, we somehow get through it.

I thought I'd give you a few little hints about the future workforce. What does it actually look like? What are we doing here? By 2020, millennials will be 50% of the workforce, and they're digital natives. I was just talking before in the break about some of my team take notes on their phone. I have to remind people and tell people that they're not texting, that these people coming through have a different way of storing the information, or communicating. We have to be able to look at that and to be able to assess that and integrate that into what we do and how we do it. There are skilled labour shortages. Particularly in the industry I work in in analytics, significant labour shortages. We see that all the time in the media, with the government with visas and things and trying to get people in to be able to fill some of these gaps.

Independent workers. We hear a lot about this, the gig economy, there's more contractors. We see it in the public service significantly, right? 5 or 10 years ago, there weren't the amount of contractors that there are now. It was a bit more and we think 15, 20 years ago, and then when I first entered the public service, my dad said to me, 'This is great, Kylie, you've got a job for life.' I thought, 'I don't know if I want a job for life, but I don't like it.' To him, it was very much that this is great. To me, I was like, 'Oh, is that what I really want?' We have to think about generational attitudes change as we move along.

Augmented workforce. Robots are here to help us, they're here to augment the decision-making. They are not here to replace us. I'll explain more about this later. But they are here to collaborate with us to help us make decisions, to help us to have our lives to be a lot more easier, and to help us in terms of making those great, big discoveries and those exciting things, like curing cancer. Just they're there to be able to do that. That's what we're trying to use them for, and artificial intelligence, not just robots, obviously.

I'll give you an example of that. In terms of automating tasks, so I was doing a bit of work with our Department of Human Services (now Services Australia). One of the key things we were looking at, and I do have a paper written on this, was about social workers. Social workers, we see in the media, continually struggle with the workloads that they have, a significant work that they have to do, very traumatising at times for them as well. They have tons and tons and tons and tons of files, and maybe not too similar to some of you in this room, right? There's all these cases that they have to get to, and they're growing every day. Then they have to triage the reports that are coming in. It's quite a complex and challenging job.

What we're looking at doing was feeding as much information we could from all the files that they have. They come in everyday, and there's a stack of files, and you think, 'Oh, I've got down in a few of them and I'll go out in a few visits,' because we probably want them doing more field visits than we want them processing paperwork. It's getting harder and harder for them. What we did was put a whole lot of that information into the computer. The computer comes out and says, 'Okay, well, out of all the information and the risk management tools and everything that I've looked at here, Little Johnny is at risk a little bit more than Mary, so therefore you need to go and visit Little Johnny.'

The person, the social workers got to make this decision. If they're an older person, who tends to be more experienced, been in that job for a while and just generally been out there and has a bit of a feel for it, would look at that and maybe go, 'The machine just told me Johnny over Mary, but my gut tells me, Mary,' right? What do you think they do in that situation? Yeah, so they go to Mary. But can you see the issue in terms of the machine has told them to go to Little Johnny, and then what if something happens to Little Johnny, right, and nothing happens to Mary? It becomes complex, right? The liability involved in that, the human decision-making process.

Yet, they need to be able to have the freedom to be able to override that machine. There needs to be a matrix or protectionism measure or something for them to be able to do that. Now, if the studies we've done, if they're a younger person and they're looking at the decision being generated by the machine, and that tells them Little Johnny, what do you think their reaction might be? From a younger person, what do you think? Yeah, they trust the machine. They grew up with the machines and the computers a bit more than the person who is a bit more advanced in terms of being older.

We've got this issue here where we've got people of different generations in the workforce all having a different way that they interact with the machine, and different way that they trust the decision-making process. We're looking at building models into that and risk models into that to get what point do we have that human intervention over the automation. That's where we're at at the moment. We do not want a system that just produces a thing that says, 'The machine said Johnny is at risk, you need to go see Johnny and you just have to go and see Johnny,' right?

We need something where it could go maybe the top 10 children that you should visit today because they only have eight hours in the day, although the ones I know work 14, 15, 16, for them to be able to help their job. The idea of the machine should be that helps them prioritise, and they'll look at maybe the top 10 or 15 instead of having to look at 100 or 200. The idea that you can see is augmenting the decision-making process, but we can't leave the human out of it. Does that make sense? Yup. Okay, that's how we should be using it.

Uniquely, human traits becoming much more valuable. The things we're good at as humans that robots and artificial intelligence can't do, they're the growth areas. You can see there, emotional intelligence. It's very difficult at the moment for AI, and it always will be, to have that emotional intelligence. I don't know any of you have seen ... Is that Nadia? The Saudi Arabian, it's a robot that went on a date with Will Smith, and she was very blunt. I think you don't want a relationship. Sorry. No. Very blunt. It's very difficult to teach artificial intelligence, emotional intelligence.

Creativity. Again, very difficult. Persuasion. We were discussing robot comes out and we can get them onto natural language processing to try and be as human as possible. But the end of the day, humans relate to humans. We will always relate to humans. It's very rare that we're going to relate to artificial machines. We're going to need people with those skills that can help humans relate to humans. Innovation. Yes, we have machine learning and we have all these fancy things, but the end of the day, it's the humans who are thinking differently, who are needing to collaborate, human-to-human contact, these are the things that are driving the innovation forward. It's not the robots and the artificial intelligence and the machine learning and that. It's the humans behind that that are driving the innovation. We're also stopping it where we need to stop it as well, but I'll talk a little bit more about that.

A really cool thing that's happening is more multidisciplinary teams to get rid of bias. You would have seen driving trait in the workforce is diversity and inclusion. Where I work in algorithms, this is really important. I'm not sure if any of you are aware in the '80s, they had ... I'll go back to the '80s because there's some cool examples in social media. But in the '80s, they had voice-activated lifts. I'm really excited because this was a new technology at the time and you got into the lift, you didn't have to press a button, and you just go, 'Third floor, please.' It was great. It was really good technology.

Then they said, 'Yeah, this is really going to work,' and they put it in at the Hilton, it was, and worked well for the first test run and a group of men got in there and they said, 'Third floor,' and it worked great. Then a female got in there. She went, 'Third floor, please.' 'I don't understand.' 'Third floor, please.' It did not recognise a human female voice. Why do you think that was? Being programmed and trained by a male, who had thought about it, that males are going to use the lift. They had not thought females use the lift. People with different accents using the lift. It was simply because of an unconscious bias that they were doing it for them and their cohort and the people that they were working with. We had to be very aware of that. We have to bring as much diversity and inclusion into not just the workplace, but into these new technologies as much as we can to avoid this.

Physical environment. It's pretty cool. The workplace is going to have biometrics. We can see that with the border processing and that. But in some places, some of you may already have thumb prints or facial recognition to be able to get in instead of the physical passes, sensors for comfort. In my workplace, we were looking at sensors to be able to work out whether people were hot or cold and they could put it into their keypads, that was AGO down in Melbourne. They were able to make the workplace a better place for everyone. This section over here where Gretchen is, in the corner for those in other areas. She may want it at 26 degrees, but over in the back corner, they may want it at 22 degrees. There is the ability to be able to do that and to have that with a technology, that more comfort.

Auto translation. I did in my previous role a lot of global work. I was working with Vietnamese, Thai, Cambodian, German, and English-speaking people on a particular project. We would use Google translator a lot of the time, because everyone would always expect in the past it would have to be in English. But that's pretty arrogant to expect that. What we wanted to do was have it so that we could bring in the expertise of the people who didn't necessarily have the English as well. We were using that live in the workplace. You'll see that more and more, probably more so in places like Singapore and that, where there's a real mix of cultures. But into the future, we'll see more and more of it.

Flexible and better working from home options with cloud computing. We may already see that a little bit. I think the public sector is probably slower in this than it could be. I know in tech companies that I work with, we have people sometimes that very rarely come into the office. You'll be dealing with people who you very, very rarely meet in person. It's exciting when you do. You do do a lot of video conferencing, just like we're doing today, to be able to help build some of those relationships. Whenever you obviously go to that country or that location, it's always great to be able to meet up and see them in person, because nothing can replace that face-to-face interaction. But we will see a lot more of that.

In terms of thinking about that, I wanted to give you a real-life example. Everything's moving, we've got this digital disruption, we've got to think about how we integrate ourselves with these exciting robotics and artificial intelligence and how we navigate that. People don't often think they go into a job that, 'It will impact me.' 'I'm nice and safe in my job and I don't have to worry about that.' Yet, it can impact you and it can impact you in a pretty cool way. We've got Cardia here at the front. It's a little robot. Stand up if you can't see her and you can have a look at her. She is a gift from my team to the National Heart Foundation. She's a great example of where someone working at the National Heart Foundation may never have thought that they'd be working with a robot.

She goes with field offices out to the schools, and she encourages children, particularly children who are very addicted to Fortnite, everyone knows what Fortnite is. Yeah. Now, the team are trying to program some Fortnite moves into her at the moment. What she tries to do is to engage with the next generations coming through, because a person standing up in front of the classroom in a traditional way isn't necessarily cutting through. I'd like you to meet Cardia and we'll get Farhad to show you a little bit what she can do.

Cardia:Hi. My name is Cardia. Nice to meet you.

Kylie: Just think about it, the person, the field officer did not have her, operating with her in their [inaudible] statement when they applied for their job. Here, she is doing push ups. They can get the kids to do the push ups with her as a bit of a novelty. I need to say it was only a few years ago that robots couldn't get themselves back up again. That's how advanced we're becoming. Here, she is dancing. A few Michael Jackson moves in there. We'll have to update it with Fortnite. Okay. Thank you, Cardia.

She's supposed to say thank you back to me.

Cardia:Thanks, Kylie. Over to you.

Kylie:A little delayed there. It just gives you example there, because you're field officer, you're out in the National Heart Foundation, you're a charity worker, you don't necessarily realise that you've got to have a phone and app on the phone and you need to have a user guide to be able to work with her. My team might program her for them as a charity thing that we do, but they've actually got to go out in the field with her, and they love it. Absolutely love it.

The next thing is some things to think about. You don't have to answer these now. But in the current workplace and where we're going, and not just the workplace but beyond, would you go under a robot's knife? Always interesting for people to think about, 'Would I do that? Would I trust a robot more than a surgeon, a human surgeon?' When you think about the long hours that the surgeons are having to go through in terms of what they do, and the mental processes, and the stress that they're under, it's pretty interesting to see. It's usually about a 50/50 split. Some people are just like, 'Nope.' Other people are like, 'Yes.' But actually, there are robots working in a lot of private hospitals at the moment quite effectively.

Do I trust my computer when it predicts something ethically challenging? We're thinking about the caseworker study with their social services before. Do you trust it? Where does the robot go on the org chart? Can you imagine reporting to a robot? It's something we need to think about. Do I open the door for my robot colleague? This one catches people out a bit. Anyone here, would you open the door for your robot if they were a colleague working alongside them? Would you open the door? Yup. Yup. But stop thinking about that and unpacking it. Do they have a security pass or are you just trusting that they're a robot and they should know how to get through the door? There's a whole lot of things behind that that we need to unpack here.

Would I defend my robot if someone criticises it and they're part of my team? I know Farhad would. He's up here in my team here. He defends the robot. They become aligned. You do, you become aligned with these things. Do I sacrifice myself or the pedestrians in a potential crash in a driverless car? Much more serious ethical thing to think about. I have had someone say to me, 'Well, I would build an ejector seat so I would crash into a building, and then I'd eject, and then I'd be safe, and everyone else would be safe,' right? It's just not that simple.

Unintended consequences. There's always unintended consequences. There's always things that we don't think about. We try and think about everything. A really good example here. It's a small picture, which I'll explain. In China, driverless car, and there's a Chinese person standing on the roof. This is not something that came up in the discussions about driverless cars when they were first thinking about them. He is drunk, right? We have a drunken Chinese driver on the top of the car, trusting the car is going to get him home. Not something that it was factored in when we were doing all the ethics discussions and talking to the safety people. It's not something that comes up. We really have to think about some of these things.

How do you prepare? There's a thing called purple people. I want you to all try and be purple people. Red is technical and analytical. Blue is more business communication. Probably most of the people in this room may be business and communication. But what I'd like you to think about is trying to be a little blurry. You can see here, we don't have a definite ... It's a shading here. What we want you to do to get into the future to prepare yourself is to not identify that I'm either this or that, to try and become more like the other.

My team is predominantly technical and analytical. I encourage them as much as I can to try and be more of a blue person. The blue people try to be a little bit more of the red. You can see there if you can try and be a little bit more of the purple person, that will help set you up for the future. I won't go into each of the dot points here, but you'll get a copy of the slides. You'll be able to just go into that and have a bit more of a look and explore what that actually means. But just think about it, be a purple person.

Think about these new job titles. These did not exist 10 years ago. Customer success officer, change manager, so that's come up quite a few times. It was not an industry. Humans are amazing in the way that we're resilient and we can cope and we've constructed the idea of a change manager because we know it's needed. We know we need to get through all this digital disruption and all this change, so we've gone and looked at it and said, 'Well, we need the psychology of this. We need to unpack this,' so we've gone and created a position that can help us do this. This did not exist 10 years ago.

Social media manager, Uber driver, cloud computing, design thinking facilitator, all these things did not exist. There's a picture down here of one of my team, Don, and he's playing with virtual reality. Now, he did not think when he joined us that he was going to be playing with virtual reality. We'll probably have a virtual reality assistant or something in our team next.

Other practical ways that you can prepare. Adopt an open mindset. You do need to get used to change. It's been an overriding theme for today, and you do need to get used to it. I'm sure all of you in the room probably are on board. You're here for a start to pick up hints and tips on how to change. You do need to get used to it, and to communicate to other people that they need to get on board as well. We've been doing it for a long, long time, right? We've been doing it since caveman. It is not new, the concept of change. It's just a little bit faster than it used to be.

Be open to flexible working. Don't panic about working from home a bit or having to work from a different site or having my desk which I really hated. I remember in my career in public service, I just wanted my own office. That was my thing, if I've got my own office. Then I got EL2 and they said, 'We're not having offices for EL2 anymore.' I was like, 'What? I spent my whole career wanting my own office, and then you're just taking it away from me?' I was not happy, I can tell you. I had to learn and go out there, and the next job I got was completely open office. Everyone was open office. I loved it. I'm an extrovert. I love talking to people. I suddenly realise I didn't really want my own office. Most of the time, I've got an office again because we get one from where it's allowed, but I'm quite often out there with the team if I'm not on a teleconference. I just love being out there. But you need to get used to change.

Push yourself re: tech smarts. Download a podcast. Who here listens to podcasts? Good. Those of you that don't, think about it. If you're bored in the car, go and download a podcast, you learn things. Excel course. Go and get better at Excel. Go and get better at Word. Go and get better at graphic design suite, whatever it is. Push yourself just a little bit. Go and do that. Go on Instagram and stalk your children, right? Go to the cloud. This is what I do and stalk your children by having their photos come down into your photo roll. My goodness, you learn a lot, I can tell you. You don't have to stalk your children, but go in there and know a little bit about what they're doing and what's happening if you are in that space.

Put the phone on Bluetooth. Whatever it is, try and become a bit more purple. I challenge you today, think about something where you could be a little more purple if you are in the more business communication side, and you do need to go more into that tech space. Think about it. Go this afternoon, tonight, in the next three to four hours, think about what it is and enact it. Do what you can.

Learn, learn, learn. Ask questions, ask questions, ask questions. Go back to when you were first in the workforce and you asked a million questions. Go out there and ask those questions again. Ask from people above you, ask from colleagues alongside you, ask from people in other departments. Ask, ask, ask. Look at what matters to you. Jump on a passing ship. There's a lot of times when I'm not happy. I don't like it. I don't like the change. I don't want to be here. I'm just not feeling it. Well, you know what? There's a passing ship. There will be a passing ship. Jump on it, right? If you're not happy, you need to leave where you are, you need to go somewhere that makes you happy.

Now, there's going to be change there as well. You're going to need flexibility. You need to be open to options. But if you're really not feeling it, you need to jump on that ship, okay? I can guarantee you that most people I know that have jumped on the next ship have been happier. If they haven't necessarily seen that work out, they jump on the next one, right? Take a few career turns if you need to. Don't always do what you've always done, because you know this cliche, you'll get what you've always got. Determine how to get there. If you don't know how to get there, talk to people, mentor people, read, find out what you need to do, but be proactive.

Lastly, if you want some insights, this is Deloitte Access Economics Future of Work. This is where the predicted growth professions are. If you really want to jump on the passing ship and you want to get involved in the change and the digital disruption and what's going to be around in the future, a lot of these are actually people-oriented type work. There's a tech work in there as well. But there's also, look, physical therapy, psychology, nurses are the fastest-growing sector, people to actually relate to people, humans relate to humans.

Tourism. People still need to go places. They still need to have that experience with tour guides and that rather than just putting on those little headsets and having to hear it that way. A lot of times, we've got people in the tourism industry that can give us those personal experiences, and hearing from people that live there and work there and play there is much more important than just listening to a recording. That's just a few examples there. That is available and you've got the slides there. There's a whole little unpacked spreadsheet I can send around later on other ones and on priorities with them.

But that's it for me. Just at the end of the day, I just want you to think about, it is happening, it is fast, I can do it. The people around me can do it. We've been doing it for a long time. Let's go in the next four to five hours, try and look at what you can do to improve how you get on the bandwagon. Thank you.

Speaker 3:Thanks so much, Kylie. That was fascinating. I can see my son's desire to be a drone operator is growing, so maybe I should stop trying talk him out of it. He's got to get a drone first. That's the first hurdle. We haven't quite got to that point yet. Just on your point, too, at Future of Work, some of you might have come across Lynda Gratton, who is a professor at the London Business School.

She, amongst lots of others, has written extensively about the Future of Work. There's a couple of books she's written, one called The 100-Year life, and a second one called The Shift. It's really interesting. One of the key themes that she talks about out of her work in this space is there's a couple of human dimensions or human competencies that cannot and probably will never be replaced by technology, namely relationships/empathy, caring, some of those things you talked about, and judgement, decision-making.

She talks a lot about how even very sophisticated medical science can be supplemented or augmented with aspects which automate data compilation and synthesising and making sense of something, but ultimately cannot fully replace human decision-making. Of course, with the human piece around empathy and connection, it's almost the chemistry, in inverted commas, empathy piece, which is still incredibly important. Really, really interesting. Thinking about that then, I suppose, is a massive change message around technology laid against a future of work context. I suspect that the implications for staff wellbeing of which mental health is one thread are really, really significant.

We've got a little bit of time for questions. Probably only a couple of minutes actually, not heaps of time. But, look, just a first question I might kick off and then I'll see if anyone else has got anything else. Kylie, how would I or could I respond if someone tells me that Elon Musk and/or Stephen Hawking were worried about AI?

Kylie: Good question. At the end of the day, amazing people, absolutely amazing with great insights into what's going on, but they're technologists, right? They're people who are really wrapped up in that whole technology space. When you look at what they're actually saying, they're saying, we need to be careful, we need to consider it, and we need to think about it.

In some of the other comments that they've had, the media sensationalises that, 'Oh, well, you need to be careful because it's scary,' and they're saying it's scary, when you unpack what they're actually saying, they're saying we need to look at some regulation. We need to look at ethics committees. We need to look at how we actually move forward with this. They aren't scaremongering themselves and they're not out there just going, 'No, no, no.' They are saying, 'There's caveats around this and we need to think deeply about it.'

Speaker 3: Thanks a lot. Questions? Any interstate questions? First, Gretchen?

Kylie: Oh, thank you. I love them, too.

Speaker 3: There you go. You got the [inaudible] fans. Question here. We'll just get your mic, so just a sec.

Kylie: Cardia is, probably she's about ... Sorry. I think Cardia is only about $600. We do have to program her, though. She does come pre-programmed with some things. But the team ... What program do we use? Built software, so it's pretty easy for someone to be able to ... Yup. My next ambition is there's a bigger one. I think it's about $2,000. It's a bit bigger. Yeah. A bit interactive. Yes, my 10-year-old can program it. You probably need the bigger one maybe. She's a bit small.

Speaker 3: Yeah. I'm just conscious some of those answers to the previous question might have come through. Just for the benefit of Interstate, there's a question about what software is used to program Cardia. The answer was basically proprietary software that comes with Cardia. Then the other comment was, there is a bigger version of Cardia that's worth 2K, I think $2,000, which can even do things like recognising voice, I think you said, and also you can ask it to do stuff like putting entries in your diary and so forth and so on. Was there a question? Anything else? No? Other questions, last question? Natalie?

Natalie:Thanks, Kylie. That was awesome. That was fantastic. One of the things you touched on was about remote working on about the need to kind of embrace that. We have seen those changes happen across various industries. I guess I'm interested to hear given your experience and breadth of work that you've done, I think connectedness is something that becomes a challenge. What have you seen? What do you think is the success in actually making remote working actually work?

Kylie: Yup. Here's my own experience. I was based in Canberra, but assigned to the Singapore office. I looked after Vietnam and Malaysia and Korea and Japan and Australia, New Zealand and Thailand. Have I mentioned Thailand? We had a team, or we had a number of teams. A key thing is to have the team have a common purpose, have a team name, a common purpose. We set up WhatsApp groups, which my current team do. We're not necessarily ... In fact, we are a bit remote, aren't we? Because even today, everyone's out on different client sites. There's only one person back in the office.

We do have a WhatsApp group. I know last week, one of the team members wrote, 'I'm lonely.' It was similar to that, and then everyone came in and was sending little cool things and stuff. Just making sure that everyone feels included. We do lots of Skype meetings. We used to have lots of Skype meetings with either teleconference or video con. We have to be really careful because of the time zones. You don't want people having to video con and they've just taken off their makeup and they got wet hair because they've got out of the shower. Then other people are sitting there, raring and ready to go in the office during the work day.

Very much real strong communications emphasis. We will have weekly circulars about what was going on. For my team in particular for what I do now, we have what we call fly home Friday. The team are all out on different sites. They're out in Sydney, they're out in Melbourne, they're in Canberra. As much as possible, we get people to come inside on person or to interact on a Friday. I was told by someone that is outside of consulting. She said, 'You're mean. You have like four o'clock meetings on a Friday.' I'm like, 'No, but they're awesome. We all get to see each other and socialise.'

It's just fantastic. The people from Sydney and Melbourne all will try and find a space together to be able to ring in as well, so they might be on different client sites but be able to ring in. I have the same model throughout Asia when I worked on that as well. There are probably lots of other things that we did like group emails and the more proper little newsletters and things. But to me, the WhatsApp actually works really well. Farhad, what do you think? Do you like the ... Yeah. Put you on the spot?

Yeah. Farhad was just saying you can share and joke around and stuff. You can reach out and say, like the team member last week, 'I'm a bit lonely, someone talk to me.'

Speaker 3: I think we've got one more question. It's going to be our last question. Where from? Melbourne? Excellent. Melbourne folk, over to you for the last question.

Speaker 5:Kylie, at the Comcare conference, [inaudible] spoke about the importance of offering program in school. How important is it for workplaces to be starting to offer programming skills and stuff?

Kylie: Absolutely important. My middle daughter's in year five, and I had her teacher hitting me up the other day for what could she do to get them on board. I was giving her some information about the best type of programming, which would be for that age Python, but because there's just fantastic support networks for them to do that and they can do a whole lot of games and things in that, too. I think children relate to programming games and stories and things first, so not getting them at the younger ages to do anything too intense or too complex, but then moving that along as they go.

I know that I had a person work for me at [Defence] a few years ago. In India, they're learning coding and programming from when they're five. Those people, and we wonder, why there's a lot more people from India out here in these spaces and have really advanced significant, really good programming skills. It's because they backed them and the school system backed them. I really think we need to be doing that in these training schools, too.

Speaker 3:Kylie, thank you much for your presentation. Obviously, got lots and lots of engagement. Thank you, Cardia as well. I don't know she's taking that on board, but sincere thanks for me to her. We've obviously got a little symbol of appreciation for Kylie as well. Could you join me in thanking Kylie?

Kylie: Thank you.