The Business of Transformational Leadership (episode 2)

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In our second episode on Transformational Leadership, we explore how businesses can respond to AI, and harness big data to make decisions that benefit employees, customers and shareholders

The era of artificial intelligence (AI) is upon us. While it might not look like it does in the movies, its arrival is affecting organisations and the way we work.

Host Emma LoRusso is joined by Professor Toby Walsh, Laureate Fellow professor of artificial intelligence at the University of New South Wales, and lead on the Algorithmic Decision Group at Data61. Toby speaks to the present and future state of AI, and the challenges and opportunities it presents for business leaders.

Emma also speaks to Kristi Barrow, Principal Consultant at digital analytics consultancy, Kritikality. Kristi explains how business leaders can become advocates for incorporating data analytics into the decision-making process of their businesses.

Finally we hear from Magnus Gittins, Director, Executive Education, at the AGSM. Magnus outlines the important role of the education sector in preparing students for the ‘now’ and future of work, and conversely, how those with established careers can leverage the drivers of transformation to upskill and thrive in today’s accelerated world.


  • Emma Lo Russo, (AGSM MBA Executive 2013), CEO and Co-founder of Digivizer
  • Professor Toby Walsh, Scientia Professor of Artificial Intelligence at UNSW Sydney and Research Group Lead on the Algorithmic Decision Group at Data61
  • Kristi Barrow (AGSM MBA 2006), Principal Consultant at digital analytics consultancy, Kritikality
  • Magnus Gittins, Director, Executive Education, AGSM @ UNSW Business School


Emma Lo Russo: The terms artificial intelligence, big data, and machine learning have all entered the language of business , and they have important implications for navigating work as it is ‘now’, but as importantly, the future of work.

These technologies signify organisational change, alternate work arrangements, and new skill requirements among employees. With as many as 60 per cent of the jobs expected in the next decade not yet conceived, leaders will need to plan these new futures, and take their organisations on what, for many, will be disruptive journeys.

In this episode, we take a closer look at this period of accelerated digital transformation and its impacts on people and organisations.

I’m joined by Professor Toby Walsh, Laureate Fellow, professor of artificial intelligence at the University of New South Wales, and lead on the Algorithmic Decision Group at Data61. Toby shares his fascinating yet rational perspective on the challenges and opportunities brought about by AI.

My second guest is Kristi Barrow, Principal Consultant at digital analytics consultancy, Kritikality. We hear from Kristi on how business leaders can become better advocates for incorporating the use of data analytics and new technologies to aid decision-making within their organisations.

Lastly, I’ll talk to Magnus Gittins, Director, Executive Education, at the AGSM. Magnus explains the role of the education sector in preparing students for the ‘now of work’, and conversely, how those with established careers can leverage the drivers of transformation to upskill and thrive in today’s accelerated world.

First up, let’s hear my conversation with Professor Toby Walsh.

Welcome, Toby to The Business Of.

Toby Walsh: It’s great to be here.

Emma Lo Russo: I’m very excited to be talking to you about a topic I think lots of leaders, organisations, boards are talking about. How will AI change the way we do business, and how should leaders be thinking about this in approaching it?

Toby Walsh: Well, it’s hard to think of a part of business or even a part of any part of our lives that AI isn’t going to touch. I mean, there’s this often repeated quote due to a colleague of mine, Andrew Ning that AI is going to be like the new electricity. And I think there’s some truth to that metaphor. Electricity is an incredible part/fabric of our lives. Everything is powered by electricity, all our data is sent by electricity. It’s hard to imagine how we could live today without electricity. And I’m sure in 30 or 40 years time, we’re going to look back and realise that we couldn’t live also without artificial intelligence.

Emma Lo Russo: So we shouldn’t be afraid of it, I guess is the point there. It’s coming. So how should leaders prepare their thinking and their organisation to be on this journey?

Toby Walsh: That’s a great question. I think that part of the problem is ignorance. People don’t understand what artificial intelligence is. They think what Hollywood has told them, and much of what Hollywood would tell you is actually wrong, is perhaps way, way in our future if ever going to happen. And the uses of AI are much more prosaic. And indeed, AI is already a hidden part of many of our lives. I mean, every time you ask Siri a question, it’s some artificial intelligence that’s helping to answer that question, to transcribe the spoken text into a question, and then look up the answer.

Therefore, the first thing that business leaders need to do is to try and understand what are the opportunities? What are also the risks? Because also I think it’s a technology where things can go wrong and there’s plentiful examples already of even the most switched-on companies that have made mistakes and fallen into pitfalls in the process of trying to wield AI to improve their business. So the first thing I think is education to find out what is possible, and the second to realise is that there aren’t a lot of people who actually understand the technology, and there are even fewer people who understand the technology and your business at the same time.

And so you’ve got to somehow square that circle and work out how do you get people with those two skills? And actually, I suspect in many cases, the easiest way to do that is to train those people up, to invest in your people, which are of course the most valuable part of your business, because those are people who do understand your business and you can pick up the technical skills to work out how to use the technology to advantage.

Emma Lo Russo: I think you just nailed it. I was going to say, how does someone start thinking about their organisation, to find the place that AI could be used to better the experience for the customer, or like you said, make better evidence-based decisions? Is there a framework or a mindset or an approach that they should take to getting to the heart of those questions?

Toby Walsh: So much of this is being driven by data and ideas like machine learning requires lots of data. And so to begin to work out how you might use AI in your businesses is, you’ve really got to go back to the data. You should have a serious data plan, and you should be asking yourself, “Well, are we collecting the right data? And if we are collecting the right data, how can we use that to lift our game, to improve our product, our service, to understand our customer better?”

One of the important things, and this is different to past technologies, in particular our IT technologies that you might have integrated into your business is that it’s a very difficult technology. It’s not push button technology. When you brought spreadsheets into your business, that was pretty much a push button technology. There wasn’t a lot of risk and there wasn’t a lot of capability needed to actually use it, whereas that’s not true today for artificial intelligence. It requires a lot of skill and expertise and there’s a lot of risk. And you should therefore be careful and mindful of that.

So the first thing to do is to realise is that you need to get your feet dirty, but in a way that’s not going to destroy your business at the same time. So don’t start off by putting it in something that’s mission critical. Find some part of your business, some small little area where you can build up your skills and expertise where it won’t matter if it goes wrong, or if it doesn’t do quite exactly what you want it to do.

Emma Lo Russo: So what constitutes responsible AI? What do we need to think about when that’s also being considered?

Toby Walsh: That’s a really important question, because we’re discovering that there are plentiful things that can go wrong, that will cause reputational harm to your business, or even greater physical harm to your business and then you’ll be facing lawsuits or the like, if you’re not careful. There’s plentiful decisions that you could be handing over to machines that will be problematic, and there are lots of examples. I mean, we’re seeing how the disaster of handing over marking of exams through algorithms in the United Kingdom, that’s caused a huge, great problem, a huge, great knock on effects to universities, huge, great problems to the government and a huge, great stress and challenges to school children and their parents within the UK, all of which was entirely unnecessary, all of which could have been predicted from the start, because in that case, they weren’t asking the right questions. The problem there with that example was that they were trying to avoid grade inflation. They weren’t trying to accurately rank children so that you could decide who went to which university places.

Emma Lo Russo: What’s the role then of the government in creating frameworks, what frameworks do help ensure that bias isn’t in built?

Toby Walsh: I suspect like many other areas that we’re going to need regulation and norms set at all different levels. So that starts at the international level. We’re already seeing international efforts within organisations, within the United Nations, within the World Economic Forum, other bodies like the OECD trying to set international norms and standards. We see bodies like Standards Australia, an ISO trying to come up with appropriate standards, so at the international level. But equally, actually much of this I suspect is going to be set by national governments. They have the right level of control. We’re not perhaps going to have the same standards or want to have the same standards as a country like China, or even as a country like the United States.

And so, there’s a very important role for governments to provide suitable regulation and frameworks, through to things like kite marks, our chief scientist, Alan Finkel has proposed a Turing stamp as a way of providing consumers with confidence that they’re buying products and services that meet various ethical standards, to a level where it will also be a distinguishing feature between companies. I mean, you can seek commercial advantage by positioning yourself as someone who respects your customer’s privacy, for example, and that you see already in the tech industry. You see a company like Apple has a very strong reputation of defending the privacy of its customers, of never selling their data to other people, whereas a company like Facebook is quite different in its attitudes towards customers. And I think its reputation is perhaps less strong and less good as a consequence.

I think it’s a way you can see of distinguishing yourself from your competitors, the internal standards you have. And it’s not only advantage in terms of your business and your customers. It’s also in terms of your staff loyalty and the values that you strive for as a business. And increasingly I think we see, especially with millennials, that they want to work and only want to work for businesses that meet the standards and norms that they find acceptable. And so as a business, it’s important that you actually respect those sorts of boundaries.

Emma Lo Russo: I’m curious just about the Apple and Facebook examples at both ends of where it might be protection of data on one hand and commercial use of data on the other, what do leaders get wrong here? Is that something that starts at the top? Is it the framework, is it government that’s going to help manage that? How do we build trust around AI and integrating that into organisations?

Toby Walsh: It really is important that we build trust. And I think we are seeing some loss of that trust already. We’re seeing the tech-lash that is happening, that people are actually starting to be somewhat distrustful of the tech companies and their aims. I think it starts from the very top of an organisation. The values are important and they need to be more than just a window dressing. They really actually have to mean things, and people have to really believe in the business.

I think the really important idea is that customers have to feel that they’re getting an appropriate return, and you can see why this breaks down. Why is it that people are logging out of Facebook? I’ve closed my account down. I haven’t used Facebook now for many years. But why is it that people find a company like Amazon, who actually also use your data quite extensively, why do they find a company like Amazon much more acceptable? Well, in the case of Amazon, you feel you’re getting a proper return. You feel that you’re getting access to goods that are competitively priced, you’re getting next day shipping for free or for the price of your Amazon Prime membership.

So whilst you are giving up data, that is true when you use a service like Amazon, you’re definitely are getting a proper return. And I think that’s where some tech businesses have been going wrong, that they have been hoovering up your data and people are thinking, “Well, wait a second. I don’t seem to be getting a suitable return for the privacies I’m giving up here anymore.”

Emma Lo Russo: So you touched on where it goes wrong, but where do leaders get this wrong?

Toby Walsh: Well, I think it comes from having good values at the end of the day, I do think that the boards are going to start having another person in the C suite – the CPO, the chief philosophical officer. Because there are some really challenging, ethical questions that businesses are having to face. There are a whole raft of wicked problems that the planet is facing, whether that be the climate emergency, the knock on effects from this disastrous pandemic that we’re seeing, or the increasing inequality that we see within our societies. The rich getting much richer and many of us being left behind, or the toxic environment that many of our political debates are turning into, and sadly, the tech industry is perhaps contributing to. So there are a whole bunch of really challenging global problems that we face, and the businesses are actually part of, in some places contributing to, in some sense, being part of the way that we’re going to tackle these problems. And so businesses and leaders of businesses, I think increasingly have to make some really challenging decisions.

Emma Lo Russo: And Toby, just for the benefit, because I know I see this question get asked often from leaders who don’t know the difference, or whether there is a difference between machine learning and artificial intelligence, would you help unlock that?

Toby Walsh: Yes. I mean, machine learning is a part of artificial intelligence. It’s a very important part of artificial intelligence at the moment. Many of the recent successes that you would have read about probably be involved some machine learning, but it’s not the only part of artificial intelligence. Artificial intelligence is trying to get computers to do things that humans require intelligence to do. Our intelligence is largely learned. I mean, when you were born, you couldn’t read or write, you couldn’t do math, those are all things that you learned. But equally there are other parts of our decision making, which aren’t just the things that you learn. They’re about deliberation, about planning and so on. And so AI includes all of those other things than just machine learning. But machine learning is definitely a really big component of it at the moment, but it’s unlikely to be the only part of it. Certainly, as we move forward, as AI gets more sophisticated, there’s going to be other components that we’re going to start using more and more.

Emma Lo Russo: To me, this is an area that I love. I’ve got a company that – we’re a data company, so we’re very much in this space, but what excites you most in terms of the opportunity of AI?

Toby Walsh: I always like to tell people it’s the four Ds. It’s the dirty, the dull, the difficult and the dangerous. That’s what we will get machines to do, the dirty things that we didn’t like doing because they were dirty. The dull things because we got bored by doing them. The difficult things because they could do things that humans can’t do. They can look at data sets bigger than humans could look at. They can actually perform at superhuman level on narrow tasks. And then the dangerous things, as an example, they can go and clear out the sewers or clear minefields and do other things that are too dangerous for humans.

And so when I hear people saying and complaining or worrying that the computers are taking over jobs, I normally say to people, “Well, I don’t know much about that job perhaps, but I suspect we should be celebrating. The very fact that we got a machine today to do that particular job tells me almost certainly it was a dull, repetitive thing and we should never have gotten humans to do it. And therefore now is a moment to celebrate. We’re no longer degrading humans to do things that machines could have done.” Of course, it raises challenging questions about, well, what do we do with those people whose time now has been displaced?

I think that’s a really important lesson, which is we could do one of two things. If you’re a leader of business, now you’ve automated some part of your business, now you could do two things with those people. You can either use that to save your bottom line and reduce your head count. I think that’s a really shortsighted idea because that’s a race to the bottom, we live in a high wage economy. Our neighbours around the Pacific rim are going to beat us in any of those races. And the alternative view is to realise, well, that’s an opportunity. We’ve got those people’s time freed up. We can now use that to get those people who understand my business, understand the customers, to get them to innovate and create, to use those human skills to lift your game, to improve your product, improve your service and not race to the bottom, but actually make yourself a better company by serving your customers better.

And that is if you want to be a business in the long term, if you want to be around in 50 or 100 years time, I think that’s where you should be thinking about, now that’s the opportunity we’ve got. We’ve got an opportunity to lift our game.

Emma Lo Russo: If data’s at the heart of everything we’re talking about,, I imagine that the first thing is make sure you’ve got a good, original learning set or data set where you’re ensuring you haven’t got those biases in built,, but if we’re going to take full advantage of AI, how do we make sure that bias is removed?

Toby Walsh: You can’t eliminate biases in some settings, and then some bias, there will be a bias. You’re making a decision, you’re picking a subset of people out that there’s a bias and you’re just going to be happy to reflect the norms. I mean, it throws out these deep philosophical questions. Well, what does it actually mean at the end of the day to be fair or unbiased if that’s possible? And we’re discovering that actually is a difficult, philosophical, mathematical question. We actually don’t have exact answers to these problems. Despite hundreds of thousands of years of philosophy, we’re discovering that it is throwing up deep, challenging, ethical questions.

Does it mean equality of opportunity or equality of outcome? What do we really actually mean to say, we don’t want to discriminate on the basis of sex or age or race? If, for example, unless the groups are truly identical, you are going to be treating some in disparate ways. And so you have to work out what is the societal norm? What are the acceptable values within our society? And it really is important that we have these conversations. And in some sense, they’re old conversations. They’re ones that have actually troubled us throughout history. It’s just that when we get computers to try and answer these questions for us, and anyone who programmes a computer knows how frustrating the literal computers are. And so, it’s actually requiring us to actually face up to these difficult questions, which troubled us in the past, but we didn’t have to be so prescriptive when we were getting humans to make them, but now we’re getting computers and programming computers and all that it requires.

Emma Lo Russo: Coming back to the role of government, so we talked about the frameworks and the policies that can help here, government’s also sitting on some of the biggest data sets and could potentially influence some of the biggest positive outcomes, so whether it’s health or services. What’s your view of, if data’s at the core of this, how do we manage our data in a way that could get the best outcome from using these technologies?

Toby Walsh: You’re right. Actually government is in some sense, the organisation that is set perhaps best to benefit from this revolution. Government collects more data than almost any other organisation within our country. Government is trusted in many cases with that data, more than businesses. A third of our GDP is what the government is doing and spending. It’s a significant chunk of our economy and they’re always trying to deliver better their services and increasing demand on their services. So there’s a lot that government can do.

And equally, unfortunately, we’ve seen some mistakes the governor’s made. Robodebt is one of the more recent examples, which I think the challenge there is if government becomes too cautious and conservative because of the mistakes that were made there, when actually it should be one of the – it has the opportunity to both use AI and also to drive the adoption of AI, both through procurement standards and by setting examples for other businesses. I’ve been spending quite a bit of time in the last few years, actually talking to departments within governments, trying to help them down their journey because actually, it is one of the most important ones.

And as an example, in a country like Australia, where we have a huge, great opportunity, if we can seize it, is in medical data. So we have a joint up healthcare system, which is a great advantage we have over many countries. And if we are careful and mindful of all the challenges around privacy and the like about the data that gets collected in our system, we have a great opportunity to lift our collective health and our individual health to actually all of us to live longer, and as a community to actually increase our life expectancy, and not just our life expectancy, but the quality of that life as well.

Emma Lo Russo: So Toby, you’ve published a book 2062: The World that AI Made. Tell us about this book. What’s the story?

Toby Walsh: I probably should explain the title, why the year 2062. I surveyed 300 of my colleagues, other experts in AI around the world, and 2062 was when they said on average machines would be as smart as humans. And I imagine they’ll be not only as smart as us, but very shortly after that, much smarter than us. They have many advantages. I think it would be terribly conceited to think that we couldn’t build machines that were smarter than us.

But whilst we’ve got a way to go to get there, perhaps 2062, my colleagues weren’t saying it’s going to take hundreds or thousands of years, it’s something that’s going to happen in the lifetime of our children, and if we’re lucky in our own lifetimes. And that’s going to surely be a very profound change to the planet, because we look around the planet today, it is the product of our intelligence. We have 8 billion people on the planet, courtesy of the invention of agriculture, courtesy of the invention of vaccines and medicines and all those other things, electricity, all those other things that keep the planet ticking over.

And so, if we’ve got another intelligence on the planet that’s possibly even smarter than us, it’s going to be profound what we can do with that. And that’s going to change almost every aspect of our lives, and that’s going to happen in the next 40 or 50 years.

Emma Lo Russo: Toby, I love that you’ve unlocked artificial intelligence and the good that it can have on our world, and helping us in ways that it should.

Toby, thank you so much for joining us today on The Business Of.

Toby Walsh: It’s been my great pleasure.

Emma Lo Russo: What a fascinating conversation. I hope you enjoyed it as much as I did. AI can be a subject surrounded by trepidation and caution, from people and organisations. I think Toby does a superb job of drawing out the positives and putting the challenges into perspective.

My next guest is no stranger to the world of big data and how to incorporate it for accurate data-led decision making. Let’s hear from Kristi Barrow on how data can help create a culture of digital advocacy within organisations.

Hi, Kristi. Welcome to The Business Of.

Kristi Barrow: Hi, Emma. Thanks for having me.

Emma Lo Russo: I’m very excited to hear about your new venture, Kritikality. Do you want to share what your journey is?

Kristi Barrow: Absolutely. So last year I left my corporate role that I’d been in for about six and a half years and decided to open up my own consulting business. And I think really the reason why I did this, when I think about it is that I love solving problems. I have an engineering undergrad, and I like hooking things up and solving problems. And I definitely saw an issue or a problem in the market where a lot of companies spent a lot of money on digital marketing technology and their data and then don’t get the maximum ROI on their investment for different reasons. A lot of it comes around to resource and skillset, and I saw a bit of a gap in the market to go in there and really help people set themselves up for success in this space.

Emma Lo Russo: And where do you see organisations get this wrong?

Kristi Barrow: I think it’s really about not understanding how to resource up properly for something like this. I think, you go in and you buy this technology and in some cases potentially there’s this feeling it’s a little bit set and forget. We bought this, so we can do it. And now we’re going to move on and buy the next shiny toy. And absolutely something like any of the marketing technology. A, there’s so much of it at the moment, B, it’s very complicated, C, it can be quite expensive.

One of the big issues is that a lot of companies spend a lot of money in the millions of dollars on data that is potentially collected in an incorrect way. And often the way the data is collected in the first place in the digital space, it might have touched three different divisions in a company. No one actually has a KPI on that quality. And then a lot of this data is then used for the marketing team to spend this money with some of the very big players in the market to get as much as possible out of their marketing money. Now if that data is wrong in the first place you could be spending on wasting a lot of money and not getting the return that A, you thought you were getting, or B, that you should be getting.

Emma Lo Russo: And why do you think that falls down, is it the lack of knowledge or skills or capability in an organisation? Is it not the right questions at that first outset, is it the lack of collaboration of the one team, one view around the one customer? Like why does that happen do you think?

Kristi Barrow: I’d say it’s that, it’s still very much a I think, and some people would disagree with me on this, a structural and team and culture issue. Because even if you have people who understand how to put it together, if they don’t have the tools and the support to do it, they can’t do it. So I think it’s very much the fact that digital seems to be still quite siloed with all the pieces that are involved in getting it right. So just about every large organisation will have developers sitting somewhere else. And the developers are often the very, very start of capturing digital data. They often won’t have KPIs on digital data quality, nor do they necessarily have time or the priority to do it as well as they might like. So all of the great intentions in the world from everybody in an organisation, it’s really hard to get over an actual structural problem.

So I think that’s sort of one of the big problems is how everything is kind of siloed. You might have the website team also might be different to the marketing team and not everyone knows what’s going on. And if all these people are in areas where they ultimately bubble up and their only common leader is the CEO, it’s really hard for them to work together. Because everybody has their own agenda, everyone has their own KPIs and targets and what they’re working towards. That’s just how a big company works. You have to structure it somehow. You can’t just have a free for all, because it would be chaos. And I think the companies that are starting to succeed in this space actually have this setup much better than the more traditional, we sort of have IT here and finance here and operations here and marketing here and product here.

Emma Lo Russo: Do you see it starts at the top in leadership, that’s where the best outcomes are? Or what’s your recommendation for where they should start and how from C level down it should be owned ?

Kristi Barrow: Yeah, look I think the C level definitely needs to be involved, but it’s probably that one level below. And maybe the one level below that, that really has to push this kind of thing. I think if you are an organisation that gets up there and says, “We base our decisions on data,” then you have to walk the talk. So it’s got to be that level where priority, funding is given to actually collect accurate data.

Emma Lo Russo: Is there other things that you think kind of like, “If they followed these three things that have this start to the right foundations,” and to answer the questions they need ?

Kristi Barrow: I think one would be, give data the priority or make it a priority. The second one would be, make decisions on data and actually really communicate that you have made this decision and done it, and also be consistent. Don’t just make some decisions on data that suit you, and then the ones that don’t suit you, you kind of just ignore it or not talk about it. You know, I think often there’s a tendency as people who’ve got this great idea, and then they go and find data to support that idea. And there’s an inherent bias in coming at a problem from that perspective. You have a problem that you need to try and fix, use the data to come up with a solution as opposed to the solution and then finding the data to kind of back it up.

And I think the other thing is also really get educated around what data can do for you. There’s, if you think of maybe your favourite products or your favourite services, try and understand why they’re your favourite products and services. And customers’ expectations are so much higher then they are this year then probably even last year and definitely higher than five years ago. So if there’s some really basic things that have been on your roadmap that you’ve just kept putting down to the bottom because you didn’t think they were that important, five years ago you’ve suddenly realised that you’re behind in customers’ expectations.

Emma Lo Russo: In terms of, I mean, you talked about how much technology is out there and there’s a lot of new technology hitting the market all the time. What should organisations be thinking about or looking to?

Kristi Barrow: I think there’s a lot of discussion around artificial intelligence and machine learning and how great it is. And everyone wants to jump from here to here, which is awesome. But , if you don’t have the right foundation, it’s not going to work anyway. But also understanding what that means. And there’s, I have an example from Professor Nico Neumann, and he does a lot of research into data and tracking and attribution, all this kind of stuff. And he always has this great example, which I wanted to steal, but I thought I should give him credit, of attribution, especially with digital marketing.

So attribution is around giving credit to a channel that caused some kind of conversion. So really easy one might be your business has sent an email to a customer, that customer has then opened the email, headed to your website and bought a product, or perhaps filled in the lead form. He said when he was younger he got a job working for a nightclub. And the nightclub said, “We’ll pay you,” I don’t know, let’s just say, “$5 for everyone who comes in with a flyer.”

So, the idea of the nightclub owner was that Nico and his friend would go all the way everywhere, hand out these flyers all over the place, and they’ll get a bunch of new people coming to the nightclub. What he did was stand around the corner from the entrance and handed out a flyer to everyone coming past, because that was the easiest way of getting to his goal. It wasn’t the same goal as the nightclub owner, but it was his goal.

And a lot of AI technologies in the programmatic space work that way. Their goal is to touch as many sales or conversions as possible. Their goal is not to get you more sales and conversions in a way. So I think understanding how all of this stuff works and the limitations and how to set it up is super, super important. Because otherwise you don’t really understand what’s going on and you’re not getting the most value.

Emma Lo Russo: So you talked about that education piece, how do the executives and business leaders stay across this so that they can be informed to make the right decisions for their business?

Kristi Barrow: Yeah, that’s an interesting one. And one I’ve been thinking about a lot and really you need to invest your time into it. You have to believe it’s important. So there’s a lot of different ways of doing this. You know, the easiest one is I can just say, “I’ll go and read some blogs around that.” That’s not really going to help. But I think there’s two really good ways. One, talk to your team more. There was a lot of discussion I think last year around reverse mentoring and kind of things like that. So if you don’t think you understand, you don’t need to go out and hire a bunch of consultants like me, probably doing myself out of it. To do that, go and talk to your team. They probably know a lot more about this, but talk to them with an open mind, right?

And I think the second thing is that you know, there is some formal education or semi-formal education is really important as well. There’s a company called Decoded that runs an excellent course called App in a Day. And they take anybody, it doesn’t matter what their skillset. And at the end of that day, you have built this little functioning app. And I’ve been through it and it’s absolutely fantastic. What I’ve struggled to do is to get senior leaders to take the day off to do it. And this is I think comes back to the priority thing. You have to want to do it, and you have to invest your own time. You can’t just go and ask someone else or delegate this to somebody else in your team. You have to actively want to spend the time and invest.

Emma Lo Russo: We explore the theme of doing well and doing good. And technology, there’s been a lot of talk about the technology companies. You talked about talking to employees, but this is the ultimate measure of your customer, and customers are choosing what they want for their data. What do you see is the future for getting that balance right of the customer’s happy, and so is the business and data’s at the heart of it?

Kristi Barrow: I think there’s definitely a movement that has happened over the last couple of years around what’s now acceptable practise for companies. I think companies often always held the power, and as a consumer you had to just sort of go along with it. But there’s been a lot of things that have happened. Especially if you think about the Royal Commission into Financial Services and things that have come out of that. Where I think that’s highlighted that the community now doesn’t say some of those practises as being acceptable. They might be legal, but they’re not acceptable anymore.

I think now with expectations from the community and customers being higher, you can use digital to actually really improve the customer experience, not just save money. I have an example here. Very, very large Australian corporate of which I had a service that I tried to cancel. You couldn’t cancel on the phone, because you couldn’t find a phone number. You couldn’t cancel on the desktop or mobile website. You had to download their app to get into a chat, and that was the only way you could cancel. Now, after doing

all that, I was pretty frustrated and pretty angry. Kristi Barrow: I have no more services with this company and anyone whoever asked me, and I won’t name, I will never ever recommend this company and I will never go back to them ever again. Now, the reason why I was cancelling the service was actually to do with the outcome of COVID. And I probably would have started it up a month or two ago, but there is no way now that I will go back to that company to buy the service ever again, because my experience was so bad.

So they use digital as a way to try and A, save money, but make it harder for me to leave as a customer. And for me that’s not acceptable anymore, and I won’t do that. So I applaud companies, I think a company like Netflix, Stan, I watch a lot of TV in my spare time. That’s my downtime. They do make it easier to turn it on and off. Kayo is another one, right? And that’s the way things are moving. And I will always go back to them because I was really happy with the way that they treated me as a customer and my wants and needs. And I think now with digital, there’s that opportunity to go, yes, you can save money, but B, you can also improve the customer experience.

Emma Lo Russo: Kristi, thank you for taking the time today. I think you’re right. It has to delight the customer and make it easy and frictionless for them. And that information and organisation needs to be raised to the level that good decisions are made. Thank you for giving us some guidance.

Kristi Barrow: Thank you, Emma. Thanks for having me.

Emma Lo Russo: A number of great insights there from Kristi, executive teams sometimes seek out data that supports our ideas and agendas that as leaders it is important to remove that bias in the data and seek the truth of data form the solutions not the other way around.

My final guest for today is Magnus Gittins, Director, Executive Education, at the AGSM. Let’s hear Magnus’s views on the role of the education sector in preparing future leaders with the skills required to thrive in the now and future of work.

Magnus welcome to the ‘Business Of’.

Magnus Gittins: Thank you, Emma. It’s a pleasure to be talking to you today.

Emma Lo Russo: So I think everyone understands technology’s changed the way we work and a lot of people are thinking about the future of work, but you talk about the now of work. What does that mean?

Magnus Gittins: Yeah, so I think a combination of, or confluence, of factors have really kind of accelerated what everyone had anticipated and brought it much further forward. So we’re seeing as a result of technology adoption, as a result of the COVID-19 pandemic, we’re seeing a lot of what we had anticipated coming down the track in sort of three to five years time, actually impacting the skills that are required of the workforce today. So looking at things like automation, looking at things like technology and digital adoption, these rates are accelerating to the point at which they’re outflanking the workforces current skills and those skills are not just technical skills, but they’re the soft skills. I hate that term soft skills because in actual fact, it very much sort of diminishes the importance of those things like leadership, adaptability, dealing with ambiguity, dealing with an accelerated and increasingly complex world.

So I think the now of work is important because it focuses us very much on what we need to do today, which is reskilling and up-skilling people, so that they’re able to lead, that they’re able to work in this new dynamic environment and COVID-19 has presented many challenges, but it’s also been really positive to see how we’ve all responded to this and how we’ve stepped up and stepped into it.

Emma Lo Russo: So Magnus how do organisations get themselves ready? They might have, are they more open to it because they’ve had to accelerate that with COVID-19, but what’s your advice?

Magnus Gittins: So what we’re seeing in the workforce is both skill shortages across the economy and in increasingly important areas like cybersecurity, for example, or risk management within financial services, for example, where that has been legislated as a requirement, as opposed to be a nice to have. So we’re seeing skill shortages but we’re also seeing new occupational categories emerge as a consequence of automation, as a consequence of artificial intelligence and as a consequence of cybersecurity and the opportunities and challenges that that entails. So what we’re seeing at AGSM is sort of an enterprise level assessment of skills. Whereas before, just a few years ago, organisations would invest in talent and they’d invest in talent, in a bit like in a pyramid, the more senior you were or the more high potential you were considered to be, the more investment dollars would flow into your learning and development.

What we’re now seeing is across the board investment. So actually getting entire sort of up-skilling and re-skilling across the organisation and that’s presenting some interesting challenges because that comes at a cost. But the interesting conversation I’m having with organisations is how they look at the cost of talent, acquisition, and retention, they look at the cost of retrenchment as a consequence of some of these factors, and then they look at the cost of up-skilling and reskilling. It’s interesting to see that many organisations are now saying it would be better for us to see if we can up skill or reskill the people that we have rather than release people and then go into a competitive market for talent when that talent just isn’t always there.

Emma Lo Russo: So when they’re hiring anyone in an organisation or they’re looking at up-skilling the talent in their organisation, what do you think the characteristics are that helps someone be future-proofed and ready for whatever the future looks like?

Magnus Gittins: Good question, so adaptability. Actually being able to pivot and be able to switch gears is really important. So not having a fixed mindset, having a mindset that’s open, it’s not even so much a growth mindset. It’s more just an open mindset that is appreciative, curious, desirous of change in many respects. So I think those characteristics of someone who is able to say, you know what, I’ve done this for a decade, but I’m open to the possibility of doing something new and leaving what I did behind. So I think the people who will be successful are those who are adaptable and who are able to actually to look at the changes that are working their way through the market and actually to embrace those changes and actually bring a degree of adaptability, openness, lateral thinking, curiosity, those types of characteristics, which take someone out of a traditional fixed mindset and actually enable a change in occupational or a change in skills. And so I think that’s going to happen with increased frequency over time. So, not no longer having the certainty of an occupation that spans multiple decades or an entire trajectory of someone’s working life, but actually being able to step into something new, to experiment, to pivot.

Emma Lo Russo: And Magnus, you’ve done a lot of study of that global kind of view of the workforce. What do you see as the opportunities for businesses when they’re thinking about either the opportunities for their own team or a global view of that whole kind of marketplace?

Magnus Gittins: Yeah. So it’s interesting how we were very much heading towards very much sort of a globally mobile workforce and how COVID-19 has just kind of put the dampness completely on that. So I’m, sort of, in some respects a product of that, I moved from the UK, moved to pursue an opportunity here in Australia, and so I think global mobility has really been, the acceleration has been put on hold. I mean, there was no mobility. So I think the idea of teams and how the work forces then resource for talent and for skill shortages is going to be something that governments are going to have to wrestle with, and organisations are going to have to wrestle with because a country like this one, which relies on immigration in order to meet its labour market requirements and its demands, that’s just not going to happen right now. So, that’s going to force an investment in talent, it’s going to enforce investment in development, if only to meet the skill shortages that are currently anticipated, let alone the ones in the future.

Emma Lo Russo: So you obviously are looking from an education perspective of how to arm the future employee, how are you working with organisations to understand how to get their team ready to think and solve those challenges?

Magnus Gittins: Well, in the first instance, those organisations need to think about what is their strategic future. So what markets do they want to operate in? What is their aspiration in terms of their growth ambition? Organisations need to, sort of, think several steps ahead about what they want to be when they get five, 10 years down the line. Admittedly, that’s a lot of scenario planning, the days of the five-year strategy and the five-year plan are gone, but organisations still need to have a view about what is the Flag on the Hill, because everything else then becomes a translation of that aspiration.

So I think it’s all about starting, and this is where we see organisations getting a little confused. So a lot of organisations will gravitate towards what they know, which is how do we achieve incremental improvement in our operating models. So how do we get better processes? How do we get better systems? How do we improve that archaic billing system that has never been addressed for 20 years? But unless you start at the very top, which is the strategic ambition and the purpose, then those decisions get compromised because they’re all made in the context of the here and now, rather than what the organisation wants to become. Learning and development of people is in no way any different to that. There are investment choices that need to be made into capabilities and skills, but they have to start with an understanding of what the organisation wants to achieve.

Emma Lo Russo: So when you look to what drives the change and maybe the speed, is it typically technology or a mindset change or competitive threat?

Magnus Gittins: The Confluence of those three factors, I think. So when I look at technology and the role of digital and data and you look at incumbent organisations like banks, and you look at how they’re being challenged, not only by global digital giants, like the Apples and the Googles, but also the FinTechs and the Startups, it’s an uncomfortable place to be. If you’re running legacy branch networks, where you have huge investments in infrastructure and technology, and yet you’re being challenged on both fronts. So I think the interesting aspect of COVID, but also technology is how that’s really shaken those existing business models to the core, whereby in actual fact, and we as a university are equally vulnerable, when our last point of competitive protection is the fact that we can issue degrees under a charter, that’s not necessarily the best place to be. I think technology sort of, enables new sort of ways of thinking new business models, but also it poses challenges to incumbent organisations about how to not only deal with that, but then also deal with new ways of working as a consequence of the pandemic.

And I think that’s going to necessitate a whole scale sort of reappraisal around what skills do we need in this economy in order for it to thrive. And it’s interesting that when, there are, it’s hard to imagine if you put yourself in the shoes of a young person today, how would you actually sell an employment proposition to that person from a large multinational or a large domestic organisation? When those individuals will talk to you about how they want to start up their own social enterprise, how they want to start up their own business. And so that’s going to create interesting dynamics as to how do these large organisations attract, develop and retain individuals. And can they actually expect to retain those individuals for significant periods of time anymore?

Emma Lo Russo: So how does the AGSM answer maybe that same question, right? How are you going to arm me, I’m thinking of my study, so I am going to be equipped to deal with all this change in the future. What do you see is that role?

Magnus Gittins: Nick Wailes and I talk a lot about the importance of lifelong learning. So someone who graduated with an MBA in the late 90’s, early 2000’s will have graduated with an MBA that preceded the introduction of the iPhone. Now, how would that person with that MBA standing alongside someone who graduated last year, who’s done digital marketing, who’s done all of these other contemporary topics, and yet they have the same qualification from the same institution.

So in my mind, how would we sort of reauthenticate or if you like apply the Canstar to an MBA that demonstrates that it is always contemporary and always relevant. And that’s where I think the notion of just in time learning, topping up learning, skilling, is something that AGSM can do. And I think that what we bring to the table there is the empirical rigour, the power of the UNSW Business School to actually to be if you like a disinterested party that knows that we can advise you on what skills you need and we’ve got the research behind the learning that actually assures the individual that they’re getting a quality, well developed product.

Emma Lo Russo: When you look to the future, what do you see is the perfect kind of relationship between organisations who know that they need to maybe up-skill their organisation and think about even their offering to employees in that lifelong learning and the education sector, and then its role in making sure Australia stays competitive or organisations stay competitive. What do you think is that model moving forward, to deal with the now of work or the future of work?

Magnus Gittins: Yeah, so I think the notion of doing a three or four year degree, and then that’s it, I think that notion has now been completely dispelled, given the occupational shifts and skilling shifts within the economy. So I think people knowing that they need to be in that learning mindset on a longer term basis beyond their foundational study, regardless of what that foundational study might be, is increasingly important. And I think as educators, but as educators who don’t have, our proposition is we want to up-skill and that’s it. We have no other dog in the hunt, we have no other desire other than to help people do better. So I think it’s how do we translate that purpose into a learning proposition that is personalised, that is focused on job ready skills, that allows people to shift in their careers, to shift at their different stages in their lives, and how do we actually make that accessible to a broader population in the community as possible.

This is what is actually important right now, is actually being able to transpose entire categories of employment into new things. So when you look at the forecasted impact of automation to the accountancy profession, for example, well, we’ve got, I know the numbers because we’re working with them, but Chartered Accountants Australia and New Zealand’s got 166,000 members. Now, how do we help those members think about the future of their profession and their role within it, when automation increasingly takes away some of those day-to-day tasks, what then replaces those tasks? And I think that’s where universities and other institutions can step in and assist because we do have the benefit of being able to educate at high volume and at a quality premium.

Emma Lo Russo: What do you see the role for small business and learning and where does that also come into play, is it the same principles?

Magnus Gittins: It’s really interesting. So we at the AGSM have historically had what we call a Custom Learning Business and an Open Enrollment Business, and the open enrollment business was previously described as sort of a B2C, people sign up and they enrol. But when you look back and you peel back the data, what actually it says is that, that business, the open enrollments business, 95 per cent of the participants in those programmes are having their fees paid for by a small to medium sized enterprise. So there’s no lack of commitment from SMEs in terms of investing in their people and their high potential people, because we see 4,000 of these individuals every year. I think the question is how to improve accessibility and I think that’s where the role of government thinking about how to unlock lifelong learning budgets, and align those with industry needs, and get better coordination between all parties can only help.

I mean, SMEs are the lifeblood of the economy and I think it would be to our collective disadvantage if they were excluded from those learning opportunities. So I think the good thing about technology enabled learning is it’s reducing the cost of access and I think that will hopefully enable a greater sort of leverage of investment dollars by SMEs into their people, and sort of move beyond the high-potential, the person who’s agitating to go onto a programme for all the right reasons, but how to actually get more people from that sector into a programme, can only be a good thing.

Emma Lo Russo: Magnus, I love how you’ve set the context of don’t think about it as a future of work, you need to make these changes now. I love that kind of call to arms in the now of work. So thank you for your time today on the ‘Business Of’.

Magnus Gittins: Thanks very much, Emma. I’ve really enjoyed the conversation.

Emma Lo Russo: For me, this episode has really shone a light on the importance of digital literacy in leadership, and the need for continuous reskilling and up-skilling today’s workforce – not just in technology, but also in leadership skills. As Magnus said, the value of skills like adaptability and managing ambiguity can not be understated in an increasingly complex world that continues to change and undergo disruption.

One of the consequences of COVID-19 is that digital transformation has become a bigger imperative for organisations, and many are transforming much sooner than they had imagined. AI, machine learning and big data will continue to infuse all areas of business, and we as leaders have a responsibility to drive and support new skills for the job market of the future and to ensure our businesses stay relevant.

Thanks for listening to this episode of The Business Of. I’m Emma Lo Russo, see you next time.


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