What AI really means for organizations, workforces, and the future of human work

In this episode of The Middle Ground, Dr. Kathryn Ritchie sits down with Dr. Sean Gallagher, founder of Humanova, to explore what AI really means for organizations, workforces and the future of human work.

Their conversation covers why AI is a talent strategy, not a technology strategy—and why the organizations that treat it as an efficiency tool are missing 99% of its upside. Drawing on Humanova's research, Sean explains the growing chasm between organizations that have merely adopted AI and those building genuine capability with it: adopters see an answer machine for summarizing emails and drafting documents, while highly capable users see creativity, innovation, new business models and entirely new horizons.

Key themes from the conversation:

• Agency and accountability are what make humans distinct as AI takes over cognitive work—humans are good at navigating the unknown; technology is good at extrapolating the past.
• Efficiency and productivity are the lowest-value outcomes of AI. The real prize is creativity, innovation and business model transformation.
• Engage with AI as an intelligence, not a tool. Humanova doesn't train people to use a tool—it shows them how to collaborate with an intelligence, creating a human–AI hybrid way of thinking.
• AI is a horizontal, workforce-wide capability—the layer that forces organizations to build the capability architecture they need to execute strategy.
• Culture is the main game. Turning AI from an individual pursuit into a team sport requires psychological safety, sharing, comparing, and permission to make mistakes. Highly capable AI users are three times more likely to collaborate across functions—AI is breaking down silos.
• Leadership vision comes first. Organizations with a clear "why" for AI are more than two and a half times more likely to have an AI-enabled workforce. Leaders must elevate people to higher-value work—or waste human talent.
• East versus West. While fear dominates the Western narrative, nations like China, India and Singapore are treating AI as a system-wide uplift—investing in education, reskilling and workforce capability at national scale.

Watch the full conversation above—and read the complete transcript below.

Full transcript

Dr. Kathryn Ritchie (00:01):

Dr. Sean Gallagher, just delighted to have you joining us in our Middle Ground podcast series. I think one of the things that has really always been meaningful for me and our connection is that we really see the role of both the human impact and neither of us let go of all of those very significant elements that relate to the finances and the edge that a business has to maintain. And your work with workforce has been crucial. You know the work that we do in strategy execution. And we now find ourselves in a time where there's an extraordinary global set of sentiments, but we also have an explosive influence through AI. And I want to not lose sight of the broader context, but actually give you the opportunity to share with us some of your thoughts about the impacts and so forth in terms of workforce, organizational capability. And I'll get tighter with a question or two, but I just want to set that context that we're not going in just to AI or just a workforce. We're looking at this in the bigger context. What I'm particularly interested in is AI has all sorts of implications. It could be commoditized. I mean, the language has been thrown around. Organizations know they have to address this. What are your initial thoughts? How should this be considered in terms of an organization that's wanting to continue to perform and drive out its strategy?

Dr. Sean Gallagher (02:06):

Well, thanks Kathryn and thanks for having me as a guest on the podcast. Very much looking forward to our conversation. I think AI is without doubt probably the most profound technology that is ever going to come into our lives, be it at a work or personal level. It's going to impact society and the economy in ways that I think are going to be—well, no one can really crystal ball that stuff—but it's going to be fairly profound. And as the father of a 12-year-old daughter who still has five years of high school left before getting into university or whatever her world looks like after that, I think about this keenly in terms of not just organizations as you're talking about, but for her and what her future looks like. I've been in the future of work space for fifteen years or give or take. And one of the things that drove me initially at the start was trying to understand how exponential technologies—at the time it was digital disruption and Industry 4.0—how they were impacting and transforming work and what that meant for workers. And one of the little insights, I suppose it's kind of like a North Star that I quickly began to realize early on, was that as exponential technologies get more sophisticated and take over, we need to become more human at work. And I think that that is not only true with respect to AI, it's even more true with respect to AI. Particularly because digitalization of our organizations, Industry 4.0, were incredible technologies, and they still continue to be, as was earlier forms of AI—predictive AI and machine learning. Really valuable stuff. But generative AI, the current form of AI, is really beginning to take over a lot of the cognitive work that we used to do. And I think it forces a frame that really forces us to ask the question: well, what is the role of humans in this increasingly sophisticated space with technology that tomorrow is going to be able to do things that were unheard of today? It's moving that quickly. And so fundamentally I sort of see it as: what do humans—what's unique about humans in this space? And to me it comes down to a couple of things. One is agency and accountability. From an agency perspective, it's taking risks, using our judgment based on curiosity and our imagination and empathy and so forth. But fundamentally, where we're different to technology is humans are really good at figuring out the future. Technology is good at understanding where the past might take us, but humans really are great at navigating the total unknown. And accountability—of course, you can take a narrow view on accountability in terms of, well, an AI in theory is not going to go to jail or pay a fine. It has to be a human. But I think a more interesting version of accountability is that as humans pursue that agency and take risks, they learn things as they go along. And the accountability is they need to be accountable for sharing that learning—either with their team or with their organization. And that sort of virtuous cycle is really valuable. And you might have heard me say, and certainly talk about it with all of our clients and elsewhere, that AI is a talent strategy. It's a workforce play. And it's about how do we not only make sure that we take away a lot of the drudgery, a lot of the mundane work that humans have been—the low value, high volume stuff that humans have been thrown into jobs to do—we've been paying humans to execute as opposed to paying humans to use their expertise to take these risks. So I think from an organizational perspective, and particularly from a workforce perspective, how do we begin to evolve the workforce to this higher value work. And I'm sure we're going to have lots of opportunity to dig deeper down into that as we have our chat today. But fundamentally I really want to make sure that people have a takeaway that seeing AI as a talent strategy being a workforce play and not a tech one.

Dr. Kathryn Ritchie (07:16):

So one of the things—I mean, I've looked at AI in the context of our business and I see it really has allowed us to free some space up. In fact, it's allowed us to elevate the things we do and to create more space, more thinking time. And I don't feel in the least bit actually threatened from a business standpoint in terms of AI. However, that's not always the experience. Are there things that you can convey or help businesses think about how they consider AI and its impact such that it becomes a workforce uplifting strategy?

Dr. Sean Gallagher (08:05):

One of the things—so in addition to working with some clients across many industries here, we also do research. We also want to understand how AI is impacting work, what that means from a workers' perspective, and how their organizations are responding to that. And what we're finding is that a lot of organizations are seeing AI as a traditional technology, in terms of it's about adoption, it's about measuring use cases and usage and so forth. And that's not unhelpful, but if that's the only way or the only lens that you have onto AI, you're missing 99% of the upside with respect to that. And so what our research shows, as well as helping those that we work with, is focusing on capability—moving away from adoption, number of uses per week, how many people opened up the tool this week, to what do you actually do with it? And what we're finding is there's a huge chasm between those who have adopted it and those who can actually demonstrate real capability in using it. What we find is that, and this is a really interesting thing in our research, those who are highly capable at using AI see a totally different world with AI than those who have just adopted it. Those who have just adopted it sort of see it as an answer machine—transactional use: summarize this email, draft this document, help me figure out an agenda for this meeting. Not unhelpful, certainly of value, no doubt about that. But people who are highly capable with AI see things like: what complex problems can I solve? What critical thinking can AI support me to do to really push my boundaries, to understand something that I haven't really been able to nail before? What new ways can I see our business model transforming and innovating? Those are the real distinctions that we're seeing. And it's interesting, when you look at what's the most common view across an organization in terms of the value that AI brings—from those in the office and frontline to those in senior leadership—the most common is efficiency and productivity. But that's actually the lowest value outcome you can get from AI. When you look at those who are super capable and using AI all the time, they see creativity, innovation, new business models, as well as continuing to evolve the organization in ways that they can't currently do. And so one of the things that I really like and encourage people with AI is to deeply engage with it, and to build up an understanding of how AI works, because you'll actually be able to see a horizon much further away than just the work that you have in front of you. And so that's certainly a great journey for everyone to go on.

Dr. Kathryn Ritchie (11:52):

So what that conjures up for me—because what we do when we're in strategy development and so forth is we talk about pictures of success. And as a part of that, we do an envisioning exercise where we want leaders and their teams to think out and envision the future. And then we architect back from that. It's hard for many who haven't taken that dive into the detail or haven't experienced it, and they're only thinking, as you say, productivity, remove duplication, make it a bit more efficient here and there. How do organizations start to bridge that gap between where they are and how they could genuinely engage in a real envisioning exercise to see what could we be, and then they can work back? Because I see that being able to do that—that's where you will start to understand the bigger role of the human and you'll be able to see what AI can do from a creative and more expansive standpoint. So how do organizations go down that path? And think of a bit of a range—those who have hardly started, those doing what you're saying—most organizations are doing stuff around productivity and so forth. Where do they go from there? How do they take that step and sort of leapfrog to a more thoughtful path?

Dr. Sean Gallagher (13:36):

It really comes down to fundamentally engaging with AI not as a technology or a tool, but as an intelligence. And at Humanova, one of the things that I pride ourselves on is that we don't train people on how to use a tool. We show them how to collaborate with an intelligence, because it's constantly evolving. It's almost like—I don't want to get too sci-fi—it's almost like another species has landed on our planet, and we humans have to figure out how to work with this species. And in doing so, it changes our cognition. It really is about this human–AI hybrid way of thinking and doing stuff, as opposed to, yes, AI can help me summarize that email and do that kind of thing. So when it comes to envisioning a future, what I think is valuable is asking more interesting and valuable questions. And of course, no one has a crystal ball. And it's very difficult to understand exactly, both from a market force perspective, but also from the way that the technology and the emergent capabilities are going to go. But by having this almost symbiotic partnership—both at an individual level and a team level and an organizational level with AI—the questions that you're able to ask are so much more valuable and interesting, and to be frank, existential. Often companies that come to us start with one or two things. Everyone's heard about AI, you can't avoid the headlines. And they come in and they say, well, we've got this one particular use case that we want to solve. Or we really need to work on efficiency and productivity. And of course, that's a great conversation starter. They realize that AI can support them in doing that, and they just want to go straight to the solution: show me how I can make this workflow 20% more efficient or whatever it is. And we're sort of like, well, hang on a second, let's build up from the fundamentals first. Let's change your understanding of what AI is and how you work and collaborate with it. And not only are we going to be able to address those particular concerns that you have, but we'll also be able to do 99% of the other things that you haven't actually identified. And I think that's one of the real values that AI brings to organizations and workers that leaders just engaging with it haven't really had the time to think through: it's not a special purpose technology. It's not just dedicated to solving these two or three particular problems. It's there to support everyone. But in doing so, it's changing our cognition, as I mentioned. It really is allowing us to see problems in a more sophisticated way because our mental models on the future are changing dramatically. I did—I won't mention their name—there's one of our spy agencies here in Australia. And I gave a couple of keynotes to them. And they, for understandable reasons, were very worried about data security and privacy, and that we can only use Copilot. And that's really the only AI that we can possibly use. And I told them, I was perhaps a little bit frank. I said, well, that's not good enough, in the sense that every criminal network out there is going to be using the most advanced form of AI to think of the most sophisticated crimes to commit on Australians. And I said, yes, you have to protect our citizens' data and privacy and all of that. But you need to figure out how to play with this technology in a safe and responsible way to understand what it means for your ability to fight these crimes. And unfortunately, we had a cyber incident here in Australia where criminal networks had been using these sophisticated AI models to commit even more sophisticated crimes. And the point being—and this is not throwing shade on anyone, I understand that organizations have legislative and regulatory compliance obligations and all of that. I'm not in any way dismissing that. But there are ways that you have to figure out: how can we see a world that we can't currently see by engaging with this technology at its most advanced level? Because that future is actually going to be upon us pretty quickly. And not just people trying to commit crimes, but in the traditional business sense, your competitors being able to do things that completely change the purpose of your industry, or the business model that you're on is all of a sudden on quicksand. That is really where I think we need to go with AI. And so coming back to how I started framing this: collaborating with an intelligence so that we continue to evolve with it, so that we can see and develop an understanding for what might be in a way that we can't currently. I think we just really need to engage.

Dr. Kathryn Ritchie (19:48):

So one of the things that we find—and I would go back a couple of the decades that I've been working in the space of organizations wanting to strengthen how they perform and execute and so forth—is organizations look for quick fixes in my view. Oh gosh, we can now do that, we can now do this. That's a very exciting thing. And with AI, there is the risk of doing that. I think one of the great benefits of AI is that it's increased speed and we can't go slow and we also can't think linearly. We have to think as a system and I'm deeply grateful for that because it forces organizations to think in a more sophisticated way about what they need to do because they are a system. What we now are thinking about—and had been before AI—is the way organizations need to improve, or can improve, is to really build the capability architecture, deepen the capability through their organization in order that they execute. So it doesn't matter whether this CEO or that—they've got the capability in their organization and they've got the directional strategic architecture from mission, vision, competitive strategy. They've got that all there. And now they keep building the capability to execute. What we see is, beneficially—because it's a hard slog getting that architecture, building the capability, getting organizations to recognize that this is a capability game. You need to build this in your people. AI is forcing the hand here because it is a capability—it's a horizontal capability. As you've said, this is something that needs to be embedded with a lot of other things. How do you—

Dr. Sean Gallagher (21:52):

It's workforce wide.

Dr. Kathryn Ritchie (22:02):

How are you encouraging that, enabling that? Are you using facilitators? What is the way that you are activating a greater understanding, focus and commitment in organizations to really deepen their commitment to building capability? Because that's what they have to do in order to execute. And AI is a layer that transcends.

Dr. Sean Gallagher (22:30):

I couldn't agree with you more. And in fact, let me double down on that further. Our listeners to this program might think we workshopped this answer, but we haven't. And my answer is very much in line with yours. It comes down to leadership. First and foremost, not just from the research that we do—and it shows that those organizations that have a vision, understanding the why of AI and the purpose, are more than two and a half times more likely to have an enabled workforce with AI. So it really is critical. But it also comes to challenging and confronting leaders about their mindset. I have the opportunity of speaking to a lot of boards and executive leadership teams about AI. They bring me in for a strategy session. And one of the things I often start with is—I'm there to help them better understand AI. And I say, AI is an exponential technology. And they all nod around the room. I said, I know you know that, but you don't really understand that, in the sense that when you're going to walk out of today you're going to feel like you have a much better understanding of AI, which is great, and you will—but tomorrow AI will have moved on. And so your thinking needs to constantly evolve with AI. And don't necessarily think that the most recent op-ed or the headline or a use case that you saw is AI. That may be an element of it, but it's constantly evolving such that we need to likewise have organizations that are constantly evolving. We were talking before we came on camera about Peter Senge and his great work in his book The Fifth Discipline and the understanding of this learning organization. It is fundamental, I think, for any organization to build the capability that you're talking about—both at the individual, the team, and organizational level—is about learning. And I think it was Arie de Geus who said, learning faster is your only sustainable advantage against your competitors, or some version of that. And Peter Senge made that line famous in his book. But it really is true in that because AI is moving so rapidly, and in nonlinear kinds of ways, we don't understand the emergent capabilities that are going to come. Unlike Moore's law, which was perfectly logarithmically linear—we doubled the compute every two years. That was pretty good. Great exponential, but AI doesn't behave in that way at all. And so the only way to really understand how this technology is impacting not only us but also driving value is being able to learn faster. If you give me another couple of minutes, one of the things I often talk about is the war in Ukraine as a metaphor. And at the start of the war, the soldiers on the front line said to the generals, we want drones. And the Ukrainian military machine—the command and control, the top-down—said, why would you want drones? They're just for wedding parties and taking photos. What a waste of money. We're going to give you tanks, we're going to give you howitzers, all of the traditional kinds of things. But the soldiers went out and they crowdsourced and got drones delivered to them on the front line. At first they were just better binoculars—they could see further, they could cover tanks and infantries that were making forward advances. But about four months into the war, that was the first time a soldier strapped a hand grenade onto a drone. And it totally changed the nature of warfare. And a year later, the Ukrainian military was buying them by the thousands for the front line. But the really valuable lesson from this, from a capability perspective, is the innovation ecosystem that the Ukrainian military then established. They gamified war—I know it's a gruesome kind of topic—but every frontline unit was competing with everyone else and they got points for taking out targets of value. And those leading could convert those points to cash to buy more of the latest AI-driven software or the hardware. And then that was a really positive feedback loop to the manufacturers to then allow everyone on the frontline to buy that kit. And this innovation ecosystem is what's building the capability of the Ukrainian military. And one of the things that's I think is—okay, we're not at war, and how relevant is that to us in business? There are many different examples of these little bits of innovation. You look at Zara—Zara is able to do fast fashion, not because of the sales data that gets up to head office. It's the conversations that sales assistants overhear in the stores and feed that back. It's a real surveillance, a little radar onto the future. Or if you even go back fifty, sixty years to Toyota with the andon cord on the assembly line. If a defect was found, a worker stopped the line and everyone gathered around to troubleshoot it to improve the quality. And so where I'm going with both the version of the Ukraine military, but also those couple of examples and many others, is delegating authority to where the action is happening, but then having a system that can capture it to evolve the organization. I think that's really where AI is going to force organizations to go. And that's how capability is going to continually develop from that systemic frame that you mentioned earlier.

Dr. Kathryn Ritchie (29:34):

And so what's leapt out in listening—a lot of what you've described is about creating the conditions in order for the capability to have the impact that it can, and to be embedded and have the resonance ultimately that it can and the impact. This is about culture. And we talk a lot about culture. I would always go back to Lou Gerstner, the old story where he said, look, when I was at Harvard I kind of walked my way through all of those human studies and thought they were a bit of a waste of time. And then when he got into the seat at IBM he realized that it was the main game. So what are the cultural things, what are the conditions—and yes, I'm saying AI, but actually this is much more than AI—what are the environmental conditions that leaders need to deliberately shape so that people can engage and embrace and adopt AI in the way it can? AI is somewhat symptomatic, but it also forces a whole lot of other uplifts.

Dr. Sean Gallagher (31:05):

I'm really pleased you asked this question because fundamentally it is about culture—both at the individual and team level as well as across the organization. And let me answer this from at least a couple of perspectives. One of the things that we have found—and it's certainly research-backed—is that to create this learning organization, if you like, it really is making sure that we have a culture of learning, workplace learning, of sharing, and so forth. And psychological safety is part of it. It comes through from the water cooler. It's part of the organization. Because one of the things that is a real blind spot that most organizations have around AI is they see it as an individualistic pursuit. I'm working with AI and it's helping me in my job. And of course, that's great. But we need to turn it into a team sport. And to do that, from a cultural perspective, we need to build up this culture of sharing. We need to build up this making mistakes and comparing and so forth. So one of the things that we do in the enablement training work that we do is we get people to do an exercise in pairs—not one person on AI and the other observing. They're both doing exactly the same exercise in parallel. And because AI is a non-deterministic technology, they both put in the same or very similar prompt and they get wildly different outcomes. And they're sort of seeing, well, how did that happen? And that instance is one way to begin them to realize, hey, actually we're starting to talk about AI. How did you get that? What did you do? What was the difference there? How do I now make sure that my response or the way that it's formatted looks like yours? And actually this has given me a really great idea because this technique or this use case I can now apply elsewhere. That is right at a fundamental, at the atomic level in terms of beginning to build this culture. And it's one of the most fulfilling things that I do from an enablement perspective—training and seeing these light bulbs go off, and just the chatter amongst people talking about their own experiences with AI with their colleagues is incredibly valuable. From a research perspective, the other thing I wanted to mention is that this has a lot to do with the work that you do at Ignition and The Middle Ground: AI is breaking down silos. And those who are more capable at using AI are three times more likely to collaborate with colleagues outside of their function than those who are not. And the mechanism is really interesting because AI allows people to not only do better work upstream within their own role, but it allows them to do at a higher level of work in areas that are adjacent to their roles. And so we're not only seeing this vertical, but also a lateral expansion of the work that people are doing. And so someone who might be in HR can actually have a much more sophisticated conversation with someone in finance. Because they're using AI not only to expand their HR work into the finance space, but increasingly they're also beginning to do some of that finance work themselves. Obviously there's broader policy issues and questions for organizations in terms of QA and whatever. But this is perhaps a mechanical driver of the change of the culture in an organization. So how do we leverage that? How do we amplify that? It really is this culture of learning and culture of sharing and a conversation across the organization that must occur. It really has to. I'll just say one last thing. I could come in and give a hundred training courses and not cover all of the use cases. The only way you're going to amplify AI across your organization is by sharing and comparing and showing and doing—taking it away from being an individual pursuit and increasingly to the team level. And that's going to really help change the culture.

Dr. Kathryn Ritchie (36:32):

And I also hear no judgement and mistakes. Let's learn, let's fast fail and so forth. However, if you think about where an organization might be, what's like the first step that they might take to go down that path of starting to break down those silos, to create that environment of feeling safe to share, to collaborate? I mean, most organizations talk all of this. They say, we're doing all of this. But the reality is not so much. And there's still a lack of permission and so forth. If you had to give CEOs and C teams one or two really simple, very practical steps to move in that direction, what are those?

Dr. Sean Gallagher (37:27):

I will give you two. Number one, it has to be the leadership vision thing. Without knowing why your organization is engaging with AI and going on this journey, you can throw as much money as you like into training and it's not going to return much at all, because people don't know why they're doing it. Is this a test to see whether I'm good enough at AI and potentially I might get replaced? They need to really have clear—it's not necessarily a strategy for AI, because most leaders don't understand AI itself. But having a vision, the why: this is about empowering our workers to do more valuable work and engaging and doing less volume-type stuff so that everyone can grow and evolve in their roles. That is really helpful and helps any future investment. The second is just showing workers that the individual work is actually going to disappear. The work that someone's sitting at their desktop going through the routine repetitive BAU within their particular role—increasingly that'll be done by agents or some version of an AI solution. But the future for those workers is to elevate them to much higher value work: the complex problem solving, the critical thinking, the innovation, the creativity, which increasingly is best done collaboratively with colleagues, because the diversity of views, particularly from an innovation perspective, is incredibly valuable. And so those two—talking to leaders, making sure you have a vision in place, but also recognizing that if you don't elevate your workers to that higher value work, then they're going to be made redundant. And I think that's a waste of human talent.

Dr. Kathryn Ritchie (39:30):

Yes. And I would imagine—I've got one more question, but I'll just add to what you've said. I would imagine that helping people see that they can be successful in that higher order work will be incredibly important and that they'll get the support, the training, whatever they need to go there and not be seen to be redundant. I imagine that that is an important staircase to go from where they might be today and where their work might move to—to be greater for them but also greater for the contribution.

Dr. Sean Gallagher (40:20):

I've got a quick little anecdote here. GoFarm was one of our first clients a few years ago. They're an agriculture investment business, and they're looking for underperforming and distressed farming assets to invest in, and then they come in and bring their technology and increase the value that they can get from the land. And as you might expect, they're constantly talking to investors about opportunities. And they found this one particular property. And they went to the investor and said, what do you think? And the investor said, well, have you done the seven-year carbon budget modeling on this? Just to give me an understanding. And of course, they hadn't. What they would have normally done is gone to a consultant—three weeks later, 30 grand, come back with the budget modeling. But one of the research team at GoFarm thought, well, I'm actually going to use AI to model this. And so they were able to get back to the investor within two hours, save a truckload of money, and keep that opportunity warm. And so this is lifting yourself to doing this higher value work. And I suppose the most important thing is that the organization realizes that actually now that we can do something in two hours as opposed to two or three weeks, it's no longer a nice to have. This actually now needs to be the way that we work. And this is one of the beautiful feedback loops of doing this higher value work in someone's role and seeing the organization take advantage of it. I think that's a great little example of valuing this higher value stuff.

Dr. Kathryn Ritchie (42:16):

It certainly is and it actually leads me into my last question, which might be a bit of a wild card. As we were talking before we joined the call, I've just come back from this extraordinary program with the MIT Learning Center and Transformational Center. And they've obviously been paying a lot of attention and they do really deep immersion—I don't say just research, it's real immersion. And one of the observations—Otto Scharmer, who was a leader in that environment, has recently been in China and he set one of the pre-reads, an article he created. And I'm going to simplify this and I'm probably not going to get it right, so Otto, I'm sorry—with all permission—but I still am going to play it out because I think I can get the core streams of it. What he said in that article from his observation was that there are almost two camps. And I'm going to simplify this by saying there's the Western world—the US, Australia and such like—and then we can almost go the East. And I'm not even going to try and qualify or quantify that. But notwithstanding, there are two groupings. And again, I'm going to simplify the circumstance: in the US, there's a lot of fear. There's a lot of fear around loss of jobs and, my gosh, our kids are not going to have careers and I'm going to lose my job and all of this is going to go. And actually ultimately—and this might be a little bit political—all to the benefit of a few benefactors at a financial level. And so you could say a constraining execution landscape. Whereas China—and he used quite explicitly—said in China, AI is being looked at as something to uplift. It's to enable those that don't have, that aren't educated. To uplift the education profile of people, the working environments—it's an enabler and we're going to push this through our environment to uplift all for the better of all. And as I say, I've simplified it, but it struck me as—therefore they're not frightened in China. They see this as great. Help me, get me onto that train, get me onto that pathway. Whereas in the US it's like, gosh no, what's that going to do? How's that going to destroy? How's that going to constrain and take? What are your observations about that perspective?

Dr. Sean Gallagher (45:28):

I think it struck you because there's a lot of truth behind it. If I look at China—particularly given China is a country with central control of a lot of technologies—it has strongly encouraged the use of AI. China, by many measures, is developing the best open source models for large language models anywhere in the world and workers are increasingly using it. And I think what you mentioned is right. From a government perspective in China, I have no doubt that they're also thinking that we've got a diminishing labor force and an aging population. We need to increase productivity and so forth. So there's no doubt that it's a win-win—but it's also the uplift of workers. If you look at a country like India, the uptake of AI is just extraordinary, because people see this as a leg up. They really see this as an opportunity for them to not only do better within the work they do in India, but also globally. You look at Singapore—you'd hardly call Singapore a cavalier nation—but compared to Australia, they have twice the amount of uptake of AI within work. The capability across the workforce is much greater. But also in Singapore, the government is not in any way wearing rose coloured glasses. They realize the disruptive potential of AI. And so they've set up a national education fund so that anyone over forty, I think it is, can get money to pay for AI courses to help them pivot or to ensure that they themselves, if their work gets disrupted, have other opportunities. So I think that certainly, East versus West—Eastern nations are very much seeing this as a real opportunity to leapfrog. The West is—in Australia, our government here is very risk averse. We've had a number of cybersecurity incidents, and understandably so. And so we've been overly heavy on the AI security side of things, whereas in my view, we've built the seatbelt before we've even designed the car. And I think that has unfortunately sent some negative signals about AI. Well, is it going to make wealthy people richer? Hasn't that been the case for four hundred years? I don't know. I mean, yes, is it an issue? And do they have power? They've always had power. We're not going to solve that problem. But what we can solve is engaging and enabling people to do valuable and more valuable work within what they're doing, and create things that they weren't able to do otherwise.

Dr. Kathryn Ritchie (48:55):

So thank you, Sean. I would come back then—it was a beautiful set of observations that you just made, and you brought it right back to the system. And the way I look at it—whether it's Singapore or China—they are each looking into their systems of their landscape, their economies in the global landscape, and they're going right. In China, they're taking advantage to upskill and increase productivity and do what they need to do there. And Singapore, they need to offer training and face into some of the diminishing opportunities work-wise or whatever that might lead to. They're thinking of it as a system as a whole and I just don't think—I'm not experiencing that in the US. I don't observe it. I'm not close enough to Australia. But I think the whole thing is: AI is a new capability. It's with us. We have many other capabilities, but if we don't think of this as a system enhancing force and look at what that means for each of our environments and in the overall global environment, we're going to miss out. And so I bless and thank you very much for your commentary and your expertise. We're delighted to have shared this time with you. Thank you very much.

Dr. Sean Gallagher (50:22):

Thanks, Kathryn. It was a great conversation and look forward to hearing comments and feedback from your listeners. Thank you.

Dr. Kathryn Ritchie (50:29):

Thank you.