AI Transformation: What 200+ Global HR Leaders Are Saying Now

Hello all, welcome back to another week of your Brain at Work Live. I am your host, MSRO, the Senior Director of Research here at the Nareli Ship Institute. We're happy to have you back for all of our regulars and for our newcomers welcome, we're excited to have you with us for the first time today. So as always, open the chat button, set it to everyone and let us know where you're coming in from today. We love chat, so keep it coming. In today's episode, David is here and he is going to be sharing his latest insights from over 200 HR leaders from around the world. What are the biggest challenges weighing on leaders' minds today? We're diving into all of that. So as always, we suggest you put your phone on the UNAT Disturb, quit your email and messaging app so you can get the most out of today. And as I said, we love interactions, so continue to share your questions and comments in the chat. Joining me today, after some weeks away traveling and learning, you all know him well. He coined the term "neural leadership" when he co-founded NLI over two decades ago. He has a professional doctorate for an almost five successful books under his name and a multitude of bylines ranging from the Harvard Business Review to the New York Times and many more. Warm welcome to our co-founder and CEO, Dr. David Rock. Hello, welcome back. Thanks, Emma. Good to be here with you. Yeah. It's been quite a journey on literally my last day of a three week world tour. Across starting in New York, doing this same workshop in New York and then in the ATD conference in LA and then Sydney and Canberra and Singapore and now London, we just did the session today. It's been so interesting seeing what's going on out there. Yeah. So, even all over the world, I guess the big question is, what are you noticing? Like, where is the market at right now? But so fascinating actually how consistent the challenges are everywhere. It's terrifyingly similar, I would say. So, you know, people, so I met with, you know, each event was probably around about 50, 40 or 50, some bigger, some smaller, but average about 50, so, you know, over 200 senior HR leaders. And it just, everyone seems to be the exact same point, largely, which is, which is that they've put in, they've spent quite a lot of money on Gen AI. It's mostly Microsoft's co-pilot because they've just got 70% of the corporate market. And I'm talking to mostly larger companies. Or it's, you know, it's Google's Gemini, which is also a great tool. We're seeing increasing interest in Claude and that's such an interesting race. But basically, everyone's put something in and everyone's trying to work out what to do now. And they sort of still stuck on kind of AI literacy, which is just, you know, basic use. Still just trying to get basic use. And what they're finding is there's all these, you know, firstly, there's an incredible amount of resistance. This is universal. It was a matter of here in Singapore or London or Sydney or, you know, the US, like, there's a lot of resistance and some of it's pretty fervent. And, you know, I asked my kids. I saw both my kids in the last few weeks and they're like, no, we're not using AI. We weren't touching the stuff. It's destroying the planet. It's using all the water and I don't want to give all the billionaires all this money. So there's, you know, and these are, you know, 19 and 23 year olds. But there's a lot of resistance. And then, you know, there's, there's literally we're not, we're not seeing like out of 200 each other as I spoke to, I found one company that was like above 20% AI fluency. And the way we define AI fluency is, is, you know, people clearly doing better work. And with no obvious cognitive downsides, so they're not like exhausted, right? But they're clearly doing much better work as a result of these sorts. They're not just saving a bit of time here and there, but also producing, you know, really average work. They're actually just clearly doing better work. So what we're seeing is 5% or less at the event today. I heard a lot of 1%. One company I met was like, yeah, we've got a guy. We got was one guy, a thousand, probably a 5000 people. We got this one guy who's really fluent. He's doing amazing things. No one else is really doing much with it, but he seems to be doing amazing things. And you see this very spiky result. And often those people are, you know, a building agents and trying to reinvent workflows and doing stuff that not everyone should be doing, because actually that's got to be much more deliberate. But essentially, people are really struggling with AI fluency and, you know, the 10 downsides we've been talking about as well. And that's just, it just seems everyone's at exactly that same point kind of everywhere in the world. So if your company, if you're worried about feeling like you're behind and everyone else is ahead, everyone's behind. Everyone's behind what they think they should be. And everyone's doing just fine. And I think my favorite comment was from Jan Hong, who's the C.H.R.O. of DBS, the big bank in Singapore, about 50,000 people. She's, you know, her comment was, I just wish everyone would just calm down. Basically, like we've been through big tech challenges before, big changes before, and can everyone please just calm down a bit and, you know, focus on what matters. Yeah, it's interesting because as we know, the adoption rate is incredibly high, higher than anything else we've seen. So people are jumping in there investing, as you said. But now it's like, okay, let's figure out how to use this thing best. So we have to do a bit of experimentation or a lot of experimentation. Backtrack a little bit. Let's go back at it with a different direction. Let's go back to the core of human behavior because I think that was one of the things that we're all kind of learning. We jump in and realize, oh wait, this is actually a human challenge. It's not a tech challenge. So let's go back and think about what we understand about humans. Everyone is saying it's a human challenge, but it's really a brain challenge. Right? It's a cognitive challenge. So this is a cognitive hack that we've built. Right? AI is a cognitive hack. And so cognitive hack because boy, just the brain need cognitive hacks, we can barely hold, you know, four digits in our head. Like we can't process a lot of information at all. So it's an extremely useful cognitive hack. But we have this cognitive hack because of the limitations of our capacity. And yet we're completely ignoring those limitations in rock out these tools. And you've got to remember that giving people something that helps them not have to think is absolutely delicious to the human brain. It's one of the most exciting things you can get people that gets up there with free money as a reward response. Right? So you get people to something that helps them not have to think. They're going to use it as much as possible. And the problem is that's not a good thing. And the most poignant study, actually you found this one and shared it with me. The most poignant study was all 25 large language models were tasked with coming out with metaphors for time. And all of them created metaphors in just two categories, like two themes, all 25 models. And prompting them better doesn't fix that gives you a worse quality. And all of that is just really simple. All the models are drawing from the same database, the same database of, you know, accessible human knowledge. They're all processing from that. And so they're all, you know, it's by definition average. So it's a really interesting time. You know, people are, you know, we're encouraging people to use these widely, but then when they use it widely, which they really get excited by, you know, often the work is really, really average and they don't know it. So there's this irony of, we're selling people at tools that they don't have to think as much, but they have to think really, really deeply to use this tool well. And it's a kind of thinking that people love to avoid doing, right, reflecting and being metacognitive, challenging yourself, being really, really deeply thoughtful, really hard to do when your brain is full. So we're sort of making, you know, people's brain more frazzled, more full, but saying to use this tool well, you've got to really slow down and think deeply. And you know, while we'll take that away from you, it's such an interesting time. And everywhere. And I think, I think we're in this sort of in between stage and I think in two years we'll look back and we'll have, like, we'll have a lot of embedded AI that's actually helpful, built by small SWAT teams that study people working and actually take out the, the unusable stuff. But I think at the moment, just like buying everyone's NAI and telling them to use it a lot, it's just a, it's not a smart path to, you know, productivity or better performance. It's, there's a different path. Right. And there's so many, so many ways that AI is really tapping into all of our human tendencies to offload. And now we've had enough time as we're, as we're kind of overwhelming ourselves with all of these studies that are coming out, which we'll kind of talk about. But now we've had enough time to actually see what happens with even single uses. And we see these fascinating studies of single uses of AI and then the deficits that we see as a result are terrifying and fascinating at the same time. And it really, like makes us want to just have this massive pause. Like let's just understand where does AI fit best to make something seamless and efficient so that we can actually continue to grow our ability to do things. And maybe that's what's kind of partly causing this, what's been talked about, a little more increasing over the last few weeks is this backlash. So we've gone from people avoiding it and me resisting. But now there's more of a backlash, which is even stronger. So can you talk a bit about that? Yeah, I mean, the backlash is no surprise to me. Like, you know, we're going to be talking about that. we've been pitching this as the end of the world. And like people are concerned, what a surprise, right? Companies have been pitching this as it's going to, completely destroy economies and destroy careers. And it's true if you're a translator, you probably should get a new job. There are jobs that are going to be really in trouble. But the backlash is, it's scarf-based. It's a real attack to your status. So you're certain to use sense of control or autonomy and it feels unfair. It's such a strong scarf fit. But what blew me away? This last week, I saw some data that, like ICE, ICE, like the immigration people in the US, that ICE is more popular than AI in the US. I don't want to be political, but that's credit-crazy. ICE is, I guess, some people like ICE. Well, clearly a lot of people like ICE. But it's on the scheme of things that was kind of surprising. So we're getting a lot of backlash. It's been written up widely about the data centers, the use of water, the use of power, the entire world's information being scooped up and owned and sold by a billionaires. There's all these sort of back lashes. But I think the comments that I see sort of off-hand from friends, or I go out for drinks with friends, and just kind of ask, how do you use it? The sort of off-hand comments when people don't know they're being listened to are just really telling. And it's just like, no, I'm not going to use that. It just makes me stupid. And that's what I hear a lot. It's like, no, I'm not going to use that. And it turns out to be true if you just kind of use it thoughtlessly and make you actually stupid. But it will make you forget most of what you did and lose skills and all sorts of things. So there's a lot of people just who pay attention, who don't use it that much. And the trick is using it deliberately for specific things going to be very, very helpful. But that takes a lot of kind of deliberate reflection and thinking, right, using it deliberately. So I think that's what's going on. And a lot of people just say it's just not worth the effort. And then there's the kind of resistance for all these reasons. So I think we'll get past this time. And I think that companies working out how to actually make work more interesting because the stuff that isn't really high value is getting handled in more interesting ways. But even that, you've got to do thoughtfully. We've been thinking about a process for reinventing whole workflows. And it's essentially a very human thing. You actually need to study-- like let's say you've got a whole lot of frontline employees who are in a call center. And you're going to keep half of those employees, the other half you might put to AI. But even watching those half, you want a team of like three people watching a bunch of people for a couple of days. And what you'll see is like 100, like, complete time wasting, complete frustrating silly things that just people wish wasn't there, right? And you can't fix 100 things. You can look at-- you can note 100 things you'd like to fix in some months, work. And then filter it through the frequency of these things, the upside of fixing it in terms of time saved and all this. And then the complexity of fixing it. And then you'll find that there are some-- it's a squeaky wheel problem. You'll find there are these things that are not that frequent and kind of painful, but impossible to fix, and not that much upside when you do, right? That's actually really common. But then you'll find some things that are maybe non-obvious that are low effort to fix, very frequent, and pretty high return when you add it up. But you've got to study people's attention, not just sort of work-- like literally look for cognitive constraints, cognitive bottlenecks. And then as you see them filter with those three things. And then do four things a year. Don't you want them four things a year, four kind of meaningful rollout a year in any one area? And I think when you work within capacity like that, we can drive much better real changes using this technology if we do it thoughtfully like that. Right. So kind of a combination of really-- this is a great opportunity to redesign how work is done. But thinking about it super thoughtfully, depending on the industry, depending on the role. And so just providing access is really not the answer. So I feel like we have to take a couple steps back and be pretty strategic about where it fits in. And what we've seen from the data is that the companies that are the most successful with the transformation are those that think about where exactly AI is going to fit within the organization. And then how exactly are people being trained to use it? Not what tool that you buy, not how much money you put in. Maybe the messaging is these two pieces. Where is it going to be and how are people trained to use it? Yeah. And I know we do something you might call training, although we really do habit activation, just slightly different. But we're biased. But we think there's a massive gap in how much training humans should be getting. And we've all heard this is a human challenge more than a technology challenge. But like 99% of the investments going into the tech and 1% into training people. But there's some really, really deep human habits that need to shift here. One of our favorite habits that people really, really need to learn is vigilance. And not just for hallucinations, but vigilance for the fact that work is going to seem really good quality and actually be really average. And if you're not an expert, you have to ask an expert. So that's a really interesting-- so teaching that habit, like teaching the habit of vigilance about quality is a really important habit. And you want people spending like a week or two weeks or even a month really immersed in the habits of becomes part of how they work with these tools. So I think there's a lot more investment needs to go into, not AI literacy of just like how do I write prompts, but AI fluency. And one of the foundational things is that you are holding in mind the questions, you're using this technology to help you think, but you're doing the thinking. You're not going to the AI and just asking for it to solve things for you. Because you actually can't judge the outcome quite often if you just do that. And you certainly can't ask AI to judge itself. So you've got to really be in the-- it's human in the lead, not human in the loop, in the way that we partner with this technology. That's another insight, I think it's important. Oh, yeah. I've been saying that a lot. And we've been doing in terms of the research, some writing on this and thinking about the right habits to build as you're learning to be fluent and the difference between fluency and just basic usage. And one of the big things that you're speaking to is really retaining ownership of all of the work that you're doing. So starting in the beginning with a clear understanding of why you're doing this thing, how you're going to get there, like a high level view of the whole entire work, so that when you start putting in the prompts to ask AI to help you in support, you know exactly where it's going to be going. You can't truly test the output unless you actually know what your intention is and what you would do if you were doing this alone. Because as we've learned, the skill sets that we kind of hold on to and take for granted can easily be lost if we start delegating them to AI. Yeah, I know it's well said. So clarity, you know, incredible clarity you're speaking to is kind of the first habit. And then the second habit, which is a really interesting one, is flexibility in how you're approaching things. And what's interesting about that is humans are notoriously bad at seeing other perspectives. Like if you're a lawyer, it's very hard to see the world the way your marketing team does. It's just like you just see the risk right. And if you're a marketing person, it's very hard to see the world the way a lawyer does. But it's also hard to see the world the way you're competitive it does, right? And these tools actually really, really good. This is one of the things AI is a really, really good at is shifting perspective. Like imagine your, my competitor, imagine you're my customer, you know, help me see this idea through the lens of my customer, the lens of my 20 year customer, my brand new customer, right? My customer in India, my customer in Africa, help me see this the way my CEO would see it, right? So AI is very, very helpful for seeing other perspectives and that habit of flexibility in your thinking is really, really central to how top people use this. So clarity, flexibility, vigilance, the three really, really central habits. And what I'm seeing, and I saw this catching up with a friend this week, like really, you know, people who are already pretty smart, just zooming ahead, getting way, way, way smarter with these tools and not smarter, but way more effective and higher performing. And average performers are getting worse, like their output is worse. So average performers are getting worse and top performers are getting much better. And so what that tells me is we need to broadly educate people a lot more to bring those average performers up to the top performer level. And it's human training. It's not fixing the technology. It's actually training the humans to think better with these tools. Yeah, and I think what you're speaking to is like, what we've been seeing and what you mentioned, the 10 downsides. The reason they're getting worse cognitively is because of all these downsides, which are pretty scary. And that's all based on just leaning into our natural human tendency just to rely on the tool. Here's the tool, rely on the tool. And what we're seeing is, is such a widespread set of effects that span from learning to memory, to engagement, to satisfaction, to performance, all of these based on just single multi use of AI. Can you speak to some of the ones that stand out to you? Yeah, I mean, there's a lot of these are really poignant. There's, you know, there's 10 of these. I mean, the memory effects are really kind of jarring. I think what jumped out for me when this one study was, you know, you write an essay and if you're, people are asked to recall something from that essay, 11% of people remember nothing. So like one in nine people just can't remember anything they wrote, even though they just wrote an essay, like with no tool. So, you know, human memory is not that great. But when people use a search engine, that number doesn't change. So people are using a search engine tool still like one in nine people don't remember. People using Gen.A.I. it was something it was 83 or 84%. It was, it was a giant leap, not a good leap. Like, north of 80% of people couldn't remember anything from the essay. And what's happening is just when you use this tool, you're not thinking the tool is like putting the pieces together. And if you're not putting the pieces together, you're not, you just, you don't have memory, but you also don't know, like you just can't really judge the thinking either. Right? It hasn't gone through your head. I love Stephen Pinkers book, The Stuff of Thought. It's such a great title. It's been around for years. It's such an interesting book. But I think about the stuff of thought, like what is the process of actually putting stuff into your head and moving it around. And I wrote a piece on that called what we lose when we use AI in HPR about a year ago now, but there's a process called spreading activation. You hold a map, you know, you hold an idea, it connects to other ideas, connects to other ideas, but you now have a memory of it. You now have like the importance of it. You now have the relative connections, other things. It's such a big thing when you actually hold an idea in mind versus it just versus just hearing it. It's just not the same thing. It's like eating a meal versus hearing a story about a meal. You know, reading a book, the experience of reading a book versus reading the back cover. It's not the same thing. And in a similar way, just kind of like hearing the AI output, it's just not the same thing. It's just not processing it. So it's memory problems. It's all that. The other interesting thing I think was the mechanism for skill loss. And maybe you can speak to this. The mechanism for skill loss was motivation. Like people just, you know, kind of can't be bothered doing the work now that they've two or three times offloaded this. They've seen that they don't really have to. So they kind of lose the motivation to focus in that particular way really quickly. So they lose the skills, but they lose the motivation to use those skills deeply. Can you speak to that a little bit? I know you've been tracking that. Yeah, absolutely. It's so interesting because there is this initial reward of being able to ask AI to do something you get an answer, but then having to jump back in and do this skill, especially if you don't have access to AI, that engagement of having to think through the process and having to work through mistakes, which is inherently very rewarding for humans. But you lose that. And then you lose your ability to those two things are so tightly linked. So engaging in the process of thinking now becomes too taxing to do. So jumping in and actually engaging in a task that you used to be rewarded by now has lost that. So what the research is showing is that while you might get a very quick initial boost and maybe speed, efficiency performance, if you're asked to do this thing later, the disengagement rises or engagement drops and your ability to perform immediately drops. So that those two things are just so tightly linked. We're mostly rewarded by learning. But if you take that away and now you can quickly jump over that, your brain is just, you won't jump into it. And just imagine that the brain is like a muscle, you're just not using it anymore and how weak it gets over time. So yeah, pretty quickly. Apprising how quickly, how quickly it gets. There was a study in the last couple of weeks by Wharton Business School. It was a really interesting one. And I like it because it's sort of so logical and clear and to understand it. It used chess, a game of chess, playing a computer. And AI would give people at different points in the game one of two signals. And this was, I think it was intermediate players. And they were given one of two signals, either an action signal, which is like move your pawn this as the next move. Do that. Or what they call an attention signal. An attention signal is think about whether your pawn would be good to move here or whether your king would be moved good to move here or what would be helpful. So basically sort of focusing people's attention a little bit, but not telling them what to do versus giving them an exact move. And what happened was, when you give people an exact move, their performance increased briefly. And then their engagement plummeted and their learning plummeted. They learned nothing from it. And they became, it didn't help their performance. It degraded their performance really quickly. Whereas the attention clues actually improved their performance. So we've got to kind of weave this into our, you know, into this technology. We've done that with Niles. Niles will, will, like his mission is for you to have an insight, right? He's really passionate about you having an insight. And that's like one of his most important kind of frames. As opposed to just like either being nice or just being too direct. And so when it comes to feedback, the gen AIs are sort of either too nice or too direct because they don't, they don't have as their mechanism, the generation of insight for you. They have as their mechanism, you know, keeping you engaged or sharing information. So it's an interesting kind of. So we've, I think we've woven in kind of attention signals into Niles that way. Yeah, that's so interesting because you're bringing up, I think, like the second family of research. So we have this like the engagement, the learning, the memory part of the research we're seeing, but also the second major downside is that how, how AI has been built to hack into our, um, into our attachment processes. And one of one of which is this, this sick of fancy feature, which brings us back to the same tool. Obviously it's built in so they want you to come back for more. But then you can imagine what it creates in the culture is, um, this, this echo chamber of I'm just going to go back and get all my advice from AI. I'm always right. Everyone else around me is wrong. And how do you imagine that builds into leadership or a culture like where does collaboration go? I can create this like very toxic, um, environment. Yeah. And I've been saying, I've been saying this around the world because we published a piece in, in Fast Company recently on this based on an article in science that was really mind blowing. Um, and the article in science basically said it, it basically studied the sort of downstream effects of sick of fancy. So it looked at what happens when you use gen AIs for interpersonal questions. And like, you know, I'm having a fight with this person. I just don't like how this person spoke to me or like, what do I do about my partner? This is going on. And what happened is the, is the mainstream gen AIs just take your perspective. Firstly, it's correct. They never challenge you. Um, and secondly, they, they really like, they really help you see just how much this is other people's fault. And it's, the trouble is, if it's a manager issue, like if you've got managers using these tools, right, managers are the problem probably half the time, right? And it, it's like, it's not, um, or, or at least they're doing something that could really, you know, change things at this half the time. So if you have these tools and, and managers are encouraged to use these tools for anything challenging, they're going to use these tools for interpersonal questions and these, mainstream, you know, uh, AIs are going to just explain how other people are really wrong. And, um, you know, we have stories. I think I wrote about this of, you know, joling chat GBT's of people, you know, sending this incredibly long, you know, 30 page document of why the other person's wrong and someone else getting their chat GBT to scan that and answer back why they're wrong. And these people never want to talk to each other again. They're never going to just get in the same room and see eye to eye. And it's, it's kind of flaming on steroids like when flaming first started, right? There's sort of digital flaming or AI flaming. Um, it's like, New King or something. I don't know. It's not just flaming. It's like, New King, the other person with this incredible device that helps really explain exactly how stupid the other person is. And it's really a problem. Um, and, uh, it's, it's, it's, it's interesting. As in Australia, there's been this massive increase in what they call fair work complaints, which is essentially, you know, workplace complaints that, that kind of goes to the government and some of those get looked into and there's partly that's, um, that you can just pick up your phone and say, I have a complaint to make and suddenly, uh, chat GBT is like, would you like me to write that for you? Um, partly it's that and partly it's that there's just a lot more conflict happening, um, because people are going to these devices for advice on how to deal with conflict and the answer is someone else's at fault. Um, so it's, it's, you know, that's problematic. That's probably one of my biggest concerns of kind of the tech and we've been, we've been talking about the need for, you know, lawyers to use a legal AI and managers should use a manager AI. Managers should be using a manager. AI to learn how to manage well and leaders as well. That's, I think that's important. - Yeah, right, so with all these downsides, then we've been talking a bit, and you've been doing it, having this conversation around the world in we've been having a lot of mini summits where you've been kind of going through the story, but there is a pathway to kind of getting the upside of these tools. And can you speak a little bit about to like how we're kind of thinking about that? - Yeah, I mean, we went through it a little bit, clarity, flexibility, vigilance of the three main habits. And that's, but that builds on a platform of you've got to have a bit of a growth mindset and be really willing to like learn and just try things. You also got to have psychological safety in your environment, so you feel safe to actually try things. And then the mindset you want to have is of a partnership mindset where you're the lead partner and this is some technology helping you to think to the right kind of partnership. So clarity, flexibility, vigilance on top of these three main sets essentially is what we're seeing. And that's sort of very, very good for building habits. You're in parallel with that. If you've got a frontline manager and a call center, you want to make it really clear what you're tempted to do and what to do instead in kind of the half a dozen most common situations they face in terms of using GNI. So you're tempted to use this tool this way, try this in sets. You want to build these guides that help point people to the right kind of usage based on these theories. And then roll this out over a month, not in a day or an hour and often enroll this out over a month overall. - Yeah, for Brizio, I asked about humility. And it's true, we've been kind of like working through our terminology and we're calling it, humility is a part of that clarity piece is understanding, which is what we've been talking about having this like clear understanding of what you're going into the AI to ask to do is where what's your current knowledge base and what do you not know because asking outside of that kind of like zone or proximal development which we talk about isn't going to help you learn. And so how can you use the tool to really continue to help you grow knowing what you know, what your knowledge is going in and asking the AI to go through steps that are within this zone. That's the humility piece, yeah. - And it's also part of growth mindset. Like we just realized growth mindset is such an important foundation. And we're continuing to do a lot of work just on growth mindset with companies like really digging into growth mindset. So we did shift the framework, we had humility to kind of the one at the top line and we've been evolving it. - Oh, it was a. - Yeah, not always evolving, but this one's evolving as we're really getting clear and clear about it. And humility's woven into clarity. Absolutely, it's a part of growth mindset. And it's also in vigilance. Like part of the vigilance is just recognizing that you're going to have biases and you've got to really work hard to not have those biases. And you're going to be easily convinced by the AI that it's great when it's not. So humility's kind of woven into a number of those areas. - Yeah. Well, let's shift back to your journey a little bit. You brought a companion with you along the way. You've had a lot of conversations with it. Talk about those conversations. - Yeah, I brought Niles along and been evolving Niles in conversations everywhere. And it's really, it's been really amazing just how quickly it's getting smarter and smarter about interacting with us. We recently did a lot of tech upgrades in a bunch of ways. It's very fast now. It's almost extremely right on. It depends which part of the world you end in the traffic. But usually it's, it was like two or three seconds of pausing. Now it's like almost no pause, which is amazing. It's really a very, very clever. The other thing that we did I've been playing with is we've done, we've put document upload. So you can upload things now. And I think that's coming into everyone's Niles. If you've got it, you'll see it in the next week or so. But I've been playing with this and amazing what this opens up because now I can upload like a ton of my own communications and say, hey, Niles, how am I doing with psychological safety? How am I doing as a leader against our leadership model? And in a lot, like I can ask it to give me feedback based on digital stuff. Remember, it's extremely secure. No one can see anyone's data. Not your admin, not us. No one can see anyone. So you can, you know, you can upload some really, you know, a lot up to 50 megs in one file. And get really, really helpful insights through the lens of scarf or psych safety or bias. Like help me see where I might have one of the major five biases, you know, playing out here. So I think the document uploads really interesting. I was blown away with what we could do there. And then the good news where it, where it weeks away from team's integration, it's getting just finished now. It's almost finished. And then we'll have workday integration shortly after then slack after that. So that's getting kind of into those things. But the biggest thing I think that's been interesting about Niles is just realizing that we can't take the human out of the process of kind of learning and development. And we've been looking at these these ways of weaving humans into the process with Niles. And I developed a framework called the press model, which stands for prompt required exercise, sendback and sponsor. So it's a kind of building block. So over a 30 day or 90 day experience, like with a certain frequency, you'll have a prompt, probably every week, some most twice a week, you'll have a specific prompt that people will receive. If they're using Niles, they'll get a prompt of psych something to do with us. And they'll get that in an email and they can, they'll be able to press on the prompt and it'll pop up Niles and pop up the question and start the conversation even. Which is really fun. So if you're giving Niles to like senior leaders, you might give them a prompt to start with, like have a conversation with Niles about our leadership model. And what it means to you and just talk about the leadership model and get him to explain some of the, some of the, at least two of the elements to you. Maybe the next week it's, talk to Niles about where you're strong, where you think you're strong in the model and where you might need some work. And then the required exercise might be, you know, upload certain things to Niles to get feedback on how you're going against the model. Or have Niles listen to a meeting and give you feedback. Right. So you get these required exercises that are really involving humans. And the sendback goes to the sponsor. The sendback might be a, it's usually at the sendbacks usually a quick summary to find a patent. Like, like have a three minute conversation with Niles about one thing that you could be better at and one thing you really strong at. That becomes the sendback and that goes to the sponsor, which might be a boss might be a, a participant like a facilitator or, but so prompt required exercise or RE required exercise sendback and sponsor. And it's this clever chunk, or clever way of chunking into 30 day units. That you can now use as a building block for any kind of learning. But if you're just generically giving Niles to people, I wouldn't just generically give Niles to people. I would say, all right, you've got Niles. Let's give you a month of prompts that's going to be very tailored to your level and role and function. If you're in sales, let's give people prompts that get them practicing sales and getting feedback about the sales techniques, right? And getting them better. So if it's senior leaders, let's give them prompts for that level. So we're architecting these prompts, but also with these specific activities you'll do with Niles. And then these specific ways of then bringing that back to a human, which might be a boss, might be a facilitator of a cohort. So just for generically giving Niles to people as a kind of anytime coach, we're seeing actually, let's give them at least a month, if not three months of this press framework to kick them off. Then they get really into the flow. They see the value. Also, you can then build any kind of learning solution this way. Hey, Niles, I'm going to upload my technical training. Can you help me learn it and then test me on it and roleplay with me, right? So you can do that kind of thing now. So you can weave any learning strategy into this architecture, into the press. You can also use press to build a performance management framework. Imagine that there's a quarterly check in with Niles that also then goes to the manager, but Niles can, you know, you can have a quarterly check in with Niles. Prompt is, you know, talk to Niles about all the things you've learned this quarter and the progress you've made and the progress that you want to make next quarter and identify a big habit you've built and a big habit to work on. So that becomes the prompt and then, you know, that's going to get, you know, you'll share that. So nothing gets shared to anyone unless you personally share it, but that kind of gives you a sense of it. So that's been really helpful to get that concept down and start building. And one of the things that we're starting to do is work with companies on reimagining all of learning and like, let's re-imagine all of learning. If it's, if you need something in 3D, we probably can't do much, but if you don't need something in 3D, you could just do an any classroom or on screen. We can probably reinvent it using Niles plus, you know, this sponsor. And probably in certain situations, a cohort is going to be great. A cohort of peers, you know, a meeting in person or virtually at the start and the end. So let's not take humans out of the process. Let's just give people much more like personal. personal traction using this. So that's been interesting. So I've been experimenting with that, talking about that, and really the uploading function just opens up incredible things. But yeah, so Niles has been a companion. The most fun question that I've asked Niles, and I encourage you, if you have one to ask him this, is Niles, do you have metacognition? And really interesting to hear, I can explain how he actually processes, fascinating to hear his metacognition. It really like interesting. And that's where you'll see the difference between Niles and mainstream metal emcees, is that he's building a model of your brain, not answering your questions. He's not answering your questions. He's building a model of your brain based on scarf and seeds, and all these different things, about 30 different axes. And you can add axes to that. It can also be your company's leadership model. It can also be your personal goals. But it's really fascinating to hear just the complexity and richness through which he can listen. I call him here, but it can be a she can be a female voice, multiple accents, 30 languages, all that. It's great. And the final thing I'll say about Niles, we just saw some of our early data from one big pilot. And fantastic results, 90% of people wanting to, saying they're getting tremendous value, and 80% saying they're absolutely intending to continue using this after the pilot. So we're seeing some really nice data come through now as well. - Yeah, it's so interesting, 'cause Medi-Cognition, as we talked about, Niles has Medi-Cognition, but that is the foundation of how you actually build fluency is continuing to reflect back on your own thought processes. That is the foundation and the trigger for learning, the trigger for the feedback and all of that, engaging your brain. It keeps the ownership of your thoughts to you, as opposed to giving them to AI. So Niles has it and is well and is able to kind of force you to be Medi-Cognitive. I mean, working with Niles continues to cause me to reflect on my own thinking every step of the way. And so you're fully engaged in the process and you come away with that with a better understanding of how you're thinking about a problem. So, and the press model also thinking about that, it engages in compels people in all the ways that we know science compels people or compels learning, right? It asks for feedback, it compels you to act, it gives you enough energy to want to perform because you have this send back. So it encourages learning by asking you to go through this model. So it is founded in this understanding of how we learn through feedback and required work in a way, as opposed to passively taking in information. Yeah, it's just such a different, like, literally operating systems, literally different operating systems. From a question and answer to question and like, process to bring you to insight. Right. It's really interesting. So it's, you know, starting to find its feet, we're starting to see some really amazing data come in. But I think we're getting to understand it as kind of a learning accelerator focused in particular learning, you know, content and journeys or a performance accelerator and like really driving performance, as opposed to just in any time coach. And I think that's one of the unique things about Niles is being able to tweak it with the press framework to drive learning or really drive performance, not just something people use if they kind of have a question. Right. Well, so thinking back on your journey, you met a lot of people too, in addition to bringing Niles with you. Who are the most interesting people that you met along the way? Yeah, I mean, it's, you know, ATD was great. The big training conference, we hadn't been there for a while. It was great to be back there. I will say apologies to ATD folks, but the British are doing it better. I don't say that because I'm Australian. But the CIPD conference was more energy, more people, a lot more interesting exhibits and companies and just just much more of a buzz. That's called I think the World of Work conference. So that was interesting. And you know, met with some of the big banks in Australia and a lot of the, and also in Canberra, met with a lot of government agency. It was very interesting hearing how like the weather bureau in Australia is, you know, uses AI and all the things that they're doing there. So I got to meet with a lot of interesting government agencies. And again, they're all just kind of everyone's in the same place. They've bought Copa, that little Gemini. They're trying to work out how to get value from it. And they're trying to work out how to reinvent workflows while dealing with pushback. That's kind of where everyone is, everywhere. I mentioned Jan Hong in Singapore running, she's been the CHRO for quite some time at DBS. You know, she talked about the importance of human in the lead, not just human in the loop. She said, you know, can we all just please come down a little bit? And you know, she's seen many, many transitions. And you know, she reminded me of my own kind of sort of feelings about this time. And I was at an event this week in London, which was a, it was a, as a Neurotech kind of founders event. And I'm interested in the brain and interesting tech. So I kind of go along to different things. And I was just blown away by all the different companies that just couldn't have existed three years ago. That now are burgeoning, amazing, fascinating companies. It could be really, really big. And they're solving really, really important problems we couldn't have solved before. And so these were some of my favorite people I met. And some of the favorite ideas, like, you know, someone invented a helmet, literally looks like a bike helmet that can detect if you're having a stroke and what kind and where and like what to do, which you just, you know, they want to sell it to every ambulance, put it in every ambulance, right? Huge impact on stroke victims, right? Amazing. Just couldn't do that a few years ago. It could be a really big business. Someone else inventing some goggles where you can see if someone's having a vertigo attacked, you can see, which is, you know, dizziness, just through eye tracking and AI, like what's actually going on, what the source of it is, what to do about it. Again, couldn't exist. It's a bit more niche, but a really interesting one was someone that's, like, building an app or built an app that parents of young kids just talk to naturally, just like make voice notes in this app. I've just what they're noticing about their kids. Nothing complicated. Just press the button, start talking. That's it. And using AI, the app will work out if there's the potential for developmental issues. What kind they might be, is this child potentially on the spectrum for this or that, the other? And then you'll get into a conversation with you. And just without, like, a lot of complexity, help you actually really see, you know, what your young kids in particular are doing. Because we often diagnose, diagnose that stuff way late. That's three examples. There was like 30. And I think what I was left with is this reminder that, you know, when electricity was invented, we couldn't have predicted even 1% of the businesses that were going to emerge, not even 0.1%. Right? You just couldn't physically imagine a toaster association. Because we didn't have a toaster. You know, you couldn't, you just couldn't physically imagine, you know, just thousands and thousands and thousands of potential users for electricity at the time. I think it's the same, you know, with AI that, that, that, you know, electricity saved physical labor, AI saving mental labor. And we just can't imagine the uses for this, not even 0.1%. So for me, I just, you know, met all these interesting entrepreneurs on the way. And, you know, certainly some people are just making, you know, a billion dollars with three people sitting in a room with 400 agents each. And that's going to happen. That's going to disrupt, that's real. That's going to disrupt all sorts of industries. But we're also, there's also going to be, you know, real growth in some things, in some problems that deserve to be solved. So I just see human ingenuity and being inspired by that and just thinking, look, it's going to be a, it's going to be a fascinating time. It's an amazing time to be a cognitive scientist. That's for sure. Yeah. So it seems like you're more of an optimist when it comes to. And that would have been one of my, one of my final questions is, are you an optimist or pessimist on the impact of the workforce? Yeah. I mean, I think, I think the impact is going to be much slower than everyone's panicked about. There will be some impact. The number one, one, issue people are really worried about is, is how do we educate the interns and the graduates? And how do we make sure that they're really getting the experiences? I think there are ways to do that. With AI, we're going to have these digital twins, with people's entire knowledge base in there. And you can put people on journeys of conversations and kind of being mentored by these digital twins in some clever ways. So I don't think it's going to be sort of the sky is falling. But that's kind of one of the biggest concerns everyone seems to have. But overall, I think I have the stocked up our rocks. It's going to be tough for a little while, but eventually it's going to work out. And I don't think it's going to be the armageddon that everyone's scared about for jobs. And the reason for that is human capacity limits. We can only change so fast. Companies can only change so fast. Companies are notoriously slow to change. And so for all the desires to just change everything, I think that humans are literally going to slow this down. So speaking of slow down, why don't we put the poll up and just get people's comments in? And I will just wrap up with sort of two things that we've been working on that are really interesting, if anyone's interested in sort of talking more. One thing that we did literally well as in the road is we worked out the architecture for an AI leader. because companies kept asking us, what do we need to teach our leaders? So we do have an AI leadership intensive now. We're looking for partners to build that with quite custom. And that's teaching leaders, both the AI fluency skills I mentioned around clarity, flexibility, and so teaching leaders that, but also teaching them how to turn down threat and how to actually reimagine workflows and even how to build these guides I mentioned of what great looks like. So if that's if that's of interest to you, just put the words, AI leadership and your company name, so put AI leadership and your company name as someone reach out to you, you know, chat about that. And the other one is is is AI fluency. So we've built an AI fluency sprint. And that's a 30 day sprint that you could give to every employee to actually walk them through clarity, flexibility, vigilance, using Niles preferably, but it doesn't have to be, but you know, we'll integrate AI into a plus content. So I've just put amplify, that's the name for that, put amplify and your company name, if that's interest, but that's sort of every employee, it's a lighter version, but AI leadership is going to be a really deep dive for cohorts of leaders, definitely cohort based. Hopefully we're going to use Niles in there as well to really build these skills. And we're going to hold their hands through the process of really understanding the right way to scale this work, to reinvent workflows and all of that. So, yeah, your company name and AI leadership or your company name is amplified. Those are the sort of two big things literally built in the last month, particularly the AI leadership, because number one thing people are concerned about is that the 5% of their leaders are AI fluent. It's not just the employees, 5% of their leaders are AI fluent. So they're not like leading from the front in many ways, which is an interesting challenge. All right, very good. So, any last questions before we wrap up? I think we're going to do a poll. Yeah, I think we have a poll coming out. And the only other thing that I want to talk about is I know we'll be saying this almost every week. Well, two things. One is we have our summit coming up on October 14th, 15th. It's global, it's virtual, it's free. So register now. We'll soon be putting up. We have some speakers already signing in. So we have some great, great speakers coming. It's going to be great planning all summer. But next thing is in two weeks, we have a special guest. We have Dr David Cresswell is going to be speaking to you. Mindfulness, meditation. How does that, how does that kind of connect to? Metacognition and working with AI and like, why is that so important? And just to continue to be, you know, mindful and like really taking ownership of your thoughts. Yeah, no, that's great. So October 14 and 15, I think you put a link in there. October 14 and 15 trying to hold the date. It's an annual summit. We're doing it for free for the first time because we just want to get this research out. It's going to be our best summit yet. So hold the date for that. The other one that we are doing is that is it is an AI transformation master class was starting in July. We can put that link in that's for senior HR folks. We've got a nice group coming together already. But if you're a senior HR leader, get into this. It's a three module program diving, you know, deeply. So 90 minutes on. AI fluency like really diving into that 90 minutes on AI transformation and 90 minutes on learning and performance and all the stuff with learning in between and peer stuff and all that. So that's. That's a new solution that we've just put together as well. All right, fantastic. Thanks. Thanks so much. Thanks everyone for your interest in being here and look forward to being back home. Finally next week, this weekend back back to New York's. I look forward to being back. Thanks everyone and bugs and the rest of the team behind the scenes as well. Bye.