How EY Is Upskilling 400,000 People for the Age of AI
The fastest way to learn AI may be to stop learning about it and start building with it.
In the most recent HR Leaders Podcast episode, I had an inspiring conversation with Simon Brown, Global Learning & Development Leader, Partner, Talent at EY.
He explains why organisations need to move beyond simply teaching people what AI is and create learning experiences that help employees build confidence by using AI in the context of their actual work.
By combining protected learning time, hands-on agent building, responsible AI principles and a stronger focus on human judgement, EY is exploring what it takes to build AI capability at scale while keeping learning relevant as the technology continues to evolve.
5 things you’ll learn from this episode:
How EY is moving employees from learning about AI to actually building with it
The capability Simon believes people need before they can delegate meaningful work to AI agents
Why making AI learning part of everyday work can change how quickly new skills are adopted
The reason stronger AI tools may actually make human expertise and judgement more valuable
How learning leaders may need to redesign their approach when AI capabilities change faster than traditional training cycles
81% of People and HR roles at leading tech companies already expect AI skills. Is your team keeping up?
Leapsome analysed 5,000+ data points across 100 leading tech companies to understand how AI, revenue per employee, pay transparency, and changing workforce expectations are reshaping the HR playbook.
The companies pulling ahead are not just adding more AI features. They are rethinking how HR works across the entire employee journey, connecting hiring, core HR, performance, learning, goals, engagement, and compensation through a more unified people data foundation.
That is the direction Leapsome is building toward with its ATS, core HRIS, full Talent Suite, and cross-functional AI agents, helping organisations move away from fragmented point solutions and toward one continuous talent workflow.
Explore 50+ findings in the 2027 Workforce Trends Report and see what the companies moving fastest are doing differently.
[01:00:00:06 - 01:00:22:07]
Hey Simon, welcome back to the show. How are you doing, my friend? Very good. Great to see you, Chris. Really pleased to be here again. Yeah, it's been a while. I feel like the plants grown a lot. You added more. I have, I've thought you were literally all the way across now and around. Yeah, sort of running out of places to put them. So, next time. Yeah, you'll be all over the screen. Just take over the whole room, basically. Yeah, but it would look like Jumanji. Someone.
[01:00:23:10 - 01:00:55:17]
So, my ones, I don't have real ones. I feel like I'm a pretender with my fake plants in the background. So, because I just can't keep them alive if I'm not. I'm a terrible dad. I need ones that I only need watering in three or four weeks. Oh, okay. That's the secret. How have you been anyway? I mean, it's been a lot of change since we last spoke. Yeah, exactly. So, yeah, I'm two and a half years now here at UI and yeah, lots of exciting stuff. Yeah, I know. I'm already. Oh my God. Wow. So, what's happened? Catch me up. What have you been up to recently?
[01:00:57:11 - 01:01:05:03]
So, let me recap the last two and a half years what's happened. So, I understand the new organization.
[01:01:06:09 - 01:02:05:00]
Figure out what we need to do from a learning strategy perspective. So, we now have a five-year plan for our learning strategy, which around offering the best opportunities to learn and develop for our UI people to thrive and for the business to grow. Set of strategies of how we support that. And now we have our learning team all around the world working towards how do we do that. And probably the biggest part of that, you won't be surprised, is around AI and leadership development at the moment. And how do we make sure that we're upskilling the organization to have the skills that we need to be able to support our clients. So, I suspect that's where a fair bit of today's conversation will go. Yeah. Some super exciting pieces that are happening in that space. We'll start with this week. Or do you have an interesting week? Is it ahead or last week? You had AI week? AI month. AI month, sorry. We're doing the whole month. There's not enough for a week. I mean, there's too much for a week. If it was a week, you might be off on holiday and might miss it. So, we're making money. There's no escape. Fair. Okay. Good idea.
[01:02:06:24 - 01:03:01:16]
So, this is our AI month. So, Janet Trunkali, our CEO and Chief AI Officer launched that last week. And then we have a series of global webcasts over the course of the month. And then about 200 or so events happening between virtual events and local events all around the world. And then a whole series of on-demand sessions as well that go deeper into learning about all aspects of AI. And then we have our AI badges. And so, it's really sort of a great opportunity to bring focus around the importance of everyone upskilling around AI. And yeah, to create some excitement around it with lots of activities and events all around the world. Nice. What are key outcomes you're looking... I mean, I don't think anyone needs to answer the question of why you're doing this. I think it's pretty obvious. But what are the outcomes that you're looking for that are meaningful? Yeah.
[01:03:02:21 - 01:04:36:00]
So, various different levels, I guess. But a core one is around where everyone is super busy. There's always an excuse why I'll learn about AI, but I'll do it next week. I'll do it next month or whatever. So, we've got phenomenal learning resources available. So, use this month as a focus to get people talking about AI, get them working within their teams around AI, get them building skills around AI. So, it's really around getting people's attention, creating the opportunity to have the discussions, and then be able to also apply the learning that we've got into practice as well. So, whether that's teams that figure out what agents we're going to build together as a team, let's all do the agent building training and let's build our agents and compare notes on how we're doing. Maybe we'll do an AI badge. We just launched an AI for good badge. And so, maybe as a team, we'll work through and do a badge for good or AI engineering or genteque or whatever. So, it's really creating the focus. And off the back of the focus, then people's attention to be able to learn about the latest with AI, build their skills around AI, and learn about the great stuff that's happening. I mean, we have 400,000 people in 150 countries. There's amazing stuff happening across the organisation. And this is a great opportunity to be able to showcase that, whether it's sovereign AI, physical AI, agentic AI, etc. We can profile the great stuff that's happening and then connect people to people learning as needed. Yeah. I love the fact that you're kind of doing it across the month and it allows everyone in one go to be part of something as opposed to just doing it in pockets, right? Which is always difficult.
[01:04:37:00 - 01:06:42:05]
And I've heard the other companies trying to do something similar. How have you made space for this? People still got their day-to-day job that they got to get on with. What have you put in place to ensure that you've not said, "Hey, this is a great month for us to do this work," but then we haven't created a space or maybe even a psychological safety for people to actually get involved. Yeah. So, I think there's several ways to answer that. So I guess at the top is actually creating the reason to pay attention and create the space. So, I mean, we prioritise what's important to us and what's important to us as an organisation. So having many of our leaders visible in events across that, whether that's global leadership, whether that's local leadership, that creates, this is important for us as an organisation. Our leaders are paying attention to it. So that's one element almost to the culture or the importance around it. Second piece then is just a practical bit. So we're a professional services firm. People charge their time to charge codes, et cetera. We look at where people spend their time. So we have something as part of our talent strategy, our HR strategy, called Thrive Time. And so Thrive Time enables you to put time down to learning and also to some of our corporate social responsibility and wellness, et cetera. So we provide people with the charge code to be able to put your time down to learning. So you can also track that, I'm assuming? Yep. Okay, great. Yeah, yeah, yeah. So there's various ways that we sort of track learning in terms of consumption of content, but yeah, time spent, et cetera, et cetera. But yeah, this gives them the permission that you've got the leadership teams talking about it, sharing what they're learning, attending events, et cetera. But also there's the practical piece of removing the barriers to things that might block people doing it by actually providing the codes that people can use, et cetera. And also as a team, by having a whole month dedicated, it's like, right, what are we going to learn as a team? So yeah, we're going to focus on Agenic AI and we're all going to do the training around how we build an agent and then we'll compare notes and we'll see what we've learned, et cetera. So that helps to create the space if you're using your team meetings for it or discussing it as you go. Love that. Love that. One of the things I was excited about when we were talking about this podcast was agents of change.
[01:06:43:07 - 01:07:14:07]
Could you walk through that with everyone and share more details? Yeah, so this is one we're particularly proud of. Yes, if I look at our enterprise-wide AI learning started two, three years ago, in fact, before I even joined, there was a video tutorial on what is AI, why do I need to pay attention to it? It was a voluntary training for everyone, most popular voluntary training we've ever done, 350,000 people going through it. So that was the what is AI. Then things moved on and we had...
[01:07:15:09 - 01:07:16:08]
No, mine's gone blank.
[01:07:17:15 - 01:07:19:16]
All good.
[01:07:22:20 - 01:12:01:00]
Generative AI. Yeah, it's all right. At this point, I can't keep up, so every week there's something new. Yeah, so we go from, you know, stand there into generative AI and then now onto authentic AI. So generative AI was then, you know, how to have AI as a thought partner. So we did gen AI as your thought partner, two modules, sort of one that introduced how you could work with gen AI, but then the second piece was around actually hands-on with gen AI, discussing how it would impact your role. So I would put my job description in and it would say, yeah, you know, as a chief learning officer, then this is ways you could actually use gen AI in your role, how it might evolve your role, et cetera. So you actually get to experience it. So the sort of, I guess, learning by doing. Fast forward to this year and it's like, okay, agents is the piece that we want everyone to be learning about how can we teach people around agents? And it's like, well, okay, best way of doing it, learn by doing, is actually have people experience an agent. So we came up with agents of change. We developed this agent we call Maestro and it's an agent that teaches you how to build agents. So you'll go into the training, you've got a sort of 10, 15 minute tutorial that introduces some of the sort of responsible AI principles, et cetera, you know, tells you about agents. Then you get introduced to Maestro and Maestro will take you through and say, hey, Chris, in your role, what would be a useful thing that an agent could do for you? And you'd discuss with Maestro, come up with you, actually. I want you to help create podcast questions for me for my guests. If I give you a guest name, I want you to research them and I want you to come up with 20 using questions or whatever. And it would tell you how to build that agent. And by the end of the training, you've built your podcast question agent and it's there in co-pilot or in our EYQ studio. And you've learned it by interacting with an agent. So it's a very cool training. About 75 percent of people are saying it's a better way of learning. They found it better than the previous ways of learning. About 130,000 people that have been through it now since May. 130. 130,000. Yep. It's a sort of 60 to 90 minute experience, depending on how you how long you spend on different pieces and whatever. But what we're finding, majority of people are saying it's saving them an hour a week in terms of the time that it's the agents now doing network. Exactly. So the podcast questions, they're now using agents to save themselves. I literally have an agent that does exactly what you just said, as you can imagine. Exactly that. Yeah. Majority save an hour a week. About 14 percent say they're saving at least three hours a week. And of the total, yeah, about four percent that they're saving more than five hours a week even. So it's landed really, really well. And it's a great way to experience AI by actually using AI. I think the best thing is you're making it personal to the individual, right? So they understand them, their role, and then it's literally asking you, hey, what are some of the things you're doing? OK, here are some things that agents can do for you. It's immediately something that isn't just another course or another thing you have to go through or something that, oh, actually you can see immediately the impact that this is having. And I do the same thing on my team. I kind of go function by function, role by role when I show them examples. And they're like this sort of light bulb moment. They're like, oh, wow, like that's I read that some sort of manual work, for example, that I really don't want to spend three hours a week doing that. And actually, I'm getting better insights, not just the fact that it's saving me time, but it's also getting me, for example, my podcast notes. Sometimes it will pull information on guests that I never even knew that would give me a different angle. Well, hey, did you notice that they just did this post because I even have it look at people's social media accounts and they'd be like, oh, did they just posted about this? So just like things that you just wouldn't even catch and it's consistent. I just turn up, it's there. It's ready. And again, it gets better. You can improve it over time. It's never perfect straight away, but that's the whole point. You just keep feeding it. I think that's one of the really interesting things with AI versus other pieces is just how quickly it gets better and it gets better often within a tool. I mean, copilot's the tool that we use. And so is your is it built on copilot? Like the actual agent itself or is it? What's the back end? So it's on Azure. So it teaches you to use copilot or our own internal agent studio, but it's built using Azure as the underlying. Okay. And one of the things I noticed about companies, they're making a bit of a mess of is like, how is this embedded in the flow of work? Like how do they access this tool? Is it hidden away in some intranet somewhere, which is a nightmare? Like, you know what I mean? Like with these tools, there's too many right now. So you're making sure that it's accessible and easy to access. Where does it live?
[01:12:02:02 - 01:16:58:17]
I guess your agent lives in copilot. So in your Microsoft Office suite. So the one I built was an inbox prioritiser. And so within my Outlook, I have an agent tab, the copilot tab on the left. I can click on that and I see my agents and I can click on that. When I click on inbox prioritiser, it has like three pre-populated prompts there. One's like, review my emails from the last 24 hours and prioritise. And then one's like, what are the top five I need to respond to now and draft me a response or whatever. So it's super simple in the flow of work. And that's the agent. I guess the training programme, you get emailed and notified and it's in our training systems. But the agent that you actually build sits in your work. So you do the training and then you almost could do it side by side. As you're doing the training, you can literally open up a copilot and build the agents there and then. And actually it teaches you how to do that. And actually one of the things I learned as part of it is how you can pin a one window on one side of the screen and one window on the other. No, I love that you could even say that this is my setup. Like what's the better way of doing that? Exactly that. You have the agent down one side, you have the copilot on the other and it's giving you things to copy and paste across and you're refining the prompt and the scope and things and moving from one side to the other. What I like about that is you're not building it inside the same training tool. You're building it in the actual in copilot, which is where you work every day. Like we've talked about the flow of work. People talk about all the time, but most of the time it's kind of overused as a term. But in this case, I love the fact that you're building your in team just right there. It's where you spend your time as well. You said to me, you know, on a last call, you had four and a half thousand people in just 10 days go through this after how long ago was this when you launched this, by the way? We launched it in May. I think we soft launched it first of all, because we didn't know how it would behave at scale. We had done all the sort of new normal testing, et cetera, but we wanted to soft launch it. So people quickly found out about it and word of mouth and we had like four and a half thousand go in within the first week or two. And then we started to roll it out, sort of cohorts of people at a time to sort of stagger it to just manage the demand, if you like, as people go through and make sure everything was stable. But, yeah, we're now where are we may so June, July, August, September, four months on and yeah, one hundred and thirty thousand people have been through it. Yeah. So you mentioned that our saved, right? What are some of the things that those advanced users are doing that others aren't because we every company has their sort of power users, even in my company. What are some of the things that you're seeing that they're doing that other people aren't? Yeah. So I guess they're building their own agents and sharing those agents. So in some of the stats, let me just bring up as we're talking some of that. So twenty eight percent of them are of the people gone through are already applying their agent design skills and twenty four percent, quarter, essentially, of built and shared agents that others are now using. So in terms of the, I guess, the adoption and use of agents, it's very much having that desired effect of people applying those skills that they've learned and being able to build the agents. And more broadly, the feedback we were getting was ninety eight percent intend to continue to use AI. So fantastic. We didn't put everyone off that it stopped them from using it. And 72 percent now expect to use AI frequently. So I think a big part is actually getting over just using it as a search engine and starting to use it in a more sophisticated way. And by having your agent, that's the vast majority of people actually intend to use it far more frequently and use it in a much more powerful way as a result of the agent and understand how it works within your ecosystem. How does it work to share an agent? Does that curiosity? Yeah, different. So governance is super important. Yeah, that's what I was asking. Especially in an organization like ours. So these are personal productivity agents. So they are, if you like, the lowest level of agent that you can have. So these I can create my own agent, I can use it for my own productivity, but I'm not going to be sharing it widely with other people as we go then through more sophisticated agents, as you would expect, much greater levels of governance and checking and rigor, etc. So at the point where you've got then an agent in our UIQ agent marketplace, for example, that's been through very rigorous testing because that's then available to the organization. So yeah, different levels based upon the use and how that will be shared. How have you managed that? Because one of the challenges out here, we had one of our People Analytics Summit last week. We had three and a half thousand people analytics leaders and AI leaders on there. And one of the things that we're talking about is who's responsible and who's accountable.
[01:17:00:13 - 01:17:39:17]
Because it's great that you can create these agents that can do these tasks, but who is now accountable and how you're communicating that to employees. Because I had an example in our marketing team where we had an agent that we built to do some of the outbound emailing. It was an absolute nightmare. I ended up sending 200 people the same email six times, but just because the way it was, it's not perfect to be able to do it. And in that case, it was like, oh, who's accountable for this mistake? Is it us because we didn't give you enough good enough training? Is it the individual? I just want to understand how you approach that.
[01:17:40:23 - 01:18:38:04]
The campaign that we have at the moment for our AI month is all around be the hands that shape AI. So it's around here. It's not about building AI and setting your AI off-going. It's around humans do far better with AI, but it's a toing and a throwing with AI. So how do we use it to give us superpowers and supercharge what we're doing? But we are still the judgment, the critical thinking, the approver of the things that the agent needs to do. And that's why we do all of the responsible AI training and our ethics training and all of these other pieces. And before you get access to these tools, you need to understand the data implications and all of those pieces. So there's a critical role for us from a learning perspective to make sure that people do understand those things so that then they can maximize the benefits from those Yeah, because that's the issue I'm seeing, right? I think you said that
[01:18:39:09 - 01:27:59:24]
only 12% of employees globally have access to advanced AI training. So that was the research that we did across 1500 organizations. Yeah, about 88% of people saying that they're using AI regularly, but only about 5% of those actually using it as what we would call an advanced user. Yeah, very simple way. And yeah, a small proportion having access to the sort of more advanced tools. Yeah, I like the analogy. I think the analogy that I saw was, you know, we're giving people keys to Ferrari without teaching them how to drive it. I think that is that's to my point about safety and security, etc. Like that part, I don't think we're spending enough time, time on ahead of time. Because even now, if you look at the stuff you can do with some example, chat, GBT, Astra, and you've got that connected, you know, it's amazing, but also, you get stuff really wrong as well. If you do the wrong way. The power of the latest models is phenomenal in terms of what we can achieve. But yeah, you then need to be able to understand how to do that, use it in an ethical, responsible way. And yeah, make sure that the right guard rails and the right controls are there around how to use it. And a big part of that is knowledge and some of that you can put into the systems to make sure that we have limits around token usage and things like that. So we don't necessarily clock up huge bills without being unaware of what we're doing from that side of it. But then yeah, the training need is critical around responsible AI, etc. What was some of the most interesting skills, or maybe unexpected outcomes that you saw? People were very creative. So I'm interested to know what some of the things that popped up for you in the team and use cases that really stood stand out to you. Yeah, so when we're talking around agent building, we came up with a model around the three D's of sort of agent management. So the three D's was around delegation. So it's like, what do I do? And what do I give to my agents to do discernment, which is around then, you know, how to sort of judgment, essentially, how do I make sure they're doing the right thing? When do I interrupt them? And so, you know, you're heading off down the wrong path there, you need to go and do this, how do I make sure I give them a clear brief in terms of what I expect them to do not to do the path to take, etc, etc. And then debugging because, as I'm sure you probably found it with your podcast one, you know, it comes up with a whole bunch of things the first time you do it. And it's like, that sounds great. But have you actually sourced that and show me where you sourced it from? And you go through sort of several rounds of debugging in order to get it to where you want it to be and still have an eye from that discernment piece of a that doesn't look right what it's saying about that guess you give me the source of that. It's like, Oh, sorry, I made that one up. So, so having that sort of what to delegate and when to delegate that discernment to know and judge what you're getting back and the path that it's taking to get there and then that debugging element to constantly refining it and improving it as you go. Yeah, I think along with that, I think one of the things that's really helped me in the team is kind of really in part of our training is like making sure the team understand that you have to challenge the you have to be curious you have to you know, like, for example, whenever I'm writing a prompt, I'm always asking for opposing views, maybe whatever, whatever not for of, you know, like, I'm always, you know, like the curiosity, you know, we spoke about curiosity, a lot of times in our conversations, the critical thinking I feel like some people are just asking questions and sort of getting a mirror effect and not really using sort of critical thinking and curiosity to is that makes sense, by the way? Yeah, it does completely. And so I've got a playground of about 30 something agents that I use to sort of experiment with and to try things and they they each have sort of personas and roles, etc. And so one of those is the agitator agent. And so, so I love to include that one in like a roundtable discussion between the agents where you ask them to do something, you'll drop the agitator in there. And the agitator will take the contrarian view, like the tennis view or whatever. And so it will be thinking, you know, complete tangent to what normal thinking is, and it will provoke the others. And then there's a challenge mode I have in there as well, where they'll, they'll look for things they disagree on, not just like agreeing with each other and coming up with a sort of a happy solution to it. So yeah, that's sort of challenging it and coming up with different views, I think is a critical part of how to get the most out. I love that. And but that's also the reality of the world we live in, right? Like, you think about in our office, like, whenever I have a meeting with a team, we have different proposing views and perspectives and ideas and challenges in the room, right? And that's where the best ideas come from is being in that. And if you don't get that friction, you're losing a huge part of that as well. And I think that's one of the challenges. Yeah, and it's one of my favorite prompts that I use is create a diverse panel and then do a premortem of this idea of why it failed based on a diverse panel, how it's better. Really? I love that. If you do that two or three times, then it gets you to so much better place because you're sort of already going in with the assumption that it's wrong and it can be better. And but with a diverse panel of different personas. Yeah, that's one I like using a lot. Yeah. How does this all change in your mind, you know, how and how what people need to learn? Because it's like, it's I don't think there's ever been a bigger issue. We say it's there all the time. But I feel like this is a shift is beyond many of the transformations that we've gone through from a learning perspective. It is. I think it's I mean, it's unprecedented from from many aspects, I think. But it's, I think it's super interesting, because the way I look at it, I don't see the skills that we have and the skills that we've historically built going away. So I see it as like a layer on top. So if I'm if I have if we if we look at into our organization, I have accounting skills, just because I have AI skills doesn't mean I don't need AI, I don't need accounting skills, I still need all of those accounting skills. But now I need an extra layer on top of how I can use AI, how I can work with agents, how those agents can help me to do things. But if I haven't got that core technical skill, then I don't know when an agent is. What questions to ask, right? Yeah, exactly. Exactly. So so the way I think about it is it's everything that we have before in terms of all of those technical skills and things with an extra layer on top of how to get the most out of AI and the how to manage a set of agents, etc, etc. And probably then even more than dialing up the curiosity, the judgment, all of those elements as well. And then from a leadership perspective, I think it also changes the dynamics of the skills that our leaders need to be more around, you know, how to operate in ambiguity, how do I deal with anxiety if my team are worried about the impact so is going to have, etc. How do I create, you know, clear strategy in a path forward when there's so many moving pieces, etc, etc. So I think it changes the dynamics maybe of which leadership skills are most important, and then adds this whole extra layer of knowledge and skills on top. And to your point, an extra layer that is moving so rapidly as well, because what it could do six months ago, he can do so much more now, and it will be able to do so much more in six months time. So you need to then have that sort of constant learning constantly updating your thinking and constant experimentation curiosity to sort of keep working through it. Yeah, I think my head of engineer in Marvin always tells me like, you know, because everyone's like, oh, I've broken code now. But he's trying to explain to me the other day that, yeah, I've got like seven agents now that are working for me coding. But if something goes wrong, unless you have that experience in engineer, you have no idea what's broken, where it is trying to find it in tens of thousands lines of code. And he was showing me some screenshared. And I was like, Oh, my God, I would have no idea how like the smallest little break right in the chain, you'd never know where to look, let alone try to figure out how to fix it as well. So we're a long way from completely replacing the technical side. But it is becoming more and more accessible, to be able to use things like prototyping, where maybe the robustness is less important, then being able to throw back to the agents, this is the error message that I'm getting, or here's the screen grab of what's going wrong, or whatever. Often that's enough for them to then be able to sort of work through and resolve it. And certainly in some of my own experiments, and they're sort of, yes, sending them screen grabs, sending them error messages, and sort of getting them to fix it. And they'll go in and figure out where in the code there's a problem and resolve it. Yeah. But to your point, though, like if you get good at prompting, you do that ahead of time, right? You say before you present it back, check it, right, go for it, look for bugs. And then the more you start to understand it, the more you're like, Oh, I did all that upfront, save me a lot of time. Listen, I got a lot of things I want to talk to you about. And I don't have a lot of time. But is this something you're gonna roll out to customers? Because I can see an application for customers of yours to access a similar tool, right, to help upskill, reskill and answer their questions.
[01:28:01:11 - 01:28:10:22]
It would be used externally as well as internally. So start that again, Simon, because I just lost your audio for a few seconds. Start, start. Sorry. Yes. All right. Go for it.
[01:28:12:00 - 01:32:35:02]
Yeah. So the, the, the Maestro tool, or the, that's a, that agent teaches you how to build agents. So we've, we've built that so we can use it internally, but we've also built it in a way we can use it externally with, with our clients as well. So yes. And that's, that's often an approach that we will take is where it's something we've been investing in significantly. Okay. Is this something also that can benefit our clients as well? We have our, what we call our client zero approach. So that's where we'll sort of test things out on ourselves. And then, you know, learn from that what works, what doesn't work. And then we can use that with, with our clients afterwards. Nice. As you look ahead, obviously, this is ongoing, like we just said, but what are you most excited about? Cause there's a lot of fear and concern and there are some, there is valid of course, but for you, what are you most excited about? Yeah. I think it's the opportunity that AI can, can provide that it's essentially, if you can think of it now, then AI can help it for you. So sort of, so many of the constraints are taken away that, yeah, it sort of gives a whole set of superpowers that, yeah. I mean, like, like the coding example, I tried coding C plus years ago. I got my big thick textbook and I made it a few pages in and I was like, this is just too hard. And yet now, you know, all sorts of incredible things that I'm doing either within work with it, but also outside of work. I've had Claude basically reprogram my house and make it all smart. So like the blinds go off and down and the lights go on and off and the door locks and it does all sorts of incredible things. And it, I've gone down a rabbit hole and it's laptop to do it all. And none of that, I know how to do it. It's all, you know, it's given me these superpowers that I would never otherwise been able to have. So yeah, it's crazy how like, I've got the same thing at home. We could do a whole other show about that. But like, I've even got, I went over to my neighbors and he took it to a whole new level where he had his Claude and he has like connected to the weather forecast for an API. And then he showed me on the screen and then all of a sudden I looked in his garden and the garden water sprinklers popped up and watered the garden because it was linked to the forecast. Don't get me started. I literally track the sun as it goes out based on the angle and the heat and the temperature and the lines will sort of open on that side. Oh man, we're the same. My wife's like, you're buying another thing now. I've got this other, like, I mean, we could, I've got another thing that it kind of pops out of the ground and through AI tracking targets each plant and gives each plant the exact amount of water it needs. And then goes away and people are like, what the hell, Chris? And I'm like, well, I can do it. I don't have to think about it anymore. And my grass looks amazing. And it's something that I don't even think about in anymore. Same with my robot lawnmower and everything else. Yeah. It sounds like we should do a show on that one. We should. Yeah. Yeah. Yeah. Before I let you go, where can people learn more about yourself, obviously connect with you directly, but also learn more about the great work you and the team are doing? Yeah. LinkedIn is probably the best way. I try to share fairly regularly what we're doing through posts on LinkedIn and things, but yeah, by all means get in touch with me as well. And there's various conferences and bits and pieces that I do over the year as well. So, but yeah. The podcast, you still doing the podcast? Oh yeah. Still doing the podcast. Yes. So we have the curious advantage podcast as well. Yeah. I'm sorry. I should have, should have think of that one as well. So that's every week or two, we have a great discussion around curiosity and AI and actually we've got a new book coming out as well called the curious imperative, which is a lot of the things we've talked about, you know, how you need to be super curious in the, at the age of AI. So that'll be coming out in the next month or two, final touches to it at the moment. So nice. Well, listen, it's always a pleasure catching up with you. And for everyone listening, wherever you're listening, watching right now, I'll put all the links below cause Simon always forget. So I'll put his podcast link, every else link to the book is LinkedIn profile, wherever you're listening, watching right now, or even on our newsletter, the links will be there for you. But it's always a pleasure, my friend. And let's do it again soon. But congratulations to all the work so far that you and a team have achieved. It sounds super exciting. Thanks. It's great to chat as always. And yeah, look forward to our deep dive into house automation at some point. See you later, my friend. Thank you.
Simon Brown, Global Learning & Development Leader, Partner, Talent at EY.