USA 
United Kingdom
As organisations grapple with AI, shifting skills demands and the future of work, EY is rethinking how learning and talent development are delivered at scale.
In this episode of the HR Grapevine Podcast, Alex Laurs, Chief Learning Officer for EY Americas, explains how the firm is building a skills-powered organisation, balancing human and technical capabilities, and using AI to personalise learning, coaching and career development.
He also discusses the future of early careers, leadership development and the challenge of ensuring employees continue to build expertise in an increasingly automated world.
Host: Hello, everyone, and welcome back to another episode of the HR Grapevine Podcast. I'm your host, Benjamin Broomfield, head of content at HR Grapevine, and I'm joined today by Alex Laurs, the chief learning and development officer for EY Americas, where he leads enterprise learning across one of the world's largest professional services organizations. His work sits at the intersection of leadership skills and emerging technology, with a focus on advancing a future focused learning agenda that helps businesses adapt to rapid change and shape the future of work. Today, we'll be discussing EY wise approach to learning skills and of course, the impact that artificial intelligence is having on all of that. So plenty for us to dive into across the course of the episode. But first and foremost, Alex, a very warm welcome and thank you for joining us.
Guest: Thank you Ben. It's an absolute pleasure to be here.
Host: Lovely. Well, we're going to be talking all about skills and the learning strategy in place at EY at the minute and perhaps a good place for us to start is with this concept of the skills first organization, the skills based organization. Certainly a buzzword, perhaps, that we like to talk about in HR and learning spheres, but what are some of the ways that the EY is tangibly shifting to, to adopt that skills first approach?
Guest: Yeah. Thanks, Ben. It's a it's a great question. You're right. You alluded to it. There are a lot of phrases that are used skills first, skills powered, skills based organization. And we're certainly seeing the importance of having skills data. One of the ways that we actually frame it internally is to aspire to have skills, intelligence, which basically means that we can make better decisions based on skills. And as it relates to our overall talent decisions, I think that's an important distinction to be thinking about, because when we look at ultimately what our charter is, which is about helping the organization transform and improve organizational performance and change behaviors, we recognize that there is a much broader tapestry of things involved that really drive performance. It's not to say that skills are not important. They absolutely are. But they are one dimension of what contributes to performance. But it is interesting that AI and other emerging technologies now are reshaping roles as we speak. And as a result, the skills that people have and need to be successful going forward is really important to keep an eye on some of those, on those activities, the truly, uniquely human skills. And we can talk a little bit more about this. The skills that were traditionally human and now can be augmented as well and amplified through AI becomes increasingly important. But yes, skills is important, right the way through the talent lifecycle. And starting with skills based hiring, we do firmly have the belief that we should be hiring based on potential, not pedigree. I think that's really, really important to see that, especially as we think through just the broader context of socioeconomic mobility. And you know, that talent is evenly distributed, but opportunity is not EY. I firmly believe that we can kind of shift from a broader talent pool, increase that that level of diversity across the organization and that, that, um, people from different backgrounds can certainly be successful at EY. So it starts there on the, on the hiring side of things. Um, we've started to look at, you know, tactically speaking, some skills based assessments as we bring on our new hires. And then that thread pulls right the way through into our learning and development strategy and talent processes, where again, the more data we have around skills, the better decisions that we can make. And classically as a, as a professional services firm, you know, we historically haven't had, you know, a classic product or widget per se. We are in the business of, of, you know, human capital just generally. And a lot of the projects that we end up working on with our clients get staffed through a broad understanding of the skills and capabilities and experiences that individuals have. So in many ways, we've been operating as a skills based organization for many, many years. So it's just the next evolution of that for us.
Host: When we talk about that next step, I definitely be interested to hear a bit more about what that means for the learning and development strategy that you have, especially when you factor in some of the concepts like skills, intelligence that you mentioned, having that much clearer picture of the skills that you will need to help drive performance forward within the organization. So how was your learning and development strategy changing? Is it becoming very sort of focused on skills, or are you just refining how you go about that approach? What does it mean for your overall learning strategy?
Guest: Yeah, there's a couple of different dimensions. There's what are the skills and capabilities that we're trying to build in the organization? And I mentioned those two intentionally because, you know, skills are elements that we look at, you know, arguably at the individual level, but capability is also equally important. And that when I say that word is maybe important for your listeners to understand, you know, what we mean by that, that that is really about organizational capability. So how do we look at the actual things that EY is good at doing in order to service our customers? So how do we build our engineering capability as an example? How do we build our workforce planning capability. How might we build our change management capability? Those would be examples of organizational capabilities that we know we want to build to be successful. As our market evolves, as our customer demands change, we have to be constantly thinking, what is it from an organizational design standpoint that we actually need to be able to build? And that's, you know, bottoms up. What skills intelligence feeds is an understanding of the truly uniquely human things that we need to train and upskill in service of a particular capability in the organization. So it's important, again, to kind of frame it within the context of our overall strategy. So that's the kind of level up is that our skills and learning and development strategy has to connect to the business strategy and very broadly to the, to the talent strategy that we're that we're focused on. And then I think what's uniquely challenging about the age of AI is we're getting much more involved in, especially around the needs analysis phase around what the future of work looks like. So I think that's a fundamentally new conversation. Again, just much like we are breaking down work into skills and maybe to kind of pick up from the point before, you know, I think historically there's been this obsession of like, okay, if we're going to break down a human and work into something, it will be skills. I would offer up that it's arguably just as important to break down work and what humans do into tasks. And that's another area that we focus very heavily on is we're working on our workforce evolution. What we are doing is looking at an inventory of the different tasks that are executed to deliver the different types of work that we do. So I think that task intelligence is just as important because that helps you understand what the future of a particular role, function or workflow looks like. And from that, you can work backwards and infer what the learning and development needs might be. So that's one of the, you know, front end ways, if you like that, you know, having a skills based or, you know, this broad approach to skills and the future of work is really impacting what we do in our learning and development strategy. It starts with that business into our learning and development consultants, having a really good understanding of what capability we're trying to build in the business, and then how that then translates into evolving work and workflows and the tasks and activities that we need to, that we need to be training. So that's just an example at the front end. I'm happy to talk about more examples of what it means for, you know, the typical the types of programs and offerings that we have and the, what we're training on. But I wanted to give a kind of tactical example of like, you know, that interlock was the, with the business, I think is, is hugely important as we become more skills based.
Host: Yeah, absolutely. And I think it would definitely be useful to, to dive into maybe, I guess a little bit more in terms of how that comes to life, because I think some of the areas you touched on are where a lot of organizations are thinking at the minute is, is that that impact of AI, but then thinking about the future skills you're going to need, really emphasizing a bit more on the human side of that. Those are sort of those deeper skills that are going to prevail over time as well. But obviously trying to balance that with some of the current and the pressing needs that the business has. So that sort of balancing act, I know is a challenge a lot of people are working through. So what does it mean for you in terms of some of the human skills that that really matter? What have you identified as sort of some of the skills that are going to be important for you to focus on. And how do you go about making sure that they're being developed, nurtures rewarded within the organization?
Guest: Yeah. And again, it's we look at this holistically. So I actually just recently created a function within the learning and development team called the Future Skills Lab, because again, as we were looking at this space, we wanted to make sure that we were seeing the signals. You know, what we know is that the sort of like the half life of skills is changing pretty rapidly. I think there's lots of research that people have read about that. So we want an organization that was waking up, you know, a team, sorry, I should say, on that's waking up every day thinking about those signals, doing applied research within EY to understand what are the most important signals that we are seeing, both from the market and internally from our own work, that help us understand what skills are going to be more important in the future. And that's how we're sort of defining future skills. They could be existing skills, or they could be new and emerging skills. And the two combined are really, for us, what we're referring to as future skills. And I think, again, that's an important one. So we don't just overrotate on one particular area. So yes, some of the those, you know, traditionally human skills are involved in that. So collaboration, creativity, communication, empathy, those, those, those kind of areas certainly are getting amplified. Um, there's other future skills. I would kind of point to around critical thinking systems thinking all the thinking's, you know, become definitely very, very important here. And so we have a range of different programs that we use to upskill individuals on those. And again, it's really a blended approach. So it's not like we're doing a particular course on one particular skill. We'll try and weave that through all of our offerings because when we when we're thinking about the evolution of work and when we sort of seeing the practical application of artificial intelligence in people's work, what we recognize, again, with the goal of behavior change in mind is that that behavior change isn't just a result of training on a particular skill, it's mindset, skill sets and tool sets. So when we think about that in a in the context of a training program, we have to make sure that we are we are ourselves empathetic to the role that an individual is operating, and we understand our learner, and we think through the various kind of mindset, skill sets and tool sets that they need to be successful in the role. So that's, you know, just a little bit about how we're kind of infusing human skills into all aspects of, you know, updated workflow training, AI training. We always kind of marry it with some of the human skills side of things.
Host: Definitely. I think we've also seen a bit of a conversation in recent years around, I guess, this concept of continuous learning or learning in the flow of work. Again, one of those other areas, we love to come up with a buzzword around. And obviously, yes, there's definitely a place for sort of those, those really specific dedicated spaces for training and development and those sort of courses, that style of learning and blending that obviously with that, that kind of learning that takes place continuously, especially when we're talking about some of these, these human skills. So they're practical ways that you go about delivering that kind of continuous learning, making sure it's happening, that it happens in the flow of people's day to day work as well, in any areas that you've seen have been particularly impactful in making that happen.
Guest: Yeah, we do actually. So there's learning that is delivered at every single stage of the of the career here at EY. And I think again, as, as I mentioned before, there's a professional services organization. Our product is our people. So it is just so important that we do invest in the skills and capabilities of every single role in every single business unit. And we have a pretty comprehensive learning curricula for every role and rank. We're also a regulated industry as well. So we have for certain parts of our business, we have mandatory continuing professional education requirements as well. So that also helps shape some of the curriculum. So just culturally we are wired to be continuous learners, which is a great advantage and a great environment to actually be operating in. But then, yes, we, you know, there's continuous learning in especially some key areas and maybe tying it to the previous point around human skills. We've been investing a lot now in the leadership development and leadership development skills. I think it is truly one of the Uber human skills that will be important in the AI world and at every single rank. Now we have leadership pathways, as we call them, that we've designed and developed that kind of give a curated learning path for new entrants at a transition point. Maybe they've just been promoted and kind of walks them through a curriculum that, again, is a blend of experiences over the course of that year that again, are both in the flow of work. When you look at the flow of work being an annual life cycle, but then there's also tools and technologies that we're using that various AI technologies that again, integrate into Microsoft Teams, which is what we what we use internally for, for some of our team collaboration capabilities that give nudges around both registering and attending training, give nudges towards behavioral patterns. That kind of learning science component of, of spaced repetition. There's ways like that, that we're introducing learning in the flow of work. And then also a lot of the kind of custom tooling that we have. So not, you know, rendered off the shelf software. We are, you know, technology enabled organization. We have learning and performance support. I would say that at the point of actual, the point of need as well. But again, I this phrase, which gets used a lot learning in the flow of work, it's important to study it as well, I think. And again, this future skills lab that I mentioned, we have a behavioral science team there that are actually studying how some of these new tools and technologies are impacting work and impacting behavior change. We have the belief that work should be designed with a human centered lens up front, and we want to make sure that we're avoiding, you know, de-skilling or this kind of cognitive atrophy that you've kind of likely read about in the age of AI. So again, we're being very intentional about both how we design work and how we design learning experiences to, to present that. And we know that, yes, we want people to be learning all the time, but they also need time away to learn. And we have to balance our offerings with that. So people need time to connect, work with others, reflect and really, you know, work towards two different models. If I kind of, you know, put them in in contrast with one another, they're just, they're just as important. What I think is wonderful and genuinely exciting about learning in the flow of work, though, is truly, I believe now with AI, we have the best possible opportunity that we've ever had to have this kind of concept of hyper personalized learning. That's what I'm incredibly excited about that, you know, an AI now will know your organizational context, your personal context, or know your skills and development areas and will actually develop custom based learning right there in the moment of need. And again, we're actively have a number of different pilots around that going on right now, which is, which is truly exciting. I think that that is, is where the power can really be seen.
Host: Definitely. Well, maybe come back to that side in just a moment and sort of talk about long term kind of career coaching and how that level of personalization can support that. Just before we do, I wanted to come back slightly on something mentioned maybe around that, that cognitive atrophy that that can take place. And I think that's sort of the concern that, that you maybe see sort of in a few different studies is around, I guess, as we're shifting to, to AI, and AI is replacing some of these tasks that are perhaps quite repetitive, quite administrative, but these are maybe sort of the baseline practices that help people build up knowledge and accumulate that, that skill set and capability over time, especially early in their careers, not necessarily at that stage, but I think concentrated in that area is where there's been a lot of sort of conversation around is, is there that erosion taking place in terms of the practices that would help people build those skills? So how are you making sure that that's not taking place? And I guess really the question is, how are you supporting those early careers individuals and making sure that they're still building those really baseline fundamental bits of expertise that they're going to need as they scale their careers over time?
Guest: Yeah, it's a great question. It's a it's a really interesting challenge because again, that frontier of what's required is changing every single week, arguably. So we have had a concerted effort to kind of redesign that early career experience, to focus less on the routine tasks specifically and more on the broader capability building. One of the programs that we have right now is called three hundred sixty careers, which is where our new entrants can actually do like a rotational program and experience different parts of the business. So they're getting broad based capabilities that are transferable, get a good understanding of how the business operates, and really spend the first few years participating in a variety of different structured, skills based experiences across the firm. I think that sort of builds more resilience in our early career hires. It gets them more exposure. And so far, it's just it's a great way to kind of think through how we can not just focus on the individual task and process that when we're upskilling and onboarding, which, like we say, has the risk of being completely irrelevant in six months time. I think it's also important, again, that we have this proximity to where the work is being done and where the work is being redesigned so we can very quickly work backwards to, what do early in career and onboarding programs actually look like? You mentioned the word experience as well, which I think is super interesting because, um, that is one of the things that we're studying is what does it mean to be experienced? What does, you know from the concept of judgment to just knowing how to do a task? What does that actually look like? What can we learn from other domains? You know, pilot still has to know how to fly every aspect of the plane, even though there is a copilot and an autopilot capability turned on, it is still important to train those. So it begs the question for us, you know what, what still needs to be trained? Even though a machine or an AI may be executing that work, and what is required in order to for an individual to constitute having experience. Another thing we're thinking of doing to kind of compress the time it takes to get experienced is we're working on the design now of an early career residency program where we're looking at, you know, what kind of skills and capabilities would typically be built over a maybe the first. And again, I'm going to use general numbers here because these aren't the specifics because we're so early in the design, but I thought it might be interesting for your listeners to hear about it. But so let's say, for example, what are the kind of skills and experiences that people would likely get in their first four years of the career? And is there a way that we can compress that into just very similar to, to doctors into like a six month residency and give them exposure to those types of experiences. And again, take advantage of the latest tools in AI enabled learning, as well as some of these kind of concepts, like the three sixty career, as I mentioned earlier, and just really condense that experience window. So that's another area that we're that we're looking at.
Host: Fantastic. And I promise we'll come back to, to discuss really that personalization of the learning experience that you've mentioned, and perhaps to explore what that can mean for, for long term careers within the organization. Obviously, looking just beyond that kind of entry level experience for, for people across the business, what do you think organizations can look at doing in terms of now being able to really deliver coaching at scale, perhaps far more so than we've been able to previously to really, again, help give people that that full career support across their life cycle within the organization. What do you think of, I guess, some of the exciting ways that that's time to take shape?
Guest: Yeah. I think, you know, before I get into the really exciting world of kind of AI coaching, what that actually means for scaling something that, you know, previously maybe would have been a little bit more challenging. Again, I would say that within the context of organizational culture, I think coaching in any kind of delivery mechanism is incumbent on having a deliberately developmental culture where coaching is seen as just a really valuable activity. Just generally, I think, again, having organizational shorthand and, you know, clear leadership expectations of what you expect the leader to be doing day in and day out also is an enabling factor to ensuring that coaching can scale at an organization. So that's what we do is we embed our coaching capabilities and methodologies into our leadership expectations, as well as our broader kind of learning and talent systems. So it doesn't live inside. Like this is a coaching program. It's part of our everyday sort of leadership model and everyday language When we look at executive coaching specifically as an example, that had typically and historically been referred restricted to a certain population in the organization, we now have an active pilot with an AI coaching vendor to scale AI coaching across about eight thousand of our fifty, sixty thousand folks in, in the US specifically, where again, we're scaling that capability down to the individual contributor level and new entrants into the into the organization. So it's a, it's a really important capability. Again, I think the... I was at a conference recently, which was kind of interesting. Somebody mentioned that, you know, leadership is one of those high performance sports, if you like, and it's the only one that doesn't have a coaching staff for everybody, you know, which I thought was a really interesting kind of contrast to the to the sports world. But now with artificial intelligence, we generally have the capability to scale coaching. But, you know, again, I mentioned we study things here. I think it is really important to, to look at studying how people interact with an AI coach. Will they react the same way that they interact with a human coach? Are they likely to receive the advice in the same way? Again, I think there's a lot we need to learn from a deployment and a adoption, and more importantly, the adaptation of these types of technologies to see if it actually creates behavioural change in our employees as well. So really interesting time to be in the learning and talent space as it relates to coaching. And again, while I'm bullish on the AI coaching side of things, again, I come at this from a position of balance. We think that the AI coaching can help amplify existing human coaching that exists, and it is also a close cousin. And, you know, it's something that complements this, this idea of apprenticeship that we have at EY where everybody can be a coach and a mentor to others as it relates to the work that they're doing on a day to day basis. And again, I think all of those are important. As I circle back to the original point of like having a culture that values being deliberately developmental and everyone having a role in, developing those around them is truly where the power is.
Host: Perhaps as a final question again, to tie a lot of the threads together, we've spoken about some of those different studies that are going on, the pilots, the experimentation, but also that need to really see how that is driving behavioral change in employees. And you spoke earlier about the need to connect a lot of this strategy to some of the business level results as well, and making sure that thread is there too. So what does that mean in terms of measuring the success of some of these pilots and especially these, these new areas of experimentation, new programs, new approaches? What are some of the typical success measures that that you're looking to keep a record of? And how do you, I guess, take that on board in any feedback to, to kind of really iterate and make sure that you're kind of improving and that hopefully these, these programs are either working in the way you would hope or that they're at least giving you a bit of direction for, for what to try next.
Guest: Yeah. So there's a couple of things. Again, I point to, I think ultimately we're in service of the business and building the business capabilities that are required to execute our strategy. So that's where I try and anchor my entire team is to focus on what is this in service of? What are we building in the business? And were we successful in building that capability? That's one of the main ways that we we sort of track value and impact that we're having on the organization, because we want to be connected to the most important change initiatives that are going on in the organization. So we certainly look at the more detailed numbers, everything from, you know, from sentiment to participation, the again, this kind of concept of skills, intelligence, this is where we want to look a little bit more detail and actually have some discriminant analysis that we can look at to say, hey, when we invest in these kind of things, can we do a test and control? Are the behavioral differences? Are they actually resulting in performance changes at the individual and team level as well? So we do look to look at a range of different performance behaviors and really ensure that we are using that type of multi regression analysis to understand, okay, does investing in X actually translate into a business performance in a way that we can actually distinguish? We do that in a selective way, because it is very expensive to actually measure that on masse across every kind of area. So we when we're when we have new requests from the business, again, actually this future skills lab where our measurement team actually sits, I promise we have more capability in the team than just them, but they're very front of mind right now. They will they will help design a measurement strategy for some of those key programs based on the objectives of what we're trying to build there. So I think that's really important to have that understanding. So we're not making assumptions behind the developmental programs that we're putting together, or just kind of speaking in generalizations to say, you know, that we yes, we need to improve creativity skills. Therefore, let's create a program on that. And we assume that will improve performance. Performance is contextual, and we need to study the context to be aware of how effectively our learning programs are doing. So again, hosting approach from the tactical to the strategic is, is what we're looking at. And that fundamentally is at the core of what is behind being skills powered and having skills intelligence. We're not perfect, we continue to work on it. I want more data, as I think everybody does and all my peers do. The more data, the better. So we can kind of run various scenarios and test different hypotheses, because there are so many questions that I have that I'd like us to, to study across the firm.
Host: Fantastic. And you did a lovely job of taking us back full circle there to the first question. So appreciate that. Thank you. Alex. I think that's just about all we've got time for. It sounds like you're hugely busy, man, so I will let you get back to it. But thank you so much again for taking the time to talk us through all of the different experiments and shit you've got going on at the organisation, giving us that that real run through of how EY is thinking about this shift towards skill space and what that means in practice, and again, really being fantastic to hear all about the Skills lab, I'm looking forward to hopefully doing a bit more of a case study on that at some point as well. But Alex, thank you so much for taking the time to join us on the Grapevine podcast.
Guest: It was my pleasure. It was fun. Ben. Thanks.
