Decoding AQ with Ross Thornley Feat. Sandra Loughlin - Chief Learning Scientist at EPAM Systems

Speaker A: Hi and welcome to decodingaq. Helping you to learn the tools, mindsets and actions to thrive in an ever changing world.

Speaker B: Hi and welcome to the next episode of decodingaq. With me today I've got Sandra Laughlin. She's the chief learning scientist at EPAM Systems. She's an organizational psychologist and self proclaimed skills nerd. Sandra's dedicated career to transforming the way businesses approach learning, employee enablement and technology driven transformation. So welcome to the show Sandra.

Speaker C: I'm thrilled to be here, thanks for having me.

Speaker B: And for those of you who are listening and not seeing, Sandra's got some awesome heart uh, shaped earrings. And it was the first thing that hit me and I said how I love your earrings. You said I've also got just for record, heart, uh, um, shoes too. So I thought that was brilliant. Absolutely brilliant. So Sandra's at the forefront of helping organizations really cultivate business, uh, agility, engineering excellence through AI enabled skills based strategies. So her work has been featured in lots of places, be it Forbes, CEO magazine and is recognized as a global leader in consulting as well. And sharing her expertise today about how do we really build cultures of continuous learning and innovation. Before we get into that, I'd like Sandra to give listeners a little bit more context about epam because I think it's quite a special organization where it's perhaps been on this skills journey for three decades. Um, and EPAM and SKILLS have been married from day one. It's not as if it's come in later. So give us a little bit more context about epam, Sandra.

Speaker C: Okay, and then, and if you have never heard of epam, like don't worry, like pretty much nobody has. We um, are like a 5 billion dollar um, ah, company operating in 56 countries. So we're a pretty large sizable organization. Um, and our business is professional services specifically related to software engineering, data and AI. So what we do is we, you know, for a many, many different companies in the world, like you know, all the companies, the brands that you know, we come in and we build their, you know, very sophisticated digital platforms. Um, so you know we build for, yeah, for digital natives, we build for enterprises, but that's what we do. And so our entire organization is made up of uh, software engineers, data scientists, AI experts, you know, you know, just that's who we are. Um, and I get, I want to underscore the point that I, that you just made, um, which is that this whole Skills thing is something that we have um, been built around as a business. So for 30 years skills has been the language of the business. Um, and so it's been really cool to see this, you know, more recent shift to the skills based organization concept because that's really reflecting, you know, who we have always been.

Speaker B: And you have quite a novel way of thinking about skills and giving skills some perhaps context, uh, through tasks, um, as a way of making them more real, more understood. So can you share a little bit more about that of your thought around tasks and the relationship with skills?

Speaker C: Yeah. So our whole skills journey, just to give you a sense, started because we needed to staff people to client projects, right. So we had to find the right people to do the work that needed to be done. And from the beginning we realized that those are two sides of a coin. Like you have to understand the work, the tasks, the specific deliverables and activities that need to be done and you need to know who has the skills to do those tasks. And so we have, you know, are, we're built around skills. That's true. But skills by themselves is not a solution for us. We have to understand both. And so when I say skills based organization or I say skills, I'm inherently meaning skills and tasks, understanding workers and the work to be done.

Speaker B: And when we were getting set up in the green room before recording, you were explaining about something that you're facing right now. A call that you've just come off of a very, you know, complex but real challenge around AI integration systems and sort of this transition of where HR systems and the records matter. But it's also about where data comes from outside of those systems in terms of just doing the work. Tell us a little bit more and share about what are you working on right now. What are some of those, uh, challenges? Because I think that's a great place to explore our conversation today.

Speaker C: Absolutely. So, uh, as a person who didn't understand technology at all until I came to EPAM a few years ago, I have learned that the technology, um, architecture of any business and the data architecture reflects how work gets done. It reflects the processes and people in the organization. Um, the reason that EPAM has been able to function as a skills based and task based organization is because we are our business architecture. And the data infrastructure at EPAM has been designed to be interoperable, meaning that we don't have data silos. We don't define skills in one system this way and another system this way. Um, we don't like, we pull data together from systems of record. So all the HR related systems, your hcm, your lms, your lxp, ats, Systems, for example, but also equally importantly from all the systems of work, you know, where, you know, getting evidence of what people's skills are and how well they're deploying them by looking at their work product. So as a company we've always been designed this way and the call that I was just on um, was um, trying to figure out how to help our clients achieve that kind of data and technology integration reflecting this like 2.0 version of an organization. Like if you don't have, if you have data silos, you can't understand your people. If you can't understand your people, you can't figure out who's optimized to do what work. And you cannot figure out how to build an organization where the workforce consists of people and AI. And so right now we are working with a few different um, clients trying to help them re envision their HR tech stack and their, their whole technology back end to reflect this new world being defined by skills and tasks and AI.

Speaker B: And um. One of the challenges for organizations that aren't EPAM is that this is often something that's coming in after an operation without it. So they've been operating, doing, doing their job, doing the work. And now these new opportunities of technologies and tech stacks and different platforms, but also a different view of what is the right work, what is the work m that we need to do tomorrow and how we might achieve that. It's all fluid and all moving. And so where you mentioned, you know, the, the art of the possible, EPAM is a great sort of showcase of that, but hasn't been on the journey of going from the silos, the mess and transforming themselves into a skills based organization. So organizations and you know, even our small business, it hurts when you leave a system, right? When you've spent money, time, effort in it and you have a relationship with it and when you then come into a new opportunity of a new way of doing things, uh, a new bit of software, new tool. It's that duality of excitement and disappointment of well, we just spent all that money and effort doing it this way and now I can do it so much easier and quicker. I feel awful, but I feel great. What is that? Um, how are you approaching that with your clients when they're facing perhaps on a much larger scale than we are? That duality.

Speaker C: That's a great question. And it goes to like just the psychology of individuals and entire organizations, right? Skills and the whole concept around, you know, understanding your people and using that information to inform the whole life cycle, who to hire how to give feedback, um, you know, internal mobility, how to craft job, all of that, um, is implicated. And without a compelling, very compelling reason to do all of this differently, individuals and organizations will not change.

Speaker A: Right.

Speaker C: Like this is just like, as you said, like there's a, there's a, there's a comfort, there's a sunk cost concern. Right? There's so much, um, and so when we are helping organizations who want to become skills based, we start by saying like, why? If it's, if it's because you're trying to follow a fad, I mean you're not going to get very far. Like that's, there's, that is not a reason to change everything. If you are trying to solve for specific business challenges. And they could be HR challenges or actual business. Right? So AI to me is a business challenge, internal mobility, you know, um, high turnover, like these are HR challenges that affect the business. Um, you have to be able to conceive of the connection between this whole skills thing and whatever that issue is, and you have to be able to articulate it in a very compelling and meaningful way. And so a lot of what we're doing in these conversations is just asking the questions to pull out, what, why are we doing this? And then we can help companies figure out how they're going to do it. And that is, it involves, uh, addressing all the different levers inside organizations that sustain how things are done today. So it's not enough, I mean, I'm a learning person. It is not enough, for example, to throw out training on whatever you want, whatever the change is. Right. First of all, people aren't going to take it up unless they have a good reason to. But also, even if they know how to function differently, they generally won't because their incentive structures haven't been changed, the processes haven't been changed, the tools haven't been changed, the metrics haven't been changed. And so when we help companies think about, um, you know, shifting to being a skills based organization, we help them think through all of those components. What is the operating model? How, what do people do differently? How does work change? Like what new processes need to occur? And then we come back to them and say, okay, you had this vision, you wanted to do this big thing, here's what it takes. Are you sure? Are you sure you want to do that? And if you are, great, right? Then we can help with pieces of that. But a lot of companies and individuals, when faced with the reality of what that change really entails, will say, maybe next year, yeah, And I think that

Speaker B: is a very real situation that compounds right is at uh, the moment we're faced with any sort of shift has the potential to be great in a pilot, in a small team, in a pocket and make real tangible, you know, business outcome, impact. And every single one of them, when we then go now, uh, want to do it org wide, they're so integrated, they're so complicated. It's nah, uh, maybe next year until there is no next year. And I think that's the reality that maybe many companies are facing is, is that next year will only exist when those are taken forward. And that's a scary reality rather than, no, I'll be okay for another year, I can do these things in a pilot. And they're faced from many angles. These shiny things, skills based. This technology, this piece must do this, this is imperative. It is overwhelming if we're, if we're embracing this and the companies that uh, exist in the next five years will be the ones that embrace this way of thinking. Yes, there's no doubt about it. There's no doubt about it. And um, so therefore it's about how do we do it without making ourselves ill, without making ourselves so stressed that we get there and think, God, I'm nigh on dead as a human. But we made it, we want to make it to this better place while maintaining health and maintaining well being. What are some of the things in this art of the possible that EPAM have been doing to make that a little bit more real for some of the listeners, um, around how, you know, how are you doing it? How have you done some of these things?

Speaker C: So um, the best analogy for skills transformation is something that EPAM understands extremely well, which is digital transformation. So you know, 30 years ago it was a back office function that just got thrown over, like, thrown over the wall, like just go do this thing, solve this problem. They were a support function. Um, and it has taken 30 years and I'm sure emotional just. It's, it's been a rough um, and very long transformation for the IT organization. Um, and when it got started there it was very difficult to see the art of the possible. Like they were just going on this journey because they had no choice because you had companies who were born digital, right? Competing and forcing essentially organizations to change. Um, and so the thing that, the first thing that I do when I'm talking to companies about this is first of all make sure that I'm talking to the right people. HR is not the only or arguably the most critical stakeholder in this thing, it's the business, it's your. It's. It like human capital. So skills is about human capital. Human capital is one of the biggest costs in almost every organization. And so you have to be able to frame this message to the right group of people. That includes your coo, your CEO, generally speaking, your cto, like, you know, the people who understand and are, um, uh, incentivized to optimize the business. And the whole skills thing is component of that. So start with talking to the right people. The next thing we do is set expectations exactly saying what you just said. If you don't, like, you know, we say if you, if you don't do this thing, here's what you're facing. If you do do this thing, here's what you're facing. And it's two terrible options. Like, there's no, like, better way to say it.

Speaker B: Uh, line my wife and I always, uh, re quote each other from. I don't know whether it was master and commander or something. It was, um, uh, the lesser of two weevils. And, uh, they were there. Which one would they eat on? Well, it was Russell Crowe. I can't remember. We always use it. Oh, it's the letter of two weevils. So you just made me think of that.

Speaker C: I'm gonna do a whole LinkedIn post just on that. I love that.

Speaker B: The lesser of two weevils.

Speaker C: Two weevils. But it's the idea, it's you basically, like, this is where we are as a world. And, uh, and I'm super sorry that we're here, but yet we are. And so first of all, pick your path, pick your poison. The next thing that we do is help companies think about, um, simultaneously holding in mind two very, very different concepts of change. So one is the big picture. What is the operating model? How are you going to, like, how are you going to be set up? What are your processes? Who's doing what? Just the very, very big picture of like, what does the end state look like from a very tactical point, uh, of view, and then simultaneously start doing some of those pilots go solve some real problems that people have today that are not, you know, boiling the ocean. You can't do that. You have to find, you know, burning challenges that are rel skills, you know, and task data informs and helps to address. And you go, you solve this one and you solve this one and you solve this one. But if you do, if you do the, um, the pilots and you have no vision of where you're trying to go, it's very difficult to pull those together and like make the vision. You have to do both of those things simultaneously. And then in the middle, and this is like, this is where it gets overwhelming. But you have to split, you know, divide and conquer the work. Right. The technology organization has to now be re engineering the business architecture, the technology and the data to allow all of that stuff to happen. And so if you think about it from the big picture, the small picture and then the back end picture and you can allocate that across the business leaders and across the different, um, functional areas, it's still going to be overwhelming. But you have a plan and you can execute against that and you have wins coming from your pilots. And so there's that emotional like reinforcement of like this is worth doing painful as though it might be.

Speaker A: Yeah.

Speaker B: And it's building that um, confidence and momentum and also the speed of that feedback loop to evolve the vision. What we can envisage today will be very different when we've got a few of those other things under our belts. And so it's that, as you said, the duality of having the zoom in, zoom out lens, but also having the flexibility of what is possible shifts, uh, whereas previously, oh, the North Star was the North Star and we work on it for decades, um, and now that'll be a year, uh, or we are Nvidia creating technology to solve the problem that we think exists now and creating a business and value on it that in a day can shift of how people approach solving a problem. It reminds me of a story, um, of the biggest problem in New York back at the turn of the last century that they were working on. The biggest issue they had was horse manure in the streets. And it was causing huge issues and they were all working on how to solve it. And in a short space of time, a decade or so, it was no longer the biggest issue and problem because the automobile came along. So compute power was the big challenge, the big issue. We need to create, create, you know, these chips, quantum computing, all of these, uh, areas to take complex, very high data, uh, compute value in. And now people come along and say, well, how can we do that on less resources in a different way? And it shifts what the business model is. So there's another point I'd like to touch on, and it was a post that you were talking about of how we might structure our businesses differently. And so you sort of pose 2 thoughts of 1 thought where there's a new role, a uh, C suite sort of role to oversee the integration of the people, the processes, the technology or do we foster tighter collaboration between them that already exists with better KPIs or joint incentives of those things? In reality it's probably a combination of these. But I'd just like to explore your thinking and perhaps what's going on in uh, EPAM or some other organizations, uh, that you've seen. How are they approaching it? Is there Route A, route B combination or C and D?

Speaker C: So that's a great question. And I, since that post actually talked to a company where they have uh, I think they called it the Chief Administrative Officer and the Chief Administrative Officer, um, had reports from the cio, uh, from Chro and from the coo. It was like exactly what I had talked about. I was so excited about it. Um, but it was because, and this was, you know, before even AI was heading, they had started thinking about um, this challenge of like if you have these people process tools, organizations running in parallel, not like not tightly aligned, you're going to be inefficient as a business. So I think that's one way to do it right. And it's, and it's a very different way of approaching this. You have to have your AI in there, wherever AI lives and it's different in different companies that has to live in there too because AI is now part of your workforce. So like you have to think about things differently. Um, so that can work. The way that EPAM is set up like is so different. Um, we, we're a hybrid model so I'm not sure that we're a great example. But I do think it's possible for organizations to do a much better job of um, um, um, um, aligning the people process organization or people process tools functions in an organization. Um, if you have the right people in the role and you have the right CEO because really the CEO is the one to be pulling all this vision together and make, and directing the business in this new way. And so the CEO, uh, people have said are very responsive. Like people are very basic, complex but also very basic. They respond to incentives and metrics and KPIs and processes and stuff. And so if the CEO does it correctly, like putting together KPIs that multiple orgs have to work together to address. Right? Um, you know, going and looking at, you know, what are the things in the company today that are reinforcing this parallel play that we see and removing those, it's possible, um, the, and there's trade offs with each one of those. So if you just do the Chief Admin Officer or whatever you're calling it. Right. That is uh, some very powerful signal like. Right. But you are going to upset a bunch of people. You're going to, it's, it's going to be really difficult to take people who have been reporting to the CEO and like drop them. Like it's hard. On the other hand, you know, if you're trying to completely and uh, to, to take the same people and have them work very differently, that's also super hard to do. So again, you have these different trade offs and I think that companies will need to make this decision based on the people that are in the seats today. And it could change. Right. You might shift from one to the other, but regardless, that coordination is no longer um, an option.

Speaker B: Yeah, yeah. And I think traditionally what I've observed is that what tends to happen with those types of initiatives and types of roles tend to slow things down, slow innovation down, become a red tape, a bureaucracy, a uh, how do we now play the political games to get my, you know, resources I require for my project? And that joined up nature of collective incentives is, is very often not really there. And I think a lot of them, it's this balance between are we incentivizing, rewarding, recognizing behavior or output, uh, output and result and the, the, the reality for a lot of businesses is they function on the output and result. And yet we need the behaviors to shift within that. So again, we need this duality, we need this, you know, flexibility around how we both perhaps incentivize behaviors and outcomes and results, even if the outcome and result wasn't what we ideally needed for the business. Um, but the behaviors we needed, we needed those collabor needed to join people up, which may slow things down, may speed them up, depends on the context of those. Right.

Speaker C: And I think in, in anything like skills or any major transformation, you will necessarily slow down before you can speed up. Because again, every business is perfectly designed to get exactly what they get today. Perfectly. And in order to get something different to transform, you have to figure out what is reinforcing the status quo. And then you have to like decide to address it. And addressing it is not easy because it goes to compensation, it goes to the fundamentals of the business and stuff that's frankly hard to change. Even if it's not financially difficult. It's going to be like, you know, political capital has to get spent and you have to decide you want to do it. And this is why so many companies, uh, and I'm scared for them, are not going to lay the skills groundwork today that they will need so that when AI is hitting, when there are AI native companies who are just operating so much leaner, so much leaner.

Speaker B: It's thing, it's night and day. Yeah, I interviewed the um, uh, head of future of work for Sodexo. Now Sodexo is you know 18th largest employer in the world and he as an individual has five AI, you know, uh, workforce, you know, uh, cool them agents, colleagues to do different things that augment his ability to do his job. Whether that's informing him of research, whether that's uh, supporting communications that previously would have been tasks for a human that is now tasks for an AI. And so that individual becomes a force multiplier when they have those things around them. Now the balance as we become more and more from you know a tool as a service, software as a service to a result as a service. At the moment we use humans to get a result as a service. We're going and transitioning to result as a service with technology. So we just ask it and get the end result rather than help me do those things. And that's going to be another very swift change that a couple of years ago we were still you know, uh, oh look at this new chat system to now it can go and really do things for me. Books and flights, you know uh, OpenAI have just launched their agents and operate and ah, model and there's many of these and they'll be plugged in through workflows. A quick response to just what I've said and then I've got a question that I'd like to um, pose to you that I'm curious about something with skills.

Speaker C: So love. Yeah, uh, on the agent thing. Okay. I think that um, a lot of people particularly in the HR learning space don't understand and differentiate. There's three different kinds of AI that need to be understood. So one is automation. Well let me start with assistive. AI is like the chat GPT stuff that people know like you're asking, you're prompting it faster and better, right? Yeah, like that is what most people understand, um, and that's disruptive but it's nowhere near as disruptive as the next two. The second one is automation. This is where this already exists today. It's going to exist in more um, be more robust and have more options but it's where you take an entire workflow and you just pop it over to technology and it just does it. Agents kind of are like in the middle um, but they are the Most disruptive of all three. And it's where they take pieces of a workflow. The other pieces are still being done by humans and they are now interacting with humans, as you say as a colleague, but not just doing one little thing. They are, well collectively they're doing a bunch of things. But what happens is you are re completely reimagining how work gets done. And you know most companies aren't seeing this yet because it's going to happen versus happening right now in software. Right. Like EPAM is one of those companies who our entire model has always been we, you know, we sell people's time and that's our business. And we are actively disrupting our business today because we know uh, that that model is going to be broken. And so we are the ones who are you know, looking at the software development life cycle which is a huge process with lots of handoffs and all these different groups. And we are thinking, okay, how can we completely change this process? By introducing agents and the companies that can figure that out are going to be the ones that, that win because you have just cut your costs.

Speaker B: Accuracy, efficiency.

Speaker C: Yes.

Speaker B: Yeah, uh, it's the you know, Model T4 production line of reimagining. How does one person build that entire car to. Yes. We then segmented that out to specialists so somebody was specialist in each component of that. We then put it into a uh, factory line that enabled speed, capacity, consistency. Then we put in technology to look at how do we you know, lift uh things differently, place things differently, screw them differently. And so in a software and knowledge base we're doing that. And they'll also interplay with the physical world. You know there is now even existing manufacturing plants that are fully autumn, uh autonomous with robotic enablement to them. Um, I saw a uh, piece on our local news uh of here in Dorset we've got a robotic arm which is one of the uh, strongest robotic arms for lifting very heavy equipment. And uh, they were talking about using carbon fiber for uh, plane wings. And a process that would take uh, many, many hours, could be done in seconds with this big arm that could essentially maneuver sheets of carbon fiber into the shape that it needed to be to go from a production of you know, uh, you know, thousands x production per hour of this particular component. And so when we layer in yes, these massive uh, robot robots to humanoid robots doing things together with agents that are, you know, knowledge based solutions and how that all integrates it is going to be so vastly, vastly different, so very, very quickly that you can be boom, uh, or bust on a daily, weekly basis as an organization. Um, so a lot of that for many people is sci fi for others. They really understand how this is right now and it is happening. But one of the things I really wanted to ask you because I'm conscious of our time. Sandra was around skills and skills mapping and you know, I have read about some of your work, some of your videos I've listened to in posts and I liked the idea of tasks helping to give it context. One of the things I've experienced is my skills change depending on, on who is in the room and who's on the team with me. So I might get better or worse at uh, certain things that I'm not an isolated individual in when I do my work. And uh, you know, be it uh, a simple skill like writing or communicating, my writing and communication will differ depending on if I'm in a room with Sandra or having an interview and we're on a podcast to a team member of how good I am or not good I am or what I need to evolve or even need to learn differently within that skill. So I'm fascinated as to the thinking of skills not only from an individual sport, but from a team sport basis and what's your thoughts and thinking around that.

Speaker C: So this is like just a fascinating area and I have to give epam credit like they have been thinking about that too because again our work is teams of people to solve problems. Now it's gonna be teams of people and agents, but still it's people collective. Um, and this is where tasks become so critical. Um, the team is going to be collectively charged with getting um, all these tasks done. Right. They're going to get allocated to people and they'd have multiple people in redundancies but they're going to. People will be doing pieces of that. Um, I don't, I have to think more about this and talk to you more. I don't know that skills change, but I think who is most skilled to do it will change. Right. Or um, whether I am pushing myself to the limits of my capabilities or skill in any given context. But what we are doing and we're not, we can't finish solving for this. We're in the process and I mean solving is a relative term. But um, we, because we understand the tasks and we understand um, every person on the team what tasks they've done already in their role. Um, and we can look at the um, ah, the like commonality between previously done tasks and these new tasks they have to work on. Right? They could be like, you know, the same thing again. Or it could be that they're bootstrapping and having to, you know, take some of the skills that they applied in this task in a certain way and leverage them in a new task in a bit of a different way. Um, we, because we have that intelligence, we are able and we do think about team composition when we are building teams. It is not perfect. Like don't get me wrong, um, because really you'd have to, it's a, it's a massive data problem where you're looking at multivariate, multivariates trying to address one issue. Right. And so you have like all these different permutations and combinations. But big picture, we are starting to have enough data and see the business value of applying that data to optimize teams. And the whole AI thing is going to just continue to push on that for us because we want to be as efficient as possible and as effective as possible. And understanding how individuals, their skills and the tasks interact is like a valuable thing to solve for.

Speaker B: And I think you made some interesting points about, you know, efficient and effective. Right. Because we can be efficient in something that's no longer relevant and effective. And I think skills can sometimes have uh, that double edged, you know, sort of shadow to them that historically leadership and management came in, stood tall, were proud because they wore the T shirt, you know, they have the scars. I've done it before, therefore come to me and I'll be able to give you the answer for how to do it next. That those that were once the springboard have now morphed into a ball and chain and suddenly is what's holding us back. And something we have in our model is about unlearning, you know, that uh, I have this skill but actually that's what's holding me back. Because I'm so wrapped up in using this skill, using this tool, using this way, that I, ah, apply that skill in this task that is now limiting me. It's limiting me and I need to shift away from that. And I think that is equally as important as we try to break through the immune system of decision making and organizational development.

Speaker C: Yeah, it's like the hammer analogy. If I have a hammer, I see everything as a nail. And that is like, um, humans are largely incapable of rising above that. And that's where data comes in. Right. If we know that certain tasks require certain skills, we can go to someone and say, hey, did you know that you actually have 77% of the foundational skills required to do this thing? That you've never heard of before. Right? It allows people's minds to be opened to new possibilities. Um, and just the, you know, people tend to get better at anything and more skilled, um, when they can leverage that skill or that piece of knowledge in a variety of different contexts. So we actually, by doing this, are building that expertise that is so critical and important in the AI world, in particular, by helping people see their toolbox for what it is, not just the hammer.

Speaker B: And it's the analogy of bridge making that we need to go a, uh, bridge from where I am today to where I need to be and using the resources that I've got. So those tasks are the Lego bricks that I reformulate to reimagine to a different way of articulating it. Uh, I recently watched the original, uh, Lego Movie again with our grandkids, and I found it fascinating of this, um, you know, I missed it before that the creativity is gone. If you've got, this is the model you're creating. This is the model. This is what it is. And glue it all together and isn't it beautiful and perfect versus look at what we could make and the imagination. And there's great AI apps for it. Right? You can have the massive mess of all of the bricks. Take your pick on it, and it will show you hundreds of different things that you can imagine and make and play with. And so it's reimagining those skill brick applications to new bridges, to new areas where what needs to be done is going to be different and how we do it is going to be different. And I think that's a, uh, great kind of encouragement to give. Uh, before my final question, which I ask to every guest, uh, that I've had on the show that I'm going to ask you, uh, is there anything that you would, uh, have wanted to express that we haven't expressed that you've been thinking of as we've gone through the conversation?

Speaker A: Sandra?

Speaker C: I guess, yes, it's not a small thing. It's actually a huge thing. But it's something that I always like to touch on. I'm a learning person. This is what I care about. This is my PhD is in the learning. And I just want to underscore that this whole skills, um, uh, and tasks leading to business agility is through learning. And that learning is, um, incentivized. People have to want to learn, right? And they have to see value in that learning, um, at epam, and as far as I can conceive of it, um, that context that Incentivization. That directionality is provided by skills based performance management. If you, if people cannot keep their jobs and they cannot grow in their career, if they're not, if they don't have the skills to do their job and you tell them that, do you know what happens? People tend to want to learn stuff because there is inherent value in doing them. Um, and so I just want to underscore the criticality of learning but not training. We're not talking about throwing training at this problem. We have to build the organizational and incentive structure and support, support structure to get people to want to learn, which will in turn create business agility. That is the, you know, the promised land for skills.

Speaker B: You touched on something, um, and you know, you're a self confessed training hater. Uh, articulate the difference for us uh, between learning and training.

Speaker C: Okay. Training is something that you do to people, right? It's, it is information dissemination. The focus is on the content and the trainer, whatever that's a book or a person or. What do you mean? Chat, GPT, whatever it is. Learning is a process that occurs in the mind of a person. And for the most part learning doesn't happen in a training context. Most of what we learn as individuals happens from research, from reflection, from observation, from asking the right questions and getting feedback from doing stuff and failing and learning from it. And so to me learning is um, there's like a, uh, there's an overlap but they are very much not the same thing. And when companies focus on training and focus on number of seats and how people clicked through whatever, then they're looking at endurance of content. They're not looking at what has changed in the mind of the person that they can then translate into business value.

Speaker B: And I think we have got a, an awakening of what learning capabilities we have as a species coming from um, not only neural net brain computer face interface chips, uh, to uh, some of work that Mary Lou Jepsen's doing with her open Water project, uh, which is mapping what's going on to uh, you know, how we might stimulate the neural net differently to layer in that learning, I. E. Plug in matrix, I know Kung fu to how we might be able to stimulate that without degradation uh, of uh, cells by putting things in. That's going to be fascinating together with, I mentioned in our green room some of the work that uh, Kyle Jackson was doing in virtual uh, reality, augmented reality. It gives us an opportunity to cross that chasm into learning out experiential learning, the reflection, dynamic learning that your journey is Sandra's journey. Ross's journey is Ross's journey. I E N of one learning has that opportunity that technology can now start to deliver out. And I think it's going to be a whole new world. Uh, that is just, it's super exciting. So the last question I ask every guest, Sandra is. Is linked to curiosity. It's a homage to my first guest, uh, Dr. Diane Hamilton, who wrote the curiosity code, uh, and is a mad nut on curiosity. And it is. When was the last time you did something for the first time?

Speaker C: And what was, uh, must have been playing pickleball. Like I do not. I am ish, sporty. I am not a sporty person in general. Like, um, and I hate doing things that I'm not good at. Like, and so like I like physical things, like intellectual things. I'm all about like change and learning new stuff. But physically I feel so incapable. And so I think it must have been when my husband dragged me to play pickleball. And I was sometimes good and mostly bad, but it was, I felt my, I don't know, I felt like my neurons were expanding. My brain learned something that day and my ego did too. And so it was, it was something that uh, yeah, that, that helped me a lot.

Speaker B: And, and it's great, right? We can all find these things in our lives that we haven't done before. And even uh, you know, those regular listeners know I like the idea of seeing things with new eyes. Something you do every day but treating it as the first time. My last guest, we both inspired each other. I was going back and I was going to hug my wife like it was the first time I hugged, hugged her. Um, and these things of novel experiences can be literal novel or they can be the perception and eyes and mindset that we have. And I think that is a great uh, skill to encourage us to uh, see things with those naive, you know, first principle, first eyes. And to your pickable piece. I played it for the first time at our in person summit in Spain. I'm not sure of the difference between pickleball and paddle. Um, um, but I think they're kind of similar. They might just be slight nuanced difference of the name, sort of this combination of, between squash and tennis, um, type thing. And I really enjoyed it and we played it with a load of uh, our certified partners in Spain and uh, great fun. Hadn't done it before. So yes, you, you uh, shot a fire of a happy memory for me of learning something new as well. Well, if people want to get in touch with you to connect, to figure out, maybe even they've been listening and want to find out more about not just what you do, but what epam, uh, does for their organizations. How do they get in touch?

Speaker C: Sandra LinkedIn is the easiest way to connect, I think in general now. Um, so start there and um, if it's a great discussion, then we'll move to email and then to a call.

Speaker B: Beautiful. Thank you so much for your time today. It's been a real pleasure to get to know you more and learn, which I love doing by having conversations with awesome people on this show.

Speaker C: Same like I love the AI focus, the technology, the business, the neuroscience. Like this has been a great conversation.

Speaker A: Do you have the level of adaptability to survive and thrive the rapid changes ahead? Has your resilience got more comeback than a yo yo? Do you have the ability to unlearn in order to reskill, upskill and break through? Find out today and uncover your adaptability profile and score your AQ. Visit aqai IO to gain your personalised report across 15 scientifically validated dimensions of adaptability. For a limited time, enter code PODCAST65 for a complimentary AQME assessment transforming the way people, teams and organizations navigate change. Thank you for listening to this episode of Decoding aq. Please make sure you subscribe on your favorite podcast directory and we'd love to hear your feedback. Please do leave a review and be sure to tune in next time for more insights from our amazing guests.