AI and the workforce: Beyond efficiency to real value creation

Speaker A: M welcome to Pragmatism in Practice, a podcast from ThoughtWorks, where we share stories of practical approaches to becoming a modern digital business. As organizations accelerate in their adoption of AI, one thing becomes clear. Success isn't just about tools or algorithms. It's about people. How do we equip individuals, align teams, reshape roles and build organizations that can learn and evolve in an AI enabled world? Laura I'm Laura Berger and today we're bringing together two voices at the intersection of talent and AI. The people and culture side and the technology and transformation side. I'm joined by Joanna Park, Chief talent officer at ThoughtWorks and Rachel Laycock, chief technology officer at ThoughtWorks. So welcome Joanna and Rachel.

Speaker B: Thanks for having us and very excited to be here today. My name is Joanna. I'm the Chief talent officer at ThoughtWorks. I've been with the company for 22 years. I started as a software developer and I've played lots of different, different roles over the years, uh, including leading our North America business and uh, leading talent for the last eight years.

Speaker C: Hi everyone, I'm Rachel Laycock and happy to be here again on the podcast. I have been, uh, at ThoughtWorks for over, just over 15 years, which I can't believe, um, and I have played, uh, many different technical leadership roles across ThoughtWorks. I've led our modernization, platform and cloud service line. I've been a technology lead across North America and then for the last two and a half years I've been the chief technology officer for ThoughtWorks, uh, which has probably been some of the most disruptive times in technology.

Speaker A: AI is moving faster than anything most organizations have experienced before. And from where you sit, um, how would you describe this moment for talent and work? And maybe we can even talk about the leadership aspect of that.

Speaker B: I would say this moment is one of the biggest disruptions that I've seen in my career. And I think it's in the very early days and we're just beginning to understand, uh, what it means, uh, especially what it means for the world of work, uh, and talent and technology, uh, in terms of leadership, I think what we see is a lot of leaders really struggling with what this means for their organization. How can they drive adoption and what benefits will they get, uh, from, from the new technologies. So uh, we're going to dive into this more. But I think, uh, our view is that it's much more than just individual adoption of technology and that leaders really need to think about, uh, broad impacts to the teams and the full organization, uh, in terms of how work gets Done.

Speaker C: Yeah. And I think the thing I would add to that is, unlike disruptions that we've had in the past, let's say, you know, digital transformation was a big disruption and a big change in our industry. We kind of knew what the end state was that we were working towards. And I think what's different about what's happening now is no one quite knows what the future is going to look like in terms of how we build software, how we run systems, how we're operating systems, and therefore, given we don't really know what it's going to look like. Um, we also don't know exactly what the talents landscape looks like, which makes it hard for leaders to navigate. So some of the stuff we want to share today is how we've been navigating that. Um, and um, what are some of our, like, based on our learnings, like what are our hypothesis of the stuff that's changing?

Speaker A: And there's a lot of leaders that are very excited about what AI can mean for their workforce. Maybe you can talk a little bit too, because there's a lot of uncertainty. What does that mean? Um, relative to some of the biggest misconceptions that we're seeing.

Speaker C: The biggest misconception I think, is this whole, you know, AI is going to steal your job, which I believe is somewhat related to the conflation in the industry of, you know, oh, I'm laying off all these people because of AI, and I just don't buy that. I actually think that, uh, companies are laying off people for more economic reasons around their business and that's impacting everybody. Right. We know we're in a hard place when it comes to economically in the workforce in general. And so those things get conflated and then people have a lot of fear around AI. Um, and um, the reality is for people that are adopting it now, it's making them more effective, maybe in some cases on an individual basis, more efficient. We'll talk a bit more about why that's such a problematic, ah, thing to measure, especially at scale. Um, but that I think that misconception is kind of creating more resistance in the workforce than is really necessary. And I don't think it's really true. Like, I don't think it's true in the short term. Obviously it's very hard to predict the future right now. And I'm saying that as a CTO, and I think most CTOs would agree with me. Uh, so it's hard to look beyond like the next 18 months and usually that we're looking kind of three years out. Um, and um, so because of that we don't know exactly like how jobs are going to look like in the future. But most people that are adopting these tools, it's making them more effective. They're finding um, you know, better ways of working, interesting things that the gen AI can do and things that it can't do, like pushing it to its, its edge. Um, and I think if we can move away from this idea of it's going to steal our job because it's, it's not in the short term. I don't know what will happen in the long term, but it's not in the short term. I think we'll get less resistance for people adopting it. And then when you've got more widespread adoption, that's where you start to push the edge of what the technology can do and then people can innovate, uh, not just on their individual work but how they work as teams and also how organizations work.

Speaker B: I just want to add to that. I was reading some research the other day where they did sort of a deep dive into all of the job losses over the last year and their conclusion was that less than 1% of job losses were actually due to, you know, work being automated or replaced by AI technology. So it's, it's certainly overblown. Um, and Rachel's right, it's two things happening at the same time that are being conflated. Um, the other thing I wanted to add about misconceptions and I think we hear this one a lot, which is that we don't need junior talent, uh, that we don't need junior developers. And what we're finding when we talk to teams is that the magic is really happening where you have a great balance in a team between people earlier in their career and more experienced technologists. From the junior people coming into ThoughtWorks in particular, we see a lot of um, just being open minded. These people are, if we can call them AI native. They're learning how to use these tools and technologies from the get go and they have a lot of passion and curiosity, uh, about how work can be done and they don't have a lot of, let's say, historical baggage about how things used to be done. That being said, they're lacking the experience, the hard earned experience of having learned uh, how to develop technology, we can call it the hard way. And um, they really need the benefit of that experience for more senior technologists who can teach them, you know, what good looks like, how to think critically about the output of AI, um, how to develop good technology practices, which are still more important than ever.

Speaker A: Both of you have recently authored articles around this topic. And there's that central tension around the AI. Benefits only matter at the organizational level. Maybe you can talk about that too. When we're thinking about the organizational level, the team level, the individual level, what's your perspective on that? And Rachel, maybe you can talk a little bit to start.

Speaker C: Yeah, it's interesting. So when I think these tools first came out, and I think the early adoption of a lot of these tools was in the software delivery space and in, you know, creating new software. And um, there was early claims that, oh, um, this is going to make you 50% faster, which is very challenging for any technology leader when the business is like, cool, you guys, 50% cheaper now. And it turned out that wasn't really true. Um, and in fact we, you know, we did many tests, we were pretty early adopters on these kinds of, you know, coding tools. Is that, you know, uh, the best case at the time on an individual level it was like, could be 13%, you know, more efficient. And there was tons and tons of cavities caveats around that. But that's just a software developer, right? The software teams are made up of developers and uh, QAs and BAS and project managers and product owners. And you know, it's a, it's a team spot. Um, and so one, you know, bit of efficiency, efficiency on one person doesn't necessarily mean that the whole team is more effective because in reality if I can build software faster, then, you know, the amount of code reviews that needs to happen is speeding up the amount of tests and QA process. You know, you're just creating a bottleneck somewhere else. So we realized that really early on and said, well, okay, well how do we start reducing some of these other bottlenecks? But then you just plain whack a mole against a process. So we were like, let's take a step back. How do we approach this when we're helping clients look at how can they be more effective in the software delivery cycle today? And we use a, uh, very well known technique called Value Stream ma, where you look at like I want to do, I want to get this value into production. So from inception of idea into like the thing is in the customer's hands and what are all the challenges in our current ways of working, which is usually cross team, um, and we identify the waste in the system. It's a very like classic lean, uh, management approach. It's very old, it's Been out a long time. Um, and that helps you then look at not just an individual or a team, but start to look organizationally at what you can do. And now when you layer, you know, Gen AI, uh, coding tools onto that, or other tools or gen in general, or large language models, think about it. If you just, at least in this context, think about it as a tool that can make people more effective. You can look at that whole value stream and go, hey, let's try this use case and see if we can put gen into that use case and make that use case more effective from the perspective of getting value from idea into production or change from change needed, you know, issue in production into production. And that, that's more of an organizational view than an individual view and an individual level. Like as I said, even if you get one person to be more efficient, more effective, what does that mean in the context of the entire team and the entire organization? It starts that 50%, if you can even achieve that, that number gets smaller and smaller and smaller when you start to think at scale. So, uh, that's the approach we've been taking. Yeah.

Speaker B: And I just want to add on to that. I think it's, you know, it's very natural that many organizations, including ThoughtWorks, really started at the individual level. Right. These are new tools and technologies, and there's a lot we don't know about them yet. So for organizations, you know, getting the tools into the hands of their people and encouraging a culture of experimentation and continuous learning is so important. So, so you start doing that and you get people to really explore what's possible. But then the, you know, the next phase that needs to come is this more, um, I don't want to call it top down, but more broad organizational view that needs to happen to say, okay, what are we lear, where are we seeing the benefits? Where does it work well? Where does it not work well? Um, and there certainly are places where it does not work well. Uh, but then as Rachel talked about, you know, what is the value we create for customers and how can we do that in a different way, maybe a better, faster way, um, to really unlock value. And I think, you know, because of the macroeconomic environment over the last few years and the fact that these tools are not, uh, inexpensive.

Speaker A: Right.

Speaker B: It's natural that a lot of the focus has been on cost, uh, savings and productivity leading to cost savings. But I think what will be really impactful is the organizations that start to shift that view to value creation. And we can tell from many, many historical, um, technology innovations that that ultimately is what will happen and that the winners out of this transformation will be those that figure out how to leverage the technology for growth, uh, and for creating more value.

Speaker C: I would add on to that. I've been speaking to a lot of different technology leaders across many different industries. Um, and they're saying, you know, those, those productivity metrics, those efficiencies, they're just, they're not being realized, I guess for many of the reasons I just described. Just because you do something in the small doesn't mean you actually can see that at scale. Um, and um, where they're actually seeing value of uh, gen, the tools and the models is improving of effectiveness of their employees, which is a little less easy to measure than efficiency. But that leads into what Jon is talking about. When you think about effectiveness, it's usually around how can I do my job better, do more, you know, and that's more in the growth mindset, value creation mindset. Um, and if that shift is happening in technology leaders, then I think that shift is going to start happening across the industry, um, and we're going to move away from just the pure productivity metrics. But as um, Joanna said, it's, it's the economic pressure that's creating that. And nearly every technology leader I've spoken to said I'm not getting extra budget to invest in AI. So their initial hope was well, you know, hopefully that this creates an efficiency that I can then use to invest back in AI. And that's not happening. So we, we have to take more of a value creation, uh, approach to it and look at what kind of metrics we can set around that.

Speaker A: Joanne, I want to pick up on too something you were talking about with that healthy balance and that top down, or I should say bottom up, top down approach when we're looking at that, maybe you can talk a little bit about how that would look, um, and how we want to best balance that for success, um, because we want to give people that freedom. Um, but there's also kind of that balance between the freedom to create and the governance and the structure and how do you really meet in the middle. So maybe you can talk a little bit about that.

Speaker B: Yeah, I think organizations truly do transform um, from the activity that's happening on the ground. It's a great place to start and getting people excited about and bought into the possibilities and really taking ownership um, over their own learning journey and their team and how it's evolving. But it's really hard for individuals or even teams to be able to See the big picture. And so that's where I think senior leadership needs to come in and really think about the organization, how they create value for customers, what are the organizational opportunities, uh, and sort of devise a strategy or an approach on what are the most critical business challenges that we want to capture opportunities around. So it's then kind of marrying the two as you said Laura, and figuring out how do we crowdsource uh, from employees all of the ideas that they have, uh, all of the innovation that's potentially coming out of their experiments and figure out how to channel them into those important business use cases. Um, and then thinking about organizational structure, how work gets done, how teams are organized. You know a lot of the um, organization organizational structure builds up over time, uh, and it's a really difficult task to kind of unless unpack or untangle, um, and think about a different way of work getting done. But I think we're seeing from our teams that the way that teams are interacting with each other is changing a lot because of this. And so having that end to end view, um, is really important. And it's only this senior leadership that kind of has that really big picture view of the entire organization at this pace of change.

Speaker A: Obviously careers, skill sets, career paths are changing. Any thoughts around how that's going to evolve and what we're going to see maybe even more near term because of that?

Speaker C: Yeah, I can share some of my thoughts that I've been heavily focused on the software delivery life cycle, you know, how we build software, how we operate it, how we run it, how we modernize it, um, and um, how people are working starts to give you some idea of like what roles could start to look like in the future. And this is just in this context. I think every function will have to look at that, how things are being used and then take that step back and think about how, how could this progress. So one example is that what we're seeing is this, the art of like greenfield, like creation of code, um, is going to be more and more automated. Right. And this, this field of spec driven development is kind of rising around that where um, the developer or you know, the product person spends more time focused on the initial specification in order to get a great result in terms of what's generated. And that the stuff that gets generated is not only the code, it's the unit tests around that. Um, and so the whole green field, so like creation from 0 to 1 of applications I think is massively disrupted. And then if you just think about what, what as I said earlier, what, what would a team look like ordinarily? Like a kind of standard development team might have like two pairs, so two developers and a QA and a BA and you know, a product manager. And potentially those qaba and product manager might go across you know, several teams. But that's kind of your standard setup for a development team. Um, and there are tweaks around the edges. But if we're saying like a developer is writing the, the spec and then everything else is getting generated, um, then who's their pair and is that necessary? Um, and then we need somebody who knows the business requirements. Now that could be BA could be a product manager, like you know, whatever the role is in that organization. And that's kind of it. Um, and that's a totally different setup. Um, now you know, immediately that leads to the problem of okay, well if we only need a very senior engineer and we need a senior kind of pretty technical product owner, um, a, what does everyone else do and B, um, how do we grow those people? Because you don't just come out of university as like a senior level engineer, right? You and the skills that you learn, which is currently all the hardware and all kind of manual is, you know, we, we do pairing at ThoughtWorks, you know, where people sit side by side. We, we also work on lots of different projects, right? So that speeds up people's learning because not only did I sat with somebody working through a problem, learning different ways of doing things and approaching a problem, but they're also learning lots of different cross functional concerns, potentially different industries, seeing things go into production, seeing what goes wrong when things go in production. And that's basically how we all learn. Like the professionalization of our industries is kind of low. If you compare us to like real engineering industries or architecture or even the medical field, like it's much more structured. There's kind of an expectation of you having certain levels of learning before you can do certain things. Um, and that makes me think that, okay, we're going to have to nurture these people right from university all the way up to this senior level. So I think we have to take a step back, break down what it is we actually learn. And, and that leads me into essentially kind of an apprenticeship model or a professionalization of our industry. Because if you also think that I was just talking about building software, we haven't even talked about modifying it and running it in the future, uh, we're going to, there's going to be lots of agents doing things we're already seeing that happen from an Operation perspective. Right. There's an issue in production. Oh, it's a pretty simple fix. The agent figures it out and fixes it and then some. A developer reviews it and says, yeah, that's good, let's go. Right. That's totally different to the manual approach that we've done in the past. Um, so you're going to have these very much smaller teams covering much larger pieces of software or much larger systems. So they're just not going to have the depth and the understanding of those systems. So what they need to have is the depth and understanding of what could go wrong, what's good answers to things that go wrong. And those are all architectural and engineering concerns around software, which just, it changes the field. And I think then that pair, in my mind, and this is me hypothesizing, is that, you know, you've got a senior engineer plus their apprenticeship apprentice, and then you've got, you know, a senior technical product owner and. Or a product owner and their apprentice. That's a theory I have that we'll be kind of testing out, uh, at thoughtworks. But I don't think it's far off from what other people have talked to me about because otherwise we're just going to have a massive dearth of these. Everybody wants these senior engineers and it's like, that's great, but there's not going to be enough of them to go around. Um, and, you know, do these senior engineers like what they're skilled at? It's not very well written down. It's not like you can say, like, Rachel's a level seven and so it's assumed. I know all these things. It's not, you know, we're not at that stage at the industry, but I

Speaker B: could see us going there and the pair programming. What's really interesting when I talk to our teams, you know, there's this idea of, like, well, isn't the AI, ah, tool your pair? Um, but really what they're finding is you obviously need that human discernment to, uh, look critically at what the output is and what they're doing. Um, especially when you have a pair of maybe a more senior and a more junior person is, rather than the pair kind of necessarily spending all of their time handcrafting code, the pairing is used to teach people how to use these tools properly. And so the topic of the pairing is maybe changing or evolving or the discussion that's happening. I mean, certainly there's still the notion of how should we approach this problem, how should we solve this problem? What should we do next that leads to really high quality. We know from pair programming history. Um, a couple more things I wanted to add about. Since you asked about career paths. I think one interesting pattern uh, that I'm seeing again when I'm talking to our teams. If we go back, um, way back when Rachel and I entered the workforce as software developers, we were what we called full stack developers. And you know, you could be a full stack developer because the stack was infinitely less complex than it is now. And then as technology evolved it became almost impossible because uh, the full landscape of technology was so big that you couldn't be an expert in all areas of it. I was speaking to one developer who shared a story with me that he really viewed himself as more of a back end developer expert. But with the augmentation of these tools he said I can be really uh, proficient now in front end development because it can kind of fill in some of the knowledge gaps I have about the stack. And I think we're also seeing people, you know, the lines between roles are blurring. You can create a decent user interface, um, you know, you can create the business requirements and you can use it to generate test data. So I think this, we're kind of coming full circle from a full stack or very generalist approach. We went very specialized as an industry for a while and I think we're coming back full circle um, to more uh, of that generalist view.

Speaker A: So let's shift gears a little bit and talk about um, earlier we hosted the internal AI for Software Development festival. What surprised you by that? That was obviously a really, really big undertaking. Tremendous effort, um, some really good outputs I think from that.

Speaker B: Yeah, I think maybe before Rachel answers that I'd like to just share a little bit about the origin of the festival. Um, so we were, I was having a conversation with some of our senior technologists and I was really trying to understand, you know, this world is changing so fast every day there's new, you know, new tools, new models coming out and how are you staying on top of it. And one of the things they shared was uh, we have something in, in the talent space called the 70, 2010 model. It's, it's a well known model about how people learn. And it's Basically, you know, 70% of your learning happens on the job, 20% of it happens through social interactions and only 10% through formal learning. And they really reference this notion that because things are moving so fast, they have to rely on their network, um, to, to really share and understand. So whether that's people they follow on social media or things they read or just spending time with other senior technologists kind of talking about what they're doing, the experimentation. So uh, the idea for the festival was really born out of this concept of social learning, um, which is something that's very near and dear to us at ThoughtWorks. We have, we talk a lot about our culture of cultivation, which to us means that everyone is both a student and a teacher, uh, always. And so the, yeah, the intent of the festival was to hear from thoughtworkers what they were doing, whether that was in their client projects or in their side, uh, side projects, and really share with each other, uh, so that we could accelerate learning.

Speaker C: Yes. And uh, in terms of, to answer your question directly, Laura, like what surprised us? Um, we had a lot of hopes, the, the energy that the festival would generate. And um, I was just surprised about like how much energy it generated because at the end of the day, outside of what's happening with AI it's, it's all kind of doom and gloom and not great news.

Speaker A: Ah.

Speaker C: As an industry perspective, as we stated earlier, lots of layoffs across the globe. Um, and so I was you know, worried that people would be like, yeah, yeah, that's nice but you know, I've just got to get on with my day job. You know, I got to get my head down. But, but what I realized, and I guess it's, it's true, we proved it is. As much as we as leaders can say this is important, it's really when people hear from their peers and other people, they respect that they pay a lot more attention. And I've experienced with this recently because on um, the off the back of the festival we've been doing more of this kind of internal sharing of finding out what projects are doing interesting things and then asking them to share with a wider group. And those are way more well attended way. People are way more interested than what me and Joanna might say and other leaders will say it is what it is. Uh, we're okay with it. Um, but no, it's, it's the whole. Once they kind of see their peers and folks like them doing the work using these tools, what results that they're getting and you know, what, what's working and what's not, um, it kind of encourages people to, to, to really use these things. And we had an uptick in people uh, uh, you know, requesting the training courses that we'd built. We had an uptick in. We have a bunch of different chat groups around one that's Just in AI One that's around AI assisted software delivery, uh, in this space, like lots more people joining that and that's a buzz every day. That's actually my starting point. In the morning I'm like, okay, what's, what's, what is what's been happening in that chat space? Because that's usually where all the links to all the articles and everything is happening. It's not just a random article or something that appeared in your inbox, which you don't know if it's just marketing for some product. It's like people that they respect or that they think is interesting, uh, stuff to follow. Um, and so the energy that that created, which we've kind of, as I said, we've been building on, um, and then um, we're actually planning to do another festival, uh, early in the new year as well. So that was probably one of the most impactful things we, we did.

Speaker A: Well, it's a lot of community based learning. Right. When you think about just having the tool or having the training versus learning together, it's, and I think that's such a strength here, is that community based learning. So, yeah, great. Well, and you talked a little bit about too, how you were spending time learning about the projects. And I know you've had some focus groups with our technologists. Were there any moments that have come out of that? You know, like you had said, we see a lot of strength and progress um, when we're speaking with our technologists and spending that time. Were there, was there anything interesting that came out of those focus groups?

Speaker B: Yes, loads. Uh, so over the last few weeks I've been doing some focus groups with thoughtworkers from around the globe. I've talked to people in Brazil, Spain, the uk, India, um, and it's super interesting to hear different perspectives, um, from people in different regions. I think a few takeaways I have, um, one that's very clear, comes out loud and clear in the conversations is that good programming practices, whether you want to call that extreme programming or that those practices are more important than ever. Uh, and I heard a lot of, you know, these tools, if you attempt to do something that's too complex, uh, you're likely to get back garbage. And so you, uh, know one of the longstanding good practices in software development is that you tackle the smallest possible thing next and you build quality software sort of one small piece at a time. And some of that comes from test driven development. So you, you know, you write a test for a small piece of functionality and then you Write the code to make it pass. And they're finding that that approach really helps them get the most out of these tools. And it's also instilling again in the more junior uh, technologists these good practices. So uh, pair programming, test driven development, they are finding, you know, to be more important than ever. The other thing that is interesting, particularly when I talk to the senior technologists, is just the impact to their role. Um, and so Rachel talked a lot earlier about prompt engineering. And I think what we're seeing is that a senior engineer role is moving from more of a builder role to more of an orchestrator and an integrator. Um, and what I found is different people have different opinions about this shift. Uh, some are more excited about it than others. I would say, uh, some developers really get their fulfillment and satisfaction from the process of hand crafting code. And they are not excited about the fact that they are now, you know, spending more time kind of on prompt engineering and then a bunch of time on code review is to look at the quality. Uh, and then there are others who, you know, they more get their fulfillment from seeing their ideas come to life. And so the, there's any speed or augmentation that they're getting, you know, they're, they're super excited about. So as we think about the future of talent and career paths and roles, I think it's something important for leaders to recognize is that the changes are um, different people are welcoming them at different levels and we really have to help people through the change management of how they get satisfaction and fulfillment from the craft, uh, and find a way to um, really engage and motivate people in that process.

Speaker A: So lots of change obviously happening. Um, I think there's a lot to manage too. When you think about um, employees and cognitive load moving at pace and you know, even Rachel, I think I've heard you talk about before kind of talking, thinking about the thing and not the hours like the bolts and how we really solve for that and move at pace but be very focused at times. And it's going to be a different type of probably work force workplace. Maybe you can offer a little guidance when it comes to that, how, how we're working through that today and where you've got, you know, some general opinions on that.

Speaker C: Yeah, I think it's, it's an interesting wood because it does a lot of it does come down to change and change management. And I think traditionally when we think about change where like people processing technology, but that's in a world where we know what the output is going to be right. As Joanna was saying earlier, like, when we built our like, program, I don't think, I don't know what we called it actually now I think about it. But like, we knew that we had to shift and we needed to do something in AI, uh, software. We intentionally said AI first software to kind of really push, push the edge of it. Um, and we looked at not just the people, the process, the tech, but also like, what are competitors doing, what's happening, and what, what are the tools and technology, uh, how is that changing? Um, what's our core differentiator? What is our business model? Like? We kind of looked at everything and then run it all in parallel as a change program with the. And, um, with what I call like a strategic learning cycle. So not, you know, different functions running off in different directions and doing things right. It was like it was a program we put together intentionally, kind of forced everybody to cross, collaborate. I know you were both part of it, so you know what it felt like. And I described it as, as organized chaos, which I think is a good thing. Um, so, um, yeah, lots of learnings, right? And then at least once a quarter we were like, what did we learn? Right? And what are we pivoting on? Right across, you know, the articles that we put out in the industry, across how our talent changes across how we work changes across, you know, um, how we go to market. Like, everything, um, and these things all influenced each other. And I think that that was tricky to do, but it was so important to take that approach because otherw, you're implementing change in a silo in, in a very, like, in a place where everything is changing around you, right? There's no clear, like, end state. We're not going from A to B. And then this is the change program. We're going from A, unknown B, begin change. It's like, it's a totally different way of, of thinking about it. So what we were able to do is like, learn things along the way. Like I talked earlier about at, uh, the start, we were like, oh no, this is creating a bottleneck here. And how do we make sure it's not just developers doing these tools and all. If we take a step back, how does it look at the whole process? And oh, let's break apart the stuff that's Greenfield from Brownfield, because that's really different. And when, when I say Brownfield, I mean like editing existing software and then the whole legacy modernization. Or I've got a mainframe or I've got some really Old system, that's a whole different thing. So we, like, broke things apart and then brought them back together. Like, when we put articles out in the world, we saw what the feedback was and what they connected to the industry to see what they were doing. And so it was kind of this, you know, constantly evolving and moving space, um, and the festival and other things we were doing is really just trying to take thoughtworkers on the journey of, like, we don't. I don't actually have the answer, like, first to the first question of am I getting fired because of AI? My answer right now is no. Like, I can't tell you about that. In three to five years, we'll all see. But I want you to come on this journey. And the other thing was, is I'm not doing this to you. It's not leadership saying you all must change. It's like the industry is changing around us. Like, I'm m on the journey because coming on the journey will actually help you get a lot of the learnings of, like, what does the future look like? And then to Joanna's point, that's where people started to go, like, oh, I don't know, I like crafting software.

Speaker B: Right.

Speaker C: I don't know if this is where I want to go. And it's like, well, it's better that you figure that out sooner rather than later, right? So I think it's just the big part is like, uh, being open about, you know, the change that we're all going through and trying to take people as much as possible on the journey with you and not being siloed off in different functions. Everybody kind of trying to do things their own way. It's like, that's why I called it organized chaos, because there was still some of that, Because I do believe I like the idea of a thousand flowers blooming. But then we come back together and we're like, which of the, you know, three to five flowers are we actually going to put some energy into that we think are going to grow into a real, really nice garden? And so we did a lot of that. And then as we go into 2026, we've really identified where are some of our big blind spots, where are some of the big open questions that still need figuring out? And that's either, you know, a real constraint that's going to be ongoing or an opportunity for us to innovate in, you know, the various parts of our business. So that's, that's really how we've approached it. And then when I talk to other Organizations that have also been fairly, you know, um, I guess ahead in terms of their own transformation around this, it's, it's quite similar patterns actually.

Speaker A: Right, okay, so 12 months from now, what's one thing you would love to see organizations stop doing and then one that you would like to see them start doing as a result of this? When we're talking about AI and talent,

Speaker B: I'd love to see organizations start reinvesting in junior talent. I think, um, it is an existential threat, as Rachel said, which is if we overly focus on experienced engineers, we'll find ourselves with no experienced engineers in a few years. And I also think that organizations underestimate the importance of the energy, passion and curiosity that people early in their careers bring. So that's my start doing, um, in terms of. Stop doing. I really think it's the, um, we have to shift from the obsessive focus on cost savings and turn our energy to value creation. Um, it's, you know, again, understandable why we've been there, um, in terms of the macroeconomic growth. But it's easy to fall into the efficiency trap and not achieve true, um, you know, true productivity gains and true gains in value creation. And I think the organizations that are able to make that shift are the ones that are going to emerge as the leaders in the future, I think.

Speaker C: And this is kind of related to the start doing like, take that step back and think about, okay, given the power of this, how does it impact my, you know, X value stream within my business? Um, and how could that change, you know, my organization and how do I start down that path? Right, Because I think that's a very different approach to kind of incrementally using a tool. This is better, or this is better, but for an organization once, because you have to, have to start using the tools, right. Just so people can start using it in anger and see what's, what's, what's good and what's not. Um, but then, you know, take the time to take a step back and look across your whole organization for those value creation opportunities. Right. So as I said, we didn't rehearse

Speaker B: this, but we kind of have great minds.

Speaker A: Yes. Any other parting thoughts, um, today that we haven't covered?

Speaker B: Yeah, it's been a great discussion and I, I think what's exciting about this time in the industry is that it is changing and evolving so fast. So uh, you know, we just need to approach it with, um, with curiosity and understanding of uh, how we can really engage everyone in the organization to go on the journey, um, and I'm excited to see how it evolves over the next three to five years.

Speaker C: I do have a parting thought now. Um, and I say this a lot, but I think it's important to reiterate, um, just because you can't see those immediate cost savings or whatever. The initial thought, kind of early thought were don't sit and wait.

Speaker A: Right.

Speaker C: Because we don't know the end state. But waiting is not a good strategy. Right. So starting whether it's as an individual learning them, whether it's as a team, seeing how you can work together more effectively, like, whether it's as an organization thinking about how can you create, uh, value with these things, you know? Yes. Lots of those use cases and proofs of concept will amount to nothing. That's kind of the idea with experimentation, right? Is that you learn stuff and you move on. And even if that learning is not, not the greatest idea, but waiting to see, like, what are the killer use cases and who's going to disrupt your industry, not a good idea. Like, I think organizations have to figure out how to get started and, um, take their organizations on the journey.

Speaker A: That's great. Perfect. Well, thanks so much for joining us for this episode of Pragmatism and Practice.

Speaker C: Thank you.

Speaker B: Thank you.

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