Systems Thinking in an Age of AI - Workshop

Okay, brilliant. So, I think we can start. Um Yeah, so well, maybe first to check and everyone hear me okay. And just give me a thumbs up. Brilliant. Great. So, Hello, everyone. Great to see you all here, and thanks a lot for joining us today for our event on systems thinking in age of AI hosted by the SI Tech Hub and the SI Narratives Lab. My name is Bowen. I'm a representative at the Tech Hub, where myself, Joss, and Roma are creating a space and a community to explore the intersection between technology and systems innovation. And co-hosting this event with me today is Joss Colchester, who I imagine many of you know, who is the founder of SI. So, what brings us here today is that we've been working on some research exploring the changing role of systems thinking with the recent rise of advanced algorithms, with AI of course being, you know, one manifestation of that, and things like automation. And we put out a short position paper on this, which forms the basis of our discussion today. Um and on the mirror board, which you see, um we've included a few of the recent papers that we've done on technology. Um Well, and the intersection with with systems thinking. Um but that said, it will be an interactive session today. So, Joss is going to present some of the findings from the paper. And we'll have a couple of breakout sessions, where we'll each get to digest and discuss any of these themes and ideas together in groups, and hopefully get a chance as well to share back to the group as well once we come back from the breakout rooms. Um but before we get into this session, uh we would like to start with a little icebreaker. Um Would you be able to share just in the chat, so no need to go into the mirror board yet, just in the Zoom chat, what brings you here today? And what would you What would you like to discuss in today's event? Or what types of questions maybe do you bring that you're trying to figure out yourself when it comes to this topic? Or maybe just take a couple of minutes for that part. Brings us here today. What brings you here today? And what did you hope we might cover? What types of questions you're currently exploring when it comes to this topic? Okay, so Anna has put understanding AI possibilities within complexity sense making. Yeah. And don't worry if it's not fully fleshed out. I guess, you know, a lot of us, myself included, uh are still figuring things out obviously on this topic, so just whatever comes to mind. How AI contributes to complexity, where does it simplify it? Yeah, that's interesting. I think we talk about the potential of an AI agent economy, then there's probably a lot of additional complexity that that could add. Um but you know, obviously there's more to it, but that's just one thing that came to mind. I'll maybe read out a couple more just so that in the interest of time before we move on. So Fabian put how to responsibly work with AI tools and system thinking leveraging the benefits while being aware of the dangers. Yeah, 100%. Um I think we can all all relate that there's probably a lot that AI can help with but there's also the question of okay is it good that AI helps with that? Is it is AI really the right tool for the job with certain tasks. Okay, brilliant. Maybe I'll just read the last one from Nelson, exploring the perspectives and implications of systems thinking in AI because of the curiosity. Love it. Love it. I guess we're all pretty curious about that here. Great. Thanks a lot guys for your inputs and I think at this point I'll hand over to you Josh. >> Uh thank you very much Byron. Great great to be here with with everyone today. I'm going to chance to talk on this uh you know fascinating topic came kind of crashing into our lives a few years ago and it's a bit difficult to ignore at this stage. Um I've been working on thinking about this and trying to figure it out for for a number of years. Um and you'll actually find some of our previous uh papers down here. Um we actually did an event maybe 3 months ago or so with uh our concept [clears throat] cards principles for sustainable use of emerging technologies. Some of you were were there for that. Um but also this one on technological systems innovation is kind of research that uh spans back a number of years um on this topic and uh so what we what we have here is a yeah short position paper. Byron may have seen it published a couple weeks ago and we're building up on that today and um you're welcome to take a look at the other ones there. You'll find the link uh just there on each paper. So it's a short paper and it's really about yeah as the name implies the role of systems thinking in an age of of AI. So how can systems thinking help us make sense of this this new reality, this new world? How does it help us uh sense make and adapt in that world? And how should the area of systems field systems thinking evolve as we go forwards? Um that's kind of the the second part here where we'll talk about some things becoming more relevant, maybe some things becoming less relevant. That's the second section there. In this first section, we're using some pretty foundational ideas and uh core principles in systems theory to try and make sense of uh this evolving uh situation and relationship ultimately is kind of core to it between humans and technology. And how do we innovate and adapt and um ultimately try and find that synergistic relationship uh between human beings and the things we're great at and, you know, the things that technology can do. Um and what's can come out of that? What's really the opportunities here? Uh we hear a lot of the the risks and uh you know, doomsday scenarios. What what what could the beneficial outcomes look like? So, starting at the top here, um the kind of core of this argument, I'll get to it here at the beginning. Um is is this difference between synthesis and analysis, which if you're familiar with systems thinking, you you would have heard a lot about. And it's trying to use that differentiation as a platform, as a framework, as a sense making tool for thinking about this relationship between humans and machines and, you know, what's good at what and how do we ultimately kind of integrate those. And just uh yeah, say a few words about what what what I mean by those, in case you're not familiar with the two ideas. Um so, analysis is uh a study or paradigm or worldview or a reductionist worldview uh a way of inquiring about the world which uh studies the uh parts of a system and to be takes a system and puts it into a closed environment and decomposes it and studies the individual parts and their properties to try and understand how that system works through an account of the individual parts in the system. And that is analysis and we see a lot of it around us. It's quite dominant in the modern modern world. It's pretty foundational to modern science. Systems thinking tells us there's another way of looking at the world uh way of relating this uh synthetic or more holistic view of the world which is looking upwards and outwards. Analysis and reductionism can look downwards. And it's more about putting things into context. It's about understanding them in relation to other things and them in relation to their overall environment and the function that those things uh serve when you put them into their context. And uh that we call synthesis and uh again it's a paradigm or worldview. Ultimately, you need both of these to be successful and systems thinking is an argument for a balance of both reductionist analytical reductionism and and and synthetic holism. And that's uh is pretty pretty core to to systems thinking and you know, just letting people in here. Yeah, thank you, Ben. Um it's a fascinating kind of framing that helps you understand a lot of things in the world. Russell Ackoff, you may know of him, he's a famous systems thinker. He uses this kind of uh framework to think about a lot of things. He gives the example here of the Spanish automobile and he says, "No matter of analysis of the car and how it works and its internal workings would ever tell you why it's positioned to drive on the right-hand side of the road. One could only understand that by understanding the car in its context and in a historical context of of Spain and so forth. So, he's saying some things you can understand analytically by studying that thing. Other things you can only understand by looking at them in their context and specifically what he's saying is analysis helps us understand the the how how things work. Like the car, if you want to know how it works, you take it apart, you look inside it. Synthesis helps us understand why. When you put things into their context, you start to understand why it is the way it is. So, a bicycle, you know, works the way it works and it's got this mechanical system. You take it apart, you can understand that, but you wouldn't understand why it exists until you understood human beings and their need for mobility and uh roads and all of this and then you start to understand, you know, why the bicycle ultimately is the way it is. So, that kind of framing actually helps can help um help us understand, you know, what we're talking about here, this relationship between uh computation analytical computation uh which is very good at this analysis and looking inside things and synthesis can help us make sense of the broader environments. It can help us look upwards and outwards and try to understand that environment. What's the right thing to do within that environment? What's the function of this system within the broader environment? Analysis uh looks inside that system and how it works and so forth. And the argument is that computers and algorithms and bots and AI are and will get very good at this analysis, understanding how things work and making them work, the internal workings of systems. Um but there will always be another dimension. And I mean if that's the only dimension you deal with, then it's kind of you're in a world of competition with algorithms ready and bots. But there is of course another world which is asking up looking upwards and outwards and saying why does this thing exist? What's this real purpose within its environment? How does it interrelate and work with other other systems? How should it work, right? Ethics and values. How does it need to adapt over time? What is the framing, the paradigm we're using for understanding this system in the beginning? Um all of this None of those things can be answered by looking inside the box. And yeah, the argument is that computers are getting and going to get very good at this analysis. Whether the system is a transport system or a electric grid or your house or whatever it is, the way you should organize your holiday. Bots and I would know that they're going to be doing all of that and already do a lot of it. Um and they're going to be optimizing until there's much better ways to organize a holiday than we could even think about, right? But why should we go on holiday? You know, what do we care about? Um where should we go on holiday? What's the purpose of a holiday so on and so forth? Um are actually these more contextual uh questions. And um that's that's the argument that as systems, technology systems get better at this analysis, um it's really more our job to get better at this synthesis. And that ultimately we're trying to balance. That's what system thinking ultimately argues for, this balance between reductionism and um and holism. And if we're increasing expanding our analytical capacity so vastly as we are, then we need to increase this uh synthetic more holistic uh view of the world. And it's kind of what we hear around us, right? We hear that we need a lot more of this uh sort of stuff, but it includes things like sense making. How do we make sense of very complex environments that we can't at the moment? How do we understand our paradigms and mental models that we're looking uh at the world with? And this is all part of you know, what systems thinking offers to us. Um so, here's a great quote that captures some of that. It's the humans who ask the questions. It's very hard to teach machines to tell them what the interesting questions we need to answer to. Once we pose the questions, they can help us answer it. So, the the questions are the framing of the whole thing that comes from, you know, external to the to the system, but computers are going to get amazingly good at answering those. So, it's trying to expand this uh you know, synthetic ability uh that we have to be able to, you know, reason upwards and outwards and understand context and derive purpose and meaning from broader, more complex um environments. And one one word that comes to mind as I kind of thought through this is this whole idea of non-linear thinking. And, [clears throat] you know, the whole systems approach is trying to move away from this linear problem-solution framing because computers are going to get incredibly good at that, right? Once we define the problem, they're going to get incredibly good at at solving it. But, actually what what's needed is we step up and we create we step back and we create, you know, the containers within which computers and algorithms are going to go about solving problems, right? And if you, you know, follow the developments of agentic AI and so forth, we know companies are already, you know, hosting many algorithms and bots and so forth, and it's a job of employees to define well, what do we actually need to, you know, achieve here? What's the framework, the governance framework and so forth, within which these algorithms are going to operate. So, we're already kind of shifting up from there's a problem and I solved that problem. We need some code and I write the code to uh yeah, we need to create something here and these are kind of the outcomes that we want to achieve, but I don't have to write all the code. I need to set the context for, you know, algorithms to collaborate and work together and go about doing that. So, it's this kind of context creation in relation to a purpose or um a function, a board of functions. So, it's already kind of shifting up to this non-linear approach, non-linear thinking. And um as you put this together you know, one of the sort of Bowen and uh sure or Roma were were discussing all of this and one of the the ideas that popped out was the Stacey uh matrix or, you know, the Cynefin framework, it's a little bit similar. And this distinction between simple, complicated, and complex, which is obviously a part of systems thinking. And it helps us to some extent and it's a very loose diagram, I wouldn't take it too seriously, but it helps us to kind of think through this distinction between linear and simple and predictable and, you know, all those things that can be automated and things as they get more complicated and a lot of these things complicated things now can increasingly be automated, but there's a whole kind of dimension up here um where there's many parts that are highly interdependent, but they also involve high degrees of subjectivity. These are people and all of these things come into focus out here, which are classical kind of complexity characteristics. Ambiguity non-linearity, uncertainty context, emergence, unpredictability so on and so forth. It's a realm of complexity and many things still lie in this realm like many challenges still lie in that realm, right? Some challenges down here, you know, computers are going to be building houses and fixing bicycles and making meals and so forth. And even doing some of these more complicated ones, maybe running schools, um building electric cars and so forth. These ones up here, you know, they're they're vastly out of the computers kind of reach and even out of our our reach. So, the hope is that we might be able to actually begin begin to approach these sorts of things, global governance frameworks, multicultural integration. We're vastly struggling with these. And they're not theoretical problems, right? You can just put into an algorithm and tell us the solution for multicultural integration. They're practical practical challenges that we have to work together, collaborate, and iterate and develop over time. So, yeah, that's one way of you know, framing it. This uh you can look into the Stacey uh matrix if you're interested in in that. And um this is a great quote, right? The real danger is not machines thinking like people, but people going on thinking like machines. That we go on, you know, as we do in our educational systems and in many organizations thinking analytically and reductionist in a reductionist way. When actually we need to shift out of that modality. We need to understand what it's about, but we actually need to shift up to different ways of thinking. And systems thinking is pretty uh pretty pretty core to that. So, that's a little bit about systems thinking. I'm looking forward to, you know, you guys going into the breakout room and hearing all of your voices about this. Um but it also relates in the context of systems change. And you know, in the context of systems change, we live in this kind of industrialized world with all these industrial systems. And the question is, where do we go from here? We hear a lot about broken systems in healthcare and education and so forth. And how do we respond to that, right? We're normally kind of breaking it all up into parts and trying to improve all these parts and then we get algorithms and technology to make it faster and more efficient and so forth. And that can be the kind of quick fix to things, whether inside an enterprise we've got big messes and we just put a an AI kind of interface on it to make sense of it. But the more challenging thing is actually to dig into the systems and find ways to to innovate and change and understand and transform those systems. Uh the bottom of the iceberg sort of stuff. And how do we get those two things working together synergistically? Cuz too often that they're not, right? We try to do transformation through technology and it's not as transformative as it should be. So I think that's part of the open question, right? We have the systems thinking it's going to help us adapt and change our thinking and so forth. But we actually have to adapt our whole practice and kind of ways of addressing challenges so that we can integrate these two, uh you know, modalities, the the human collaboration and all the stuff we we talk about in systems change and systems innovation, convening, you know, collaborating around complex challenges and this technology, the data, the infrastructure and so forth and putting those together in synergistic ways, I think also is key to kind of systems innovation. So I'm going to leave it there. Um those are just some ideas you can look more into the the short guides uh if you want to uh explore more of that or come back a little bit afterwards to share some more uh ideas, but maybe pass it back to you, Ben. >> Okay. Brilliant. Thanks for that, Josh. Yeah, I think um a lot of a lot of food for thought there. Uh so, this will be a good time to enter the first breakout session. I think we might reduce it a little bit uh from 25 minutes just just to make sure we're on track with time. Um but the focus of this part is more given what we've just thought about about you know, the disruptive nature about of AI and automation. And obviously, some of these things are still speculative at this time, but I think it's important that we think about it given that it could be so disruptive, given that the nature of our work may look quite different, you know, when we talk about the next year, two, three, four years. Um and so, the first part is about how can we What aspects of systems thinking or the perspective that we that we're adopting here, how might that help us actually transition into this world where there could well be a lot of disruptions, a lot of change. And as Josh said, you know, there there are different maybe types of questions that we will get to ask if some of our, let's say time and um mental capacities can be put in different types of challenges that um away from things that are automatable, let's say. So, that's that's kind of the the purpose of this, but but we also wanted to leave a little bit of time at the beginning. If you just wanted to talk openly about, you know, what was what was just shared there, maybe just digesting it, absorbing it. We don't want to be kind of too deterministic here about what you talk about. Um but that's that's that's a prompt for you guys to explore in the breakout rooms. Um and what would be great as well is if after [snorts] the breakout rooms, we can just also open it up for a little bit of discussion that maybe one person for each of the groups shares back about a few things that really stood out, maybe some really interesting points which came out of the breakout sessions. Um, maybe one thing last thing I'll just add to this as well is we're hoping that some of the inputs from this event will actually feed back into the paper as well. Um, and so it will be yeah, really curious to hear kind of what what comes out of these discussions. So, yeah, on that note, um, are you good to create the breakout rooms, Josh? >> Yep, we we got them. Yep. So, should we go 20 minutes or about that? >> Yeah, I think we can do 20 minutes. >> Okay, you should see the breakout rooms in front of you on the screen, uh, ladies and gentlemen. If not, I can assign you. >> Everyone, welcome back. Yeah, great that um yeah, we took the initiative to post post it some the on the board. Yeah, I think that could be a bit of an easier way to structure things. Yeah, I'd be really curious if if anybody wanted to share, I mean, it doesn't have to be specifically on this on this question, but like did anything kind of stand out from from the conversations or if you would just like to share maybe like a summary or key takeaways. Love to hear it. >> Yeah, um we uh said that this context uh you have to give uh the agent or the AI context. Um uh that is really and the the context is a kind of systemic thinking pers- perspective. The more you give the AI, the better answer you get. >> Yeah, thanks for that. Yeah, something which also came up in different flavors in the group that I was with as well. Maybe I can just share very briefly a couple of things which came up. Um there was a there was a couple of concerns actually which which came up or at least things that we would need to work around. One is um exactly context that you talked about, right? Like how do we make sure that a lot of the you know maybe hidden signals or you know when we talk about like cultural aspects or things which aren't necessarily really codified, um how do we make sure that it's not let's say ignored if if we're if we're using AI in our work? Um and another thing as well is that given the architecture of AI is mainly prediction machines or like predicting the next token, how do we become aware of the biases that are you know inherent in the models and making sure that it doesn't just perpetuate a lot of systemic issues that we currently experience. >> And I guess going back um to what the original person was saying there, yeah, understanding context but also understanding purpose. Like what what what are we trying to do? Why are we trying to do it? Our intention, you know, people talk about this is the shift from the internet of attention which is social media and content to intention. Like what do you want to do, you know, and then the the bot the algorithm's going to help you, you know, go go and do that for you. So I guess it's also about being clear about your intention. What do you what what you actually want to do here? The clearer you are about that um and more context you give, you know, the better you can actually work together I would say also. Yeah, we stick on. >> I'm quite curious about this green sticky note here about I'm not sure if I fully understand it but is this the idea that strategic thinking is is not something that AI will be able to do. So this is where we really need to kind of focus and make sure we get right. I'm not sure Nelson if you want to elaborate on on any of these points. >> Yes, that's a quote I have read recently about the the whole human systems will evolve in a context of AI. So, the the more human things, the feelings, the emotions are going to be things that are going to become more valuable over time because AI can provide at least up until now that kind of experience the human experience and probably won't be able to do it very accurately. And also answering the question about how systems thinking can help us to adapt to this age of AI. The first thing would be that the systems thinking help us to see beyond these AI models that are being used for all these automatizations. So, we can realize that AI doesn't have all the information. We were talking about the differences in data that is used for the training of the AI models. For example, there is many many information in Spanish, many information in cultural cultural practices that is not well represented in the in the databases of the models. So, they were you were talking about the biases. This this will be one of them. The other one is that there is people that has a lot of influence on what we can access from the AI and what not and which information is selected for the the models in training, so systems thinking help us to see beyond that and put these models into the right dimension and see them as a box out of that we need to to go beyond that in in the human experience. >> Yeah, brilliant. Thanks, Nelson. Yeah, maybe if if anybody wants to share one last thing before we move on, anything that really stood out. Or even like a question to the group that you feel is really pertinent which didn't get resolved in the session that could be an interesting one for us to reflect on. >> I'd um add one quickly built upon I think what Nelson was saying there. Um the kind of limitations of efficiency and an efficient cuz you know, the technology's all about how we can make things more efficient and do things more efficiently and if we don't if we aren't able to see more broadly, if we just think analytically, then we we come to a place where we think efficiency is everything, right? And the more efficient we get, the better we'll get and more successful. And in that world, you start to see the technology is everything, don't you? So, I think systems thinking that ability to step back and say, "Actually, getting from, you know, the efficiency, getting from A to B, yes, it's important, but it's not the only thing, right? We can create a lot of value. We can do a lot of great things by connecting across many different systems and so forth in maybe an inefficient way, but it it creates value by actually connecting them when they were disconnected previously. So, I think that ability to step back and see the bigger picture helps us see that efficiency is not the only thing. It's It's important, but there's actually um synthesis and connecting things and creativity and all all of this that comes when we put things together in new ways is equally important and that's not really about efficiency at all. >> [clears throat] >> Brilliant. Yeah, speaking of efficiency, maybe um onto you Josh for the next section. >> Josh and Stewart >> Jack, okay, great. Um yeah, I hope that was uh productive conversations for you people or fruitful uh efficient conversations, shall we say? Um and uh just to move on a little bit, we've got about half an hour left. Um so we'll get back to breakout rooms in just 10-15 minutes or so. Um but yeah, the people is also about, you know, how the world of systems thinking, the field of systems thinking and ideas and all this um can and should evolve um given this capacity of machines that may actually take away or do better some aspects of it and how how should it evolve? What will become more uh valuable and what will become, you know, commoditized uh in this world cuz I do think there are aspects of what we call systems approaches or systems thinking that that may become more commoditized. So, this is Gerhard uh Leonhard again. Anything that can be digitized or automated will become um anything that cannot be digitized or automated will become extremely valuable, human-only traits such as creativity, imagination, intuition, emotion, and ethics will be even more important in the future because machines are very good at simulating but not at being. So, it's really interesting that like sometimes we just see the technology and what it's going to do better and efficiency and all of this, but it's important to see what happens around that. Like if all that changes, then what else really opens up and becomes hugely more valuable and becomes possible for us to unlock in the way that we we couldn't in the past. I think there was so much in the industrial age that we couldn't or didn't unlock in ourselves that now actually becomes in comes into focus and becomes possible. Um So, I think a little bit about that in the context of systems thinking. Um we did a little um kind of schematic to think about it, right? These are the things on the top that we think are going to become more relevant. And the things on the bottom, the things that may well get more automated. I mean, they they'll still be there, but they'll a lot of it be done by machines. Um I mean, things that are if we think of systems thinking as just ideas and just ideas, like I have to learn this checklist of ideas, well, machines can learn checklists of ideas and they can teach checklists of ideas and all of this. So, I think that dimension, where it's just a set of ideas, I I don't see that really become more valuable. I think it'll become less valuable because a machine can do that. Obviously, many other things, yeah, workflows and best practices and all things that are kind of analytical and anything to do with information, really, which isn't um connected to a practice and a way of doing, um may well become more automated. So, you know, we've already had sessions where we looked at systems mapping as automated tools and so forth. So, and systems dynamics, that's a very that's a uh low-hanging fruit for automation and so forth. So, I think it's a lot in that space that will become more commoditized. There'll be other things, and a lot of those other things hinge around uh leadership, the human being, and their experience and the context therein. And collaboration, things that push us out of our comfort zone, and they require people to connect with other people, and they challenge us in our conventional way of doing things by bridging across borders. But also this thing of sense-making in complexity. We know there's so much out there that is so complex, it's beyond our capacity to grasp. And we aren't even able to make sense of it, a lot of it. So, anything in that realm, like algorithms, I'm not going to be able to touch it for a long time. So, like our ability to make sense of really complex uh phenomena, um I think systems thinking aids us in that will become very very valuable. And also this thing of questioning paradigms, um cuz you don't generally see that out in the world. Most people aren't aware of their paradigms, and that creates a lot of problems, whereas systems thinking does help us with that. So, I think those are some of the the things that will become more valuable, particularly the practice of systems leadership of sense-making in a complex world, in the art of collaboration, in working with emergence, in non-linear thinking, in ecosystem uh ways of working. But I think a lot of it's like a practice, because otherwise, it's kind of information, right? If it's not a thing you do, it's kind of information, and a computer's going to be uh doing a lot of uh those things that that are in the realm of information. So, we're there to add a couple of those. Um systems thinking ideas. Yes, um you know, that can be easily passed through an algorithm as toolkit or just set of tools. And a more kind of analytical approach to systems change. I mean, computers will be able to do all of those things. These are some of the things as I mentioned, you know, sense-making in complexity, um creating context, like something we see at the moment, but we don't know how to really create frame it in creative ways, right? Being able to frame things in very in new and novel and creative ways that create value, that create insight, that help people make sense of things. Systems thinking should be able to help us do this. Relational practices, anything to do with trust, with enabling collaboration between between people. And we hear trust is going to be really important, so I think that's a key aspect. And things of leadership, where people step into and take on responsibility step into uncomfort zones, they have purpose, they create meaning, they co- cohere and bring people together in new ways, and create stories around what's possible and so forth. All of these, I think, are going to become a lot more valuable given rise of automation. So, the paper does go into kind of kind of break some of those down. Won't go through all of them. I touched upon some of them there. Um I mean, I hear a lot more about this sense-making thing. People are very interested in it, sense-making and putting that into heuristics analogies and stories that help people make sense of complexity. And again, yeah, questioning paradigms and assumptions and so forth. I think some of that's happened on this call already. Um Context creating and facilitation, this non-linear thing thinking, where you kind of shift up from, you know, problem-solution or I I go and directly do things to I create the container. I create the context. I hold the space. I facilitate in spaces that are dynamic and uncertain and and supporting other people. I think these again will become very important traits. Relational thinking, and yeah, as mentioned, anything to do with people and how they work together because we know at the moment we live in a world where there's a lot of misinformation, there's a lot of distrust, there's a a of division and so forth. And these algorithms aren't necessarily making that any better. Social media has kind of created a lot of problems there. So, anything that's actually working on that level, the interpersonal, building trust, inclusive spaces, you know, it's not really helping us with that at the moment. So, how do we build those? Um and actually work together and collaborate and you know, get out of those confines cuz I don't think the technology is really helping us do that. So, I think this is stuff we need to do and we'll we'll find huge value in leadership and entrepreneurship, taking risks, seeking true transformation and emergence, you know, stepping into uncomfort zone, um and going beyond what's, you know, asked of us in terms of trying to make real lasting and deep change happen. Um and investing in that over the long term. These are truly human traits, I think, that um will become more more valuable uh over time. And hopefully, the technology can support us in doing all of those, right? Automation should free us up to have greater focus on these things, greater resources on these things. So, there we have it. I won't give a long lecture about all of that. You can look into the paper. I would love to hear all of your thoughts. We've got what, 20 minutes, Ben, to jump back into the breakout rooms? >> Yeah, brilliant. Thanks for that, Joss. Um yeah, this is So, given what we've just discussed, I mean, and also just to say, you know, you may or may not agree with this framing. This is just, you know, a a view or a way of viewing it. So, you know, obviously, you don't have to follow that, but uh you know, when you think about the type of work that you do, how you work with complexity, maybe any type of, you know, systems methods that you use day-to-day, what do you think in an age of AI automation will become more commoditized as we've been talking about and conversely which things will become more valuable? And and also maybe there are things that we don't currently get, maybe it's not even typically within the realm of what systems thinkers do, let's say, which may become additional responsibility in an age of AI. Uh yeah, it'd be really interesting to see what what comes out of the discussions. By the way, Josh, are we going into the same break breakout rooms? >> Um I have them here on my screen all set from before, so I can just open it and I think yeah, go back to the same rooms. Why why not, right? So, here we go. People, I'll open them up now. And you should see options in front of you. Uh we'll go for like 18 minutes, let's say. >> Yeah, so it's like basically we were trying to um find ways um in which, you know, like A, as we said, you know, like what what is what separates us from AI, but like, you know, how could AI actually also help us as system thinkers? Then we came up with like um all different kind of scenarios, like for instance, using AI as a tool in music making or like analyzing complicated um like legal frameworks um at work, right? But we recognized that what really still needs to happen is, you know, the human still needs to be able to discern Yeah, you know, what is actually valuable output and and what isn't. And in order for a human to be able to do that, um like it an interdisciplinary approach like um is like um you know, very beneficial in that our world actually structured around like um specialist. But, at the same time, we also didn't want to a specialist because of for instance the coding example was like if the AI code something with the help of agents and the humans don't actually understand the code, how you going to test it? So, you still need like an expert coder or like at least a software engineer to be able to figure out, you know, if this is um really valuable code or not, you know? Uh so So, yeah. So, it's becoming down on the side of like a system thinking is really going to give us the interdisciplinary skills that we need um in order to analyze things. But, if you want to put things into practice, then, you know, some kind of special specialist knowledge will be beneficial too. >> Okay, but do you want to pick up another? We just got uh you know, two three >> I think we don't we don't have a lot of time left. Um That's Yeah, I think Well, I I think maybe we we we have time for uh Yeah, if anything stood out for anybody from from the breakout rooms, we maybe have time for one uh one sharing session um before we before we wrap up. Or you're also free to to type in the chat if you prefer as well. I think maybe one thing, um, I can share from from my group that got quite quite philosophical. I feel partly responsible for that. Um, but but yeah, there there is I I think if it's also a little bit challenging the assumption there's there's a a big drive now to to kind of automate everything. Um, but there is so many things that's kind of not written down. It's not codified. It's messy. It's complex and we pick it up like kind of from the vibe in the room or from having a unexpected conversation with somebody at a water cooler, let's say, or, you know, there there is so much kind of spontaneity and novelty and things that can't be written down or codified that, you know, can AI really do that? I think is a is is a is a great question, right? And and maybe that becomes, you know, even more of a domain that that our job is to find the unexpected connections and, you know, have the unexpected conversations and bringing more of this context in. Um, but yeah, I'm aware of time, um, but thank you so much for for for joining the session today. Uh, we you know, the the tech hub is still pretty new, so we are we're really experimenting with different formats, uh, different types of, um, research as well. So, would really love to hear your feedback, um, what you thought about the session, um, and also any other topics you would like us to to explore or any feedback in general. Um, we also have some links here to uh, to join the the hub, uh, the tech hub, um, and also a link to the LinkedIn as well. Um, and uh, we have our next event also organized now for end of the month on the 30th of July. This will be co-hosted with Fabian Gump, who is or was in the session today. He's a real expert in system mapping and had a very popular event on system mapping just a few days ago. And in particular, it'll be around system mapping and AI. He's been writing some really great articles around exploring different roles that AI can assist with in the system mapping process. So we'd love to see you there if that's something that's of interest. And on that note, thank you so much. >> question. Is it the paper available? Can you download it? The your >> Yeah, I'll grab it. I'll grab it and put it in the chat. >> Yeah, I actually have the link here so I can put it in. >> I don't have the link. Okay. >> Yeah. >> Um also do appreciate your feedback. So you just grab a dot here and drag it anywhere on the scale of zero, which is not so happy or not so good, not so helpful, to 10, which is very helpful. And the stickies there if you want to type anything in. Super helpful if you give any feedback so we might improve things in the future. But thanks a lot for joining and we'll wrap up there. Hope to see you again in the future. And the link for the other event is just maybe want to drop that in the chat. >> I've already added it in, yeah, to the chat. >> Great. Well done, Ben. Well done, everyone. Great seeing you. Look forward to future event. >> Cheers, everyone. Hope you all have a great rest of the day. Cheers. >> Thank you. >> Cheers. Thanks for joining. >> Bye.