Core Systems Thinking Skills for an AI Age - Presentation & Discussion

Um so yeah, great to see you all here. Um and thanks a lot for joining us today uh for our event on core systems thinking skills for an age of AI which is hosted by the SI tech hub. My name is Bowen. I'm a representative at the hub uh where myself, Joss and Roma are building a space and a community to explore the intersection between technology and systems innovation. And so what brings us here today is that last week we published a position paper uh which set out to explore which systems thinking skills become even more important and even more relevant when it comes to how do we best adapt to and thrive in an age of AI. Um and so shortly uh Joss who I imagine uh many if not all of you uh might already know uh who is the founder of of SI will take us through the key ideas in the position paper and we will do this in two parts. So um after each part we'll go into breakout rooms to discuss it in small groups and then we'll come back together to share and discuss what came up. Um you know just to try to embody a bit of collective intelligence as we do. So there'll be basically two breakout sessions. Um and hopefully this will give us a chance to reflect on you know whether we agree with this framing. you know, what do we think are some of the most important skills when it comes to systems thinking as we imagine this future, you know, which most likely AI will only become an increasing uh influence um and increasingly embedded in the world. Um and but yeah, before we get into any of that, just to get us warmed up, um would you please be able to type into the chat, just the Zoom chat? You don't need to do anything mirror yet. um what brings you here today and what you hope to get out of today's session or what you would hope to discuss in today's session. Um maybe we'll we'll give us a couple of minutes just to just to ponder that one. Don't worry if you know you don't have a fully formed concept in your mind. I guess we're all here to to figure this out together anyway. Um but feel free just to share what whatever comes to mind. be really interesting to see what the different um perspectives and motivations are. Great. Thanks Herman. Uh so Herman is new to this field and hopes to get an overview of what is happening. Yeah, Sonia is a cy cyber psychologist and want to understand the position paper more. That is fascinating. cyber psychologist. I do not know what that means but it sounds absolutely fascinating and I would love to ask you to elaborate but unfortunately now is not the time would happily just you know maybe change the agenda item to deep dive into to what psy psychologists do. >> We could we could create a breakout room just for the psychologist and they could have a good conversation. >> Yeah. Exactly. Exactly. Um, so Paul wants to discuss the current experiences people have with AI in relation to the SR practice. Yeah, absolutely. Um, and yeah, just uh also just to um reiterate what I said earlier, that's that's what what we're really trying to do here in this hub is explore what what what this intersection even means when we talk about technology and systems innovation and hopefully try to, you know, put out maybe position papers, host workshops um to kind of explore this together. Um, great. Um, maybe the last couple I'll read out. Oh, we had a few more. So new to systems thinking, systems innovation. Looking forward to understand how AI will impact the working world generally and how systems thinking will interact with it. Fantastic. Brilliant. So I think in the interest of time uh we will move on but feel free to continue writing in the chat if if you want to share anything else around that. Um and yeah so on that note I will hand over to you Joss. >> Great. Thanks very much B. Uh good to see everyone here today and to host this conversation. Looking forward to hearing uh all of the different uh perspectives and opinions as we get into our breakout rooms. And um it's a very rich and uh you know pertinent and relevant and timely conversation I think and um it's not uh the first time we've been exploring this thread. We've really started it some time ago. So maybe some of you have been here for past uh past sessions. Um personally I started you know exploring this theme maybe five or or six years ago uh when well more even like seven or eight years ago when the first signs of all [clears throat] of this bursting onto the scene started to to show and uh way back then uh put together this uh research paper. Um but then more recently 2025 when we started this hub um we started off with this uh initial systems thinking principles for sustainable use of emerging technologies. Um so we're also thinking through it at at SI in terms of how we should use technology and what's sustainable use of of such technology. So we host hosted a workshop then gathering people's ideas on that and yeah all of these have links to them. So if you're interested in exploring, you know, past uh papers that we've we've put out, you can find them there. So then more recently, I'm sure some of you here uh were at the last I think it was the last one we did, wasn't it? B during the summer. Yes. Um on systems thinking in the age of uh AI. So pretty similar to today's topic, but that was more general uh exploration and today is a bit more specific. We wanted to zoom in on core skills and abilities. There's been quite a bit of conversation at SI about this as um we're starting to see more job applications say systems thinking and more videos and talk about it in relation to a key skill when it comes to uh AI and uh automation and so forth. So wanted to compile our thoughts on that and this is what we produced just last week. It came out core systems thinking skills for an HB app. So hopefully you're seeing a bit of a thread there. That's what we've been exploring. That's what we are exploring. We'll continue because uh I think um you guys would agree this is a super important topic or at least after today hopefully you'll be thinking that. So uh that's a little bit of context where we're we're coming from. Hopefully some of you were here for that session um uh during July or August. Um July it was and that's a bit of a framing for today. So here is the the paper. So it's a very short one and um it's really touching upon these four high level competencies, capabilities, skills, whatever you want to call them, uh that systems thinking can uh provide us with if we um well to help us uh adapt to this age of automation and AI. And that's what we're we're exploring. Um and I saw this graphic and for me it's really captured a lot um actually. So a lot of this is kind of starting in the tech world. I would say this um new reality where basic processes such as coding is becoming automated and that is shifting the the job of the uh technologist, right? the person, the engineer up from this routine, more mechanical, more linear, more predictable um kind of tasks based uh work, right? the what the coders writing code up into this more systems kind of world particularly as we go into you know agentic AI and it's the agents doing these writing the code and [clears throat] more and more we're having to build systems for these agents to do the work And yeah, Russell Akoff talks about this graphic comes from a quote by Russell Akoff who says the systems thinker, the architect is a systems thinker as opposed to the design engineer that's designing the specific parts of this building, the electricity, the heating, the the water and so forth. And they see their specific area and they work on that. The architect is the one has to see it all come together and put it all together and make the whole system work as a house, right? as a living space, as a functioning overall system. And that's uh that's a systems thinking ability. And that's increasingly as these basic functions become automated, it's more this kind of a world where we're moving into or those kinds of skills that are are needed. And if you look at job applications, there's a lot that are coming out that are making this connection between systems thinking and AI. And this is the kind of connection they're making. They're saying things are getting automated and you need to be thinking about the bigger system within which all of this is playing out and and the interactions and so forth and we need those kind of skills. So that's kind of where this is coming coming from. That's like the high level well essentially what systems thinking is about right putting all the parts together seeing the bigger system and the kind of dynamics and so forth. And that's the the starting position. We then uh break that out into four kind of specific areas here um that are like quite classical systems thinking competencies. So just to touch upon those before I zoom in a little bit. uh contextual thinking, purpose setting, pattern recognition, and enabling collaboration are the four that we we pulled out. So context thinking is about connecting everyday tasks to the larger structures they sit within. Anticipating how automated decisions cascade and aligning technical capabilities with the needs of the wider environment. Contextual thinking, putting things into context, synthetic thinking, holistic thinking is uh essentially what this is about. And it's all yeah core to to system thinking. I'll come back to that now in a minute and kind of zoom in. Purpose setting. As a AI absorbs routine work, human effort moves up to setting direction. Systems thinking and strategic judgment will help to keep an organization's high level purpose connected to its daily operations. So again, it's looking outwards and upwards to understand the broader context, define and redefine the the purpose as the context changes and align that with the the internal workings of the organization. Pattern recognition algorithms learn from the past and trends uh and tend to repeat it. Human intuition looks beneath surface events for the structures, root causes and paradigms uh that automated analysis can't surface. We hear a lot about you know biases in algorithms uh but this is the iceberg kind of thing going down to more fundamental levels to understand the actual paradigm and framing behind things uh at a level that algorithms can't can't really do enabling collaboration. So this one's more about kind of an interpersonal um ability. The data AI depends on sits in silos owned by different teams. Trust, empathy, and resilience are the human skills that bridge these boundaries and turn fragmentation fragments into cohesive systemic uh value. And you know, right at the moment, we're hearing a lot about, you know, the AI race between China and the US and the kind of challenges and problems that's kind of creating for us. Well, that's a question of collaboration. Um, if we could get collaboration, we could get much better governance uh frameworks. And that's not about technology, right? That's about human beings and how they work together or fail to work together. And that's why we're putting in here as kind of a key key aspect So, those are the four that we're going to uh unpack and explore today. I'll just zoom in a little bit on these um these first two and then we're going to jump into our breakout rooms to to talk about them. Um the first one is contextual uh thinking. So this graphic helps to capture everything exists in a context and has an effect on that broader context whether we're aware of it or not. And algorithms have limited contextual awareness at least at this stage. And human beings have vast amounts of contextual awareness that we have built up over our existence. And it's our job to try and put these more specific analytical reasoning and capacity into and align it with the broader broader context to try and avoid unintended consequences. And those unintended consequences become can become extremely uh important and powerful when we introduce automation. Right? you you're not just doing something and it has an unintended consequence. You are doing it and then doing it again again and again again and putting on auto mode um so it can stay accumulating those negative externalities and that can become very uh destructive and that's why this becomes particularly important this kind of contextual putting things into context. So in the nature of AI contextual thinking the ability to connect microlevel tasks with macrolevel structures is essential to anticipate the cascading effects of automated systems deploy innovation responsibly and align technological capabilities with the broader needs of the organization, society and environment. And certainly too much analytical thinking you know takes us in this direction of fragmentation. um and decontextualization and it optimizes the parts and what we're doing looks great but it results in suboptimization of the overall system fractured you know social relations become disintegrated urban spaces whatever it is um our environment becomes degradated um this is a challenge we've been having and continue to have but we're almost you know AI can put that on on steroids in terms of its ability for for automation So um that's topic. Uh I got a little example here. The initial AI algorithm was trained on historical bias healthcare data. Its widespread regional use could systemically or systematically prioritize the marginalized uh demographic group deprioritize the marginalized demographic group a group. This is how automated systems create unintended consequences that become amplified at scale. So we're trying to optimize one part you know improve the efficiency in our healthare system and processing of data but um if we aren't aware of the actual uh externalities and consequences of that it can lead to these unequal fracturing at the the macro level. we have to look at that macro level and as Russell Akov says always design things in the context of the whole um to ensure that they fit into that appropriately rather than just trying to optimize these parts with automation that ends up in this kind of disintegrated overall system. So that's the first one around contextual thinking. Not going to read into all the text there. It's in the uh in the paper. You can take a look. Um it's one of the topics we'll be talking about now. So looking forward to hearing your your thoughts. Uh I'll go on to the second one. This is uh purpose setting and uh yeah this ability of systems thinking to look outwards and and upwards and to synthesize uh broadly um in the environment to set purpose from that kind of direction, vision, purpose on a high level always comes from that understanding of the broader context and then to kind of work backwards from that to set the internal workings to align the internal workings of the organiz organization with with that. So as AI automates routine tasks, humans must leverage creative systems thinking and strategic judgment to continuously align an organization's high level purpose with its daily operations, ensuring agility and long-term success in a rapidly changing uh world. And this uh ability to frame and reframe to make sense and remake sense of the environment in ever more creative and innovative ways to position the organization and uh make it purposeful and meaning within meaningful in what it does and then to work backwards from that to set the strategy and so forth is is what we're talking about here. So uh to give an example of that consider the transformation of a regional public transport uh transportation authority uh adopting new algorithmic models to manage its network. The AI system handles the basic routines of scheduling and routine optimiz route optimization. The algorithm excels at this micro task, analyzing vast amounts of historic ridership data to minimize fuel consumption and reduce waiting times on the busiest routes. Because it relies entirely on past data, it will naturally allocate fewer resources to historically uh underserved neighborhoods where past ridership was low precisely due to inadequate existing services. This is where the human system leader must step up step up to the level of purpose setting. The leader recognizes that the unstructured uh uncertain challenge of social inequality can't be solved by historical averages. Applying strategic judgment and creative framing to reduce the transit authorities's overarching purpose. uh transitioning from a purely optimization driven model to one that prioritize equitable mobility and economic access for citizens. So uh again um using judgment and sense making to uh understand the broader context and derive purpose um and and I guess e ethics and so forth come come into that um to then align the actual workings of the system. without that it would uh make you know decisions that we we wouldn't necessarily desire as the overall outcome. So um maybe I'll leave it there. Um thanks for for listening. Those are the first two and we can come back afterwards and discuss the others but um going to pass it over to to B to explain the breakout rooms to us. >> Great. Yeah, thanks Jos. Um so yeah the breakout rooms uh we'll we'll give ourselves 20 minutes to to discuss. And basically um now that you've heard uh the these two core skills that we highlighted in the paper as well as some examples of what they could mean. Um what we uh what we would suggest is well first of all um we do also uh want to facilitate some sharing after the breakout sessions. So if at the beginning somebody could uh self- select as a group facilitator and uh it'd be great if you could also share your screen so everybody can see the question prompts uh which I'll cover in a second as well as of course the graphics which will be kind of the point of discussion. Um and then what we would suggest is that you go through two rounds of each person answering um you know the first question and then the second question. The first one is do you agree with these skills? Right? Um we're not here to you know obviously say that this is a source of truth or anything but of course this is based on you know research and our own you know experience and um and judgment let's say when it comes to you know which of these skills are most relevant. But we're really open to kind of other thoughts or maybe even other ideas around that. Um and secondly, what do you think are the concrete skills you or others can develop in respect to these areas? Um because obviously these are quite high level, right? So this can invite a chance to think about okay well what does this mean for me in my own context and maybe spark a bit of a discussion about that in your group. Um, so Roma has just uh added the link to the mirror board again. Uh, so it would be great if you could just um have this open because once we get into the breakout sessions, I mean, we can share it again, but um, just to make sure that you don't lose the link. Um, and yeah, I think on that note, we can probably start setting up the breakout sessions unless there's anything else I should mention. Maybe >> just in terms of timing, burn, I'm sure that >> Yeah, exactly. So 20 minutes. We'll set a timer for 20 minutes for the breakout sessions. Um, and then once we come back, we'll have about uh 10 minutes or so to discuss it together as well. So, if the if the group facilitator can maybe just share or or even more than one person in the group can share, you know, what were some things that really stood out, it will um give us a chance to discuss that all together collectively afterwards. >> Great. So, we're going to create six breakout rooms. Should be about three or four or five people per breakout room. Uh, these are your questions here on the board. And if someone could please lead the conversation, you know, guide the the questions and you're basically doing a round u with everyone in the room sharing their their thoughts and then moving on to the second one. And we'll be back in 20 minutes. So, here we go. And uh hopefully you'll fly off to Zoom out uh Zoom rooms now, breakout rooms. Oh, finally my my notetaker has been allocated to a room. So I guess I have I'll have to follow my noteaker and let AI take agency and dictate my life once again. >> [laughter] >> Okay, in that case I will join room two. Everybody have a link? Anybody needs help? You might have to look on your taskbar to find the breakout rooms. Yep. actually everybody's already assigned so there's not much we could do to assign. Uh yeah, anybody needs help joining the breakout room that he assigned to? Yeah, we're just waiting for everyone to come back to the main room. Hello everyone. Welcome back. So, it seems like we're all back in the main room now. Great. So, yeah, I hope we all had um some nice generative conversations around this topic. Um yeah, I'd be really curious to hear what came out of it, right? Um you know, any type, right? Whether you agreed with the framing or not, if there were any additional things you would add, and also, you know, if there's any, let's say, specific stories that you had um that could be interesting to share, right? Like in in in in um my group we had you know quite an interesting use case where there was lot clearly lots of different levels to consider and it really you know related quite directly to this idea of contextual thinking purpose setting. So um yeah who would like to start? Feel free just to unmute yourself um and yeah share share what stood out for you in your group. I think we selected Nadia was going to give feedback for us. >> Uh, no. Sorry, not me. >> You can do it, Paul. >> Uh, you're on mute, Paul. >> I thought you were going to do it, Nadia. Okay. Um oh that's put me under under a bit of pressure here. So um we thought actually contextual thinking and purpose setting were both very closely related. We saw that um to do your contextual thinking without uh without considering purpose behind it was uh was would be a cause of problem. Actually the two do relate to each other. Um, yeah, those were I spent that that's my first first part of it. I don't know if people in my group will add to what I'm just saying here. Yeah. What you going to say? Um, yeah, we'll add that was mine and Bowen's thinking also as we put it together, right? These ones are kind of coming from the same the same place of looking at the bigger picture, aren't they? And putting things into context and um that's kind of where purpose comes from. So they're very closely related as you as you say. >> Yeah. But we felt that that also the involvement I mean I the example I had was uh when you're talking about contextual thinking or purpose setting if you're if you're looking at a group of people they're not homogeneous. So it's by bringing the their different perspectives into it is part of that where you're pinning your tasks into the macro activities. People don't rec one one set of one group of people will not recognize necessarily another group's perspective on the same set of activities. They don't see all the pieces of it. And it it's quite there's an element of discovery in that pinning exercise. You you suddenly find that there are things that actually are not recognized in your existing process that actually people are doing or different groups in the different different sets of the groups of people are doing the things are are not doing this all doing the same thing. They have extra things or yeah or some things are blocked. You know >> it's uh interesting how many of these kind of relate to leadership um or or seem to be kind of classic leadership ability. It seems to be the space where we're getting pushed into to some extent or encouraged to move into. But yeah, uh Herman, would you like to Yeah, sorry mute again. Uh yeah, made a very good comment on on contextual thinking that um that the feedback loop is a bit missing. You see you she added a note there and and I think that's quite very insightful that it's okay to move from the immediate to the bigger picture but the bigger picture also needs to come back to the the immediate situation or context. So um uh unfortunately we don't see what's going to happen 5 years down the line but what we're doing now with AI is is is already forming that. So it's one of the I assume adventures of being in this um time of change. So I just thought that was a good perspective of Gazelle. Really? Yeah. Go ahead. >> Well, it's really something that an algorithm AI is not going to be not going to do. Is it be able to see into the future and bring that back to like what are the best things to kind of do now? The best judgments and so forth. It seems like that's not its kind of natural strength. But anyway, um yep, keep going. >> Yeah. Great. Um, and I mean I'm not sure if that was group one, but we could also do it this way. So, um, I mean I I think next would would be group two if if that was group one. Um, so I'm not sure if anybody from my group wants to share. Maybe PA. Oh, okay. Thanks for clarifying. Group three. Okay. >> I think you had certain biases in your mental model there, Bone, to be honest. >> Yeah, exactly. Assuming the first one has to be group one. Um, yeah, I'm not sure if anybody from from my group would like to share anything. Perhaps Paula, >> I I think Salassie mentioned that he would be open to to share. >> Yeah. >> Yeah. Yeah. Salassie, if you if you're open to sharing um just just a maybe a brief version of maybe how you relate to these topics given the work that you do. Not sure if you're on you're not on mute but I don't hear you Salassi. >> Yeah, I might have to come back. >> Yeah, maybe I hear the audio. >> Um, yeah. Okay. Uh, yeah. Is was there anything that you wanted to share, Paula, while while we're here that stood out for you? No, we were um gladly we were joined by Salassie and Emma. Emma with her uh uh information background and how to grasp the the the right kind of data while uh developing the right methodology or the most suitable methodology to understand the field before uh bringing any kind of uh AI or any kind of tool into the into the context and that's why for us it was so rich because Salassie works on a project about migration and we were sharing that it's the it's exactly the kind of project that can be affected a lot by uh the lack of contextual thinking and purpose setting and he was mentioning that he's exactly on the on the field right now doing the the the part on contextual thinking but I think he's trying to talk right Hi guys, can you hear me? >> Yeah. >> Yeah, we can hear you. >> No, we can't hear you. >> I think your Yeah, your audio just just cut off >> and should try again later. >> Um yeah, so and thanks for sharing that. Um PA, I think, you know, one thing that stood out for me as well, which you know, as someone who also um builds with with AI and builds things with with AI integrated into it. I think a really important thing to always think about is, you know, what what should be automated because it, you know, leads to more efficiency. it helps us to do more with less, let's say, and and all of the advantages that I think are quite clear. Um, but where there are really critical decisions that need to be made that very intentionally AI should stay out of or at least the very most play like more of a supporting role. Um, and often, you know, really thinking about this up front. It can help, you know, that it doesn't just become kind of everyone using AI for everything and and it doesn't become clear, okay, well, what what are we really choosing to make decisions on and and where where are we kind of like, let's say, delegating some of that? Um, great. Was there anything uh so just aware of time, was there anything anyone else wanted to share before we move on to the second part of the session? Okay. If not, maybe I'll pass back to you, Joss, to yeah, introduce the next part. >> Yeah, we'll also have another opportunity a little bit later. So, let's stay going. We just got 30 or so minutes. Um, so the jumping into the second two here talking about pattern recognition and enabling collaboration. So, um, pattern recognition, another one of these systems thinking skills. We have to not get too caught up in focusing on the parts and be able to step back, blur those parts a little bit so that we can see the patterns and how they're changing over time. And particularly this iceberg thing of the more fundamental patterns, what's going on underneath the um water level. To counteract the historical biases enabled in AI, humans must use intuitive pattern recognition to uncover hiddenly causes. balancing system rules with autonomy uh to proactively shift paradigms rather than blindly repeating past flaws. Um yeah, so it's this this ability to see the pattern behind the pattern, read in between the the lines and not just see the outcomes, but actually what's the the framing of that? What's the underlying structure that's creating those outcomes? um the mental models, the values, all the stuff down at the bottom of the iceberg and um be able to yeah unpack that and and and and reveal it because it's it's part of how the world works and how problems are perpetuated and created and so forth. So um let's take a look at an example of this. uh we can examine the use of an algorithm al algorithmic AIdriven evolution tool evaluation tool for uh deploying capital in systemic investing or grant making. At the tip of the iceberg, we find the surface symptoms and data events. A philanthropic fund implements an AI to screen hundreds of funding applications optimizing for efficiency. The AI blindly processes surface level data such as standard ROI metrics, keyword matches, and the scale of past projects. On the surface, the automated tool appears highly efficient at rapidly filtering out what it deems low probability applications. However, moving down the level of reoccurring themes and patterns, a systems thinker looks beneath the surface might notice a troubling trend. The AI is consistently rejecting applications from smaller grassroot or rural initiatives heavily favoring large well-resourced organizations. Delving deeper into the underlying structures and dynamics, human intuition reveals the hidden rules of the game. The historical data the AI was trained on relied on rigid evaluation structures, metrics that required extensive formal documentation, specialized financial modeling, and specific academic uh languages. Um, and yeah, it can it can go all the way down to, [clears throat] you know, the sentiments and uh um values and so forth. Um, and I think that's another kind of critical aspect of all of this. Um, yeah, being able to see uh what's going on underneath the hood, I guess, power dynamics and and all of this too, uh, that the the system is locking into place. So, um, that's patent recognition, seeing what's going on underneath the iceberg. This one here is enabling collaboration. I guess it's a little bit different from the other ones in terms of more of an interpersonal interpersonal or interorganizational um aspect. So enabling collaboration to unlock the immense value of siloed data for AI. Organizations must le leverage deeply human relational skills like trust, empathy and resilience to foster cross functional collaboration and transform uh fragmented information into cohesive systemic tools and yeah goes to security also and the value that gets unlocked from from trusts in in people and institutions. Um, and that value can't be ignored and it's not going to go away. Um, and it's it's quite a human kind of aspect. Do we trust a person? Do we trust an organization and so forth and the ability to collaborate that depends upon that and the value that's unlocked from those uh collaborations. I think certain aspects of this can be supported by technology, but there are other aspects. there's kind of always a human who or to some extent an organization that kind of owns things and do you trust them? Do you want to work with them? Do they want to work with you are always uh important considerations. So imagine a regional healthare network attempting to deploy an AI system to predict and manage chronic diseases across the population. The primary value differentiator for this algorithm is access to unique diverse data sets. However, this critical information is fragmented across distinct department sidos. The hospitals own the clinic clinical data. Local municipalities hold the social care records. The community clinics manage localized patient histories. Left alone, the AI can't function effectively because these data owners operate with different priorities, rigid privacy concerns as uh separate technical languages. As the graphic here illustrates, while the ultimate objective is datadriven, the mechanism to unlock it is inherently human. To build the collective uh collaborative bridge, a systems leader must intervene with relational skills rather than technical expertise. Um, and it's kind of both, right? It takes a lot of technical expertise to link up all these systems and create the protocols, but then there's this human layer on top of it. They must use empathy to understand the clinical department's objective stance on privacy uh patient privacy. Demonstrate resilience while navigating the municipalities bureau bureaucratic red tape. Yeah, certainly takes resilience and build genuine trust to harmonize these uh competing interests. So that's what referring to by collaboration and some of the kind of uh human traits uh required uh for that. for the sake of time. Um, I think we should, uh, jump back to our breakout rooms and make the best use of that time. So, pass back to you, Bowen. >> Okay, great. Maybe we can also cut it down to 15 minutes. What do you think, Josh? Just to make sure we're not we're not rushing with the sharing. >> Um, brilliant. Yeah. So, same thing again. Um, now that we've heard about these two core skills, um, you know, what do we think about these? Do we agree with these? and how do we, you know, develop these as systems thinkers and people exploring this field and how do we relate to them. >> Uh so yeah, we'll we'll take 15 minutes for this one and then um come back again um and discuss it together and and and share it between the breakout groups um once we're back in the main room. >> Great. I'll create the same groups. Yep. Brilliant. >> Thanks. See you soon. Uh, hey Josh. Uh, is it okay if I drop off? Um, yeah. Hello everybody. Welcome back. Hope you all had uh yeah a good breakout session. Um so we've got about 10 minutes left. Uh who would like to start? Um I mean maybe just in the interest of time I can maybe share a couple of things from from my breakout session that was that was interesting where we ended on is something which Salassie um uh raised which is that you know AI is also a pretty subjective term. What do we mean by AI? When we talk about AI in a group right especially a collaborative uh project um people have different kind of um conceptions of what that means and and how it can help them. And so just putting that on the table and discussing it and and how we we all relate to it um can also be a very helpful way of of starting that discussion of okay what might we want and what what might we not want. Um which I thought was a really really interesting point. Um yeah I'm not sure if anyone else from the group wanted to share uh anything else or any personal experiences. Okay. Um great. If not uh yeah who who else uh wants to wants to share something from the group feel free just to unmute yourself and um we can discuss it. >> Yeah. Um in our group session we talked about like you know trust is a fundamental you know basis for you know um addressing system systemic uh dynamics issues because uh in the healthcare industry because uh most of people who are I mean we have to have trust between provider and patient. We have to have trust between different departments. we have to have trust among you know different you know modalities. So like uh trust is the main thing before we even delve deeper into the major issues and building that trust is one of the biggest issue cuz you know it takes five uh up to five years to create true relationship uh trust and uh that's one of the things that system dynamics I don't think we touched so much on um cuz we can talk about all this technology but if we don't have that it's like we're just going we're just it's a feedback loop that never closes. I mean a a close feedback l >> Yeah, I might add um and that technology can often get in the way of building trust because we um use the technology instead of um taking the opportunity to build the relationship should we say, you know. Um so we don't always think about that like um what's the consequence of using the technology in terms of uh the relation rel relational dynamic between people is it um is it degrading that and doing doing away with that? um that that's kind of an externality we don't like hugely account for I would say and it's important in this context of of trust building because you've actually got to have a a relationship in some sense uh should we hear from some other people who haven't said um Aayasha any any thoughts from today before we draw to a close what were your takeaways Thanks. Sah mentioned something quite similar in our group and it was along the lines of the more we using AI the less connected we are. Um, and I'd used an example cuz I work in a software development company. Um, and I was using an example of how previously we used to pair program and now our developers pair with the AI and so that affects teams team trust or team relationships. Um, in the same way we used to have people doing QAs within the team. Now we have something called an adversarial QA. So we plot the AIS against each other. Um and so it's really struck that chord for me in terms of human connection and how do we maintain that in teams when we are being uh separated more and more because of AI. >> Yeah, it's one of those kind of externalities we don't really think about um these kind of subtle externalities, you know. Um maybe quickly uh Michelle would you like to share a word from your learnings today? >> Uh yes I was with Aisha in the group. Uh it was really interesting hearing from there because I haven't thought a lot about that the isolation problem that more and more we are working along with our own AI system. So we don't go to our colleagues, we don't need feedback on them and we are losing all this crossd disciplinary work or losing this frictions between colleagues that made ideas better or this share understanding that is a little bit like paradogical because all these skills empathy, trust, active listening, we learn from human interaction and we need them more than ever. part we are also losing it because we are more isolated with AI. >> Great. Thank thanks for that. Yeah, Bon, I'll let you pick up. We we near >> Yeah. Um I I think that the last the last um two points were were somewhat related, right? and and really really important that sometimes you know the the tension that we feel or the difficult conversations or the misunderstandings or you know I mean speaking for myself is sometimes you know the the necessity to ask stupid questions when something isn't clear you know or seemly stupid simple questions let's say like that that feels like friction but often that is actually what leads to you know the surprising you know connections or you know um the the really important conversations actually which sometimes might not happen if we're just trying to kind of almost automate that away. And so I thought that was a really interesting point. Um but yeah, so so thanks so much for for for joining today. Um everyone, um I hope that was that that was useful. Um and as Jos mentioned at the start, this is, you know, it's a it's an evolution, right? 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