Author: Alexis

  • Unlocking Flow and Effectiveness: A Conversation with Manuel Pais, Co-author of Team Topologies

    Unlocking Flow and Effectiveness: A Conversation with Manuel Pais, Co-author of Team Topologies

    Flow is one of those words every organization wants, and few consistently achieve.

    Teams are busy. Delivery slows down. Dependencies multiply. “Agile” rituals exist, but friction remains.

    In this episode of Le Podcast on Emerging Leadership, I spoke with Manuel Pais, co-author of Team Topologies, a book that has shaped how many modern organizations think about team design, platform strategy, and sustainable delivery.

    What I appreciate in Manuel’s approach is that it stays grounded. It is not a perfect target model to impose. It is a set of patterns that help teams evolve their structure and interactions over time.

    Here are a few key ideas from our conversation.


    The four team types

    Not labels, but building blocks

    Manuel revisits the four fundamental team types from Team Topologies:

    Stream-aligned teams
    Cross-functional teams with end-to-end ownership of a clear stream of value for a defined group of customers. The focus is not “owning a component”, it is owning outcomes.

    Enabling teams
    Small groups of specialists who help stream-aligned teams acquire skills, reduce gaps, and adopt better practices. Their job is to mentor and accelerate learning.

    Platform teams
    Teams that provide internal services in a self-serve manner, reducing friction and cognitive load for stream-aligned teams. Platform is not “a team that receives tickets”. Platform is a product.

    Complicated subsystem teams
    Used sparingly, for domains that genuinely require deep expertise and would otherwise overload stream-aligned teams. Useful, but risky when overused because they increase dependencies.

    This is the important nuance: the model is designed to reduce dependencies and overload, not to create a new set of silos.


    Cognitive load

    The limit leaders ignore at their own risk

    A major thread in our conversation is cognitive load.

    Even the best teams hit a limit when:

    • they must understand too many tools and systems
    • they must coordinate with too many stakeholders
    • they must navigate unclear processes and responsibilities
    • they carry knowledge that should not be theirs to carry

    Cognitive load is not just “too much work”. It is also “too much to hold in mind” as a team.

    Manuel describes how he and his collaborators went deeper after the book, partnering with organizational psychology research to better identify what drives cognitive load.

    The key shift is practical:
    Instead of guessing why teams struggle, leaders can look for the dominant drivers and prioritize actions that actually reduce load.


    Interactions matter more than structure

    A common misstep is to read Team Topologies and think the job is complete once teams are labeled.

    Manuel insists it is not the labels that matter. It is the interactions.

    Team Topologies describes three core interaction modes:

    Collaboration
    Two teams working together to solve a shared problem or explore a new solution.

    Facilitation
    One team helping another team learn, gain skills, adopt practices, and become more capable.

    X-as-a-Service
    A mature service that teams can consume independently, with minimal coordination.

    Healthy organizations intentionally switch between these interaction modes depending on the situation.

    This is especially important for platform teams.


    Platform teams should not become ticket factories

    Many organizations believe they already have platform teams. Often, what they have is a team that processes requests.

    Manuel explains that platform teams need to alternate interaction modes:

    • collaborate to discover what stream-aligned teams truly need
    • facilitate to help teams learn and adopt practices
    • provide X-as-a-Service when the service is mature enough to self-serve

    The goal is to reduce cognitive load and improve flow, not to centralize control.


    The leader’s role

    Make change safe, gradual, and supported

    One of the strongest leadership messages in this episode is about how change is introduced.

    Manuel advocates for evolutionary change, not big reorgs.

    For leaders, this means:

    • explicitly setting expectations that change will be iterative
    • supporting learning as responsibilities shift
    • investing in training, enabling help, and platforms that reduce load
    • ensuring teams are not left alone to “figure it out”

    The point is not to impose a perfect future model.
    The point is to keep learning and adjusting.


    A powerful idea: invest in flow enablers

    Near the end, Manuel highlights something many organizations overlook.

    If flow matters, someone must be accountable for noticing and improving it.

    He argues for investing in dedicated roles or groups focused on flow: people who identify bottlenecks, remove friction, and help teams improve interactions and ways of working.

    Not as a one-off transformation program.
    As an ongoing capability.

    In organizations where this exists, the return can be significant because removing bottlenecks often unlocks value that was already there but stuck behind dependencies and delays.


    A question to take with you

    If you want more flow, what are you doing to actively reduce cognitive load

    And who in your organization wakes up every day focused on improving flow

    Reference Links:

    Transcript:


    Alexis: [00:00:00] Welcome to the podcast on Emerging Leadership. I’m your host, Alexis Monville. Today, I have the pleasure of speaking with Manuel Pais, co-author of the influential book Team Topologies. Manuel is a leading voice on organizational design and team effectiveness. In ‘Team Topologies,’ Manuel and his co-author, Matthew Skelton, explore how successful teams organize themselves to achieve continuous and sustainable delivery.

    Manuel, welcome to the podcast on Emerging Leadership. How do you typically introduce yourself to someone you just met? 

    Manuel: Hi, first of all, thanks for for having me. Depends what is the context, but in terms of explaining what I do, the most difficult is to explain to my kids. So someone told me this week, I think a good way to think about it is almost like a teacher for [00:01:00] companies like I am.

    I wouldn’t say necessarily teaching, but helping organizations think about what else they might need to do to improve flow, to improve the engagement of teams. Obviously all the motivational aspects of getting teams to be more, to feel more autonomous and empowered, and but also delivering more value more independently to the customers.

    I see myself. In that way a lot. I’ve always had interest in kind of the educational part. I’ve done a lot of editing and reporting for InfoQ as well, for example. So although I’m a software engineer by background, I really like to help people and teams and organizations be able to reflect and think about, okay, what might we need to do different in order to.

    Improve our flow, improve the way we work, and also [00:02:00] provide more value to the customers. 

    Alexis: I really like the idea of increasing the value and increasing the satisfaction of the people who are within the organization. So the both things really like that. Team topologies. Incredibly influential. What initially you to the challenge of organizational design?

    Manuel: I think there were a couple of things. One is, I guess out of my. Curiosity to learn and try new things. I started my career as a developer, a Java developer, and then I had different roles as tester, release manager, and then team lead. And so that allowed me to start kind of the same things from different perspective, right?

    Mm-hmm. As someone in, in a test team look at. The work that the development teams are doing. You know, obviously I’m now have fairly fair, a fair amount of experience, 25 years, so I feel [00:03:00] a bit old, but I can remember well when there was all this friction between, you know, test team and the ops team and the dev team and the, the teams being so very much isolated and, and trying to do the best within their scope.

    But that was not necessarily very helpful for. The customer at the end that is waiting for some changes or some new product and so on. So that kind of start got me started thinking, okay, why at the end of the day, we’re all working in the, we should be all working towards the same goal, which is, you know, to deliver this product or to deliver some new value to the customer.

    So why do we have. Sort of sometimes very antagonistic views of each other. And then the other thing that happened was, this was sort of in the back of my head as I was working for different companies, and then when I moved into consulting around 2015, so together with Matthew Skelton, the other [00:04:00] co-author of the book, Tim Topologies, and we were doing consulting around DevOps and continuous delivery.

    This. Feeling that actually a lot of the issues are not really so as much the technical side as it is the people, the interactions, the sometimes lack of direction or too much isolation. Between teams that were the real problem. So we, we would often have client engagements where we were asked kind of a more technical job to, you know, implement some pipelines, help us adopt some DevOps practices, which is, which is fine and they’re helpful.

    But at the end, the real issues were happening in the interactions or lack of interactions between teams, incentives that were not. Aligned, which at the end of the day were not beneficial for the organization and, and the customers. So we, during this consulting years, [00:05:00] we were. Essentially applying the patterns that we talk about in the book team topologies and in our academy and so on, with different customers at the kind of more localized way.

    Like, let’s see if, for example, the platform pattern, can we help this team usually, for example, team that’s taking care of the CICD pipelines, can they act in a more. Platform as a product type of way that we talk about. Right. What would be needed? Well, they would need to become, provide services that are more self-service.

    They would need to reduce the amount of, you know, ticket based back and forth to reduce the time it takes to provide what, what, uh, product teams need. And so that was sort of the origin. And obviously today, almost six years after the first edition of the book, it’s really great to see. So many examples and case studies of actually applying the whole of, [00:06:00] or many of the team topology patterns together and that providing a lot of benefit and and return.

    Alexis: You already started, and I’m sure you are probably a little bit tired of doing it, but could you briefly outline the, the four types of teams that you in the book? 

    Manuel: Sure. It’s a bit like the. Playing your greatest hits. Right? But it’s totally fine. So the starting point are what we call stream malign teams.

    So this would are very much your cross-functional product teams. Type that with two, I would say two particularities. One is that it’s a kind of product team, but that is working on end-to-end, that has end-to-end ownership of. A stream that is valuable to customers. So there are some identified types of customers at the end of the day for that team that they know these are the people or the [00:07:00] type of customers that we are serving.

    And whatever we do, they are the primary customers that we need to sort of serve, if you like. And then the second thing is this idea of stream, because. You could say you have a product team, but that if they’re only, you know, maybe they’re taking care of some, one component of a large product and there’s a bit of confusion, right?

    Is it a product team? Well, they’re working on a product, but do they provide value directly to end customers? No, because they just, between quotes, own one technical component. Right? So the idea of streamlined teams is. You need to clearly identify what are the streams, and this can be within one larger product.

    You identify different streams of value to customers, which might be different user journeys, or it might be around different user personas for the same product. Or it can be, you know, one team is focused on acquisition, another on retention, and you know, whatever. [00:08:00] Makes sense from a business perspective, but that is aligned to some continuous stream of, of value to some kind of customers, and we wanted to make sure that was clear.

    And then once you have these stream aligned teams with. As much as possible end-to-end ownership. Ideally from we can actually generate ideas and maybe some experiments and things. We want to try to improve our stream for the end customers all the way to, we actually are able to build this, this experiments or features or what have you, and deploy them and have them being.

    Available to the customers, and that’s where things get a bit difficult because obviously you’re talking about owning the whole lifecycle from product ideation to customer availability of what you’re doing, and that’s where the problem of cognitive load comes in, right? This is a lot of information [00:09:00] overload for a single team and the competencies that you would need in such a team, right?

    If, let’s say they have. No help. If you would say to a team, now you are on your own doing all this, it’s going to be very difficult. And we know that as time goes on, technology tends to become more complicated and more things we need to know and and more practices and so on. So then we bring in the, what we could say are support type of teams, but they’re critical.

    To allow the streamline teams to work effectively. And so you either typically need to increase the skills and competencies in the streamline team. And for that pattern we find is very helpful is to have an enabling team. So that’s another type of team where usually a small group of experts in some domain of knowledge are intentionally so the key, the key here is that they actually.

    Are putting in, they have the availability to focus on [00:10:00] helping the streamlined teams learn the skills and, and bridge some gaps in their competencies. And they’re also in a good position to bring to the organization innovation, new ways of working, or maybe some new tooling and making the bridge between what.

    The organization does and, and uses today and what is available outside in, in the industry. And then we have platforms which typically you, I mean, we could say you might start with a platform team as in one team that takes care of some, some kind of services that are consumed by the s streamlined teams in an, in a way that makes their life easier.

    Because if we provide a platform, but actually this is just adding up more effort, setting up more need to understand how the platform works, or you need to manage work through tickets to get things done, then that’s not. A very helpful platform in the, in the sense of reducing the [00:11:00] load on streamlined teams.

    But usually it ends up being not just one platform team. For most organizations, you end up with what should be a platform group, like a grouping of teams working in a platform. Ideally, those teams inside the platform are also aligned to some streams of value to internal customers. The in the stream aligned teams, right?

    Mm-hmm. And then there’s was another type of team that we. Sort of reluctantly felt we had to include no discredit to the complicated of system teams, but we should use them sparsely when there’s really, sometimes we know there’s a component or a service or some part of a larger product that is very complicated because either the algorithm is very complicated or it could be.

    In some occasions, the technology is very outdated and you only have a few experts who understand how to make changes to this technology. There are some exceptional situations [00:12:00] where it’s say, actually from a cognitive load perspective, we need a team that takes care of this component or subsystem so that we don’t sort of.

    Impact the other streamlined teams with all the knowledge that would be required for them to be able to make changes to this component, right? Mm-hmm. But we need, we need to be careful not to overuse this pattern because it then becomes very similar to, or you risk getting into component teams and then you start having all these dependencies.

    Because if we have many component teams, then to make a change. That the customer needs, I’m gonna have to start coordinating between component A team, component B, and all these kind of issues that I think a lot of us are familiar with, 

    Alexis: unfortunately. Yeah. You spoke about cognitive load as a, as a key element.

    Can you come back to that and maybe, uh, illustrate with an example? 

    Manuel: Sure. So in terms of brief. [00:13:00] Kind of background initially, cognitive load theory from a field of psychology. But essentially what we did in Tim Topologies is if there is cognitive load limit. So there’s a limit to our working memory, right, as individuals, but as a team, we, it starts as a group of individuals.

    It, it’s more than that. But if I have a group of individuals, there’s also a limit to their capacity as a group. So what’s interesting then is that the cognitive load might have different natures, and even though we cannot cleanly split and say, well, this. Part of my working memories is allocated to the actual business problems, and this other part of my memory is allocated to some kind of more tool related problems or something like that.

    Because you know, we’re not a c plus plus program, so we don’t work like that. Everything is sort of mixed, but if you start to be able to determine, [00:14:00] well, actually, what are the things that the team is responsible or has to worry about that maybe they shouldn’t. Because it’s not really helping them deliver value to the customers better or, or more effectively are things that are more distractions, right?

    So we start to be able to differentiate, not just say, well, the workload is, is too high, or the cognitive load is, is high on the teams. That’s very common. But then. What is the kind of work that, and, and knowledge and needs that the team should focus versus what they actually should be kind of isolated from?

    And so I. That is the key idea, right? So when we talk about platforms, for example, it’s always from the point of view, how are we gonna reduce cognitive load on the streamline teams? Well, if we provide easy ways to, you know, the typical examples are, you know, provision infrastructure or easy ways to deploy their [00:15:00] changes to production with deployment pipelines or easy ways to diagnose problems in the live environment.

    All these things that, yes, the teams. Will help the teams to use them, but they don’t necessarily need to know all the details of how those things and those services work underneath. Right. That’s where we start to be able to push down and outside the team certain knowledge and details that they really should not be the core focus.

    Right. ’cause I’ve talked to teams that had, they started counting and they had like had to understand. Over a hundred different tools that they used in their lifecycle and frameworks and all this stuff. So that becomes really not very productive. Part of the work with its after the book was published in 2019 was to, let’s take a more deeper analysis of what team cognitive load really means.

    And so we partnered with Dr. Laura [00:16:00] Vais, who’s PhD in organizational psychology, and she was able to. Do the research and, and find actually different academia research and, and, and papers and, and findings that helped us. She was able to define a model to assess cognitive load on teams. And so this model that we’ve developed and has now been, we built a product based on this model, which is called temperature.

    So as in taking the temperature of a team temperature. Mm-hmm. And so. What she found is that even though we cannot measure directly the cognitive load, we can assess what are the main drivers, what are the things that are driving cognitive load up in the teams right there. So there are a number of different potential drivers.

    So it could be things related to the characteristics of the work itself. Could be about the characteristics of the team itself. It can [00:17:00] be about the work environment and tools. It can be about which processes we follow. So it’s really interesting and we start to see more organizations adopting this way of looking at almost like an indicator for team health and team productivity.

    If. You are just looking and saying, well, cognitive load is high because teams are very overloaded and stressed, but you don’t have a way to go deeper and say, well, it’s actually because they have too many stakeholders asking. Things and there’s no clear direction, or is it because they have, you know, poor tooling that makes it difficult to do their work and increases cognitive load?

    Without that kind of insight, then we’re sort of guessing what can we do to help these teams, right? And mm-hmm. We might. Be lucky, and obviously we talk to the teams. We might realize, yes, maybe we need some new platform services or [00:18:00] something else, or some training or what have you, but we might also actually be looking at the symptoms and not the real causes of that high cognitive load.

    And that means we are wasting in a way our our time because we’re not actually working on the highest drivers of cognitive load. We might be working on some things that are helpful, but are actually not the main problems that we should be looking into. 

    Alexis: I. Okay. And so in your experience, we have four labels for teams.

    The temptation could be to, to put labels on team and consider, oh, eh, that’s why you have a so beautiful design and I’m done. What kind of common missteps organization make regarding those team interactions, but ’cause it’s not only leveling them, it’s really working on the interactions between them. 

    Manuel: Yeah, no, I think you’re, you’re spot on that that is one.

    Big issue is that even many organizations or or people of who [00:19:00] read Team Topologies or they heard about this, you know, types of teams, they will sometimes think that, like you’re saying that, well, we just design a new target operating model, or however you want to call it, and we have this perfect.

    Idealization of which teams should we have, which platforms and you know, and now it’s just a matter of implementing, executing, and everything will be fantastic. And that’s not how things really work, right? So one of the. I would say the battles that we, we are still fighting is for organizations to take a much more evolutionary approach to organizational change as well.

    The good thing is there are some really great examples now that we start to see where some organizations, I’m, I’m thinking in particular a company called Yasir. I think it is not very known in in Europe or the us, but they, they are a big app in the [00:20:00] North African market. They call it a super app for doing multiple things like food delivery, ride hailing and other stuff, financial services.

    What’s interesting is that, you know, they had this typical growth spurt of the organization and things were not working very well anymore. Like they like in when they were a startup. Essentially they realized, okay, we need to change the way we organize because there’s all the dependencies. Teams are not autonomous, et cetera.

    And they looked into team topologies, but they realized it’s not that team topology tells you it’s, it’s not a, in my opinion, it’s not a model for you to follow by the book is. Giving you some building blocks to think about what kind of teams we might need, how are things going to evolve over time? The evolutionary part is key.

    So what they did that I found interesting is they actually intentionally said, let’s, I. Take small [00:21:00] steps and do, they had like four month iterations essentially, where they would say, well, we, we made this change. Like we split up this team into two smaller teams, or we tried to make this team more stream aligned, or we introduced some kind of platform service, so they would make small changes, try it out for a couple months and then.

    Reflect and see how did this help us or not? How did it work? And then use that for the next iteration. It’s really almost like if you’re, if you would be back to when you know Agile was introduced, it’s like start breaking down this huge pieces of work that we used to, to have that required this big planning upfront.

    And then at the end when you are delivering, you realize there are a lot of things that were not. Based on assumptions that were not true and all this stuff. It’s essentially the same thing, but for organizational change. Start to [00:22:00] break it down to a level where you can make small changes and learn from them and not think that you can put on paper this ideal design and this.

    Then it’s just a matter of execution. ’cause that always typically doesn’t end up well. 

    Alexis: Yeah. You mentioned something just before. There’s probably something about the platform teams that probably many organization could feel that they already have some platform teams. But you, you mentioned something about the interaction with the platform team, and it seems it’s not some, some teams to which you submit tickets.

    That’s what, so tell me more about how, how platform teams behave. 

    Manuel: Yeah, sure. Just before I do that, I think that that raises also the point that the interactions between teams, whether it’s you know, platform or any other kind of teams, are also key to that evolutionary approach, right? [00:23:00] It’s not just defining types of teams or trying to map your existing teams to stream aligned or enabling platform.

    It’s actually looking at. The evolution and are these teams interacting in a way that is helpful or not? Are is, are the interactions clear or not? So that was also the key aspect of team topology is to provide, again, some building blocks, some core interaction modes for teams to leverage. And it’s not about saying, oh, we only do this interaction.

    It’s about are this. Three types of interactions helpful to frame your communication and working with other teams so that you have a clear idea of what are we trying to achieve? Why are you, why are we collaborating? Is there like a common problem that we need to solve together? Or is this more like actually one team depends on the other because the other team.

    Own [00:24:00] some, some skills or some tooling. So we, we are actually depending on them, it’s not that we, it’s not a collaboration in the sense of solving some, some type of problem. So this framing of the, the interaction modes helps us work better with other teams, with, you know, with less waste and, and with more purpose.

    So the three interaction modes are collaboration, like I just mentioned, two teams working. Together on some common problem facilitating, which is one team that has some knowledge or skills that is helping another team learn and upskill and gain knowledge. And then we have what we call X as a service, which is obviously based on the ideas of infrastructure as a service, this kind of approach where ideally for a platform that is your.

    Your goal for you, the services you provide in the platform, is that they can be consumed independently, that you have a service that is mature enough and resilient and has the right [00:25:00] onboarding and documentation for teams to be able to self-serve, understand what service does, and use it and go on with their work.

    So. For platform teams. That sort of is one of the main interactions, but another kind of anti pattern I see is that related to the previous question, is that when organizations are. Defining and say, well, we need this platform. We need this. This teams in the platform, they typically jump to, oh yes, this service is gonna be consumed in this as a service way.

    But oftentimes that is you need to alternate between, yes, at some point that service might be stable enough and and easy to consume, but. You need to go through collaboration first to understand what do the streamlined teams really need from the platform? What is the right interface? What should we abstract versus what we shouldn’t?

    AB abstract in the platform. All that should be coming from [00:26:00] collaboration with the streamlined teams and finally the platform team. And there’s interesting. Case from Adidas that they do this very intentionally, where the platform teams should also expect to do some facilitating work because they will be typically experts in some domain, right?

    Whether that’s infrastructure or testing or you know, something that the platform provides some service, but there’s the whole knowledge of the domain of the skill. That maybe your streamlined teams don’t have, so they, they might not even be able to use the platform properly because they don’t know what’s a good practice and why should I use this service that the platform provides.

    Right. So I think a more mature view that I’ve, I’ve seen in companies like Adidas is to have this expectation for the platform teams or, or the teams working the inside the platform. You are gonna need to alternate between these three interaction [00:27:00] modes. Sometimes you’re gonna have to collaborate when you’re trying to build something that helps the teams to reduce cognitive load.

    Sometimes you’re gonna have to help them onboard and learn about the service and the domain. Other times it’s acts as a service, so you basically need to take care of the operations of that service and obviously fix incidents and provide a good kind of support to the teams using that service. 

    Alexis: It’s interesting because the way some people describe themselves tells us something about how they envision those interaction mode.

    I remember when the book was just out and a, a team of architects in the company, which was a bit surprising to me, but that’s how they were organized. Those architects owned very complex things. 

    Manuel: Yeah. 

    Alexis: That, that all the, all the other teams were supposed to use, and that was very complicated for the other teams to use that.

    And they consider themselves as, oh, we own that [00:28:00] complicated subsystem because all the others are, are so dumb, they dunno how to use it. And then, uh, someone read the book and they say, oh, well you are an enabling team. I said, based on their behavior, it does not look like that. 

    Manuel: For sure. Yes, that’s.

    Alexis: In that box was not helping because their behavior was exactly the opposite of what was needed for the other teams. 

    Manuel: That’s a really good point, and when we were writing the book that the purpose of these types of teams was also to elicit certain kinds of behaviors that would be expected for these types of teams.

    Right? Like you’re saying, you know, if you are in an enabling team, the expectation is not that you are. Sort of hoarding some complicated services and, and you’re the only experts who know how to change it, then that’s definitely not helpful for fast flow. And also it’s not expected behavior from an an enabling team, which is there to [00:29:00] teach and mentor and help others grow rather than being the smartest in the room.

    Which is interesting because effectively. Also common question after the book is, okay then if you need these different types of teams, then for example, in a startup, then you. What do you do? Because you cannot have, you don’t have enough people to have dedicated platform enabling teams. And that’s interesting to me in this sense because it’s, again, it’s more about the behaviors and the teams are almost like an implementation detail between quotes.

    At some point it makes sense to have dedicated teams, but if you are in a 30 people startup, yes, probably you, you have most everyone works in. Kind of streamline teams. Everyone can do everything, but that doesn’t mean you cannot have some enabling and platform behaviors. Where maybe in this scenario, enabling essentially means, you know, having some mentoring from people who are more senior in the company, [00:30:00] helping the new people or new teams.

    Maybe the platform pattern in a startup is actually just. A few people who dedicate, you know, a couple of hours per week to document how are we doing, how we’re using AWS, how are we setting up our deployment pipelines? You know, you don’t actually have real platform services, but maybe it’s just a wiki that helps other teams.

    Okay. If I follow this sort of guidelines and guidance, it helps me get started to deploy a new service or something like that. So the pattern is there and the behaviors are there, and then the actual dedicated teams is, might come later when you grow and you scale up. Also, you shouldn’t just never create the teams, the dedicated teams.

    It’s a matter of scale, but the behaviors can be there from the beginning. 

    Alexis: I, I really like that because that can really also facilitate the onboarding of new people on the team [00:31:00] because it clarifies how it works and it leaves some spaces that you don’t necessarily need to learn everything from in that particular area.

    You. That part, you need to learn everything there. So let’s focus on that first. That’s helpful. I, uh, I feel you probably work with a lot of leaders in organization that would like to get the benefits, let’s say, of a fast flow organization. All the things you were describing at the beginning, what are their roles in the implementation in the way you, you see that?

    Evolutionary change. 

    Manuel: So do you mean like for specific types of, of leadership, like CTO or, 

    Alexis: yeah, for example. Yeah. 

    Manuel: Yeah. I think going back to the idea of an evolutionary approach, I think that would be one of the main things, especially people in senior leadership, is sort of setting the tone that. We’re not doing this big reorg where people might [00:32:00] be afraid that, you know, their role is gonna change or the the team they work with is gonna change.

    It’s actually telling them, look, there’s gonna be changes, but we’re gonna be doing this in an evolutionary approach. So we learn and we adjust when things are not right. It’s not just, you know, one step change and then good luck and hopefully things are are better. So that would be. A big one. And it doesn’t mean that you need to be directly involved in figuring out what changes are needed.

    It’s more providing the support that, you know, let, let’s, let’s do this and, and learn and evolve. And it’s also providing the support that. People are gonna need, especially if you know it’s, there are changes in terms of their responsibility, the competencies that they need. If we’re talking about, for example, you have teams that are going to ideally become more stream aligned with more end-to-end ownership, then make sure that we are identifying what are the gaps that these [00:33:00] teams have.

    Because if they’ve never done actual user research or if they never done testing or what have you, then they’re gonna need help. They, they. Need to feel that they’re going to be supported in that journey, that there’s gonna be training or there’s gonna be some enabling teams perhaps. So providing that level of support and for people to know that we’re not sort of alone, and that we’re just being asked to do different things and there’s no support.

    So you probably need to factor that into your budgets as well to make sure that we, we can do that. And yeah, I think tho those two things. Making it. There’s nothing like set in stone. It’s about learning and taking steps towards improving the way we work and how we’re delivering value. And secondly, that, you know, people feel like there’s gonna be support in this journey.

    It’s not suddenly we’re gonna be asked to do something different without [00:34:00] necessary learning. 

    Alexis: Excellent. And so looking forward a little bit, are there emerging trends or new challenges you’re currently exploring around team and organization? 

    Manuel: Yes. I mean, we continue to do more research on team cognitive load.

    ’cause what we have so far, the model we have. It’s scientific model and, and it’s systematic, but obviously it’s not, we can never say it’s complete. There are so many factors that can influence cognitive load on teams. We have a pretty good starting point with model and with temperature that allows teams to have a pretty good view on what is actually influencing their cognitive load.

    But there are. New areas of research that we want to explore. Obviously today with artificial intelligence and all the benefits, but also drawbacks it can bring. Mm-hmm. That’s an area that’s, that’s very interesting that we want to research, like how does it impact [00:35:00] cognitive load on teams where it’s important to, if we can help set expectations.

    Right. ’cause. You could say, well, in general, our physical intelligence is going to reduce cognitive load if it’s able to do certain tasks and certain work that the teams don’t have to do themselves anymore. But on the other hand, because it’s not deterministic and because sometimes the tools don’t have the context as necessary, and you always need the humans driving that work, it might be increasing cognitive load.

    For the teams, right? If you know the way the the tools work is sometimes helpful, sometimes not so helpful. But this is something we want to research. And then the other thing that we start to see that we kind of expected from the time we wrote the book, but it’s nice to see. Happening in, in real life, let’s say, is, uh, applying the ideas from team topologies outside of it.

    So that can be, [00:36:00] there’s an example from a, a company Norway called Capra Consulting, where they actually applied the ideas to the whole organization, so to sales, to leadership. They actually. Shut down their management group and, and try to push down this decision making as much as possible to the stream teams.

    Mm-hmm. So that’s one example. And then there are even examples. I’ve been doing a little bit of guidance with NGO in Latin America, where they’re also looking at the patterns of Tim Topologies and they don’t build any. Software, right? These are initiatives to promote inclusion of socially disfavor people in kind of the digital world and the digital working market.

    And so they realized like they have some bottlenecks in delivering their initiatives, their social initiatives, and they start looking at, okay, could this team become more of a platform team so that they are not a bottleneck so that other teams can [00:37:00] self-serve what this team is doing inside the organization?

    So I find that really, really exciting and, and I think we’ll see more examples of applying the patterns. Way beyond engineering and technology. 

    Alexis: You mentioned the team temperature assessment or way of looking at the health of the team in a way, yeah. Is it something that is already available today? 

    Manuel: Yes. So if you go to temperature.com, essentially it’s a product, but you can also find details about the model behind it so that you can understand what is the research that was done, what’s, what are the drivers that we’re looking at?

    And temperature is the implementation of that model, if you like, into, into a product that’s free to use for up to 25 teams. So yeah, I would love feedback if people want to try it out and, and see what they think about the, the results. 

    Alexis: Excellent, excellent. Thank you [00:38:00] for having joined the podcast. That maybe the one thing I would like to ask you is, what is the question I should have asked you?

    Manuel: That’s a, uh, difficult question. I think we covered a lot of ground, I think in this time, and the question about, I think there’s still more questions about kind of how do you do this transformation from whether you are kind of a project oriented organization. Obviously today there’s a lot of organizations trying to be a product oriented organization.

    I think there’s even. In my opinion, another kind of step, which is a value stream oriented organization where the products are a means to provide value, but you actually have a higher level view where you understand the value streams. But this journey, you know, obviously takes time and it’s not always easy.

    One part of, like I said, is to take an evolutionary approach and, and the other thing. Is that what I’ve seen in many, [00:39:00] many organizations, they haven’t invested in internal people who focus on flow, right? Regardless how much we talk about fast flow, yes, you have transformation programs, but people who are actually there.

    Role is to look at flow and look at where are the bottlenecks, where are the frictions, where are interactions not well defined and therefore causing problems, which in my view could be a sort of enabling role, right? But from a flow perspective, how do we. Improve the flow in the organization where sometimes maybe we have to help teams understand ideas from team topologies, but maybe other times we have to help them learn about lean development and lean product portfolio or what have you.

    Right? But having this intentional group or people in the organization whose role is to, to do that, that’s something that I. I think the return on, on [00:40:00] that kind of investment is, is really high because as soon as you start identifying bottlenecks and you start to see where the work is, is waiting because of dependencies, unnecessary approvals and, and this kind of things, when you start to remove and unlock that.

    The value to the organization is can be really high. And so having some people focused on that, obviously you, ideally, the teams themselves have this awareness and they raise. Issues where we are blocked or you know, the way this platform service is provided is not really helpful. That would be healthy, in my opinion, if the organization is set up so that everyone feels they can raise issues around flow.

    But you probably. Would benefit a lot from having a group of people who are focused on this. Some organizations like ING Bank, for example, they do have a ways of working group. I’m not sure that’s still the name that they use, but [00:41:00] people who are helping. The rest of organization, learn about flow, learn about better ways of working and and things like that.

    So I see that as a kind of flow enabler approach as well. 

    Alexis: Excellent. Thank you very much, Manuel. Thank you for having joined the podcast today. 

    Manuel: Thank you.

  • One Step Higher: A Simple Process for Better Business Leadership

    One Step Higher: A Simple Process for Better Business Leadership

    This month, let’s dive deeper into a critical dimension of the Emerging Leadership Navigator: the Business Axis.

    Effective leaders have a clear understanding of their organization, its strategies, and the broader market landscape. Below are 8 essential reflection questions designed to help you enhance your leadership impact.

    Your Reflection Process:

    For each question below, follow these four simple but powerful steps:

    1. Evaluate: On a scale from 1 to 10, where do you currently stand? (1 means minimal, 10 means excellent)
    2. Celebrate: What’s already helping you to be at your current level? (Habits, resources, people, mindset…)
    3. Stretch: What would it take to move one small step (one point) higher?
      • What specific changes or improvements would you notice in yourself?
      • What would others around you notice?
    4. Commit: In the next 72 hours, what tiny signs of progress could you observe? What simple, actionable first step could you take based on this reflection?

    It is even better if you do it in writing!

    Business Axis Reflection Questions:

    1. Market Insight:
      How clearly do you understand current trends shaping your market and industry?
    2. Mission Alignment:
      How often do you intentionally align your team’s objectives with the overall mission and vision of your organization?
    3. Value Proposition:
      How confidently can you explain your organization’s unique value proposition to a new stakeholder or potential customer?
    4. Strategic Engagement:
      How regularly do you engage your team in strategic discussions about the future direction and priorities of the business?
    5. Adaptability:
      How open are you to adjusting your business strategies based on new insights or market developments?
    6. Customer Orientation:
      How consistently do you ensure your team’s objectives are informed by customer needs, feedback, and expectations?
    7. Experimental Mindset:
      To what extent do you encourage your team to test new business strategies quickly through experimentation rather than extensive analysis?
    8. Collaboration:
      How frequently do you actively pursue collaboration across different business units or stakeholders to achieve unified and cohesive project outcomes?

    Make Your Reflection Actionable:

    Take a few moments now, pick just one question above, and go through the reflection process. Then, share your insights or first steps by replying to this email—I’d love to hear your discoveries!

    Leadership is about continuous, incremental improvement. Small steps taken consistently create significant changes over time.

    Let’s keep growing together.

  • The One Exercise Every Leader Should Do Right Now

    The One Exercise Every Leader Should Do Right Now

    This month, I’d like to invite you into a practice that consistently helps leaders and teams clarify their direction, energize their actions, and align around what truly matters: Personal Visioning, inspired by the extraordinary approach developed at Zingerman’s. I was lucky enough to learn about the approach during a ZingTrain session organized by the OpenStack community in Ann Arbor, and I can attest that it is fantastic!

    At Zingerman’s, the personal visioning practice starts with a simple yet powerful question:

    “What does success look like for you, at a specific point in the future?”

    However, before diving into visioning, Ari Weinzweig, co-founder of Zingerman’s, recommends a crucial preparatory step:

    First, make a list of things you’re proud of.
    This preliminary practice helps shift your mindset toward positivity and possibility. Celebrating your achievements—big or small—energizes you and prepares you to envision a meaningful and inspiring future.

    Next, vividly describe what success looks and feels like for you 3, 5, or 10 years from now. Write in the present tense, as though it’s already happening. The richer and more specific your description, the more powerful and actionable your vision will become.

    Why Personal Visioning Matters

    When leadership is reactive—driven solely by external pressures—it can feel draining and aimless. A personal vision provides a compass for decision-making and growth, enabling leaders to move intentionally toward meaningful goals.

    When each team member has clarity on their personal vision, it empowers more purposeful collaboration and drives collective success.

    Ready to Try It Yourself? Follow These Steps:

    1. Write your pride list: Note down achievements, strengths, and moments of joy that you’re proud of.
    2. Pick your timeframe: Choose a specific future date—3, 5, or even 10 years ahead.
    3. Write vividly in the present tense: Describe where you are, what you’re doing, who’s around you, how you feel, and why this matters deeply to you.
    4. Include personal and professional details: Let your vision reflect your whole self.
    5. Share your vision: Sharing can create connection and accountability, making your vision even more likely to become reality.

    If you want to explore further, Ari’s book Zingerman’s Guide to Good Leading, Part 1: A Lapsed Anarchist’s Approach to Building a Great Business offers in-depth guidance, engaging stories, and practical tips on personal visioning.

    What could become possible if you clearly defined your personal vision? How might that clarity influence your leadership right now?

    Let’s continue the conversation—reply to this email and share a highlight from your pride list or a piece of your vision. I’d love to hear from you.

  • We’re living through a transformation, but do we have the tools to make sense of it?

    We’re living through a transformation, but do we have the tools to make sense of it?

    Robb Smith’s paper A Sociology of Big Pictures argues that we’re not just facing a set of isolated crises. We’re navigating a full-blown transformation age.

    An era where:

    – Disruption is the default.

    – Shared meaning is eroding.

    – We’re flooded with information but starving for clarity.

    And underneath it all, we face a metacrisis: ecological breakdown, sensemaking collapse, political volatility, and technological upheaval.

    The answer, Smith suggests, lies not in more noise, but in a new kind of seeing.

    He calls it the integrative worldview. A way of thinking that:

    – Welcomes complexity.

    – Embraces multiple perspectives.

    – Prioritizes collaboration, coherence, and compassion.

    What struck me most? It’s not just a theory. It’s a strategy.

    Smith outlines how integrative thinkers and communities can come together, intentionally and strategically, to create the conditions for this worldview to spread. Not as an ideology but as a shared inquiry. Not through domination, but through deep cooperation.

    It’s a hopeful blueprint for change and a direct challenge to those who believe a better future is possible but aren’t yet acting like it.

    Here’s the link to the paper: https://integrallife.com/a-sociology-of-big-pictures-network-strategy-for-a-21st-century-worldview/

    And here’s a conversation summary of the paper created with NotebookLM:

    Curious to hear your take. What resonates? What challenges you?

    #SystemsThinking #Leadership

    Here is the transcript of the conversation:

     Okay, so you’ve given us the sources for this deep dive and now, well, now we get to kind of pull out the good stuff, right? I mean, what are the really important ideas, the things that might actually change how you see the world? Yeah. You want to get right to the heart of it and, um, make it clear and fun along the way.

    Right? No one wants to wade through tons of dense writing. Yeah, absolutely. And, and the source you shared today, it takes us into some pretty fascinating territory. I gotta say Rob Smith’s, um, a sociology of big pictures. Network strategy for a 21st century worldview. And, and this isn’t just some abstract philosophy, you know, it’s, it’s a look at how the world is changing right now.

    Like these huge shifts we’re all feeling. And it even lays out a plan, like a strategy for how one particular way of seeing things might actually gain some traction. Exactly. Yeah. So, so for you, our listener, we’re kind of on a mission here, right? We’re gonna try to unpack two big things. One, what Smith calls this transformation age, these massive shifts we’re all living through.

    And two. Why he thinks a collaborative network, like people working together in a very specific way could be the way forward.

    It’s like a roadmap for a new kind of thinking or a new way of being almost.

    Yeah, exactly. So by the end of this, you know, you should have a much clearer picture of like, what are these deep changes happening and what’s this?

    This idea about how we might actually respond. Pretty cool, huh? So let’s dive in.

    Let’s do it. So to, to start, we gotta kind of get a handle on this landscape. Smith is describing, he talks about this, um, this transformation age, and he puts a pretty specific starting point, like mid to late two thousands, the time when, you know.

    Smartphones and high speed internet really took off. Right. Just like

    everything changed around that time. Yeah. His,

    his argument is that that was the moment when like continuous and fundamental change was unleashed, and it’s in all these areas that, you know, used to feel pretty stable, like our economy, social structures, culture, even just the way we connect with each other.

    It’s, it’s almost like he saw the information, age reach, like a breaking point. Right. Like it had to change or, or something. Mm-hmm. He, he even suggested this earlier that the sheer volume of information could become. I don’t know. Destabilizing. It’s interesting, it brings up Margaret Archer, right? Mm-hmm.

    Her idea of a morphogenic society. What, what’s the core idea there?

    Yeah, so, so Archer, she argues that what makes our time different is that change itself becomes the dominant force change over stability. So it’s, it’s like this, right? Think of it like instead of society being, you know, relatively steady with just, you know, occasional disruptions, it’s like disruption is the steady state.

    Now

    change is the only constant.

    Exactly. And one of her big points is that, um, variety begets variety. So these aren’t just isolated things happening, you know, it’s, it’s like they create these ripple effects where one change leads to another and things start accelerating.

    So it’s, it’s like one shift triggers another and and the pace picks up.

    Doesn’t stop. Yeah. Yeah. And, and she also talks about this, um, this convivial logic of abundance that comes out of this. It, it sounds kind of optimistic actually.

    Yeah, well it is, but it’s also, it’s nuance. So as we create more ideas, more technology, more cultural stuff, the old way of doing things like competing over scarce resources, that starts to weaken.

    We see more shared resources, more collaborative creation, think open source software, creative commons, that kind of thing.

    Okay, so we’re better at sharing. Potentially, but there’s a downside,

    right? Right. This constant flux, it also has a cost. The shared values, the common understandings that, that kind of hold the society together, those start to fray,

    right?

    Because if everything is constantly shifting, how do we even agree on what’s, what’s real, what’s important? And and Archer also mentions these, um, these demi realities, these sort of like shared. Illusions or, or misunderstandings, you

    know? Right. And this is huge. It raises this question of like, how do we even make sense of the world if everything’s always changing and while novelty, you know, it can bring progress.

    It can also create new kinds of disconnection and reinforce the inequalities that are already there, these demi realities. It’s like people get persuaded to just accept superficial appearances is the whole truth.

    It’s like we’re, we’re losing our grip on, on what’s real and Smith. He adds this layer that these changes aren’t happening in isolation.

    Right. They’re occurring across multiple dimensions. He, he even mentions integral meta theory and it’s four quadrants.

    Yeah. He’s saying these changes aren’t just happening out there in the world. You know? It’s affecting us personally too, and in our relationships and our cultures and in the larger systems that we’re all a part of.

    It’s like change on all fronts, which is why I guess this multi-level view is important and this all leads to what? Smith along with others called the Metris.

    Ooh, yeah, the

    metris, it, it sounds heavy and probably for good reason,

    right? As Smith and others like Hedland and as Bern Hargins describe it, it’s, well, it’s this interconnected web of global problems, these wicked problems that seem almost impossible to solve.

    And they’re not separate. They, they arise together and they influence each other deeply. Smith, he identifies five key areas, and the first one is the meaning crisis.

    The meaning crisis, this feeling, it’s like a widespread feeling that you don’t have a clear purpose or a direction even with all the comforts and advancements of modern life.

    Right. The question of what’s the point? Yeah. It feels like that’s hanging in the air a lot these days.

    Yeah, and it’s like despite all our progress, you know, the. The grand narratives, these big stories that used to give our lives context and meaning. They’ve, well, they’ve kind of broken down. It leaves a lot of people feeling lost and then there’s the sensemaking crisis, or what he calls hyper reality.

    This is where it gets really interesting

    hyper reality. Yeah. He’s drawing on badri art here. Yeah. This shift from a real grounded world. Yeah. To this constructed like limitless. Hyperreal can, can you unpack that for us a little bit? Uh,

    yeah. So, so what Baldry Yard saw and Smith builds on this is how our signs and symbols, you know, our language, our images, especially online, how they, how they change over time.

    Like at first they reflect reality, right? Then they start to distort it and eventually they can actually become a kind of artificial reality in themselves. These signs create what he called ra, right? These artificial environments that can actually feel more real than what they’re supposed to be.

    Representing, it blurs the lines between genuine and, and manufactured.

    It’s like we’re living in this world of, of carefully constructed illusions. And Smith brings in alderman too. His idea of the algorithmic undertow. What’s, what’s that all about?

    Yeah, so, so Alderman, he points out how these personalized information feeds that we see online, all driven by algorithms, right?

    They create these, uh, these algorithmic tunnels, I think he calls them. We get channeled into these narrow pathways of information, and we become more and more isolated in our own little curated bubbles, and, and it makes it even harder to agree on anything on a shared understanding of the world.

    Which, you know, it makes sense when you look at the, the extreme partisan divisions and the decline in public trust.

    Mm. Smith even brings up those Pew Research Center stats from back in 2019 showing this massive drop in trust in government and it’s, it a huge shift and, and bore’s quote, you know, it really sticks with me too. We live in a world where there’s more and more information and less and less meaning, like mm-hmm.

    Having more information doesn’t necessarily make things clearer. It can actually just create more noise.

    Exactly. Constant change, overwhelming information and no stable framework to, to make sense of it all. It leads to this breakdown of shared understanding, and then of course we have the big one, the, the ecological crisis.

    The Anthropocene,

    right. The, the really big one. Global warming species loss, resource depletion. It’s, it’s almost too much to process.

    It really is. And Smith, he, he mentions that UN climate report with, with the record CO2 levels. Mm-hmm. You know, and then the serious risks of, of ecological and economic collapse, the warnings about a sixth mass extinction.

    It’s like species are disappearing at a, at an alarming rate. And then the IPCC, they say we need to make drastic emissions cuts and, and he points out that in a lot of ways, all the other crises, they’re connected to this one.

    Yeah. This one underlies ’em all and then we get to geopolitics with the great release sounds.

    Sounds kind of dramatic.

    Yeah. Well, Smith, he uses this term and it comes from the study of complex systems. You know, those systems that go through these cycles of growth and stability then collapse and then renewal. He’s arguing that the global order, the one that we’ve had since World War ii, largely led by the us it’s now in this phase of release or or breakdown.

    Mm-hmm. Because of all these internal pressures, the US is, you know, it’s pulling back from its traditional leadership role, which leads to this, this more multipolar world and a much less predictable one.

    So the old order is, is dissolving and we’re entering this, this period of greater uncertainty. And then the final.

    Piece of this meta crisis puzzle is the technological singularity, the rise of ai, artificial intelligence.

    And this isn’t sci-fi anymore. With the progress we’re seeing in ai. You know, we’re facing a future where non-human intelligence is gonna have a huge impact on, well, on everything, on how we understand information, how we address climate change, global politics, you name it, it affects everything.

    Yeah, it’s, it’s a powerful picture. Bit unsettling, to be honest. All these forces. Interacting and amplifying each other. It’s, it’s a lot. And, and this is where Smith kind of shifts gears, right? He starts talking about his proposed solution, the, the growth of what he calls an integrative worldview and a, a strategic effort to promote it.

    Yeah. So amidst all this talk of crisis, you know, he sees this potential positive development, this emerging integrative worldview. And, and he mentions that, uh, ner guard, headland, and Melin, they identify it as a fourth major type of worldview, right? Alongside the more traditional, modern and postmodern ones.

    Okay. A fourth one. And we haven’t even really defined worldview yet, have we?

    Not really. No. So he brings in definitions from, from Hi and Rabi.

    Okay. Let’s do it. What is a worldview then, in this context?

    Okay, so according to, hi, it’s basically the, the fundamental assumptions we have about. About reality, like the lens through which we make sense of everything.

    And karbi, he adds that a worldview takes care of something. It it helps us navigate life, you know, and meet our needs.

    Okay, that makes sense. So it’s how we see the world and how we use that understanding to, to live in the world.

    Exactly. And, and Smith’s point is that for this integrative worldview to really work.

    To really take hold. It has to show that it can address our current problems better than the dominant modern worldview, which he says is often too focused on material things and breaking things down into smaller and smaller parts, and on competition and individual game rather than the whole picture.

    Okay. So Smith’s clearly a big proponent of this, this integrative worldview. What does he see as its main strengths? What does it offer that, that the others don’t?

    Well, in short, he says it’s the first worldview to really take into account like the full complexity of being human. You know, it draws on all the knowledge and wisdom that we’ve accumulated across cultures and throughout history to create a picture of reality that’s both scientifically sound and spiritually meaningful.

    He says it’s something that can liberate us because it recognizes the inherent value of reality and our role in it. It integrates different perspectives into a larger whole. It’s, it’s driven by. Compassion, ethical considerations. It’s sophisticated in its approach to knowledge and it’s, it’s constantly questioning and refining itself.

    Sounds pretty ambitious. Mm-hmm. And his strategy to, to help this worldview spread, it involves all these different meta trite movements, right? Mm-hmm. Like meta modernism, integral philosophy, parts of the intellectual deep web. Mm-hmm. He suggests they need to, uh, cohere around core principles, what he calls them, minimal integrative worldview, and, and then start working together strategically.

    Right. Exactly. He sees these different groups as already sharing a lot of the same underlying assumptions, even if they use different language or have different areas of focus. And his grand strategy, it’s. It’s basically a call for the leaders in these movements to connect intentionally, to figure out those shared foundational beliefs that that minimal integrative worldview, and then to coordinate their efforts to get more attention for their ideas in the wider world.

    Because in today’s information environment, that’s, that’s everything, right? It’s all about attention. Who, who gets it and who keeps it. And this leads him to, to look at the, the history and sociology of, of how ideas spread, drawing a lot on the work of Randall Collins.

    Right. And what’s really interesting is, is Collins’ argument in his book, um, the Sociology of Philosophies, that it’s not necessarily the objectively best ideas that went out, but, but the ideas that have the most effective networks of people promoting them.

    So it’s about community as much as about individual brilliance.

    Exactly. He emphasizes this really critical role of intense interaction within these networks. He talks about these interaction ritual chains, which generate shared emotional energy and sacred symbols that really bind people together.

    It’s like a shared understanding, a shared feeling.

    And Colin sees the intellectual landscape as as a kind of competitive arena too, right?

    Definitely. Idea systems. They’re like different species in a way. They differentiate to stand out or they integrate with others to build on success. Collins argues that these lines of opposition, where, where thinkers define themselves in contrast to others, those are actually key market opportunities for intellectual advancement.

    He even suggests that the most impactful ideas often create new problems, new questions for, for future thinkers to tackle.

    That’s, that’s an interesting way to look at it. Creating new problems can be, uh. A sign of a really powerful idea. Yeah. And Collins also talks about how the larger social and cultural context like shapes, how these ideas develop.

    There’s this interplay between traditional and innovative ways of thinking,

    right? Right, right. He talks about those periods that value establish knowledge and those that prioritize new discovers. And he examines this dynamic between what he calls a fractionation, where thinkers emphasize what makes them unique and synthesis.

    Where they, they form alliances and combine ideas, especially when there’s this, this confusing array of different viewpoints out there. And, and he even points out that sometimes, you know, weaker organizational structures can lead to greater intellectual consolidation and collaboration. Like, like we saw with the philosophical schools after Atkins fell.

    So the historical context, it matters a lot. And, and this brings us to Collins’ Law of small numbers. Yeah. Right. The idea that there’s only so much attention to go around, he suggests that. At any given time, there might only be like three to six really major intellectual systems competing for that attention.

    Right. But, and, and this is a big but Smith points out that the attention landscape today, it’s way more complex than in the past. I mean, we have science universities, social media, and now ai, it’s. It’s much harder to get noticed.

    Which brings us back to Smith’s grand strategy, right? Yeah. These six steps he thinks are essential for the integrative worldview to gain traction.

    The first one is to, uh, crystallize a minimal integrative worldview. What, what does that even mean?

    So it’s about finding those essential, non-negotiable principles that, that all these teal plus movements can agree on. He gives examples like the idea that reality has different levels of organization, that our understanding always comes from a specific perspective, and that it, you know, it evolves over time.

    The idea that the universe has an inherent value, a commitment to freedom and, and rational thinking. It’s, it’s about that common ground.

    So finding that shared foundation. And then the second part of the strategy is to. Um, compete for attention, and it’s, it’s a pretty bold goal. He wants to be one of the top four global worldviews by the middle of the century.

    He even sets targets for followers and financial support by 2030.

    Yeah, it’s ambitious. He, he knows they have to actively fight for public awareness. The third element is to, uh, tell a true, more deeply meaningful story to, to create a narrative that that. Emphasizes wholeness and transcendence to really focus on the inherent value of being human.

    He mentions ideas like pantheism and non-dualism,

    so offering an alternative to the, um, more fragmented or or materialistic stories that are out there.

    Exactly. The fourth component is to, uh. Build an autopoietic network.

    That sounds, that sounds pretty technical.

    Yeah, well, it’s basically about building a network that can sustain itself, you know, like an ecosystem.

    It’s not just about sharing ideas, it’s about developing a shared energy, shared rituals and symbols, things that that resonate emotionally. It’s about fostering those strong self generating connections between all these different teal plus communities.

    Okay. So it’s more than just just sharing ideas.

    It’s about building community. And the fifth element is to embrace huge problems to actually try to solve those big global challenges,

    right? And by focusing on those real world serious problems, the network can show its relevance, you know, attract people, attract funding. And the final component is to, uh, develop proprietary tools to, to create resources and technologies that actually embody and advance the knowledge of the integrative worldview.

    So put those ideas into action, build something tangible, and, and he intentionally leads the specifics of how to do all of this kind of open-ended, right?

    Yeah. He says that the practical steps, they’ll emerge as the network develops, but the, the core principle is, is commitment, right? Commitment to participation and collaboration to solving these real world issues.

    And this leads into his concept of an integrative knowledge economy.

    Okay. So what’s, what’s an integrative knowledge economy then?

    So he argues that attention is crucial for a worldview to spread, right? Because attention brings cultural influence. It offers a, a, a compelling vision that people can connect with, something that can shape their identity.

    He also highlights the importance of a strong institutional core, things like transformative educational initiatives to really transmit the potential of this worldview. Any. Specifically mentions the Institute for American Metaphysics or IAM and their focus on human development in their projects.

    Okay, so attention gets people in the door, but then you need that deeper work of education and and institutions to really make it stick,

    right?

    He talks about this cyclical relationship. You gain attention, then people adopt the ideas that leads to innovation, which then informs education and the development of institutions,

    and it just keeps building ideally. And he mentions. Jurgen Ren here. His idea of a system of knowledge with this interconnected set of.

    Models and arguments. And practices.

    Yeah. And Smith imagines how the integrative worldview could develop its own really robust and coherent system of knowledge.

    And, and he connects that to habermas ideas about how societies learn and, and generate new knowledge. And this, this idea of cognitive surplus.

    Mm-hmm. Like all this intellectual potential that could be used to solve problems if we could just. Figure out how to, how to channel it.

    Exactly. And, and Ner guard, Headland and Melin, they, they offer this vision of a, of a better society, a protopian society that’s fostered by this diverse, yet interconnected group of thinkers and organizations.

    And they emphasize this, uh, collaborative meta praxis of. Big picture thinking. Hmm. Engaging in dialogue, understanding different perspectives, generosity with ideas, self-reflection, fostering these, these intellectual friendships, you know? Oh yeah. And working on shared projects,

    creating the right conditions for these ideas to grow.

    Yeah. And Ren, he also outlines three key types of knowledge for the 21st century. Right? There’s system knowledge, which is the overall understanding of how things work. Yeah. Then there’s transformation, knowledge, how to bring about change, and then orientation, knowledge, the the ethical and moral compass,

    and.

    Those types of knowledge, they align really well with the aims of the integrative worldview. Ren says that this knowledge needs to be put into practice in research and education and public discourse, even political action. And Smith also points to I AM’s model for creating social impact. They start with an idea, then develop a toolkit.

    I. Then implement a program and ultimately establish an institute.

    It’s like a step-by-step guide to, to taking these ideas and making them real in the world. And, and this leads to this idea of exploring a social collaboration protocol. Yeah. Right? Like a framework for all these different, these meta communities to work together.

    Right. It’s about building this basic but strategic common ground for spreading this integrative worldview through this network of, of related communities. And the big goals are still the same, to to gain attention and to build this, this self-sustaining network.

    And he mentions that, you know, this protocol could take many forms.

    It could be a constitution, an agreement, an association, even a DAO.

    Yeah. But the key is that it needs to unite members around the shared values and coordinated action. And he suggests starting small, focusing on what people actually care about, solving real problems for the leaders in these communities, and building trust over time.

    He even mentions Eleanor Ostrom and her research on how groups successfully manage shared resources,

    right? And, and he highlights those factors, you know, like, who gets to make decisions, do the members have similar goals, that kind of thing. And, and he also cautions against two big mistakes, one. Putting too much faith in technology because networks are ultimately about human relationships, about trust and shared norms.

    And two, over-engineering the system. Too much complexity can really backfire, and he includes a whole table with all these strategic considerations for the protocol. You know, covering things like how to deal with factions, competing for attention, leveraging those network effects and, and how to ensure it’s sustainable in the long run.

    Sounds like a, a blueprint for building a successful movement. But of course, there are objections, right? People who might be skeptical. One big one is, well, what’s to stop this integrative worldview from becoming another rigid ideology that that could be used to justify harmful things.

    Yeah, he, he acknowledges that risk, and he talks about his own past warnings about the dangers of believing that.

    Simply growing and influence is, is automatically good and the potential for those judgmental attitudes to emerge. He says that the, the experienced leaders in this network, they need to be aware of that and develop clear communication and educational structures to, to avoid those pitfalls. He also talks about how as the network grows, there will inevitably be these more structured projects that emerge and, and then it becomes crucial to differentiate between oppressive forms and, and liberating forms.

    Mm-hmm. And he argues that the integrative worldview should, should always aim for the latter, you know, through its emphasis on these different perspectives and on ethical practices. And even touches on this, this ongoing debate about how critical they should be of, of those older worldviews. And he admits that he.

    He leans toward a clear call for, for positive development.

    And then there’s this whole issue of, of disagreements, right? Like even within the integrative worldview, there are gonna be differences in how people interpret things. Yeah. And getting diverse groups to cooperate effectively. That’s. That’s a challenge.

    Yeah. Huge challenge. And, and Smith, he gets that. He emphasizes that the network needs to actually model the kind of world they’re trying to create, like a world that’s not based on competition. And he highlights the level of maturity that’s required of the leaders. He calls it, um, turquoise Plus thinking.

    This ability to hold multiple perspectives to appreciate. Different but related theories without, without getting too attached to one specific version. The goal is to bring together leaders who, who agree on those core principles of the minimal integrative worldview and create a collaborative framework that respects intellectual diversity.

    And then there’s the last big objection, this tension between. The desire for unity and collaboration. This we aspect and, and the need for those individual movements to keep their own unique identities, their own autonomy. Like why should they all come together?

    Yeah. And he frames this as, as this fundamental challenge in human organization.

    I. Finding that balance between working together and maintaining individual agency and, and he reminds us of that problem of fragmented attention. You know, it’s really hard to get people to focus on something as important as this worldview that, that he believes we desperately need. And while you know.

    He acknowledges that positive change might just happen organically over time. He argues that those who have the ability to act, to really do something, they have a responsibility to be intentional.

    So it’s about about taking action, not just waiting for things to happen.

    Right. And the challenge he says is finding that right level of agreement.

    On those core principles to, to amplify the signal, while still allowing for those diverse interests and approaches. He even suggests the IETF, the, the group that, that manages the technical protocols of the internet as a model for how to build that collaborative governance structure.

    So to kind of sum it all up, you know, the key takeaway from Smith’s analysis is that, well.

    We’re in a time of these really profound, interconnected crises, and we need a new way of understanding the world. We need an integrative worldview, and for that worldview to have any impact, you know, those who, who believe in it, they have to collaborate strategically. And a big part of that is, is getting attention, getting noticed in this, this very noisy, crowded world of ideas.

    Exactly understanding this framework. It gives you, our listener, this valuable way to, to interpret these challenges that we’re all facing. It’s, it’s a proposal for how a different future might actually be shaped intentionally.

    It’s about making a conscious effort to, to change the future of knowledge and, and meaning in the world.

    And for you, as you think about all this, I mean. Consider this, what role might you play, you know, in, in the emergence of these new ways of seeing the world or in the formation of these collaborative networks? Even if you don’t see yourself as a leader, necessarily, think about the challenges that you’re facing in your own life, and whether this idea of, of an integrative perspective, whether that resonates with you.

    Hmm. Whether

    it helps you make sense of, of what’s happening. It’s definitely something to think about.

    It really is.

  • The Secret to OKRs That Actually Drive Impact

    The Secret to OKRs That Actually Drive Impact

    This month, let’s discuss Impact Mapping as the best way to create OKRs. If you’ve ever struggled with setting measurable, outcome-driven objectives, this approach is a game-changer.

    Too often, teams treat OKRs as just another to-do list—a collection of tasks rather than a framework to drive meaningful change. But what if we shifted the focus? Impact Mapping, created by Gojko Adzic, helps teams craft OKRs directly linked to business and user outcomes, making them more actionable and effective.

    Impact Mapping: The Best Approach to OKRs

    Unlike traditional goal-setting methods, Impact Mapping ensures that every OKR starts with why before moving to what and how:

    1- Define the Goal – What problem are we solving?
    2- Identify the Actors – Who influences the outcome?
    3- Determine the Impact – What behavior changes will lead to success?
    4- List Deliverables – What actions or features will drive those changes?

    📽️ See it in action – I created this video using Narakeet (one of Gojko’s products!) to showcase how Impact Mapping translates strategy into focused execution.

    OKRs in Focus – Insights from Experts

    To deepen our understanding of OKRs, I’m excited to revisit three episodes of Le Podcast on Emerging Leadership, each offering a unique perspective on how to set and execute OKRs effectively.

    🎙 Build a Product with Gojko Adzic
    Gojko shares his practical approach to building impactful products, emphasizing:
    – How to avoid waste in product development
    – The importance of measuring what matters
    – How Impact Mapping clarifies OKRs by focusing on outcomes over outputs

    🎙 Radical Focus with Christina Wodtke
    Christina Wodtke, the author of Radical Focus, discusses:
    – Why clear goals, roles, and norms matter in high-performing teams
    – How exploratory OKRs drive innovation
    – The role of accountability groups in making OKRs successful

    🎙 All About OKRs with Bart den Haak
    Bart den Haak, the author of Moving the Needle, brings over a decade of experience using OKRs in organizations, sharing:
    – The difference between OKRs and other goal-setting frameworks (4DX, MBOs, Balanced Scorecard)
    – Where to start with OKRs and common pitfalls to avoid
    – How OKRs push teams out of their comfort zones while avoiding burnout

    Bringing It All Together

    By combining Impact Mapping, Radical Focus, and OKR best practices, you can create objectives that:
    – Align with strategy rather than just listing tasks
    – Focus on measurable, high-impact changes
    – Encourage collaboration and adaptability
    – Help teams continuously refine and improve their approach

    So, as you refine your OKRs for the next quarter:
    – How could Impact Mapping help you define more meaningful objectives?
    – What behaviors need to change to achieve your key results?
    – Are you using OKRs to drive learning and innovation, not just performance tracking?

    Let’s discuss! Share your experiences and thoughts—I’d love to hear how OKRs and Impact Mapping have influenced your approach to leadership.

    Wishing you a focused and high-impact month!

  • The Leadership Power of Recognition: Are You Using It Effectively?

    The Leadership Power of Recognition: Are You Using It Effectively?

    This month, I want to explore a fundamental yet often overlooked aspect of leadership: recognition and its impact on motivation and team dynamics. Inspired by Eric Berne’s Transactional Analysis, the concept of recognition strokes helps us understand how the way we acknowledge or critique others influences engagement, trust, and leadership development.

    The Four Types of Recognition Strokes

    1. Positive & Unconditional – Appreciation for the person as they are.
      Example: “I appreciate you.” “I enjoy working with you.”
    2. Positive & Conditional – Praise for a specific action or achievement.
      Example: “Great job on this project!” “I admire how you handled that challenge.”
    3. Negative & Conditional – Constructive feedback directed at an action, not the individual.
      Example: “This approach didn’t work, let’s find a better one.” “I didn’t appreciate how you handled that meeting.”
    4. Negative & Unconditional – Criticism aimed at the person rather than their behavior.
      Example: “You’re difficult to work with.” “You never do things right.”

    How we recognize and challenge others matters. A culture where positive, constructive recognition is the norm fosters engagement and creates a safe space for leadership to emerge at all levels.

    Redefining Leadership – A Conversation with Russ Laraway

    I enjoyed welcoming Russ Laraway on Le Podcast on Emerging Leadership. Russ is a distinguished leader with 30 years of experience at Google, Twitter, and Candor Inc. Russ shares key insights from his book, When They Win, You Win, offering a fresh, results-driven perspective on leadership and career development.

    Key Learnings from Russ Laraway:

    ✅ Leadership Behaviors Drive Success

    • Focus on a small set of measurable leadership behaviors that predict engagement and performance.

    ✅ The Three Buckets of Leadership:

    • Direction: Clear goals and expectations.
    • Coaching: Ongoing support and feedback.
    • Career: Meaningful conversations that align personal and professional growth.

    ✅ The Career Conversations Framework:

    • Life Story Conversation: Uncovering values and pivotal experiences.
    • Career Vision Statement: Helping employees articulate their dream job.
    • Career Action Plan: A structured roadmap to achieve career goals.

    ✅ Retention and Work-Life Balance

    • Employees stay where they feel valued. Investing in their careers fosters trust and reduces turnover.
    • Prioritization is key—subtracting non-essential work creates a sustainable work-life balance.

    Leaders who actively shape career paths and acknowledge growth create organizations where people thrive, innovate, and stay engaged.

    What This Means for You as a Leader

    • Are you intentional about how you recognize and challenge your team?
    • How can you integrate career conversations into your leadership approach?
    • What shifts could you make to lead through recognition and conscious development?

    Let’s continue this conversation—share your thoughts and experiences, and let’s work towards building leadership environments where people feel seen, valued, and empowered to grow.

  • Psychological Safety: The Key to Collaboration and Innovation

    Psychological Safety: The Key to Collaboration and Innovation

    This month, we focus on a cornerstone of high-performing teams and transformative leadership: psychological safety. In a world where uncertainty and complexity are the norm, creating environments where individuals feel safe to speak up, take risks, and be themselves is no longer a luxury—it’s a necessity.

    Psychological safety, as defined by Amy Edmondson, Novartis Professor of Leadership and Management at Harvard Business School, is “a shared belief that the team is safe for interpersonal risk-taking.” In her groundbreaking book, The Fearless Organization, Edmondson emphasizes that psychological safety is not about being nice or avoiding conflict. Rather, it’s about fostering a culture where people feel empowered to share ideas, ask questions, and admit mistakes without fear of embarrassment, rejection, or punishment.

    When psychological safety is present, teams thrive. They innovate more effectively, learn from failures, and collaborate with trust and openness. Edmondson’s research shows that psychological safety is a key driver of performance, especially in environments that require creativity, adaptability, and continuous learning.

    Google’s Project Aristotle, a multi-year study on team effectiveness, underscores the critical role of psychological safety in high-performing teams. The study, which analyzed hundreds of teams across the company, found that the most important factor distinguishing successful teams was not individual talent, seniority, or even clear goals—it was psychological safety. Teams, where members felt safe to take risks, share ideas, and be vulnerable, outperformed others consistently.

    As highlighted in The New York Times article, What Google Learned From Its Quest to Build the Perfect Team, Google discovered that the best teams were those where everyone had an equal voice and where interpersonal trust was high. For more on Google’s findings, you can explore their Guide to Understanding Team Effectiveness.

    But how do we build psychological safety? Timothy Clark, author of The 4 Stages of Psychological Safety, provides a practical framework for understanding and cultivating this critical dynamic. According to Clark, psychological safety is not a binary state but a progression through four stages:

    1. Inclusion Safety: At this foundational stage, individuals feel accepted and valued for who they are. They believe they belong and are treated with dignity and respect.
    2. Learner Safety: This stage encourages curiosity and experimentation. Team members feel safe to ask questions, make mistakes, and learn without fear of judgment.
    3. Contributor Safety: Here, individuals feel confident to contribute their skills and ideas. They believe their input matters and that they can make a meaningful impact.
    4. Challenger Safety: The highest stage of psychological safety, this is where individuals feel safe to challenge the status quo, voice dissenting opinions, and drive change without fear of retribution.

    Clark’s framework reminds us that psychological safety is not a one-time achievement but an ongoing process. It requires intentional effort from all team members, whatever their roles, to create and sustain an environment where people can move through these stages and reach their full potential.

    Reflections for Leaders:
    – How are you fostering inclusion safety within your team? Are there individuals who may feel excluded or undervalued?
    – Are you creating space for learner safety, where mistakes are seen as opportunities for growth rather than failures?
    – How can you encourage contributor safety, ensuring that everyone feels their voice is heard and valued?
    – Are you open to challenger safety, where team members feel empowered to question assumptions and propose new ideas?

    As leaders, we have the power to shape the cultures we lead. By prioritizing psychological safety, we not only unlock the potential of our teams but also create organizations where people can thrive, innovate, and achieve remarkable outcomes.

    Call to Action:
    I encourage you to reflect on your own leadership practices and team dynamics. Where can you take steps to enhance psychological safety?

  • Optimizing for the Unexpected – Insights from Gojko Adzic on Lizard Optimization

    Optimizing for the Unexpected – Insights from Gojko Adzic on Lizard Optimization

    Some of the most valuable product signals do not come from your roadmap, your user interviews, or your strategy workshops.

    They come from the weird stuff. The edge cases. The misuses that look irrational at first glance.

    In this episode of Le Podcast on Emerging Leadership, I welcomed back Gojko Adzic, one of the most influential voices in modern software development, named an AWS Serverless Hero (2019) and author of Impact Mapping, Specification by Example, and his latest book Lizard Optimization.

    Gojko’s core idea is simple and powerful: pay attention to unexpected behavior, because it often reveals hidden opportunities.

    He calls these unexpected users “lizards”.

    Not because they are wrong, but because their behavior looks non rational from the perspective of the product team.

    And that is exactly why they matter.


    Lizards are not a problem

    They are a signal

    A key story from our conversation comes from the early days of PayPal.

    The founders built a PalmPilot based solution and expected the product to live there. Users, however, started using a rough web demo in a different way. Product managers initially fought the “misuse”. Eventually, the numbers made the truth unavoidable: the web path had massive adoption compared to the PalmPilot path.

    The lesson is sharp:
    If you fight users to protect your original vision, you might miss the market that is trying to adopt your product.

    This is what Gojko means by lizard optimization:
    Identify misuse, then decide whether it is a threat to block or an opportunity to amplify.


    The LZRD loop

    A practical method to work with the unexpected

    Gojko describes a four step approach that is easy to remember because it spells LZRD.

    Learn
    Observe and collect unusual behavior. Not with judgment, with curiosity.

    Zoom in
    Most weird signals are noise. Some are gold. Pick one behavior that is meaningful enough to explore.

    Remove obstacles
    Users often “misuse” a product because the product blocks the outcome they want. Remove friction that prevents valuable usage.

    Detect unintended impacts
    Even good fixes can create new problems. Watch what happens after changes, and be ready to adjust.

    What I like about this loop is that it complements user research. It helps you discover unknown unknowns. Things you would not think to ask about.


    Two examples that make it real

    Subtitle files in a text to speech product
    Gojko noticed users uploading subtitle files. That looked odd until he understood the job to be done: creating synchronized audio tracks for video content without manual editing. A small change unlocked a valuable use case for a specific segment of customers and delivered outsized business impact.

    VAT number friction and unintended impact
    Gojko tried to remove a payment obstacle by changing where VAT information was collected. The result was fewer payments. The fix made sense logically, but broke expectations for a subset of users. The mismatch reduced conversion.

    This is why the last step, Detect unintended impacts, is not optional.


    Mismatch beats blame

    A concept that fits extremely well with lizard optimization comes from Kat Holmes’ book Mismatch.

    Instead of saying “users are stupid”, treat issues as a mismatch between:

    • the user’s situation, expectations, or capabilities
    • and the product’s design

    This framing keeps teams humble and productive. It also opens the door to solutions that improve the product for many users, not only for the one strange case.

    Solve for one, expand to many.


    From products to organizations

    Watch the desire lines

    Gojko connects this to a broader idea: desire lines.

    In physical spaces, desire lines are the paths people naturally take across the grass when the official paths do not match how they actually move.

    In organizations, desire lines show up when:

    • teams route around processes
    • workarounds become common
    • people find unofficial paths to get work done

    As a leader, these are not annoyances to punish by default. They are signals to examine:
    What obstacle are we creating
    Is it intentional
    If not, what would it take to remove it


    The humbling truth

    Most ideas do not create value

    Gojko ends with a message that is both uncomfortable and liberating.

    Data from large scale experimentation at companies like Google and Microsoft suggests that a majority of changes do not create measurable value. Many ideas fail.

    That is not a reason to stop innovating. It is a reason to test, learn, and stop bad ideas earlier.

    The competitive advantage is not having more ideas.
    It is discovering faster which ideas work.


    A question to take with you

    Where are your lizards today

    In your product, your customer journey, your team, your organization

    What looks like irrational behavior might be the clearest signal you have.

    Listen to the episode here or on your favorite platform.

    References Mentioned

    1. “Build a Product with Gojko Adzic” – An episode of Le Podcast on Emerging Leadership
    2. “Founders at Work” by Jessica Livingston – Stories of Startups’ Early Days
    3. Lizard Optimization by Gojko Adzic – Learn how to transform unexpected product usage into growth opportunities.
    4. Trustworthy Online Controlled Experiments by Ron Kohavi et al. – A foundational guide on using experiments to discover what truly works for users.
    5. Mismatch by Kat Holmes – Explore inclusive design and learn to recognize mismatches in user needs versus product design.

    Here is the transcript of the episode

    Alexis: [00:00:00] Welcome to Le Podcast on Emerging Leadership. I’m your host, Alexis Monville. And today, we are joined once again by a very special guest, Gojko Adjik. Gojko is a renowned author, speaker, and recognized leader in the world of software development. He’s been celebrated as one of the 2019 AWS Serverless Heroes, the winner of multiple prestigious awards, and the mind behind several influential books, including Impact Mapping and Specification by Example. In our last conversation, we dove deep into how to build a perfect product, how to avoid waste in software development, and explore the principles of impact mapping.

    Today, we are excited to discuss his latest book, Lizard Optimization. We’ll be unpacking the core ideas in the book, how they apply to modern software development, and what it means [00:01:00] for leadership in an evolving technological landscape. Welcome back to Le Podcast on Emerging Leadership, Gojko. How do you typically introduce yourself to someone you just met?

    Well, I say I’m Gojko, it’s like Beyonce, you know, it’s.

    Does it work really well? 

    Gojko: Oh, I guess so. I don’t know. I’ve never been in a situation where it doesn’t work because maybe people try to be polite to me. I’m a developer. I kind of build my own products. Now I write books mostly as a way of. Doing a brain dump so I can leave more space for other things. I stole that one from Henry Kniberg.

    He said, kind of, he likes to do a brain dump to free up shorter memory. I think upgrading RAM in my head would be really expensive. It’s cheaper to write a book. 

    Alexis: I love the way it’s said. I have to agree with that. When you try to write something, could be read by not [00:02:00] only you, but also by other people.

    It’s really good to help you structure your own ideas. 

    Gojko: Yeah, and it gets you to clarify things that might not be perfectly clear. It’s always fun. While I was writing this, my most recent ninth book, I was trying to hunt down some quotes in exact way, the way they were said. And I’ve realized that for years I’ve been doing conference presentations and quoting some people completely wrong.

    I misremembered it the way I read in the book, but then read actually what they said and kind of, the meaning is there. I, I didn’t misremember the meaning, but Really shame on me for misquoting people. So yes, you get to consolidate your thinking and really verify that it’s still correct. 

    Alexis: You spoke briefly about your latest book.

    The latest book is Lizard Optimization. I would probably not have picked that book on a shelf. I don’t know anything about lizards. I’m not [00:03:00] really keen to optimize any lizard. That’s 

    Gojko: your mistake as a leader. I think your job needs to be to watch out for lizards and support your product teams in optimizing for lizards.

    That’s incredibly important. 

    Alexis: So now you need to explain a little bit. You need to tell me what inspired you to write the book and what does that lizard mean? So what inspired me to write the book is 

    Gojko: a really crazy growth phase for one of my products where the usage increased by about 500 times in a space of 11 months.

    So that means that things that were weird edge cases that would happen once every two years now start happening every day. And the whole 11 months was a bit crazy and firefighting and things like that. But I’ve learned a lot and I wanted to pass on what I learned [00:04:00] to other people and maybe inspire them to investigate these things on their own.

    And a lizard optimization is in a sentence, figuring out how people are misusing your product. And then figuring out whether you want to support that kind of misuse in a more systematic way, whether that should be done, or whether you want to block it and prevent it. And both of these things are valuable.

    The one example I really like that’s not from my product, but I read this in a book called Founders at Work by Jessica Livingston. Was from a company where in late nineties, the company was started because some super, super smart people built some incredibly efficient cryptography algorithms. And they had a solution, but they didn’t have a problem.

    We built this now what then somebody said, well, these are incredibly efficient algorithms, so they [00:05:00] can run on low power devices because they’re efficient. They’re not going to spend battery too much. And PalmPilot was a popular low power device there. So they said, well, let’s run something on PalmPilot.

    What do you need encryption on a PalmPilot for? And they said, well, encryption brings security. You need security when, I don’t know, you’re transferring money. And then they build a system where you take your PalmPilot out of your pocket. I take mine and we bump it together. And money goes from my PalmPilot to yours.

    That was wonderful. That was magic. And it was all insane. They had trouble getting people to know about the mobile application and to use it. Late 90s was a time where web was becoming popular and they built a website to promote the Pornpilot app. So as a way of getting people to try it easily and experiment, they had this really horrible, very rough demo thing where you could use the website to transfer money to somebody’s Pornpilot account.

    And, [00:06:00] What people started doing is they were using the website not to transfer the money to somebody’s PalmPilot account, but to transfer the money to an account and opening it even without having PalmPilot devices. The product management was really furious with that because somebody was misusing their system.

    They were not using it for PalmPilots. They had nothing to do with their brilliant app, nothing to do with These efficient cryptographic algorithms that were running on low power devices, because it was all running through the website. And people even started using the trademarks and the names on forums, like, Oh, send me money, buy this or something like that.

    It kept fighting it. The product people kept fighting it. They were going in these forums and saying, you’re not allowed to use our name. We’ll sue you and fighting with the users. At some point, somebody looked at the numbers. The website had 1. 5 million active users and 12, 000 PalmPilot app installations.

    And somebody who can do mathematics basically said, well, [00:07:00] this PalmPilot thing is really not as popular as the website. So they kind of killed the PalmPilot app and the web app became PayPal. That today is known as PayPal. PalmPilot no longer exists. And we do have. Low power devices and things like that.

    And PayPal runs on mobile phones. And of course, you know, you can, I don’t know if you can transfer money by pumping it, I think that’s like a weird gimmick, but it’s used to transfer money all over the world by doing this PayPal pilot bump, you have to be next to somebody and you, now you can use PayPal to transfer money somewhere, halfway around the world in a different time zone.

    And I think that the really interesting lesson there is that the product managers fought against users for a very long time. They fought against this misuse. They fought against people actually trying to benefit from the product in a different way because it wasn’t consistent with their vision that they were trying to stay true to the vision, not true to solving the problem.

    And I think this is where people fall in love with the solution, not with [00:08:00] the problem. And, and I think that’s kind of one of the biggest issues product companies have. So I think as a leader of a company and your, uh, listen as a leaders. Helping your product, people like focus on solving the problem. Not loving the solution is really, really important and noticing when people are misusing your product.

    It becomes important both for unlocking growth and for understanding where the market wants this product to grow, because it opens up some incredible growth opportunities. If the PayPal stayed on the Palm Pilot app, they would have had 12, 000 users and that’s it. They would have never made a kind of a decent company out of it.

    And I think this is what becomes really interesting. Lizards in this terminology are people who do things that you can’t logically explain. It looks like it was done by somebody who’s not a rational human. They’re doing something you didn’t expect. They’re doing something you don’t want, but they are effectively misusing the system.

    Now they might be [00:09:00] misusing the system or trying to misuse the system in a good way or a bad way, but kind of figuring that out becomes, I think, critical for good product management. 

    Alexis: This is very interesting because yeah, you, you take the examples of product managers fighting against misuse of the product.

    Just noticing that something is going on is already something important. And I, when I read the book, I was there and was looking at that to say, Oh, I don’t know if I would have noticed that. And the example of that video that is blank. How would 

    Gojko: you even know? And that’s really an interesting thing. So, for example, one of the products built allows people to upload different types of documents and create an audio file using text to speech.

    So, when users do something unexpected, like trying to upload an unsupported file type, they will get a decent error message. I set a number of people every day that try to upload MP3 files into a text to [00:10:00] speech system. I don’t understand why you would do that, what you expect, how that would even work.

    Converting audio to audio, you’re not converting text to speech. It’s weird. There are people who try to upload Android package files every day. I, I don’t understand how you would do that, but occasionally there’s somebody who kind of does something potentially useful. Now, with the error message that people get, Oh, you know, you’re, you’re uploading something that is not text.

    We can’t read that. In addition to showing the user an error message, I get a message. I get a log message that I can expect that somebody did something I didn’t expect. Now, I started noticing a pattern, uh, about a year ago where people were trying to upload subtitle files. Subtitle files come with video files, they are subtitles for a movie or, or something like that.

    And, um, they’re text files, they’re not images, they’re not Android packages, they’re not [00:11:00] music, they are text files. So I thought, well, I didn’t expect this extension, but why not? I can just enable that extension in addition to txt and I enable that and then I started getting complaints from people saying that, uh, the system also reads the timestamps.

    Subtitle file said timestamps when to show certain text. Yeah, you’ve uploaded a file with timestamps. What the fuck did you expect? It’s, it’s kind of reading the timestamps. It reads the content. But yeah, I wasn’t expecting it to read the timestamps. I was only expecting it to read the kind of voiceover.

    I said, well, I can understand that. So it took me five minutes to just skip over the timestamps. And then people were complaining that it reads the text too slowly. It’s like, what do you mean too slowly? It is the text at the speed of it reading the text. I mean, and then I realized talking to people what they were trying to do actually, you know, in the jobs to be done category is they were trying not to just convert text to speech a step there.

    But what they were trying to do [00:12:00] is to create an alternate audio track for their presentation video. And instead of creating lots of short clips and then aligning them themselves, what they were hoping to do with the subtitle file is to get the whole thing synchronized. Now, it was, you know, logical. I see the value in doing that.

    It was a very tiny percentage of users doing it, but it was a small change. It was a technical challenge. It was interesting to do. So I did it. So in total, you know, we’re talking about two days of work in total, building on top, by far the most profitable thing I’ve ever done. By far, what happened later is that these features were discovered.

    Like there’s an American mega church where they have these sermons, religious lectures, whatever. And then they want to have them in all the languages on earth. And they’re using my system to. [00:13:00] Somebody types over the subtitles or I don’t know how they produce them, but then they just use the subtitles to create alternate audio tracks for the priests kind of preaching.

    There’s an enterprise software company that’s using this thing for all their instructional videos. To basically automatically get an alternate. So if you’re a, if you’re a video content editor, usually what you would have to do with this is either try to record your voice or get somebody to record short clips and then suffer through hours of placing the clip at the right place in the video, where with this thing, you get it almost instantly.

    And it saves you hours and hours and hours of time. And if you save somebody hours and hours and hours of time, they’re willing to give you some money for it. Especially if it’s automated, then they can do it at scale. So although this feature is used by like a tiny percentage of my users, it’s probably contributing a decent percentage of the revenue where the most kind of profitable customers we have on the tool are actually using it for that.

    [00:14:00] So that’s the value of. Lizard optimization. I would have never guessed this without monitoring for weird file types that people are uploading. And I would not have done it in user research because I would not be doing that kind of research. I would be interviewing people who need something else done.

    And I think lizard optimization is a wonderful way to complement customer research and user research and discover the unknown unknowns. You know, you can discover through customer interviews and user research, you can discover known unknowns. You kind of, you know what people want to do, but you don’t know the details and how important something is or what, but this is really helping us deal with the unknown unknowns.

    And this is really interesting because it can open up a completely new market segment. It can show you that people want to go in a totally different direction. And maybe you don’t know that. And you need to consider it. And I think that’s why I think this is [00:15:00] such a powerful method to use. 

    Alexis: It’s interesting because we can apply that in a lot of different things.

    So of course, when you’re building a product, it’s kind of obvious, but my temptation was to say, okay, how can I learn that someone is doing something unexpected? Because as you said, in user research, you’re coming with your own assumptions about what is going on and you’re asking questions, you try to validate your assumption, but there it’s way more powerful because basically you’re trying to be on the lookout on what is going on and what are those things that you can brush saying, Oh, those users are completely idiot or maybe they just have a brilliant thing that I can solve in two days of work.

    That’s very interesting. How do you see that? Do you have a, do you have a kind of structure to help people understand how it works? 

    Gojko: So I think the process itself, I’ve kind of nailed it down to four steps to use myself. And the four steps are easy to remember because they start with the letters LZRD, like lizard, [00:16:00] The first step is to learn how people are misusing your product.

    That’s the L, learn. Then the second step is to zoom in on one behavior change. You can’t change everything. And when you start looking for weird stuff, there’s a range of incredibly weird. We’ll never understand it to this kind of makes sense. And it’s going to be a lot of noise and we need to figure out the signal in that noise.

    The zooming in is the second step. The third step is to remove obstacles from users. And the, the software is placing obstacles in front of users and not letting them do something they wanted to do or the product. And that’s why they’re misusing it. Some obstacles need to be removed for them to be able to do that.

    And then the last step, the D. Is to detect unintended impacts because these people follow their own logic. They don’t necessarily follow my logic or your logic and our assumptions about how we’re going to fix the problem aren’t necessarily true. Like I said, my first idea was, okay, just [00:17:00] support the file.

    That’s okay. But then there was an unintended impact where people were starting to complain and we increased support because we were reading all the timestamps and things like that, lots and lots of times where I thought this is going to be a good idea. didn’t turn out to be spectacularly good. 

    Alexis: Can 

    Gojko: you 

    Alexis: give me an example about that?

    Gojko: Yeah, like in European Union, kind of, there’s like VAT numbers. So with VAT numbers, uh, you need to enter a VAT number for the receipt. And with the digital product, If you’re selling things to individuals, you have to charge VAT in the country where the individual is. If you’re selling to companies, you don’t charge VAT.

    They have to account for that using reverse charge magic and things like that. Now, without going too much into the accounting details, companies want to put their, or people purchasing for business, want to put their VAT number in. If they put a VAT number in and they’re doing it with domestic transaction, they’ll usually just put the number.

    But if they’re doing it in a foreign [00:18:00] context, they’ll put the country prefix. So FR 12345 is for France. And the payment processor I use is done by an American company. They don’t understand all of that. It’s too complicated for them. And they’re trying to validate these numbers. But very often they, even if you selected France and you entered one, two, three, four, five, it’s obviously the French one, two, three, four, five number.

    What they’ll tell you is, Oh, this is an invalid VAT number. It’s not, it just, you’re not storing it correctly. And I can’t do too much about their validation. It’s their validation. It’s third party product. But what happens is I had a percentage of a good percentage of people. People that go try to purchase, they enter 4, 5, this thing tells them it’s an invalid number, and they think it’s the card number, not the VAT number.

    So then they added the card number, it fails, it fails again, and, and, and, so a ridiculous number of people from European Union end up selecting Russia as their country because Russian VAT numbers don’t have a prefix. It is, it is ridiculous just to enter the thing to, like, [00:19:00] I’m placing an obstacle in front of them trying to pay me.

    This is idiotic. So I thought, well, you know, let’s solve this and all that can’t control the validation on, on the form. It’s done by the payment provider. I can remove the field altogether. And then when they pay, I can say, okay, now to get an invoice, give me a VAT number. And then I can say, well, you’re in France, obviously the prefix is FR.

    I did that. And then I measured where the people are paying me more. And it turns out people are paying me less. 

    Alexis: Uh, 

    Gojko: Uh, yeah, so unintended impact. So what had happened is I thought I’m going to solve it, but actually people that wanted to pay for the company, they go to the form where they couldn’t put in a VAT number and then they didn’t pay.

    They were confused. They, they expected a place to put a VAT number in, and the number of payments dropped significantly. So I had to kind of go back and, and, and do some other stuff there. So that’s kind of an example of an unintended impact where [00:20:00] something that’s, you know, to me as a maker sounds perfectly logical to a user might not, or to a user of a certain type might not.

    And this is where I absolutely love, you said users are not that smart and things like, I absolutely love this book by Kat Holmes called mismatch. Because she rephrased this whole thing. It’s not that the users are stupid or smart or whatever. It’s kind of, there’s a mismatch between the user’s capability and the software.

    Now, that mismatch might be something we want to do something about or not, but we need to understand it as a mismatch. There’s, uh, people that, Expect the VAT field to be there and the VAT field is not there. It’s a mismatch of expectations. People that the user interface is very complicated, a developer can use it, but a regular person who’s not a trained developer doesn’t follow that logic.

    You can blame the user for being stupid, or you can say there’s a mismatch between what the user is expecting, their experience, the software. [00:21:00] Likewise, there could be a mismatch. Like. Visual capabilities. You might have somebody who’s vision impaired. They can’t read small letters, or you might have somebody who’s sitting on a beach under direct sunlight, and there’s not enough contrast on the screen.

    There’s a mismatch between the user situation and the app and the solution. And I think identifying these mismatches allows us to then talk about Do we want to solve it? Do we not want to solve it? Do we care about it or not? I mean, I, maybe I can’t build an app that works fully for blind people, but I can make an app that works well with somebody who’s elderly and has bad vision.

    And if I do that, I will also make it so that people on the beach can read it or, or, you know, if they were in a dark environment or something like that. And, and, and Kat Holmes talks about how You don’t necessarily follow each of these really difficult edge cases because that economically doesn’t make sense, but you figure out how to solve that and at the same time improve the product for everybody.

    Alexis: [00:22:00] You have a small population of users that could be affected by that if you look at it from one angle, but in reality it will help a large group of your users. 

    Gojko: And you just think, yeah, you make a better product. Like, for example, a couple of years ago, we had a bug report for MindMap. MindMap is one of my products.

    It’s a collaborative diagramming mind mapping tool, and we had a bug report that it does not work well on a refrigerator. Okay, well, I mean, it doesn’t work well if you put it on a microwave as well. It’s not intended for that. It’s intended for computers, not for kitchen utensils. You have these weird things where people play Doom on a microwave screen or something like that.

    How did you get my software to run on fridge? That’s the first question. A woman who stayed home in the mornings to take care of her children, this was before COVID and work from home and things like that. Because our software requires a large screen, it’s kind of a [00:23:00] diagramming thing, uh, running it on a phone is not really an option, but keeping a laptop opening the kitchen when you’re cooking is also not necessarily the safest thing to do.

    You can damage quite expensive equipment doing that. So she actually had an Android screen on the fridge that had a browser, but you don’t load it up there, but the software just did not work without the keyboard. It required the keyboard to work. So it didn’t work that the problem is not that it didn’t work on a fridge.

    The problem is it was useless without the keyboard, really, because we never really thought about people using it without a keyboard. Or a pointer device or something like that. So instead of making it run on a fridge, which was pointless, one user in 10 years complained about that. We thought about, well, maybe there’s a whole class of people who are not at the keyboard at the moment.

    Maybe there’s a whole class of people who just need to observe rather than Participate, because she wanted to observe the collaboration that her colleagues were doing. Maybe [00:24:00] there’s some stuff we can do, like changing it from a floating toolbar with really small buttons to a really large toolbar with big buttons that you can control and things like that.

    So we iterated on that. And I think we came up with a much, much better UX design for the app in general, not just making it work a better on a fridge. So it works better, even if you have a laptop and a keyboard and a mouse, it still works better for you because we challenged ourselves to improve the UX.

    Alexis: Yeah, it’s, it’s very interesting. So that was one person trying to do something, but as a result, because you observe that very carefully, you realize that could affect and improve the product for basically all the users. It’s very interesting. It’s not only discovering new use cases or probably new personalized or new possibilities of development for the product.

    It’s really improving the product overall. So there’s, that’s another class of, uh, 

    Gojko: Kat Holmes has this principle in her book talks about solve for one, expand to many. And that’s really important [00:25:00] because especially if you look at kind of lizard behavior, these are like really, really weird things that go on, but solving and doing things for such weird edge cases, it’s never going to be economically justifiable.

    I mean, you can look at a product manager, looks at the weird edge cases. Well, this is like, 0. 1 percent of our users. I can’t spend time doing this. I have to spend time doing what 80 percent of the users expect, but it’s not about helping that 0. 1%. It’s about using that as signals that your software is placing obstacles in front of people and then figuring out, well, maybe there are some obstacles in front of other groups of people as well.

    Alexis: I love it. How would you translate that into other things than software development or building products? You have a leader or an emerging leader. How would you translate that in the realm of an organization or a team? 

    Gojko: Well, that’s an interesting question. You know, I think, uh, quite a related concept from outside of software is those kind of [00:26:00] desire lines, desire lines are from usability research and things that where you try to figure out, I think there was a story about this university where they built a new campus instead of trying to figure out where to put the.

    Walk paths and, and the roads, they just planted grass and let students walk around stepping on the grass. Then they figured where the grass was stepped on and built the pathways there instead of trying to predict where the pathways are going to go. I think from an organizational perspective, that’s something that we can figure out.

    What do we want? our employees to do? How do we want to support them? How as a leader can I support people in what they want to do, not what necessarily we think they want to do? I remember one kind of really weird case, maybe it fits into this, maybe it doesn’t, when I was working with hedge funds or small investment banks.

    Small in this case means about 3000 people. So not [00:27:00] massive international giant, but not a small company as well. And they had a couple of hundred developers and we were trying to help them improve the software process, but whatever we suggested, it wasn’t improving productivity because the bottleneck was somewhere else from the systems thinking perspective, the bottleneck was somewhere else.

    And then we’ve done a kind of figuring out where people feel that they’re wasting time. One of the things where lots of people felt they were wasting time was waiting for virtual machines to start. The morning, everybody comes at the same time and they had this recent policy where for business continuity reasons, they were not allowed to keep any data on their physical machines.

    Everybody had to use a virtual kind of remote Citrix. So everybody comes in at the same time. They kind of, you know, start logging on to this. They didn’t have enough capacity and they were waiting for something ridiculous, like 40 minutes in average for access to these things because it was new and imposed, people were complaining, but they were just getting shut down because it’s for [00:28:00] whatever, for reasons.

    The leadership introduced it and we realized, well, the introducing things like continuous delivery, test driven development, whatever, it doesn’t matter really, because your bottleneck is virtual machines and they were limited by the amount of hardware they had. But developers time in a financial institution in central London is quite expensive if you think about just in salaries.

    So we added up the money. We went to the CIO and we said, look. You are spending this amount of money every month on people just waiting for virtual machines to start. With this amount of money, you know how much hardware you can buy. Can we please use some of that money and buy more hardware for virtual machines?

    And then he said, of course we can, it’s logical, but why are people waiting for virtual machines to start? Like, why are developers doing that? So, there was a company wide policy, everybody has to use virtual machines, business [00:29:00] continuity. And he said, yes, everybody, like traders and not developers, like developers don’t store data on their machines anyway, it’s in version control.

    Okay. So you want to do, I said, well, it’s idiotic. Why are you just killing productivity from people? So there’s like a totally different desire line there. There’s a different path. And I think this is an example of the company misusing its own people. I guess because when they said everybody, they didn’t mean developers.

    So I think as a leader, it’s important to kind of figure out Both when misuse is happening in one way or another way, and where if you have people that are trying to treat the system in some way, do we want to actually support that or not support that? How do we figure this thing out? And if we’re placing obstacles in front of people, are those obstacles intentionally there because sometimes they are.

    Or those obstacles are [00:30:00] intentionally there and then they should be removed like this policy where basically, yeah, if you have version control, you don’t have to use a virtual machine. Makes total sense. 

    Alexis: So lastly, what would be the one advice you would give to your younger self? 

    Gojko: One advice I would give to my younger self, I think that would be in terms of just product building, not to trust that things I do actually have value.

    And to try to validate it. I think I’ve spent far too long in my career trusting that the things I do are actually good ideas. And very often they’re not. I’m not alone. I love Ron Kojavi’s latest book called Trustworthy Online Controlled Experiments. Here’s data from companies like Microsoft, Google, Slack, Netflix.

    The data says that kind of between one 10th and one third of things they do actually [00:31:00] delivers value. 

    Alexis: That’s okay. After that, you need to be a little bit more humble. Okay. 

    Gojko: That means that these people who are supposed to be industry leaders kind of Between seven out of 10 times, things that they think are good ideas are not necessarily good ideas.

    Alexis: Okay. 

    Gojko: They don’t, they don’t improve the product in a measurable way. And with something like that, I guess it’s really interesting to think as a leader or as a, as a product manager and executive supporting product managers, what brings value to the market so we can capture some of that value, uh, back because If we’re not delivering value to the market, then we can’t really capture the value back from the users.

    And if we can’t figure that out, then we can run circles around the competition because the bad news for most listeners that have never thought about this is that, well, I’m just going to stick the range in half there. So eight out of 10 things you do make no sense. But the good news is that eight out of [00:32:00] things your competitors do.

    If you can figure that out faster than the competition, you can create a much better product. And I think that’s why these companies are winning in the market, because they can figure that out and they can understand that they can measure it. They can stop bad ideas from progressing too far. 

    Alexis: This is very insightful.

    for sharing that. 

    Gojko: Trustworthy online control experiments. Wonderful book. Wonderful book. 

    Alexis: I will add the references in the companion blog post. Thank you very much for having joined the podcast, Gojko. 

    Gojko: Thank you!

  • Leadership as First-Time Founders: People First, Focus Always

    Leadership as First-Time Founders: People First, Focus Always

    Leadership looks different when you’re a first-time founder.

    There’s no handbook for the moments that matter most — the ones where you have to show up as a human, make a decision as a leader, and keep the company alive at the same time.

    In this episode of Le Podcast on Emerging Leadership, I welcomed Héloïse Rozès and Nikolai Fomm, co-founders of Corma, a startup helping companies regain control over their software tools and licenses. Corma’s mission is deeply practical: bring clarity to the SaaS “black box”, improve employee experience, and help IT teams manage access, cost, and risk — especially as new tools (including AI tools) keep multiplying.

    What I loved in this conversation is that it’s not theory. It’s leadership learned in motion.

    Here are a few ideas that stayed with me.


    Communication is not one skill — it’s many

    Héloïse describes communication as her biggest challenge — not because she dislikes it, but because it constantly changes depending on who is in front of you.

    Co-founders. Employees. Interns. Freelancers. Investors. Clients. People you meet at an event.

    Same company. Same reality. Different language every time.

    And as Nikolai adds, the CEO role amplifies this even more: you’re often “the voice” externally, and the internal team watches how you represent the company outside.

    Leadership forces an uncomfortable question:
    are we consistent across all the rooms we enter?


    People are the challenge — and the point

    Both founders put people at the top of the leadership challenge list.

    Not in an abstract way. In a very real, operational way.

    Héloïse shares a moment many founders face for the first time: an employee announcing a maternity leave. The human reaction is joy. The leadership reaction is also: how do we adapt?

    The tension is real and constant: you can be empathetic and still compute the consequences. That doesn’t make you less human. It makes you responsible.

    I appreciated how they name it clearly: balancing care and survival is part of the job.


    Prioritization is the art of saying “no”

    One of the most concrete leadership lessons Nikolai shares is the difficulty of saying “no”.

    In startups, ideas are everywhere — especially with creative, ambitious people. Many ideas are good. The problem is that resources are limited.

    If you do everything, you do nothing.

    Saying no is not rejecting creativity. It’s protecting focus.

    And Héloïse adds an important filter she uses: if an idea doesn’t connect to market value and impact (including revenue), it may be interesting — but not now.

    That clarity is leadership.


    Culture doesn’t happen — it is built

    Héloïse and Nikolai insist on something many teams forget: culture is not a poster. It is practice.

    They talk about culture in very concrete ways:

    • being on time (or even early)
    • being available to talk when someone needs it
    • celebrating small wins
    • avoiding “two companies” inside one (sales vs tech)
    • intentionally creating cohesion

    They also created a program called Cormacolindor — an ambassador-style ritual inspired by the origin story of “Corma” (a nod to the One Ring), designed to help new hires collaborate, build spirit, and shine individually.

    I like this because it’s not vague. It’s designed.


    Radical Candor: avoid ruinous empathy

    They use Radical Candor (Kim Scott) as a feedback foundation.

    And Nikolai makes a point that is worth repeating: for teams that genuinely care about each other, the biggest risk is not aggression — it’s ruinous empathy.

    When you care personally, you might hesitate to challenge directly.

    But not giving the feedback doesn’t protect the person. It delays the learning and increases the cost.

    Leadership is not being nice. Leadership is being helpful.


    Advice for first-time founders: don’t do it alone

    Their closing advice is simple and strong.

    You don’t learn leadership from a textbook. You learn it by doing — and by talking to people who have done it before.

    Héloïse suggests a practical move: list the leaders you admire and reach out. Nikolai adds that many experienced leaders are surprisingly generous with their time when the request is genuine.

    This is a great reminder: mentorship is often closer than we think.

    Listen to the Full Episode

    Tune in to learn more about the Corma journey, leadership insights, and practical advice for emerging leaders.

    Here is the transcript of the episode


    Alexis: [00:00:00] Welcome to the podcast on Emerging Leadership. I’m your host, Alexis Monville. Today, we have both Héloïse Rosas and Nikolai Faume on the show. They are both co founders of Corma, and they will explain a little bit what it is. Héloïse and Nikolai, it’s great to have you both on the show. Let’s start with some introductions.

    How do you typically introduce yourself to someone you just met? It’s 

    Héloïse: a good question. When I meet someone. So if this is like in an informal setting, I’m going to say, hi, my name is Héloïse. I’m working in Paris as a, as a founder. And then I’ll just see if the person is in a startup ecosystem or not before knowing how to introduce myself better.

    Alexis: Okay. Nikolai, how about you? 

    Nikolai: Yeah. So I would say that I’m also Nikolai, one of the founders of Corma and then yeah, maybe say a little [00:01:00] bit more about what I’m doing in life or at the company. And then usually it gets quite quickly to what we’re doing as a company. But yeah, I do it a bit like Héloïse, see a little bit, who do I have in front of me, so I don’t give like a minute monologue that the other person doesn’t really understand 

    Alexis: or wants to know. 

    Let’s say now I’m interested and I’m saying, okay, what is Corma about? What would you say? To keep it 

    Nikolai: short and spicy. I mean, usually people work with a lot of different software tools nowadays and it’s become so many, I mean, just think of all the new AI tools that are coming in. It’s becoming a mess for all the employees to understand, okay, where do I have access?

    How do I get access to a new tool? But it’s It’s messy for the company who needs to make sure you don’t pay for seats. Nobody is using your people are not signing up to dangerous AI tools and load up their entire company secret data to some weird AI company in China. It’s also a mess for the it team.

    And basically as Karma, we want to solve that by bringing everything together in one place, [00:02:00] providing a great employee experience. And at the same time, making the life easier for the company. 

    Alexis: Excellent. I love it. Héloïse, can you do it better? Just to check that, can you make it shorter just to put some competition between the two of you?

    Héloïse: Good. I mean, I’m lucky I got a bit of time to think about the question. Corma is the co pilot of your IT for handling licenses. So that’s how I put it in a sentence. People are like, okay, but what does it really mean? I simply tell them you’re a head of IT of 1000 people. You have to manage computers, Wi Fi, VPNs, and the little mouse of the, of the computers too, but also the softwares and what’s happening in the cloud.

    But when you look at the application park, that’s your software stack represents. It represents a budget of three million dollars. It’s a black box where you don’t know what the ROI is, and you have no clue of who has access to what at what hour and if it complains. So Corma solves that problem by being the cockpit of truth of your licenses.

    Alexis: Excellent. I love it. So [00:03:00] your boss, co founders, if I understood well, there’s a third one who is not in the room. 

    Nikolai: Yeah, it’s CTO. You know, they don’t like to talk to people too much. No, but he’s, it’s, it’s, no, that’s not true. It’s he’s called Samuel. He’s also very nice and sociable, but actually right now he’s on his well deserved honeymoon.

    So we try to leave him as much alone as possible. That’s why it’s only the two of us. 

    Héloïse: And 

    Nikolai:

    Alexis: love that you’re taking care of that and you’re, you’re paying attention to that. And that’s, that’s very cool. Okay. Your first time founders, how do you experience leadership? Nikolai, could you share your perspective?

    For me, it 

    Nikolai: was interesting because before I worked for two and a half years. in another startup and saw like some good growth there. And I always look at the founder, like whenever I didn’t know something, you obviously ask the founder and expect it’s like a bit of a wizard who knows the answer to everything and know that I I’m in this position myself.

    I know this is not necessarily true. So I see it as a challenge that you need to figure stuff [00:04:00] out that nobody did before you. Obviously you have advisors, but in the end, You are the founder or you are free founders. You can ask each other. This already, this already helps at the start. And it still is a big challenge that a lot of people rely on you.

    And obviously yourself, you might have some doubts. Of course you have your vision, but you might doubt it at some times. It’s a process as well to become a founder. Then you never did it before in your life. 

    Alexis: Okay. And Héloïse, I’d love to hear your thoughts as well. 

    Héloïse: So to be a leader in a, as, as a young founder in a startup, I think is absolutely thrilling.

    I’ve always looked at leaders by leading by example, and I hope I share a good example with what I’m doing in my day and, and how I act in my life. But I think the biggest challenge for me is communication because we have so much information in our heads. We have to communicate it in one way to our co founders.

    We have to not communicate some things as well because we don’t want to disturb them at some times of the day where we’re focused on deep work, but then you have to [00:05:00] communicate at different times. You start to communicate differently to employees, to interns and to freelancers, but also to investors, clients, prospects, and people you just meet at a random cocktail for networking events.

    Communication for me is very, very important, and it’s a challenge that I’m not ready to have completely tackled yet, for sure. I think it gets even tougher when you go. 

    Nikolai: To jump in immediately and you have the extra challenge that you are the CEO, so you’re also by role in charge of the communication with the external world.

    I mean, you mentioned investors, but like people will always look to you first. So it’s. Internal different types of communications and then the whole external world. And obviously the employees see what you say outside your investors have some insights into the company as well. So I think it’s already also interesting to balance those different types of requirements of communication where you are in the spotlight.

    Alexis: What I observed so far, you are doing really [00:06:00] great. So I would say, don’t be too worried about that. Maybe that you are doing great, but I’m glad you’re taking care of that. And you’re, you’re finding that very important. So That’s very cool. There’s always challenges in early stage startups. Can you tell me what are the typical leadership challenges you face?

    Héloïse, do 

    Héloïse: you want to take that first? Yes. The first challenge is people. Okay. And I think it’s the most important challenge of all, because a company is laughing about its team. The main challenge, for example, that one of us, we had to face was our first maternity leave. It was happened during, uh, the life of a startup.

    It was a surprise for everyone, the person included. And we’re all very happy that it’s, it’s happening. It’s going well. However, it’s of course like, no one tells you as a young founder, how to react when your employee tells you that we’re going to go on maternity leave in the next six months. And you have to react to the right words in a culture setting that’s very international and with [00:07:00] an age gap that’s quite present.

    So yeah, there’s a lot of key elements to put into context for your first reaction to this type of news, which for me was quite naturally because I think Life is a Miracle was very warm. But at the end of the day, it’s also a challenge for the whole team to make sure that everything goes well for everyone professionally.

    Alexis: I have to admit that the first time it happened to me, I believe my face showed something completely different from what I wanted to say. And I saw the person, the face of the person in front of me. And I realized that my face was telling off what we will do now. And what I wanted to say is, of course, congratulations, because that’s what you want to say.

    But I was already starting to compute what, what we will do. That’s an important person. And when I saw the face of the person in front of me. [00:08:00] I 

    Nikolai: think this can summarize many things quite up. You have like really happy moments and still lots of concerns at the same time. So you obviously, we are very happy for her, but still, we still feel the responsibility as a founder, because this person also has a leadership role, you think, okay, who will cover that?

    Okay, we also have a CTO who goes on maternity leave on his honeymoon. At the same time, you have someone who has a visa needs to travel for it. So we always have A lot of things in the back of our head that we need to consider. And just because we consider, I don’t think that makes us less humane. I would say we try to be very empathetic, but still we also need to make sure that the company survives because in the end that’s the goal here.

    So I think yeah, balancing those thoughts is quite important. And then maybe to add to your point, so I would agree people challenge at the top. For me, It is the challenge of challenges because [00:09:00] there’s always so much stuff happening at the same time and you need to prioritize stuff. And this means, which I find quite difficult is sometimes you have to say no.

    Like we, by definition, a startup has very limited resources. So people will have ideas. That might be great, but still, sometimes you have to say, no, you need to prioritize because if you do everything at once, you do nothing. And that’s quite important to set like a clear guideline for yourself, for the founding team, but also for the employees.

    Héloïse: I completely agree. Especially in a tech startup where people are like tech wizards, project geniuses, call it whatever you want, but people are very creative. They always have this cool idea to do this new research or that cool new feature. But at the end of the day, maybe it’s my stage hat, but I have me.

    For me, if it doesn’t go on the front line and there’s no impact on the revenue, it’s not a good idea. It’s not good. If I cannot see where it’s going to bring more value to the market that we’re addressing. 

    Nikolai: And sometimes it’s a bit brutal. And like, because, you know, you don’t want to be [00:10:00] the no person. And I mean, we don’t say no most of the time, but it happens.

    And it can be kind of like, because you know, you want people to have ideas because maybe the idea is actually something nobody thought about and it sparks something great. But I mean, the minimum we have to do is like really challenge it. And then, yeah, sometimes we have to be a bit tough and say, yeah, okay, cool.

    But honestly, Maybe in two years, if everything goes well and that’s not always easy. 

    Héloïse: And actually the challenge to make sure that we, uh, not just challenge that, but channel that we channel the energy of the people that joined the team by creating this a new ambassador program. That’s called the Cormacolindor.

    It’s literally, so, you know, the name Corma comes from the name, the ring, where the sass of sass, so one ring from the Lord of the Rings, basically. And, um, uh, Cormacolindor is literally an elfish. Yeah. The ring bearer, the person that bears the ring, because like Frodo, when you’re Corma, you’re a Cormacollindor in Elvish.

    So we did this program quite [00:11:00] recently with the new hires to help them step by step collaborate with each other to reach the objective key results, make sure that they do things outside of work that build up the team, uh, team spirits and make sure that individually they shine because everyone is unique.

    Everyone can not, no, They cannot be replaced, someone cannot, because you’re not here, I replace you. So it’s really a very interesting program to show that, to really leverage also and make them shine as people. Because it’s not just the challenge, it’s also the main channel of how Corma is going to do great.

    Alexis: I love it. That’s very, very interesting. So you put people first as a challenge, and I can see that you are really taking care of I’ll People contribute to the company, but also how they develop themselves, how they grow into their role and grow with each other. So that’s very cool. Are there other challenges as managing people as young founders?

    I mean, 

    Nikolai: for like an important thing, it’s people, but [00:12:00] you know, as a startup, you’re in survival mode all the time until you get I mean, technically every company is survival mode, but I would say in startups is the strongest because they are young, they didn’t prove themselves yet. They don’t have as much money as they want.

    They don’t have like their product market fit yet. That’s generating profitable revenues every month. So, so it’s really. Tough also on the, on the commercial side to manage people and data founders are obviously heavily involved as well because we have investors, we have some funding, but we need to show to get revenue to prove obviously that our product, that our idea is needed, but at the same time it also pays our bills.

    And I think balancing a little bit of this financial need to just push on the commercial expansion, but at the same time, not get lost. On it. And remember, you try to sell your product because you believe in it. And if you have more clients, you get more user [00:13:00] feedback. It’s a bit difficult to balance this sometimes the need for commercial expansion or with the internal need to understand what you actually want to bid.

    If the client asks you, okay, can you do this? You say, obviously yes. And if, if it’s. Not there at all. You say it’s on the roadmap and if you know, it’s like humanly technically impossible to do it. You say, okay, we’re going to look into it for the next quarter. But obviously this has a limit. You cannot oversell all the time and you need to take a step back then and know how to balance this.

    I would say this is also a challenge. 

    Alexis: Hmm. Very good point. So when the leadership team embodies the values and principles they want to see in the organization, then I believe the organization can scale, can grow and can become something very beautiful. Do you agree with that statement? 

    Héloïse: Yeah. Culture eats strategy for breakfast.

    So like, it’s a very basic sentence to say, but really summarizes the whole feeling that we see, not [00:14:00] just at Corma as a company that has, is very strong in the values that it upheld, but also within Station F, we see the startups that are very united. Where the people are already, the cement of the whole building, of the, basically the cathedral that they’re going to build.

    It beats any competition. It goes, it just shines quick pass. 

    Alexis: So what are you doing to create such a culture? 

    Nikolai: You mentioned it’s a lot of leadership by example. You need to lift the company values and I think it’s also something we learned. You need to actively nurture it. Like, okay, people. Probably have the tendency to copy behavior, but you need to encourage it.

    And it was something that was not always super easy because sometimes people feel the founder has their unique role and they always share direction. But you know, it’s part of our DNA that we want people to lead the way as well. So there’s sometimes you need to actively encourage fine programs. Like, for example, what you said with this ambassador program, but it can be [00:15:00] small stuff to how you give praise, how you give feedback.

    So for example, this radical Canada methodology, we follow it, tried to implement it and how we do feedback and how we do development and like personal career development. It’s an active process. It takes active management. Even if we try to. Live as the best example. I think it’s still, yeah, we still need to be active to do it.

    Héloïse: Yeah. Some examples are typically by your life on time, if not on time, like this in advance, being on time is always being late. It being present for the others. Like if someone wants to talk about something, they can pick a lunch for you very easily as a founder is something that you do. I mean, sometimes it’s a career coaching.

    Sometimes I, and we talk about other things than work, which is like, what is like next five years, you know, how, how can we, how can we get you there? It’s about creating an alumni, uh, alumni group. And the alumni also inspire the current people that are present at Cuomo. And so they are inspired together and it’s about giving them the voice to be heard so that they [00:16:00] embody this leadership position that Nikolai was just explaining now.

    It’s part of not just the Ambassador program, which is. Basically setting a more formal setting to what was happening before it’s really just a mix of how you celebrate the little wins or you close the deal. That’s really, really good. Okay. How can I help you close your deal today? Collaboration on different topics and putting everyone in the same team, like there’s not a tech team and a safety mask on my team.

    That’s just one. That’s just not possible to not talk to each other. Even if you don’t understand what JavaScript is, at some point, you’re going to have 

    Alexis: to. I love what you’re saying there. So you mentioned Radical Condor for the audience. The idea, if I summarize it, is if you care personally about people, then you can challenge them directly.

    So that’s the important part of it. If people can feel that you care about them, then you can challenge them. basically give a feedback. If you don’t feel you care about them, it’s like if you were trying to put a big truck on a rope [00:17:00] bridge. It will not really work. Your feedback will not go through. So that’s basically useless.

    Is it a good summary? I think 

    Nikolai: like the other thing is even more dangerous that because you care to, because I would say we all care deeply about the, the, the team, the humans, the people behind it, that you, because of that don’t challenge directly. I think in the concept it’s called ruinous empathy, and I think it’s a big risk that you’re trying to be too nice, too cushy.

    You know, it’s like a bit of the example after lunch, you have some food stuck in your face. You don’t want to tell the person because you don’t want to embarrass them, but imagine then they go off and spend all their day with food in their face and they would have been so much more grateful to have this.

    uncomfortable moment where you say, yes, sorry, maybe clean your mouth a little bit. There will be so much that you were created this little uncomfortable moment, but overall, because you care personally, you gave some, yeah, let’s say negative or in the sense, constructive criticism. This is better. Like, so for me, just being like toxic, not caring about [00:18:00] people giving meaning.

    Feedback. Obviously there are toxic people. I don’t think we are at risk of it. So for me, it’s more the thing to avoid the ruinous empathy and to challenge directly because we have the best intention behind it. 

    Alexis: I love it. Thank you for the example. That will make it very clear to people. They will all try to look if they don’t have anything left.

    So lastly, what advice would you like to share with our audience, especially those who are aspiring leaders or early stage founders? 

    Héloïse: That’s a good question. 

    Nikolai: I think there are a lot of answers to 

    Héloïse: it. Yeah, there’s so many. I mean, it depends on what context you’re in, but do you want to start or? 

    Nikolai: I mean, for me, there are some basic things.

    You throw yourself into the cold water. I know some people that hesitated away from leadership positions because they are scared to manage others, be it because they’re still a bit young. They’re a bit shy. You have to do it to learn it. It’s not something you learn in the textbooks. So you. First, you have to bring yourself in the position to [00:19:00] lead.

    And then the next thing is you will see, you don’t need to figure everything on your own. Find yourself some mentors, find yourself some friends. Ideally. I mean, we have us three co founders, I would say we are really, really tightly linked. Then you can like with people that have similar experiences or similar learnings.

    Try to like, once you bring yourself out there, try to exchange with people that live in the same or lived in the past in the same situation and try to learn from there what worked for them, from what doesn’t. And for me, something that I usually do, like try to follow your intuition. If you feel something doesn’t feel right, maybe, yeah, reflect if it’s good.

    And if you have a good feeling of something, you also need to have to act, like have the courage to act. Don’t be like too scared in moments, even if it might seem a bit scary. Like sometimes you have to push yourself a little. I think that’s the uncomfortable part of the leadership. Sometimes you need to do things that are not super pleasant, but you have to do it because nobody else will.

    Héloïse: I mean, I can [00:20:00] only second what Nicolas just said. One thing in addition came into my mind. So when you’re young as a founder, there’s a lot of topics. But you’re going to realize that exists first in life, like managing new employees, like having a specific type of client to manage or stuff like that. It’s called like zones of hurtful ignorance that are very difficult to not difficult to observe because they’re quite come quite fast, but very difficult to, you know, on your own, if you really isolate yourself, it’s going to be a harsh on you, on your life, on your mental health.

    To actually overcome and because you, you have like 15, uh, ignorance bits to, to master at the same time. Like, uh, it’s like, just life gets in the way. What I really would recommend to a young founder, how founder starting out is you just get your phone, build out a list of the top 50 people. And so maybe five in every category, or 10 in every category that you most admire in the world.

    in your region, in your industry, and get [00:21:00] them on the phone, book a meeting with them. It might take you a year, but at the end of the day, you’re going to make it happen. Actually, I would disagree. 

    Nikolai: It won’t take you a year. Like one thing that surprised me, how happy many leaders are actually are to share their knowledge.

    I mean, if you think of some people that we spoke, like we, I mean, yeah, it’s, We’re not out of school for that long and the amount of senior people we speak to just because we reach out because, okay, they maybe get spammed by sales people, but just people asking for like founder to founder advice. They are usually really happy to share.

    So like really go out there and try to get some advice, some mentorship. I would say it’s easier than you think. I don’t think you need a year for it, but it’s super valuable to do that. 

    Alexis: I love that. That’s really beautiful. Thank you for being here. Join the podcast today. I’m sure it will be already uploaded.

    Héloïse: Thank [00:22:00] you.

  • The Future of User Experience Is Not Artificial — It’s Human

    The Future of User Experience Is Not Artificial — It’s Human

    When we talk about the future of User Experience, the conversation often jumps straight to AI models, automation, and performance.

    But what if the real challenge isn’t prediction — but judgment?

    In this episode of Le Podcast on Emerging Leadership, I had the pleasure of discussing this question with Sebastian Cao, a UX and technology leader who has worked at Red Hat and Tesla, and who recently taught a Stanford course on the future of User Experience.

    His perspective is refreshingly clear:
    technology only creates value when it strengthens human capability.


    Prediction is easy. Judgment is human.

    Sebastian draws a crucial distinction between two forms of intelligence:

    • Prediction, where machines excel by identifying patterns in massive datasets
    • Judgment, where humans rely on experience, context, and intuition

    At Tesla, this distinction became very concrete. Machine learning models could predict likely failures based on historical data. But technicians — by touching, seeing, and sensing the vehicle — often detected signals no model could capture.

    The best systems were not fully automated.
    They were guided systems, where AI informed decisions, but humans remained accountable.


    From automation to augmentation

    One of Sebastian’s most telling stories is surprisingly simple.

    When his team was called Service Automation, frontline technicians immediately feared job loss. The name alone created resistance.

    Renaming the team Service Augmentation changed everything.

    Words matter.
    Framing matters.

    By explicitly positioning AI as a tool to amplify human skill, rather than replace it, adoption became possible. Productivity improved, trust increased, and the technology actually delivered value.


    The “ghost in the machine” problem

    Sebastian uses the metaphor of the ghost in the machine to describe what happens when AI systems behave like black boxes.

    When users don’t understand:

    • where data comes from
    • how predictions are made
    • how confident the system really is

    they stop trusting the tool — or actively work against it.

    Transparency is not a “nice to have” UX feature.
    It is the foundation of trust.

    Explaining reasoning, showing confidence levels, and making decision logic visible turns AI from something magical and frightening into something usable and credible.


    Empathy is not optional anymore

    One of the strongest messages of the episode is that empathy has become an engineering requirement.

    Designing AI-driven systems without understanding:

    • user incentives
    • fears around automation
    • real-world decision-making

    almost guarantees failure.

    Sebastian insists on something deceptively simple:
    go where users work, observe them, and learn how decisions are really made.

    No amount of code can replace that.


    Open source, ethics, and trust

    Finally, Sebastian makes a strong case for openness.

    When AI systems influence frontline decisions — impacting customers, safety, or livelihoods — leaders must be able to explain who built the model, how it was trained, and what biases may exist.

    For him, open source is not an ideology.
    It is a practical condition for trust and accountability.


    Leadership in a human-centered AI world

    Sebastian’s advice to leaders is clear:

    Understand the technology.
    But never forget the human on the other side.

    The future of User Experience will not be decided by the biggest model or the fastest deployment.
    It will be shaped by leaders who combine technical literacy, empathy, and responsibility.

    That is where Emerging Leadership truly begins.

    Here is the transcript of the episode

    Alexis: [00:00:00] Welcome to the podcast on emerging leadership. I’m your host, Alexis Monville. Today, we have a fascinating conversation with our guest, Sebastian Cao. Sebastian is a visionary leader with a wealth of experience at the intersection of technology and user experience, having held pivotal roles at Red Hat, Tesla, among others.

    He recently delivered an insightful course at Stanford on the future of user experience, where he explored critical topics like AI’s role in augmenting human capabilities. So we are thrilled to dive into these topics with him today. Sebastian, welcome to the podcast. How do you typically introduce yourself to someone you just met?

    Sebastian: Thank you Alexis.  I love to be here. I usually speak that I’m an engineer that can talk about [00:01:00] Problems that could be solved with technology. I like to talk about problems. that are worth to be solved, like real problems talking about as a species worldwide, globally, problems that we have and what are good options and good ideas to solve with technology.

    So that’s my, how I introduce myself. 

    Alexis: I love those kinds of introduction where I need to ask more questions to know a little bit more about your background and all those things. But we will go through that at some point. You did a Stanford course. about the future of user experiences, and you emphasize the balance between prediction and judgment.

    Can you tell us more about that? Because I’m very curious about that thing. 

    Sebastian: Yeah, as I mentioned, I’m an engineer, computer science engineer, but I’m certainly not the kind of guy that would go and hack and make a model, an LLM model better, or just make a little bit more incremental performance out of a model.

    I’m more [00:02:00] concerned and interested about how that model could really solve human problems. We can go into that and move into Silicon Valley, like a few years ago. And you always read about all this story and the heritage around this area. And you see all this company, but it’s still a really engineering led culture.

    So everyone right now is like competing about it’s an arms race, right? Who has the biggest, boldest, more expensive model in a way. But I was concerned and we can certainly touch into that. My experience of 2 years at Tesla about how we can solve real human everyday problems for frontline workers that.

    So, when I was discussing that we engineers that were getting coding these big models back inside Tesla, we’re always. Computers will always be better than us and doing prediction made by that we mean getting a huge amount of data historical data and see patterns things that [00:03:00] are repetitive over there and say, OK, this is with a high.

    That’s a probability, right? With a high degree of a chance, 90 percent 80%. So this will happen because data show us that happened before. And that’s great. We shouldn’t be doing that. So in the case of a car company, any company, you get all the historical repairs and you know that certain type of cars in certain weather, when driven by certain patterns, you usually break this part of this subsystem X amount of kilometers or miles or whatever.

    So that’s great. We were doing that. We predicted diagnostic to Manchell learning. What I wanted to add to the equation, because it’s the part that’s easy to forget is What about the judgment? What about the human judgment? The mechanic or technician will see the car coming, and we might see by touching it, by feeling, by looking at the car, might see that something else is off.

    Maybe there was a, I don’t know, heartbreak or there was actually a crash or something that happened before that we’re getting all [00:04:00] this data and all these signals coming from the car that might also come into play. And that’s what we humans are good at. We remember seeing that before. We made a decision back in the day that the outcome was a particular outcome, and that kind of is part of your knowledge base and your experience.

    So how we can merge both how we can merge the cold data coming from the car in this case, but leaving the opportunity for the technician to make. a judgment call. So I was pushing not for a automated kind of result, but like a guided, guided diagnosis process where the machine will provide all the probabilities looking at the car and getting all the data coming from the sensor.

    But the technician will actually use that to make their own judgment and say, yes, I will do that. Or maybe now we’ll do the other thing. So I think that is a, that’s a great concept that I’ve been talking and I’m In love right now, and I think it’s really important because we’re seeing this much development in a it’s [00:05:00] thinking about a not only as artificial intelligence, not even as artificial intelligence, but more like augmented intelligent or amplify intelligent where we make humans better, they can do more because we’re feeding them with data pre analyze data with a lot of prediction by your living room for judgment.

    Alexis: Okay, so a large place for human, but not only human, the experience they have in a particular field, and that could be any field. 

    Sebastian: In this case, we’re talking about mechanics technicians, people that do wrenches, they take wrenches, I mean, with their hands, they’re not coding, they’re not engineer, they don’t care about at all about machine learning.

    How you can give them More especially there for example compensated and then we get into compensation and kind of incentives that’s a cold economical kind of analysis and there’s a lot of research about that they were incentivized by the number of cars so why don’t we tell them a story that they [00:06:00] will be able to use him.

    Augmented. Tools or machine learning or whatever, it’s not about machine learning. They can actually go through more cars through the day, through the week. So they’re more productive. They get a better paycheck. So we’re all happy. But sometimes I feel as an industry coming from any, again, as a software engineer all my life, and we met in a software company, we tend to fail to explain that we go into, okay, this is cool.

    This is the latest, this is the latest model. LLMs, but we understand who is the customer who’s on the other side consuming that technology, how they’re incentivized and what is that they’re trying to solve. And we certainly fail at telling that story sometimes. 

    Alexis: I feel there’s something deeper that we can grasp there.

    And so AI is not a replacement for human intelligence and definitely human intelligence, their experience and how they understand the world is something important. So they could be augmented. How do you do that? Practically. 

    Sebastian: [00:07:00] That’s where I learned a lot and made a lot of mistakes doing that. And I think that’s what I’m looking at.

    What is the company or yeah, a company, a software provider actually going to crack that code. I think right now everyone is fighting about releasing the biggest model and spending a lot of billions of dollars in training, but no one is certainly there might be companies doing that. But we haven’t seen that and the headlines and the stories in the media.

    It’s all about the biggest model and the competition between the providers. No one is okay. This model, whether it’s the biggest or not, it’s actually increasing productivity for it. Frontline workers, technicians, insurance clerks, customer support operators, airlines, whatever, we’re still not seeing that because we’re failing at deploying those models along human beings, working side to side, the whole copilot idea, whatever we would like to call it.

    So when I’m seeing failings first, as an engineer, we tend to, it’s too [00:08:00] complicated. We throw a lot of technology in a lot of. Explanation and a lot of, we tend to use automation and artificial intelligence a lot. And if on the other side you have someone that doesn’t come from our industry, the first thing that comes to mind is, okay, this is automation, this is going to replace me.

    No matter your intention is the first, because it’s the human reaction to that. One of the first things that I did at Tesla’s team that I inherited when I joined was called service automation. And we were supposedly, we were tasked to, okay, let’s create more tools. For internal customers to employees, more tools for them to be become better.

    I say, okay, the first thing I want you to change is the name because every time I present myself as service automation, they say, okay, you’re coming for my job. So it was super quick and everyone say, okay, that’s cool. That’s a good idea. So we change it to service augmentation and I started sharing a lot of like research, not papers, but just.

    Headlines and professors kind of [00:09:00] analysis at both sides of the aisle, as they say here in the US, both the engineers are building the software and the consumers on the other side, the technicians say, Hey, this is what we’re trying to build. We’re trying to build something that will help you go through your day that are going to augment you.

    Augmentation is still like a 20 word. As I say here, it’s like too complicated. Maybe the amplification. But just change the name because words carry a lot of weight. Right now in the media, it’s much more interesting to publish. story about automation or AI taking out jobs than talking about AI making people better.

    Alexis: It’s very interesting how we oscillate between a 1 world and 5, 000 world, and we are mixing them in one sentence and it’s scaring everybody. 

    Sebastian: It’s a human behavior and we go from, this is going to be a great feature, to Skynet and Terminator and we’re going all like the matrix. So, and that sells. So I think it’s for people like us, like you to understand the technology, but I think you need to [00:10:00] go further and explain the technology, explain what’s going on, explain why you’re using 

    Alexis: it.

    And it’s a very good point. You need to care about the users themselves, the people who will really use the technology and go a little bit further in understanding how they work and what they are trying to achieve. And it’s not a game about feature or that’s not only that it’s really about. what they need to accomplish, even if they don’t really know what kind of feature they would need on pantyhose with users, I feel is very important.

    Absolutely. You picked an example about the ghost in the machine. And I was very curious about that because yeah, I’m probably old enough to know about that album from a police, uh, from the police, 

    Sebastian: 1981 great songs that I was doing, but I always heard, I mean, I always listened to music that is. Yeah, but yeah, I got a t shirt and I used that t shirt that’s about that album that is called the policy of ghost in the machine.

    And I used that t shirt when I went to a meeting that I want to explain the [00:11:00] concept and say, the ghost in the machine is that idea. It’s a phrase that had been going on forever. It’s just. You can also talk about the Turing test and all that. Okay. If it is a machine, if it is new enough or strange enough, and I think most people got that experience with JGPD like two years ago, you do feel that there’s something else about a program there.

    That is the ghost. There’s a soul, there’s a human touch. At the end of the day now, if you delve into it and you get into the research and you do, you understand the transformer model and all that, okay, it’s a pretty big program choosing what’s the next word to use. But at the beginning, it feels magical.

    I think that is the idea is people will tend to think, okay, this is actually sentient. This is actually thinking by itself. So with the ghost in the machine, I tell the people, if we don’t explain them what’s going on, if it is a black box, that is a concept that we also use a lot in software, you’re not explaining where’s the data coming from for you to make the decision, the prediction, where it’s coming from, who selected the data, [00:12:00] who labeled the data, and then you don’t explain how you use that data to make a decision.

    And then you explain. In simple terms, kind of the, like the confidence interval, I say, okay, we’re pretty sure up to 80 percent you don’t need to use percentage worth. I was pushing a lot for like graphical representation, easy to understand that this is a recommendation based on all this data. I think we also need to get better at that with sending all these models that you ask a question, you get a response, but there’s nothing that will explain you.

    How that response got constructed, how that response came to be, and then we get into a lot of and we all we saw that already a lot of crazy stuff on really dangerous stuff about labeling and who’s bias data and all that. So I think that is an also an important concept. We’re dealing with a frontline workers say sharing with them.

    Okay, we’re giving you this recommendation because A, B and C or D happened before in my case, I was pushing, but it was a pretty simple concept to fix [00:13:00] an issue. You’re relying on millions and millions of rows of data, lines of data coming from previous repairs. The repairs, historical repairs you have done 10 years of experience, those repairs were done by other technicians just by sharing to the technicians.

    Hey, this is actually recommending you what to do, but it’s trained in a way or based on what your peers have done in the past. It’s like this shared knowledge of all your peers that you look up to. It’s not a machine that the machine is just sorting the data and just going through that really fast.

    That was a good example of how I was pushing for the ghost in the machine. We will need to explain because they will embrace it much. There will be much more open. That is just again, a black box. They’ll say, Hey, the machine is telling you to do this. Then they will know, you know what I’m doing the other way around.

    Alexis: What I really like there, it’s not just trying to explain how the feature work, but basically showing the work that is done, explaining all the reasoning [00:14:00] that got us to the conclusion. So you need to explain a few things. You did it very well to say, okay, that’s basically the model is just trying to predict what is the right word to use after the previous one based on historical data.

    That’s probably a rough explanation, but that’s pretty cool because then, okay, I understand that this is the data, this is how it works, and I can trust. that thing. In addition to that, I have a kind of confidence level that is shown to me. I can really rely on it or I can say, ah, okay, the confidence is very low.

    There’s maybe not a lot of historical data on my current situation. You probably need to pay attention a little bit more. That’s very interesting, I 

    Sebastian: feel. It’s spot on, and you mentioned such a key word, Alexis, that is trust. And the other word that I kept on using in all my meetings with product managers and engineers is empathy, too.

    You need to increase the empathy for them to say, Hey, this is actually helping me. And [00:15:00] I’m actually rooting for the software, rooting for this solution because it becomes better, the solution becomes better or machine learning, whatever the AI becomes better, I become better. We’re all peers. We’re all partners.

    If I think you’re trying to replace me, then I will do all my best to actually hijack and just kill your project. 

    Alexis: You have quite a fascinating career trajectory funding companies in Latin America, working with RADAT in Latin America and in the U. S., working in the Silicon Valley for Tesla. How do you see the role of technology in customer experience?

    in the future. And how have you seen that evolve? And how do you see that for the, in the future? 

    Sebastian: That’s a good question. You know, Alex, one thing that I keep repeating myself, just not to forget, and I keep telling friends that I have in Latin America, they go, okay, Tesla, Silicon Valley is great. And you can relate to that being in France is at the end of the [00:16:00] day here, you will see probably bigger, Ammunition, bigger weapons, or bigger things that they’re building for a global scale, we’re solving the same kind of problems.

    Cultural change, resistance to change, human behavior is the same in Silicon Valley, in Paris or France, in Buenos Aires, in Argentina, in Brazil, or in Africa. This problem of, let’s say, for Tesla, but if you’re throwing a fully automation machine learning, whatever, diagnostic to a technician. In Tesla and Silicon Valley, without explanation, they will resist to it.

    You do that in France, they will resist to it. Also, you do that in Turkey, they will resist to it. And the same in Latin America. That’s again, that was an insight that was a realization for me. I finally understood that, okay, I’m here because you get exposed to global scale of solving problems. You probably have bigger resources and tools to solve that problem.

    But at the end of the day, the problem that you’re solving is still a human problem that is the same, no [00:17:00] matter what language you speak or the color of your skin or whatever, to be honest, this is amazing because even with everything that we’re discussing about AI and all that, at the end of the day, human beings at the core, we are still the same and we fear the same things and we need the same kind of help.

    So that’s probably what I think it’s the biggest. Outcome of my journey so far, but yeah, as I mentioned here in Silicon Valley, you see that we go really fast and sometimes too fast. So I like being here and seeing everything that’s going on with AI and everything that we’re thinking about building at the same time.

    I’m super interested in how we are going to build all of that with a good adoption and with empathy. So this is a great 

    Alexis: place to try all of that. So that’s the right balance of technology and human touch. That’s the empathy that you build with the users and to foster the adoption of technology or foster the idea of innovation itself.

    Sebastian: I read as many psychology books as [00:18:00] Coding or AI machine learning algorithm books. I think we need both, especially with AI right now. Any, any you on your, what you’re working on your consultancy and we need people that talk technical because you’re going to be exposed to technical discussion or code or a solution or diagram.

    Okay. This is what we’re building, but I think we need more people that can understand, okay, we build this and we ship this product. This is going to happen. And if you don’t know, at least you’re going to, Catching a bus or a taxi and you go there with your user and you sit with them, you sit with them and see them in action.

    In my case, it was going to the Places where they were actually branching car and working with them. You have to work with them, understand what they’re doing. So if you’re just shipping code, pushing code into production without ever talking and touching and feeling your customers, it’s going to be hard.

    Alexis: I had a, I had a conversation with a really high performing team. I was looking at what they were doing every week to have a sense of what they are, the [00:19:00] things that were important to them. I noticed that. All the team members had user, real users, interviews every week. Not all of them. Every week there was a contact with a user.

    at least one. And that was different people on the team. And they had a user interview guide that was constantly evolving because they were testing their assumption with different users. And I was looking at it and say, Oh, okay. So probably a successful team needs to be in contact in touch with their users at least weekly that showed up in their work.

    Of course, 

    Sebastian: I agree with you. I think they’re really successful like B2C consumer companies. The product management team had been doing the, they know that, and they’ve been doing for AI, we’re trying to hopefully not replace, but augment decision. It’s even more that you need to be there and understand how that person is making decisions.

    If [00:20:00] you trying to build something. That person was going to use on their day to day. Now you end up with Clippy from office in the 90s. They’ll say, Hey, what do you need to do? Do you need to print? Hopefully we’ll become better than that. 

    Alexis: Yeah. That’s the first question everybody asked was how to turn off that thing.

    Absolutely. Finally, as a leader who worked in different high tech environments, what advice would you give to a new leader who want to effectively evolve in that world? 

    Sebastian: Advice. Okay. For leaders, I would say maybe what we’ve been discussing, it’s, I think today you need to have exposure to the technical part of things, understand everything that is going on, how it’s been created, why it’s been created and by who, and there’s a lot of, we know.

    Political things at stake and companies competing against each other. So you need to understand them. We probably need another podcast to discuss open source versus closed source for [00:21:00] things like AI and all that. But you need to understand where everything is coming from. But those again, those are tools in your tool belt.

    What I would like leaders. I think it’s very important. We’ve been discussing, understand, be empathetic, understand who’s on the other side. Who’s your customer? Who’s consuming that? It’s a B2C, it’s a B2B. Are your users experienced with AI or whatever technology you’re using, or they are not? Do they trust it or not?

    And if not, and if you’d make those questions and you get answers, work with those answers, I think one thing that I see a lot here is, again, we are shipping code without asking any questions and we think that code is the best and that option will follow. And I think we need a little more human touch on that.

    So that will be my recommendation for leaders. Again, human touch and empathy. 

    Alexis: Excellent. Oh, I cannot resist. People will not see that on video. I can see it on your wrist. You have an interesting message. Tell me more about that. 

    Sebastian: All right. Yeah, I am. I just I was it was lying around. [00:22:00] This is a wristband that I got from one of my favorite places in the U.

    S. That is the Air and Space Museum in Washington, D. C. That you have all these. Historical planes and the Apollo mission, all that. And this is a wristband that says failure is not an option. And there was a wristband that was created for the Apollo team before sending someone to the moon. And then you see how much was achieved in collaboration between private and public sector, different political views and all that, how much was achieved in like six years, that’s amazing.

    Alexis: I like the story, I like the message and at the same time you mentioned open source a second ago, so don’t we say fail often, fail fast or something like that? 

    Sebastian: Oh, you got me there. Okay, we have another hour and a half to keep the discussion. I don’t like the idea of with AI and everything that’s going on that fail often, fail fast.

    I mean, just releasing whatever it is because now this is something that is talking at you and many people are making decisions based on the responses that [00:23:00] they got, the answers that they have. So if it’s not curated, if it’s biased, a lot of things can go wrong. And we have seen examples. So I think for the ethos of just move fast and break things, I’ve never liked that much.

    And especially here right now with AI. And the other part that you asked me, we share that background together is I think there’s need to be a much more open source involved. And again, ghosting the machine and the black box. If that model is answering me questions, I want to understand who built the model and who made those initial training, initial answers.

    And that’s what I love. What a lot of companies are doing in France are taking the more human approach and they’re mostly based in open source. If we go and this is a personal opinion, so I don’t know, I don’t care about all the comments that we may have. This is handled by one big corporation with all the data and it’s closed.

    We have seen that before, and it’s never a good story. So I will push for [00:24:00] maybe we have seen you and I were competing against other companies. Maybe you have your closed source. That’s good. And you have your open source of that is good enough to and it kind of the similar. It’s your choice, but at least you have an open source choice.

    I wouldn’t trust my frontline workers to make decisions that will affect customers based on a model that I don’t know exactly how it was built. Personal opinion, 120%. 

    Alexis: Totally agree. And that we are back to the trust aspect and transparency is the foundation to build trust. So I love that. I’m happy that I asked the question about failure.

    Thank you for joining the podcast, Sebastian. Thank Alex. You have been great. Let’s do this. 

    Sebastian: Once again, in the future, okay? Pleasure. Take 

    care.

  • Unlocking Growth through Unexpected Insights: A Review of Gojko Adzic’s Lizard Optimization

    Unlocking Growth through Unexpected Insights: A Review of Gojko Adzic’s Lizard Optimization

    In his latest book, Lizard Optimization: Unlock Product Growth by Engaging Long-Tail Users, Gojko Adzic presents a framework for identifying and harnessing the potential of long-tail user behavior. Much like his previous works, Gojko takes a fresh, often counter-intuitive approach to product management, making this book a must-read for anyone involved in creating and managing software products.

    The core concept of Lizard Optimization is deceptively simple: instead of solely focusing on mainstream users, product teams should actively seek out unusual, “weird” user behavior. Businesses can uncover new product opportunities and unlock significant growth by understanding and optimizing for these outliers — the “lizards” in a long-tail distribution.

    What struck me the most while reading this book was how Gojko draws inspiration from real-life examples of product pivots that emerged from unexpected user behaviors. One standout example was Flickr’s shift from a multiplayer game to a photo-sharing platform, driven by users’ unforeseen enthusiasm for sharing pictures. Rather than seeing such usage as anomalies, Gojko encourages us to treat these behaviors as opportunities for deepening product-market fit.

    Key Takeaways

    1. Learn from Unintended Usage: Gojko emphasizes that product growth often lies in the outliers — those who use the product in ways the original designers never intended. Instead of dismissing these users, he suggests digging deeper into why they’re doing what they’re doing and how we can help them succeed. His method for analyzing these behaviors provides a systematic approach to discovering new opportunities.
    2. Zero In on Behavior Changes: Gojko introduces a four-step process — summarized with the mnemonic LZRD (Learn, Zero in, Remove, Detect) — to help teams optimize their products for outliers. This structured approach feels practical and accessible for teams of all sizes, offering actionable insights that can be applied immediately.
    3. Real-Life Application: Throughout the book, Gojko weaves stories from his experience with products like MindMup and Narakeet. He shares how optimizing for edge cases unlocked exponential growth, demonstrating that paying attention to “weird” user behavior can help find hidden markets and new opportunities.

    A Strategic Shift for Product Teams

    While many product strategies focus on pleasing most users, Lizard Optimization challenges teams to think differently. This book is precious for product managers, senior engineers, and anyone guiding product development. It offers a compelling argument for looking at usage data to confirm assumptions and discover new user goals that may have been overlooked.

    This book stands out because of Gojko’s ability to turn something as serendipitous as a user’s “misuse” of a product into a deliberate growth strategy. It’s not just about preventing churn or reducing inefficiencies; it’s about actively engaging the long tail and treating unexpected user behavior as the key to exponential growth.

    Final Thoughts

    Lizard Optimization is an engaging, thought-provoking read that will make you question your current approach to product development. Gojko’s method of optimizing for long-tail users offers a practical and innovative toolkit for product managers looking to unlock the next wave of growth for their products. If you’re ready to embrace the weird, the unexpected, and the unplanned, this book is for you.

    Learn More from Gojko on Le Podcast on Emerging Leadership

    In March 2021, Gojko joined me on Le Podcast on Emerging Leadership for an episode titled Build a Product with Gojko Adzic. We explored his insights on building the perfect product, avoiding waste in software development, and how to apply concepts like Impact Mapping in day-to-day work. His unique approach to product strategy resonated with many listeners, and I frequently refer back to his thoughts from that conversation.

    I’m thrilled to share that Gojko will return for a future episode of Le Podcast on Emerging Leadership, where we’ll dive deeper into the strategies behind Lizard Optimization and explore how product managers can unlock growth by engaging with outlier behaviors. Stay tuned for more!

  • Agile2024: A Week of Insights and Inspiration in Dallas

    Agile2024: A Week of Insights and Inspiration in Dallas

    Last week, I had the pleasure of attending Agile2024, the main conference organized by the Agile Alliance. The event in Dallas was a vibrant gathering of thought leaders, practitioners, and enthusiasts dedicated to building on top of the Agile Manifesto. Throughout the week, I had the opportunity to attend numerous sessions, each offering unique insights and practical takeaways. Here’s a summary of the sessions I attended and the valuable lessons I learned.

    The Opening Keynote: The Art of Caring Leadership by Heather Younger

    The conference started with an inspiring keynote by Heather Younger, author of The Art of Caring Leadership. Heather’s session centered on four behaviors she explores in her book: self-leadership, active listening, empowerment, and team resilience. Her emphasis on a “focus forward” approach resonated deeply with me, particularly as I strive to maintain a solution-focused mindset in my own leadership practice.

    1. Self-Leadership: Heather highlighted the importance of leading by example and being accountable for one’s actions. She stressed that effective leaders must first master themselves before they can effectively lead others. Heather succinctly put it, “We cannot give what we cannot have,” emphasizing the need for self-care and the importance of tending to our own emotional well-being.
    2. Active Listening: Another key point was creating an environment where team members feel heard and valued. Heather shared practical strategies for fostering a listening culture. She advised against merely parroting back what was said and instead encouraged paraphrasing with both what was said and felt, to mirror and be 100% present with the speaker truly.
    3. Empowerment: Another crucial behavior discussed was empowering team members by giving them the autonomy to make decisions and take ownership of their work. Heather illustrated how empowerment leads to increased engagement and innovation within teams. The idea, she emphasized, is to help people shine and realize their full potential.
    4. Team Resilience: Lastly, she addressed the importance of building resilient teams that can adapt and thrive in the face of challenges. Heather’s insights on fostering resilience were particularly timely, given today’s work environment’s dynamic and often unpredictable nature. She emphasized the importance of a forward-focused mindset to navigate and overcome obstacles.

    Heather’s keynote set a powerful tone for the rest of the conference, reminding us all of the importance of caring leadership in driving team success and organizational growth.

    Agile Games – Energizers Session by Dennis Wagner and Veit Richter

    I had a lot of fun during the “Agile Games – Energizers Session” facilitated by Dennis Wagner and Veit Richter. One particular energizer, “the boss worker,” stood out to me as it effectively raised awareness about the superiority of expressing intent over giving orders.

    Agile Games for Leadership by Dennis Wagner and Veit Richter

    Following the energizers’ session, I stayed for a second session with Dennis Wagner and Veit Richter on Agile Games for Leadership. During this session, I tested a few games, including one I brought to the group: “tap and guess.” This game was well-received and provided a fun and interactive way to highlight the main bias that we have when communicating with others. If you’re interested, I can share more details about how “tap and guess” works and its benefits.

    Unmasking the Secrets of Agile Facilitation – Discover the Science Behind Personal Engagement by Evelien Acun-Roos

    In the second session, Evelien Acun-Roos unveiled the science behind personal engagement through her insightful presentation on the 5Ps of facilitation. She began with the Primacy-Recency effect, emphasizing what happens first and last in a session and maintaining the right rhythm and energy in the room. Evelien stressed the need to Pay attention by incorporating novelty, meaning, and emotion into facilitation practices. Another critical point was encouraging participants to Participate actively through inclusion, co-creation, and innovative approaches. She also highlighted the significance of Psychological safety, ensuring everyone feels included and has the freedom to engage or pass as they choose. Finally, Evelien underscored the Play aspect, advocating for a playful environment to achieve extraordinary results.

    Autonomy in Action: Strategies for Energized Teams and Exceptional Results by Damon Poole and Gillian Miranda Lee

    In this workshop, Damon Poole and Gillian Miranda Lee introduced us to three engaging activities to foster autonomy within teams. The first activity, owning the retrospective, involved providing teams with a choice between three activities for each step of the retrospective process, enhancing their sense of ownership and engagement. The second activity, journey map, involved creating an agile journey map from traditional to agile and identifying individual, team, and organization behaviors to pinpoint the next steps in evolving those behaviors. Lastly, the ADKAR for agile activity applied the ADKAR change model to raise awareness about problems and opportunities, fostering a desire to change. I particularly liked the idea of using dot voting on topics that team members believe are significant issues, as it effectively highlights areas for improvement.

    Agile Identity: Embracing the Chaos by John Miller

    John Miller’s session, “Agile Identity: Embracing the Chaos,” encouraged deep reflection on implementing frameworks like Scrum. He warned of the pitfalls of “dark scrum,” where practices are followed mechanically without understanding Agile values and principles. Instead, John advocated for “bright scrum,” where these values and principles are fully embodied. The discussions at the different tables were particularly energizing, as participants shared insights and strategies for truly living Agile in their teams and organizations.

    Keynote Panel: Reimagining Agile by Sanjiv Augustine, Jim Highsmith, Jon Kern, Heidi Musser, and Ellen Grove

    The keynote panel on “Reimagining Agile,” featuring Sanjiv Augustine, Jim Highsmith, Jon Kern, Heidi Musser, and Ellen Grove, kicked off the third day, which was dedicated to an open space format. I particularly appreciated Jon Kern’s emphasis on the need for exemplars to showcase the success of Agile practices. His call to action for providing beacons of hope resonated with me, and I committed myself to contributing at least one such example to inspire others in their Agile journeys.

    Open Space Sessions

    During the Open Space, I participated in four enlightening sessions. One session with Jon Kern focused on discussing the exemplars of successful Agile practices mentioned in the keynote panel.

    Another session addressed the agile training needed for executives and managers. I shared a few strategies based on the agile awareness programs we deliver at Pearlside. These include connecting with what people already know about Agile, leveraging the 1-2-4-All technique for inclusive dialogue, starting with the Agile Manifesto, exploring the values and principles using the matrix of principles, and helping teams assess and improve their agility.

    In another session I proposed, we discussed the emerging leadership navigator, and all people were interested in taking the assessment!

    Additionally, I participated in a session on how to get people to accept change when they crave stability. I introduced the polarity map approach, which helps people see the value in balancing stability and change rather than viewing them as opposing forces. By identifying early warning signs of over-relying on one side, we can aim to achieve the benefits of both.

    It was a fantastic day filled with rich discussions and actionable insights.

    Productize Your Organization! by Jeff Patton

    Jeff Patton’s session on “Productize Your Organization!” was a highlight for me. Jeff’s product thinking approach, coupled with the practical exercise using his canvas, sparked deep discussions at our table. His assertion that “every company is a product company” resonated strongly with me. Jeff emphasized that organizations should move beyond the confines of projects and focus on understanding and addressing the needs and impacts on users and choosers. This perspective is crucial for fostering a more user-centric and impact-driven approach within organizations.

    Emotions at Work: Enabling Spaces for High-Performance People by Celeste Benavides

    Celeste Benavides’ session on “Emotions at Work: Enabling Spaces for High-Performance People” was deeply impactful. The talk addressed the importance of acknowledging and managing emotions in the workplace. Celeste warned that ignoring emotions leads to underperforming teams and can even drive leaders to seek new opportunities. The interactive sections of the talk were particularly engaging, prompting us to reflect on how we bring (or fail to bring) our whole selves into our interactions.

    Discover the Emerging Leadership Navigator by Alexis Monville

    I had the pleasure of delivering a talk on “Discover the Emerging Leadership Navigator.” The session received great feedback and sparked considerable interest in the approach, which energized me. The positive response reinforced my commitment to continue working on my upcoming book, further developing and refining emerging leadership concepts. Sharing my insights and connecting with others who are passionate about leadership was a highlight of the conference for me.

    Closing Keynote: From Cautious to Courageous: A Live Rollerskating Journey by Melissa Boggs

    The closing keynote, “From Cautious to Courageous: A Live Rollerskating Journey” by Melissa Boggs, was an inspiring and dynamic conclusion to the conference. Melissa’s journey from cautious to courageous on roller skates was a powerful metaphor for personal and professional growth. She illustrated how fear often keeps us safe and how stepping into new spaces with curiosity and courage can lead to significant progress. Melissa encouraged us to see the possibilities and take small, experimental steps forward. Her question about the kind of community we could build to become role-changers was particularly thought-provoking and left a lasting impression on me.

    Overall, Agile2024 was a fantastic conference! I am grateful to have met many amazing people and participated in such enriching and inspiring sessions. The insights and connections made will undoubtedly influence my work moving forward.