Liminal Leadership: How to Keep Teams Engaged Through Constant AI Change

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Speaker D: AI can help remove friction from the employee experience, but you can automate your way to efficiency and end up with people who feel like they're interacting with systems instead of being seen. And here's what most people don't realize. The systems you build to optimize processes and streamline workflows are not the same as the conditions you need to actually develop capability. That gap? That's where engagement dies. Katia Shippudjin, organizational strategist, author and speaker, has spent years building learning ecosystems with global companies, and she's noticing something critical happening right now. Organizations are making decisions about which roles AI will transform, which human capabilities are irreplaceable, and how to structure work in a way that feels authentically human. But most are not prepared for the conversation. Today, we're talking about the leadership and organizational designs that have to shift in order to keep people engaged in the age of AI, diving into the specific decision frameworks reshaping organizations right now and exploring the leadership approaches required to navigate this shift. Welcome to the AIHI Project, Katja.

Speaker E: Thank you. Thank you for inviting me, Nicole.

Speaker D: So let's just jump right in. In your book the Learning Mindset, you've discussed that the future of learning and development is isn't about producing more content or developing more tools. It's about building genuine human competencies that amplify the learning mindset you've Mentioned that these competencies are an important backbone across all of your work and books. Can you tell us what are these competencies and why do they matter more than everything else right now?

Speaker E: Well, it's a question that is very close to my heart because indeed, I was building this learning ecosystem, very technical, for many, many years. And I, uh, once got this question, yes, this is a very good idea, but what if people don't want to learn even when we have all the technology in place? So it got me thinking through all my work with children as well. What is it actually that we want to do with humans? We cannot make people learn. We cannot force them to learn. So can we go back to this genuine human being inside of us, which is often like the feeling of a toddler. And when you think of toddlers and you have, for me, one of the first, uh. And I always start smiling when I think of it. Imagination, the ability to see what is not yet there, to dream about it. Like novel writers, actually already predicting the future. And then, of course, the second one, curiosity. We need to be more curious about the technical parts, but also curious in what other humans are doing. And this brings me to openness. And I know many have critical thinking on the list, but for me, openness is critical thinking in a positive way. Being critical in what you see, but being open to something that doesn't feel really familiar for you or maybe gives you a little bit of an anxiety feeling. This then, of course, leads to optimism, being optimistic, which is, for me, the driving force behind resilience. We all know that we need more resilience as human beings. And for this, I believe that realism is also in place. I've worked around the world, and I see sometimes one of the biggest differences is realism for people. Understanding what. What can be achieved and what not, because that, uh, also helps with resilience. And then realism also helps with preventing imposter syndrome. Yes, it is all going fast. We cannot do everything. And for this, we also need empathy. And I'm always very strict on this. Empathy is not the same as having compassion with somebody. Empathy is taking the time to help somebody. Leadership. Empathy is understanding what we can do together to move forward. And then last but not least is consilience from the amazing book, the Unity of Knowledge.

Speaker D: I love that book.

Speaker E: Yeah, me too. It was like this epiphany moment when I first was reading it and was like, oh, this is what my brain is doing. Looking for things that are not connecting. Looking at art and science and how we can create new things from this. So consilience is actually bringing together all these really human, subtler competences into bridging the gap between what was and what will be.

Speaker D: Um, I just want to double click on two things. What is the difference between curiosity and openness? How are they connected?

Speaker E: That's a really good one. I love it. So curiosity for me is indeed taking the time to explore something, to find out more, to ask why questions, uh, to go deeper into something, to wonder where openness, for me is what I said, critical thinking with an open mind. Because I see too many people always saying we need more critical thinking. But I'm from Europe, and we took critical thinking a little bit too far, if you understand what I'm saying.

Speaker D: That's why

Speaker E: I renamed

Speaker D: it to

Speaker E: openness. Hope some people from Europe are listening.

Speaker D: And, um, the second question, um, what do you think the relationship is between, uh, consilience and systems thinking? Is it similar? Do you have to have one? Maybe it's. You have to have one to have the other.

Speaker E: Well, it's a good thing, I think, for me, feeling also what it is, the difference. Um, system thinking is too rigid. Where consilience is for me, really getting out there. I don't know if you know the story about Einstein and his Wednesday evening papers. When he was still writing in his little apartment in, uh, Germany, he was tutoring, uh, some other scientists who know nothing from his kind of science. So more the human sciences. And actually the papers that he was writing with this group of very different people with a very different background, they created the best ideas. So they use openness and fields that were not related at all to come with new concepts. I explained it as my daughter who actually studied as a ballerina, and when she was younger, she said to me, I'm not sure if I want to build robots or becoming a prima ballerina, but if you think of swarm technology, then we're actually doing this. You see the little swans of the Swan Lake coming together on the stage, and they only program the first ballerina. So you see the similarities between a drone show and ballet.

Speaker D: Absolutely. Absolutely. Well, that sort of leads to. Right now, there's a dominant question in organizations and in culture, and it's all about prompting. People are told that they need to learn how to prompt better, write better prompts for AI. But if genuine human competencies are what actually matters, would the emphasis on prompting skills kind of be like pointing people in the wrong direction? And what does that focus on prompting a distraction from what actually needs to happen?

Speaker E: Well, I love this question because Um, I see so many courses on prompting, and I was like, yes, this was relevant maybe two years ago, but what we see today is that, uh, the models are becoming much more intelligent. Uh, between brackets. Um, you don't actually need the full prompt because a model can think more contextual. What is it that you want to achieve and then build with you onto that goal? The other aspect is that, uh, when we talk about agents, you don't give them the full script anymore, what they need to do, because they become more and more autonomous. You give them the goal, and they will build the underlying prompts to get there. Okay, hallucination issues for me. Keep aware that we need to have the lumen in the you here as well. So for me, going back to these competences, then I think we need more judgment. And I don't like, again, in Europe the word judgment. I like to call it human wisdom. Reflecting on it. Where are we going? Is this answer? Is the result what we actually want? Challenging the model again, being curious on how the model got there, Imagining what is the goal, where we want to go? So you see how all these human competences actually build the way that we engage with AI. That's why I call it human wisdom in symbiosis with artificial intelligence.

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Speaker D: Sherm, you know, I'm really curious about learning cultures, and you provide guidance on how to build one and how to build a learning ecosystem throughout a company. Uh, can you walk through a specific technique from the book that an HR leader could use right now with their team to start building these competencies, to tackle a real challenge?

Speaker E: Well, this is a nice question, and I think in the new book that is coming out, Learning Ecosystems, a new edition. I actually went a little bit further in this because I saw that many organizations are struggling with this part. Uh, one of the things that I'm Looking at is the social drivers, learning together, how technology can bridge knowledge gaps within companies. Uh, because we thinking about the concept of lean learning, determining the waste of knowledge flows going outside the company, being lost, skills, people that leave the company, we cannot capture them anymore. So I think for this, uh, the first concept that I think is really relevant is that uh, when we think about human and machine symbiosis in, uh, in, in my models, in, in learning ecosystems, is what kind of a waste we can prevent with this. So the time that we get the knowledge, for example, normally when you need to go to training. Yeah, that's time waste. With learning ecosystems, we can get the knowledge that we need in the moment to apply it.

Speaker D: Yeah,

Speaker E: the other part, so that's the lean when we needed to apply it, less error. The other one is thinking um, about skills again is that a lot of knowledge is stuck in the head of people. And when we have different generations working together, when we have people with a different background, uh, neurodivergent people all working together, then I'm also looking at the way that we can socially collaborate together. And also here AI can be the bridge between all these different ways of thinking. So when I think about culture of organizations, I always try to find a way to bring people together in social learning, uh, cultures enhanced with technology.

Speaker D: I agree with you completely. I want to anchor what we've been talking about and something a lot of our audience may already be familiar with. And that's SHRM's HR Excellence Framework or HRX. And it measures how HR actually drives business performance. And one of the dimensions, we call it organizational impact, basically how HR shapes organizational outcomes through leadership engagement, change management, organizational design. This shift from content to capability, from tools to genuine competencies, it requires a different leadership and different change management. So from your perspective, what has to be true in how leaders engage and how organizations manage change for this to actually work?

Speaker E: Well, this is one of the things that ah, is actually my new research. So I'm very happy to share it. Yes, um, I'm starting a new uh, research fellowship very soon on the aspect of perpetual liminality. There is no end to the change anymore. So let me explain it. So liminality is this threshold between what was and what has to come. So the old models, they don't work anymore and nobody knows what tomorrow will be. And tomorrow is going so fast that in the afternoon we already are in the tomorrow. So this liminal period, these thresholds is now perpetual. There is no end to it anymore. So we need to understand that this feeling of the forever in between is permanent. And when I tell this to people, I see anxiety levels going up. Oh, what we're going to do with strategy. No more three months, quarterly, annually reviews, uh, anymore. Because we are in a transition period, which is not a transition anymore. It's a forever. So that's the first thing that needs to happen. Leaders need to understand that we are in perpetual liminality.

Speaker D: Perpetual liminality. It's interesting. It's like the models change so much. Nonstop they're changing and it's really, it's like, you know what? I on anything, if I couldn't do it last week, I try it again this week and sometimes they can do it. Um, and I move around from model to model because sometimes they're better. But now they're all kind of getting to the same level. They're mostly the same. And now it's just about features. And so it's just like it's perpetual, perpetual change. Absolutely. I like that, um, liminal leadership. That makes a lot of sense. What is liminal really? And how does it connect to what has to happen for organizations to make the shift? We kind of talked about the perpetual change before the organizations to make the change. Because, you know, our audience like they are in it. They're in the paint right now.

Speaker E: Well, one of the aspects that I'm doing research on, um, is who can be a liminal leader? And do we all need to become liminal leaders? Because the aspect of self leadership is also there. So first of all, HR&L and D need to be aware that we need to help everybody improve their liminal leadership capabilities. So what I saw is how can we link this really human competences, so the eight competences, including consilience, to human wisdom, and actually all those together. So building resilience, uh, what I was saying, uh, leadership empathy, listening to others, trying to understand their fears, the imposter syndrome, the fact that everybody has a label these days, uh, everybody has their backpack with them. How can we have a group with very neurodiverse people in the team? So all these together are elements of understanding what liminal leadership needs to work around. So this is a lot about situational leadership. So sometimes people ask me, uh, oh, is it about what kind of leadership? Because again, they want to label it to something that they know. So I say it is a little bit like situational leadership, helping others to move in perpetual liminality. So it is for me, when I'm doing my one on one with executives, the uh, moment that they Realize that they don't need to have the answers anymore, but need to work with people to get the answers together and have AI to help them. Then they see how they can grow together. So leadership is becoming more and more human. That's the thing for me. Less and less about strategy numbers, uh, more about connecting and building together.

Speaker D: I totally agree. But I, um, really want to know. Um, I read about um, a framework of yours called Build, Borrow, Buy Bought, um, because you've been working on cognitive learning ecosystems and so you've been seeing these frameworks emerge and you mentioned that, and I think maybe most of our listeners probably haven't heard that term yet, but you're saying that you're seeing it reshape how leadership teams even think about their workforce. So what is a Build, Borrow, Buy bottom and why is it forcing a conversation that most organizations are not prepared to have?

Speaker E: Well, I need to be honest, I'm sometimes shy to tell what I'm doing to my friends because of this, because they see me as the reason that their friends might be fired. They get anxiety, whatever. But again, be realistic. One of the human competences. If I can help organizations with it, I can still have the human view on it too. So that's why I do it. So maybe first of all I call it indeed incognitive ecosystem. So uh, the new edition of my book Learning Ecosystem, I have the level six in types of companies, how they uh, make the symbiosis between humans and machines. So in cognitive ecosystems where we have um, the model context protocol, for example, so that different agents can all work together, then you see that knowledge can really flow through an organization to all their different systems. Uh, we connect, uh, things based on OpenAI or Claude, whatever, all together. Uh, all data, what I call data repository systems suddenly get unlocked. So you see how knowledge can flow through the organization. If you understand this, then we sometimes use a task driven skill approach. So instead of looking at all the skills within the company, we are going to use all the data in the systems to see and predict what kind of tasks people will have to perform. Let me give you an explanation. Your colleague tomorrow is sick and you need to fill in the gap for his work. If you can say in the system a very easy question through your front door, your buddy that is helping you, I need to perform this task that normally Nicol is doing. Help me. And then you get the knowledge in the moment of need of everything that he or she was doing. So this is a very easy setup. When it is a human to human Replacement. However, when we can use all these technologies predictive and we know what kind of tasks will emerge in the near future and then we can say do we have the skills in the company to perform those tasks? And again, because everything is linked to each other, we know what kind of skills we have at this moment in the company. So let's say for example, we need somebody that is really good in painting my walls deliberately using an example that is uh, not in the scope. Um, then you can say, uh, well no, we don't have it, but maybe Nicole, she can learn it within an hour. So we will go for the build capabilities. Um, other companies we say we need to source those for a moment, borrow them from somebody else in the ecosystem, a partner or buy them to get part of our company. But what we often see is that we're actually looking at cost cutting effectiveness in can we have an agent taking place of this human capability and for example for script writing and many other things. And again, I'm not going to give too much examples because people might get nervous. But many of the simple tasks can be done by a bot. So that's why we see that we now source capabilities based on bbbb. By the way, it's not my model. I just build it into the cognitive ecosystem approach.

Speaker D: Yeah, you know the um. It was probably about three or four months ago that I, I was working on something and it so blew me away when I put it together that I was just like, you know this, this technology is a processing and orchestration technology. It is very good at anything, um, that is a process. And you know, I think that we have to be honest that you know, much um, of what we call work today can be done in some way by a bot. That doesn't mean it should be. Um, and so I spent a lot of time thinking about how do we identify the new work and the new jobs um, around that. Um, and then sort of like also like what would you create if you had unlimited intelligence at your um, at your uh, disposal, um, to create new things. And so um, you know I, I agree with you and I think it's um, I think that there's you know, the, the current jobs that will stay there's the new jobs that we will imagine. There's also, I think this orchestration technology gives us the chance to, to finally get invisible work on, you know, counted because that's a significant amount of work, um, and other things like that. So um, I just appreciate your honesty. Um, and I think that we have to be honest in Order to um, actually you know, manage the transition in a way that's incredibly great for people, um, and human flourishing. So um, last question. When you are sitting with a leadership team or an HR director right now, where are they actually making build, borrow, buy bot decisions? And what's a real example of how this framework is showing up in their organizations?

Speaker E: Well, to be honest, the organizations that I'm working with now that going this far into the cognitive ecosystem approach, they're big multinationals who have been investing in, first of all cleaning their data systems for many, many years. Because otherwise when you do task, uh, driven capability building. Yeah. You need to be really, really, uh, secure with your data and your numbers. So they're big, big multinationals. Um, and what, and of course I cannot give any names, uh, what we saw, for example, as an example it is always sea level driven. So I don't believe in silos anymore. This is not an LND project, this is not an HR project, this is not an IT project. This is a combined project. This needs to be driven uh, by a vision of a group of people. That's very important. To give you an example. Um, then I think low hanging fruits is in some departments because you need to test it. What we see, my approach is that uh, you have different steps because we talked about engagement. So we need to prove to some groups that actually the model works. To give you an example, one of my customers we started in marketing departments. Again this is a huge organization globally. So when we talk about marketing we're not talking about 100 people in marketing, but a lot more. And then we saw what are actually the tasks in the marketing department that they are doing. And then they could easily say which kind of tasks they were outsourcing, where they still need to have a human in the loop, where it was very easy, for example, uh, campaigns that they're building on a regular base which can be optimized. So it is really finding where is the low hanging fruit that we can show quick wins to the rest of the organization that we're actually not taking away jobs, but streamlining what is always there in a symbiosis with machines. And yes, in the end sometimes jobs will be lost. Again. Realism is very important as a human capability, uh, in these liminal times. But for me this works because sometimes you also find new tasks also for humans. So it is a very systematic approach indeed. But what is important to understand is that it is not a one person, uh, job. Nobody is responsible. It's driven by ambassadors of the projects on every level in the company. Uh, we need to have uh, fast feedback loops of what is working both on the technical part but also on the human part. And for me this is the only way that it is moving forward.

Speaker D: No silos, um, no silos. Fast feedback, um, cognitive learning, ecosystems and the ecosystem solving the future problem together. Uh, wonderful. Katja, thank you so much for joining us today. We learned so much and thanks everyone. We will see you next time.

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