“Collective intelligence is the ability of a group to solve a wide range of problems, and it’s something that also seems to be a stable collective ability.” – Anita Williams Woolley “When you get a response from a language model, it’s a bit like a response from a crowd of people. It’s shaped by the collective judgments of countless individuals.” – Jason Burton “Rather than just artificial general intelligence (AGI), I prefer the term augmented collective intelligence (ACI), where we design processes that maximize the synergy between humans and AI.” – Gianni Giacomelli “We developed Conversational Swarm Intelligence to scale deliberative processes while maintaining the benefits of small group discussions.” – Louis Rosenberg About Anita Williams Woolley, Jason Burton, Gianni Giacomelli, & Louis Rosenberg Anita Williams Woolley is the Associate Dean of Research and Professor of Organizational Behavior at Carnegie Mellon University’s Tepper School of Business. She received her doctorate from Harvard University, with subsequent research including seminal work on collective intelligence in teams, first published in Science. Her current work focuses on collective intelligence in human-computer collaboration, with projects funded by DARPA and the NSF, focusing on how AI enhances synchronous and asynchronous collaboration in distributed teams. Jason Burton is an assistant professor at Copenhagen Business School and an Alexander von Humboldt Research fellow at the Max Planck Institute for Human Development. His research applies computational methods to studying human behavior in a digital society, including reasoning in online information environments and collective intelligence. Gianni Giacomelli is the Founder of Supermind.Design and Head of Design Innovation at MIT’s Center for Collective Intelligence. He previously held a range of leadership roles in major organizations, most recently as Chief Innovation Officer at global professional services firm Genpact. He has written extensively for media and in scientific journals and is a frequent conference speaker. Louis Rosenberg is CEO and Chief Scientist of Unanimous A.I., which amplifies the intelligence of networked human groups. He earned his PhD from Stanford and has been awarded over 300 patents for virtual reality, augmented reality, and artificial intelligence technologies. He has founded a number of successful companies including Unanimous AI, Immersion Corporation, Microscribe, and Outland Research. His new book Our Next Reality on the AI-powered Metaverse is out in March 2024. Websites: Gianni Giacomelli Louis Rosenberg University Profile: Anita Williams Woolley Jason Burton LinkedIn Profile: Anita Williams Woolley Jason Burton Gianni Giacomelli Louis Rosenberg What you will learn Understanding the power of collective intelligence How teams think smarter than individuals The role of ai in amplifying human collaboration Memory, attention, and reasoning in group decision-making Why large language models reflect collective intelligence Designing synergy between humans and ai Scaling conversations with conversational swarm intelligence Episode Resources People Thomas Malone Steve Jobs Concepts & Frameworks Transactive Memory Systems Reinforcement Learning from Human Feedback (RLHF) Conversational Swarm Intelligence Augmented Collective Intelligence (ACI) Artificial General Intelligence (AGI) Technology & AI Terms Large Language Models (LLMs) Machine Learning Collective Intelligence Artificial Intelligence (AI) Cognitive Systems Transcript Anita Williams Woolley: Individual intelligence is a concept most people are familiar with. When we’re talking about general human intelligence, it refers to a general underlying ability for people to perform across many domains. Empirically, it has been shown that measures of individual intelligence predict a person’s performance over time. It is a relatively stable attribute. For a long time, when we thought about intelligence in teams, we considered it in terms of the total intelligence of the individual members combined—the aggregate intelligence. However, in our work, we challenged that notion by conducting studies that showed some attributes of the collective—the way individuals coordinated their inputs, worked together, and amplified each other’s contributions—were not directly predictable from simply knowing the intelligence of the individual members. Collective intelligence is the ability of a group to solve a wide range of problems. It also appears to be a stable collective ability. Of course, in teams and groups, you can change individual members, and other factors may alter collective intelligence more readily than individual intelligence. However, we have observed that it remains fairly stable over time, enabling greater capability. In some cases, collective intelligence can be high or low. When a group has high collective intelligence, it is more capable of solving complex problems. I believe you also asked about artificial intelligence, right? When computer scientists work on ways to endow a machine with intelligence, they essentially provide it with the ability to reason, take in information, perceive things, identify goals and priorities, adapt, and change based on the information it receives. Humans do this quite naturally, so we don’t really think about it. Without artificial intelligence, a machine only does what it is programmed to do and nothing more. It can still perform many tasks that humans cannot, particularly computational ones. However, with artificial intelligence, a computer can make decisions and draw conclusions that even its own programmers may not fully understand the basis of. That is where things get really interesting. Ross Dawson: We’ll probably come back to that. Here at Amplifying Cognition, we focus on understanding the nature of cognition. One fascinating area of your work examines memory, attention, and reasoning as fundamental elements of cognition—not just on an individual level, but as collective memory, collective attention, and collective reasoning. I’d love to understand: What does this look like? How do collective memory, collective attention, and collective reasoning play into aggregate cognition? Anita: That’s an important question. Just as we can intervene to improve collective intelligence, we can also intervene to improve collective cognition. Memory, attention, and reasoning are three essential functions that any intelligent system—whether human, computer, or a human-computer collaboration—needs to perform. When we talk about these in collectives, we are often considering a superset of humans and human-computer collaborations. Research on collective cognition has been running parallel to studies on collective intelligence for a couple of decades. The longest-standing area of research in this field is on collective memory. A specific construct within this area is transactive memory systems. Some of my colleagues at Carnegie Mellon, including Linda Argote, have conducted significant research in this space. The idea is that a strong collective memory—through a well-constructed transactive memory system—allows a group to manage and use far more information than they could individually. Over time, individuals within a group may specialize in remembering different information. The group then develops cues to determine who is responsible for retaining which information, reducing redundancy while maximizing collective recall. As the system forms, the total capacity of information the group can manage grows considerably. Similarly, with transactive attention, we consider the total attentional capacity of a group working on a problem. Coordination is crucial—knowing where each person’s focus is, when focus should be synchronized, when attention should be divided across tasks, and how to avoid redundancies or gaps. Effective transactive attention allows groups to adapt as situations change. Collective reasoning is another fascinating area with a significant body of research. However, much of this research has been conducted in separate academic pockets. Our work aims to integrate these various threads to deepen our understanding of how collective reasoning functions. At its foundation, collective reasoning involves goal setting. A reasoning system must identify the gap between a desired state and the current state, then conceptualize what needs to be done to close that gap. A major challenge in collective reasoning is establishing a shared understanding of the group’s objectives and priorities. If members are not aligned on goals, they may decide that their time is better spent elsewhere. Thus, goal-setting and alignment are foundational to collective reasoning, ensuring that members remain engaged and motivated over time. Ross: One of the interesting insights from your paper is that large language models (LLMs) themselves are an expression of collective intelligence. I don’t think that’s something everyone fully realizes. How does that work? In what way are LLMs a form of collective intelligence? Jason Burton: Sure. The most obvious way to think about it is that LLMs are machine learning systems trained on massive amounts of text. Companies developing these language models source their text from the internet—scraping the open web, which contains natural language encapsulating the collective knowledge of countless individuals. Training a machine learning system to predict text based on this vast pool of collective knowledge is essentially a distilled form of crowdsourcing. When you query a language model, you aren’t getting a direct answer from a traditional relational database. Instead, you receive a response that reflects the most common patterns of answers given by people in the past. Beyond this, language models undergo further refinement through reinforcement learning from human feedback (RLHF). The model presents multiple response options, and humans select the best one. Over time, the system learns human preferences, meaning that every response is shaped by the collective judgments of numerous individuals. In this way, querying a language model is like consulting a crowd of people who have collectively shaped the model’s responses. Gianni Giacomelli: I view this through the lens of augmentation—augmenting collective intelligence by designing organizational structures that combine human and machine capabilities in synergy. Instead of thinking of AI as just a tool or humans as just sources of data, we need to look at how to structure processes that allow large groups of people and machines to collaborate effectively. In 2023, many became engrossed with AI itself, particularly generative AI, which in itself is an exercise in collective intelligence. These systems were trained on human-generated knowledge. But looking at AI in isolation limits our understanding. Rather than just artificial general intelligence (AGI), I prefer the term augmented collective intelligence (ACI), where we design processes that maximize the synergy between humans and AI. Louis Rosenberg: There are two well-known principles of human behavior: one is collective intelligence—the idea that groups can be smarter than individuals if their input is harnessed effectively. The other is conversational deliberation—where groups generate ideas, debate, surface insights, and solve problems through discussion. However, scaling these processes is difficult. If you put 500 people in a chat room, it becomes chaotic. Research shows that the ideal conversation size is five to seven people. To address this, we developed Conversational Swarm Intelligence, using AI agents in small human groups to facilitate discussions and relay key insights across overlapping subgroups. This allows us to scale deliberative processes while maintaining the benefits of small group discussions.   The post Collective Intelligence Compilation (AC Ep79) appeared first on amplifyingcognition.

“I’m cautiously optimistic because never before has technology been as accessible as it is now—being able to interact with machines in a way that feels so natural to us, rather than in ones and zeros or more technical ways. AI shouldn’t replace what exists but augment and enhance our creativity, helping us tap into what makes us uniquely human.” – Helen Lee Kupp About Helen Lee Kupp Helen Lee Kupp is co-founder and CEO of Women Defining AI, a community of female leaders applying and driving AI. She was previously leader of strategy and analytics at Slack and co-founder of its Future Forum. She is co-author of the best-selling book “How the Future Works: Leading Flexible Teams to do the Best Work of Their Lives”. Website: Women Defining AI LinkedIn Profile: Helen Lee Kupp What you will learn Redefining collaboration in the AI era Unlocking human potential through technology Why flexible work matters more than ever The power of diverse perspectives in AI Balancing optimism and caution in AI adoption How leaders can foster innovation from the ground up Women defining AI and shaping the future Episode Resources People Gregory Bateson Nichole Sterling  (co-founder of Women Defining AI) Companies & Organizations Women Defining AI Technical Terms & Concepts AI (Artificial Intelligence) Generative AI Large Language Model (LLM) Non-deterministic  AI policy AI adoption Machine learning (ML) Human-in-the-loop Transcript Ross Dawson: Helen, it is a delight to have you on the show. Helen Lee Kupp: It’s good to be here. I love how we first started talking over an AI research paper. It was very random but awesome. Ross: Well, that’s pushing the edges, trying to find what’s out there and see what comes on the other side. AI is emerging, and we’re sitting alongside each other. How are you feeling about today and how humans and AI are coming together? Helen: I feel cautiously optimistic, and part of that is because I’ve been in tech for so long. Prior to getting much deeper into AI, I was working on flexible work and research around how to rethink and redesign how we, as humans, collaborate in a way that is more personalized, more customized, and helps more people bring their best selves to work and do their best work. It was serendipitous that around the same time, there was an increase in AI innovation. Now, we had technology to pair with the equation of redesigning work. COVID forced us to rethink work, not just from a people and process perspective but alongside rapid technological change. I’m cautiously optimistic because never before has technology been as accessible as it is now. We can interact with machines in a way that feels so natural rather than in ones and zeros or technical ways. Ross: I’m very aligned with that. One of the things you said was “bring your best self to work.” I think of it as human potential. If we’re creating a future of work, we have potential futures that are not so great and others that are very positive, where people express more of who they are and their capabilities. How can we create organizations like that? Helen: It starts with recognizing that everyone has different preferences and work styles. Organizations, teams, and leaders need to meet people where they are rather than force them into rigid structures that worked in the past. I often share this story—I’m deeply introverted. Despite jumping onto this podcast with you, I have always been an introvert. Navigating an extroverted world takes extra energy. In traditional office and meeting environments, I had to work harder to show up. However, when I had more diverse formats to interact with my team and leadership, it unlocked something for me. Instead of pretending to be the loudest in the room, I could find my own ways of expressing ideas—through text, written formats, or chat. It made work easier for me. When you think about how that manifests across a team, leaders and organizations must avoid putting rigid boxes around collaboration—whether it’s the hours we work or the place where we work. Increasing flexibility enables people to express themselves and bring forward ideas that might otherwise remain hidden. Ross: That’s a compelling vision. How do you bring that to reality? What do you do inside an organization to foster and enable that? Helen: One of the tools that helped in our research on the future of work and redesigning organizations is something simple—creating a team operating manual. The act of explicitly writing down the different ways we interact as a team opens up discussions. It allows for feedback: “Does this work for you? Should we try something different?” When these conversations don’t happen, implied assumptions remain—such as the norm of working in an office from nine to five. Explicitly stating and questioning these assumptions is step one. Then, organizations should give teams and managers the flexibility to define how they work within their sub-teams. Having operating manuals, sharing what works for your team, and bubbling up insights allow for a more bottom-up approach rather than a top-down one. It treats people like adults who understand their preferences and styles. Ross: That’s really nice. PepsiCo had an initiative where teams coordinated among themselves to determine their availability and collaboration methods. I wonder if we can push that further. People are often conditioned to fit into roles and adjust to their environments. Can we help people recognize their self-imposed constraints and flourish beyond them? Helen: This is where I’m cautiously optimistic about AI and how we integrate technology into work. When people start using AI, the initial question is often, “How can I do this more efficiently?” AI is a powerful tool that shortens tasks—like a calculator removing the need for mental math. However, once people move beyond efficiency, they begin asking, “What can I do differently?” AI allows us to do things we couldn’t before. It helps break conventional thinking. For example, if you use a large language model to generate 10 variations of an idea, it removes emotional bias. It shifts the conversation from defending one perspective to evaluating multiple ideas. This fosters creative discourse and integrates seamlessly into workflows without feeling like extra work. AI should not replace what exists but augment and enhance our creativity—helping us tap into what makes us uniquely human. Ross: So, AI helps individuals bring different perspectives and expand their thinking? Helen: Exactly. One of my favorite things to do with large language models is to open up the funnel. Whether it’s brainstorming writing styles, problem-solving, or scoping solutions, AI presents multiple potential paths. This reminds us that there is no single correct answer—only possibilities to explore. Ross: Gregory Bateson said wisdom comes from multiple perspectives. We now have multiple perspectives on demand. You work with leaders to redesign organizations. What guidance do you suggest? How can organizations evolve from existing structures? Helen: I don’t have the perfect answer for what the shape of organizations should be. However, we’ve been transitioning from hierarchical structures to teams-of-teams for a while, with varying success. The biggest challenge is breaking out of our mental paradigms of control. Flexible work means allowing managers and teams to design their workdays and collaboration methods rather than enforcing a company-wide approach. AI introduces another paradigm shift—it behaves unpredictably compared to traditional technology. Leaders must accept that they don’t have all the answers. Some of the best AI-driven innovations come from employees who work closely with the technology daily. For example, a data scientist evaluating AI’s role in data processing can quickly identify where it adds value and where it falls short. These innovations emerge at the edges, from individuals experimenting in real time. Leaders must create environments where experimentation, sharing, and collaboration thrive. Instead of dictating policies top-down, they should spotlight grassroots innovations and scale them across the organization. Ross: So, you’re describing emergence—where leaders set conditions for innovation rather than dictate precise rules? Helen: Exactly. Constraints breed creativity. If there are no guardrails or structures, people stick to the status quo and don’t innovate. Leaders must provide the right nudges—whether through hackathons, dedicated experimentation time, or open Slack channels to share discoveries. Some organizations set up “experiment hours”—weekly meetings where teams explore AI applications in a low-pressure, fun environment. This fosters creativity and keeps innovation moving. Ross: That’s a great example. Speaking of multiple perspectives, one of your recent ventures is Women Defining AI. What is it about? Helen: Women Defining AI started as an experiment about a year and a half ago. I had been working with generative AI models and noticed a significant gender gap in AI adoption. Data showed men adopting AI at higher rates than women, and anecdotally, I saw the same trend. Initially, it was just a study group where I shared what I was learning with other women. Within days, 50 people joined, and by month two, we had 150 members. It became clear that women wanted a space to ask questions, learn together, and experiment without judgment. Now, Women Defining AI is a virtual community that helps women at different stages of their AI journey. Whether it’s understanding AI’s role in their work, automating tasks, or building solutions, we guide them in gaining technical confidence and shaping the field. Some members have landed AI-related jobs or joined AI policy teams at their organizations. Having diverse perspectives in AI is crucial. Women in our community, particularly those from HR and other industries, quickly identify biases and blind spots that might otherwise go unnoticed. We need more voices questioning and shaping AI while we’re still in its early stages. Ross: That’s fantastic. Looking ahead to 2026, what excites you most? Helen: Personally, I’m excited about having our third baby! It’s a reminder of the new perspectives each generation brings. For Women Defining AI, 2025 will be the year we build in public. We’ve been experimenting and learning internally, but now we’re sharing real stories and projects to inspire more builders and technologists. Ross: That’s fantastic. Thank you for your time, insights, energy, and passion. Helen: Thanks for having me. The post Helen Lee Kupp on redesigning work, enabling expression, creative constraints, and women defining AI (AC Ep78) appeared first on amplifyingcognition.

In this discussion, Alexandra Diening, Co-founder of the Human-AI Symbiosis Alliance, and Mohammad Hossein Jarrahi, Associate Professor at UNC Chapel Hill, unpack the nuances of human-AI interactions. They explore the delicate balance between automating tasks and augmenting human capabilities, emphasizing the irreplaceable role of human intuition in critical decisions. The conversation highlights the need for responsible AI practices to foster positive symbiosis, and advocates for a future where humans and AI co-evolve, reshaping our definitions of knowledge and existence.

Rita McGrath, a top expert on strategy and innovation and Professor at Columbia Business School, shares her insights on the intersection of human creativity and AI. She discusses how AI can enhance strategic decision-making and navigate transient competitive advantages. McGrath highlights the significance of inflection points in business evolution and their effects on consumer behavior. Furthermore, she reimagines the future of work, advocating for a human-centric approach in an AI-driven landscape, emphasizing the importance of continuous learning and collaboration.

Christian Stadler, a strategic management professor at Warwick Business School and author of 'Open Strategy,' dives into the transformative role of AI in decision-making. He emphasizes AI as a co-strategist that enhances boardroom discussions rather than replaces human judgment. The conversation covers the shift toward open strategy, highlighting how diverse perspectives drive innovation and improve execution. Stadler also discusses the need for political awareness in leadership and engaging employees to foster a culture of innovation.

Valentina Contini, an innovation strategist and technofuturist, dives into the fascinating intersection of AI and creativity. She shares insights on being a 'professional black sheep' and how generative AI can enhance human innovation. Valentina emphasizes the role of AI in freeing up cognitive resources, fostering critical thinking, and generating immersive future scenarios through AI personas. The conversation also touches on the importance of embracing technology and lifelong learning to harness AI's potential for a positive future.

Anthea Roberts, a leading authority in international law and founder of Dragonfly Thinking, dives deep into the art of 'dragonfly thinking'—a method for examining complex issues from multiple angles. She discusses the shift in human roles in AI collaboration, emphasizing metacognition in decision-making. Roberts also tackles the biases in AI and the importance of integrating diverse knowledge systems, advocating for a cognitive renaissance to navigate the challenges and opportunities of AI advancements and enhance our collective problem-solving capabilities.

“To be a flexible leader is to make sense of the world in a way that allows you to intentionally ask, ‘How do I need to lead in this moment to get the best results for my team and the outcomes we need?’” – Kevin Eikenberry About Kevin Eikenberry Kevin Eikenberry is Chief Potential Officer of leadership and learning consulting company The Kevin Eikenberry Group. He is the bestselling author or co-author of 7 books, including the forthcoming Flexible Leadership. He has been named to many lists of top leaders, including twice to Inc. magazine’s Top 100 Leadership and Management Experts in the World. His podcast, The Remarkable Leadership Podcast, has listeners in over 90 countries. Website: The Kevin Eikenberry Group   LinkedIn Profiles Kevin Eikenberry The Kevin Eikenberry Group   Book Flexible Leadership: Navigate Uncertainty and Lead with Confidence   What you will learn Understanding the essence of flexible leadership Balancing consistency and adaptability in decision-making Embracing “both/and thinking” to navigate complexity Exploring the power of context in leadership strategies Mastering the art of asking vs. telling Building habits of reflection and intentionality Developing mental fitness for effective leadership Episode Resources People Carl Jung F. Scott Fitzgerald David Snowden Book Flexible Leadership: Navigate Uncertainty and Lead with Confidence Frameworks/Concepts Myers-Briggs Cynefin framework Confidence-competence loop Organizations/Companies The Kevin Eikenberry Group Technical Terms Leadership style “Both/and thinking” Compliance vs. commitment Ask vs. tell Command and control Sense-making Plausible cause analysis Transcript Ross Dawson: Kevin, it is wonderful to have you on the show. Kevin Eikenberry: Ross, it’s a pleasure to be with you. I’ve had conversations about this book for podcasts. This is the first one that’s going to go live to the world, so I’m excited about that. Ross: Fantastic. So the book is Flexible Leadership: Navigate Uncertainty and Lead with Confidence. What does flexible leadership mean? Kevin: Well, that’s a pretty good starting question. Here’s the big idea, Ross: so many people have come up in leadership and taken assessments of one sort or another. They’ve done Strengths Finder or a leadership style assessment, and it’s determined that they are a certain style or type. That’s useful to a point, but it becomes problematic beyond that. Humans are pattern recognizers, so once we label ourselves as a certain type of leader, we tend to stick to that label. We start thinking, “This is how I’m supposed to lead.” To be a flexible leader means we need to start by understanding the context of the situation. Context determines how we ought to lead in a given moment rather than relying solely on what comes naturally to us. Being a flexible leader involves making sense of the world intentionally and asking, “How do I need to lead in this moment to get the best results for my team and the outcomes we’re working towards?” Ross: I was once told that Carl Jung, who wrote the typology of personalities that forms the foundation of Myers-Briggs, said something similar. I’ve never found the original source, but apparently, he believed the goal was not to fix ourselves at one point on a spectrum but to be as flexible as possible across it. So, we’re all extroverts and introverts, sensors and intuitors, thinkers and feelers. Kevin: Exactly. None of us are entirely one or the other on these spectrums. They’re more like continuums. Take introvert vs. extrovert. Some people are at one extreme or the other, but no one is a zero on either side. The problem arises when we label ourselves and think, “This is who I am.” That may reflect your natural tendency, but it doesn’t mean that’s the only way you can or should lead. Ross: One of the themes in your book is “both/and thinking,” which echoes what I wrote in Thriving on Overload. You can be both extroverted and introverted. I see that in myself. Kevin: Me too. Our world is so focused on “either/or” thinking, but to navigate complexity and uncertainty as leaders, we must embrace “both/and” thinking. Scott Fitzgerald once said something along the lines of, “The test of a first-rate intelligence is the ability to hold two opposing ideas in your mind at the same time and still function.” I’d say the same applies to leadership. To be highly effective, leaders must consider seemingly opposite approaches and determine what works best given the context. Ross: That makes sense. Most people would agree that flexible leadership is a sound idea. But how do we actually get there? How does someone become a more flexible leader? Kevin: The first step is recognizing the value of flexibility. Many leaders get stuck on the idea of consistency. They think, “To be effective, I need to be consistent so people know what to expect from me.” But flexibility isn’t the opposite of consistency. We can be consistent in our foundational principles—our values, mission, and core beliefs—while being adaptable in how we approach different situations. Becoming a flexible leader requires three things: Intention – Recognizing the value of flexibility. Sense-making – Understanding the context and what it requires of us. Flexors – Knowing the options available to us and deciding how to adapt in a given situation. Ross: This aligns with my work on real-time strategy. A fixed strategy might have worked in the past, but in today’s world, we need to adapt. At the same time, being completely flexible can lead to chaos. Kevin: Exactly. Leaders need to balance consistency and flexibility, knowing when to lean toward one or the other. Leadership is about achieving valuable outcomes with and through others. This creates an inherent tension—outcomes vs. people. The answer isn’t one or the other; it’s both. For every “flexor” in the book, the goal isn’t to be at one extreme of the spectrum but to find the balance that best serves the team and the context. Ross: You’ve mentioned the word “flexor” a few times now. I think this is one of the real strengths of the book. It’s a really useful concept. So, what is a flexor? Kevin: A flexor is the two ends of a continuum on something that matters. Let’s use an example. On one end, we have achieving valuable outcomes. On the other end, we have taking care of people. Some leaders lean toward focusing on outcomes—getting the work done no matter what. Others lean toward prioritizing their people—ensuring their well-being and development so outcomes follow. The reality is that leadership requires balancing both. Sometimes the context calls for one approach more than the other. For instance, in moments of chaos, compliance might be necessary to maintain safety or order. In other situations, you’ll need to inspire commitment for long-term success. A leader must constantly assess the context and decide where to lean on the spectrum. Ross: That’s a great example. Another one might be between “ask” and “tell.” Kevin: Yes, exactly! Leaders often believe they need to have all the answers, so they default to telling—giving directives and expecting people to follow. But sometimes, asking is far more effective. Your team members often have perspectives and information you don’t. By asking rather than telling, you gain insights, foster collaboration, and build trust. Of course, it’s not about always asking or always telling. It’s about understanding when to lean toward one and when the other might be more effective. Ross: That makes sense. In today’s world, consultative leadership is highly valued, especially in certain industries. Many great leaders lean heavily on asking rather than telling. Kevin: Absolutely, but even consultative leaders need to recognize when the situation calls for decisiveness. If there’s urgency or a crisis, sometimes the team just needs clear instructions: “Here’s what we need to do.” Being a flexible leader means being intentional—understanding the context and adjusting your approach, even if it doesn’t align with your natural tendencies. Ross: That brings us to the concept of sense-making. Leaders need to make sense of their context to decide where they stand on a particular flexor. How can leaders improve their sense-making capabilities? Kevin: The first step is recognizing that context matters and that it changes. Many leaders rely on best practices, but those only work in clear, predictable situations. Our world is increasingly complex and uncertain. In such situations, we need to adopt “good enough” practices or experiment to find what works. To improve sense-making, leaders must build a mental map of their world. Is the situation clear, complicated, complex, or chaotic? This aligns with David Snowden’s Cynefin framework, which I reference in the book. By identifying the nature of the situation, leaders can adjust their approach accordingly. Ross: The Cynefin framework is a fantastic tool, often used in group settings. You’re applying it here to individual leadership. Kevin: Exactly. It’s not just about guiding group processes. It’s about helping leaders see the situation clearly so they can flex their approach. Ross: That’s insightful. Leaders don’t operate in isolation—they’re part of an organizational context. How does a leader navigate their role while considering the expectations of their peers, colleagues, and supervisors? Kevin: Relationships play a critical role. The better your relationships with peers and supervisors, the more you understand their styles and perspectives. This helps you navigate the context effectively. Sometimes, though, you may need to challenge others’ perspectives—respectfully, of course. If someone is treating a situation as chaotic when it’s actually complex, your role as a leader may be to ask questions or provide a different perspective. Being intentional is key. Leadership often involves breaking habitual responses, pausing to assess the context, and deciding if a different approach is needed. Ross: That’s a journey. Leadership habits are deeply ingrained. How do leaders move from their current state to becoming more flexible and adaptive? Kevin: That’s the focus of the third part of the book—how to change your habits. First, leaders need to recognize that their natural tendencies might not always serve them best. Without this realization, no progress is possible. Next, they must build new habits, starting with regularly asking questions like: What’s the context here? What does this situation require of me? How did that approach work? Reflection is crucial. Leaders should consistently ask, “What worked, what didn’t, and what can I learn from this?” Another valuable practice is what I call “plausible cause analysis.” Instead of jumping to conclusions about why something happened, consider multiple plausible explanations. For example, if a team doesn’t respond to a question, don’t assume they’re disengaged. There could be several reasons—perhaps they need more time to think or the question wasn’t clear. By exploring plausible causes, leaders can choose responses that address most potential issues. Ross: That’s a great framework for reflection and improvement. It also ties into mental fitness, which is so important for leaders. Kevin: Exactly. During the pandemic, we worked extensively with clients on mental fitness—not just mental health. Mental fitness involves proactively building resilience, much like physical fitness. Reflection, gratitude, and self-awareness are all part of maintaining mental fitness. Leaders who invest in their mental fitness are better equipped to handle challenges and make sound decisions. Ross: Let’s circle back to the book. What would you say is its ultimate goal? Kevin: The goal of Flexible Leadership is to help leaders navigate uncertainty and complexity with confidence. For 70 years, leadership models have tried to simplify the real world. While those models are helpful, they’re inherently oversimplified. The ideas in the book aim to help leaders embrace the complexity of the real world, equipping them with tools to become more effective and, ultimately, wiser. Ross: Fantastic. Where can people find your book? Kevin: The book launches in March, but you can pre-order it now at kevineikenberry.com/flexible. That link will take you directly to Amazon. You can also learn more about our work at kevineikenberry.com. Ross, it’s been an absolute pleasure. Thanks for having me. Ross: Thank you so much, Kevin! The post Kevin Eikenberry on flexible leadership, both/and thinking, flexor spectrums, and skills for flexibility (AC Ep72) appeared first on amplifyingcognition.

In this discussion, Alexandra Diening, Co-founder and Executive Chair of the Human-AI Symbiosis Alliance, delves into the vital concept of human-AI symbiosis. She emphasizes the importance of designing ethical frameworks to enhance human potential without causing harm. The conversation touches on the risks of parasitic AI, the balance between excitement and caution in AI deployment, and practical strategies for organizations to navigate this integration responsibly. With her background in cyberpsychology, Diening highlights the transformative impact of AI on human behavior.

Join Kevin Clark, President of Content Evolution and author of Brandscendence, alongside Kyle Shannon, Founder of Storyvine and AI Salon. They dive into the transformative impact of digital twins and collective intelligence on creativity. Discover how generative AI can help overcome creative blocks and facilitate deep dialogue. The duo emphasizes the importance of asking meaningful questions to enhance interactions with AI. Together, they explore future content evolution and foster collaboration in an increasingly AI-driven world.