Collaboration Over Competition: A Smarter Way Forward Today at the Knowledge Management Global Network course on Knowledge Creation & Innovation, we had the privilege of learning from Vadim Shyrayev, who led a thought-provoking session on: From Competition to Collaboration We're so accustomed to competing that we rarely pause to imagine what could be gained by choosing collaboration instead. Vadim challenged that mindset as collaborating, even with competitors, can: Expand our playing field—from a red sea to a blue ocean Save tremendous energy otherwise lost in unproductive rivalry Simply... make us smile more But collaboration isn’t just teamwork. It’s about crossing boundaries—working with people outside your usual circles, across organizations, toward a shared purpose. Vadim shared practical tips to cultivate this mindset: Aligned Mission, Purpose, and Values Collaborate with those it is important to, not with those it is easy Nurture a Collaborative Culture Train for Mindflex – the ability to shift perspectives and realign as context evolves Build Shared Knowledge Assets Strengthen Your Own Differentiation – it builds trust and unlocks willingness to partner True innovation isn’t about guarding ideas—it’s about growing them together. Let’s dare to be open, even in spaces where we were taught to compete. Previous Item Next Item

Final Post from KMWorld2024 How can a KMer with a limited budget start becoming a "New-KMer" in the KM-AI era? Here are some practical steps to get started: Learn: Dive into online resources, subscribe to newsletters like Bernard Marr’s, attend conferences (like KMWorld), and join roundtables (Knowledge Management Global Network). Knowledge is power! Get Educated: Explore KM-oriented Gen AI courses (like what ROM Global offers). Experiment: Use Gen AI tools in your daily KM tasks: Facilitate debriefs (e.g., using 5 WHYs). Suggest taxonomies for your projects. Organize portal menus. Draft KM articles for service support. 4. Expand Documentation: Use AI to transcribe and summarize conversations or meetings. Make it a habit to build your organization's knowledge base. 5. Train Your Team: Provide Gen AI education to different roles. Analyze how AI can enhance their work, teach them to add context, and foster critical thinking to improve AI suggestions. It's all about conversation and augmentation. 💬 6. Design AI Think-Agents: Build or adopt AI agents to support knowledge-intensive processes. As a KMer, you know how to capture the knowledge needed to transform these agents from "good" to "excellent." Start here, and you'll be well on your way to thriving in the KM-AI future. Previous Item Next Item

Collaborative Positioning: Celebrating the difference As a knowledge manager, you may feel less comfortable with the concept of positioning, thinking it only concerns marketing professionals. However, positioning is a crucial process for effective competition and collaboration management. It can help you differentiate yourself in the eyes of your customers and succeed in the new era. Positioning involves classifying and differentiating products, companies, people, territories, and brands. It enables you to distinguish yourself from the competition and stand out in the minds of your customers. Moreover, positioning can also serve as a lever for collaboration. When working alone, positioning helps you celebrate the difference between yourself and your competitors. However, when collaborating with others, positioning takes on a more profound significance. When collaborating with the entire industry or a specific team, you have the opportunity to build a joint positioning based on your differences. This collaborative positioning is much stronger and richer than what you can achieve alone. In a team collaboration scenario, building a joint positioning involves celebrating the differences twice: within the team, creating a strong joint positioning through these clashing differences, and between your team and the rest of the world, celebrating differences based on the positioning you have earned. While collaborative positioning may sound complex, it is a powerful tool that can work almost like magic for your business. By leveraging your differences, celebrating them, and building a strong joint positioning, you can differentiate yourself in the market, succeed in the new era, and collaborate more effectively with others. Previous Item Next Item

Unlocking the Power of Tacit Knowledge in Interviews Diving deep into the nuances of capturing tacit knowledge, I wish to share my learnings from Knowledge Management Global Network's course on "Leveraging the Value Creation of Tacit Knowledge. " The last session, led by Dan Asher (phd)., dived into the mastery of interviewing. Some takeaways: The What of Asking: The essence of extracting deep, tacit knowledge lies in crafting the right questions. Reflective Questions prompt introspection and sharing insights that go beyond surface-level information. Scenario-based Questions encourage interviewees to project themselves into specific situations, revealing how they navigate complexities. Open Questions about Approaches open the floodgates to exploring a person's thought process, unveiling not just what they think but why they think it. The How of Asking: Equally, if not more important, is how we engage in these conversations. Building Rapport is not just a precursor but a continuous process significantly affecting the depth and quality of information shared. The journey towards building trust might seem long. Still, small gestures—like a genuine smile or a moment shared from one's personal life—often bridge the gap, enabling deeper tacit knowledge capture, while also benefiting an enjoyable, and relaxed conversation. This all reaffirmed my belief that in the mastery of interviews, especially within the context of knowledge management, the "what" and "how" are inseparable allies. Each enhances the effectiveness of the other, paving the way for a richer, more comprehensive understanding and value creation of tacit knowledge. Previous Item Next Item

The 5 Levels of AI Implementation in Knowledge Management Where is your organization on the path to smart KM? As knowledge managers, we are uniquely positioned to lead the gradual and responsible integration of AI. Here's a quick overview of the 5 levels of AI in KM: Level 0 – Tool Enhancement - Upgrading existing software tools with light AI features to improve operational efficiency. Level 1 – Conversational GenAI Educatio n - Equipping knowledge workers with conversational AI tools to work faster, smarter, and more effectively. Level 2 – AI Agents: Data-Level (RAGs) - Deploying retrieval-augmented generation agents to enable precise, efficient knowledge work. Level 3 – AI Agents: Action-Level (Automation) - Using AI to trigger and execute automated actions—accelerating workflows and reducing manual effort. Level 4 – AI Agents: Instruction-Level (Knowledge) - Allowing AI to interpret and act on complex knowledge instructions—enabling smarter, contextual KM interventions. At every level, one thing remains true: AI needs a human mind to guide it. The synergy between responsible humans and intelligent machines is what unlocks real value—and ensures AI is used wisely, ethically, and purposefully. The future of KM is not just smart. It's inspiring. It's human-AI brilliance in action. And it's already here. Previous Item Next Item

Why Knowledge Management Needs Both Precision and Passion For many years the KM society held an interesting debate about the definition of knowledge management. In the first decade, people didn't really agree on its scope, some including only knowledge-in-our-heads, others also adding documented knowledge as part of what this discipline deals with. The next decade focused on how to turn our shared understanding of knowledge management into one sentence that holds it all. I remember a post titled "47 definitions of knowledge management and still counting". Study books included a series of possible definitions. They were all proper, each highlighting another perspective of KM. In 2018, ISO30401 was published, including a concise definition: "management with regard to knowledge." This new definition was very accurate; however, it hardly added clarity. Two notes were added to this definition, yet the fog remained. A few months ago, we published an amendment to 30401, including new and improved definitions. KM was redefined as: "Holistic, cross-functional discipline and set of practices, focused on knowledge, that improve organizational performance Note: Knowledge management includes, but is not limited to, the creation, acquisition, application, maintenance, sharing, and protection of knowledge to create organizational value." So now we could sleep better. A shared definition exists, published by a formal and respectful institute, and not too many will argue about its accuracy or clarity. Well... not exactly. It was all in it- but the soul. Reading this definition, one could understand, but less sense KM. And that is where I want to come up with a suggestion: Let's keep this formal definition. It is required, and having one source is essential. However, let us add an alternative, non-formal definition of KM, adding the emotional component into this equation. I suggest a first draft of this alternative definition: "knowledge management is understanding the importance of knowledge as a main asset and key to success, therefore handling it wisely, with care and tender." Additional ideas are more than welcome. Previous Item Next Item

The New KnowledgeWork - Post 2: DecisionMaking GenerativeAI opens new opportunities for enhancing many aspects of knowledge work. When we began our work in the KMAI coalition, decision-making was quickly prioritized as a key area for improvement. We formed a team led by Kristina Mirchuk, including Dragan Petrov, Larriza Thurler, Alex Bennet (Alex & David Bennet), Israel Fischer, Susan Montgomery, May Rubinstein, Maya Shaked, and myself. Our findings were fascinating: Some decisions are made very quickly and easily, and we likely want to maintain that efficiency. As we learned from Daniel Kahneman in his book Thinking, Fast and Slow, there's a good reason we don’t meticulously analyze every decision but rather respond almost intuitively (System 1 vs. System 2). We sought a process to help us make the comprehensive decisions. We quickly realized that AI knowledge-driven agents could be the solution for such processes. GPTs, like those in the GPT store, can serve as coaches, leading us through best-methodology-based decision-making processes. By enabling web browsing, these agents can also act as assistants, providing additional knowledge and information relevant to the decision at hand. For defined processes, one might develop specific AI knowledgedriven agents. However, for the sake of this coalition, we decided to develop a general-purpose AI knowledge-driven agent capable of addressing any comprehensive decision process. Practically, we developed two agents (Dragan and Moria), trained them repeatedly, and conducted experiments and pilots in various organizations, situations, and contexts. The results were astonishing, pushing us towards commercial usage (ROM Global, QME) and a research paper (Larriza, Moria). Where does this take us? We strongly advocate for organizations to embrace the use of AI Knowledge-Driven Agents in their decision-making processes, whether developed internally or by trusted partners like ROM Global and QME. This Shift can make the decision-making process faster, more systematic, and better managed, leading to more profound decisions. For years, decision-making has been crucial yet not systematically managed. We are now in a new era where this can change, and it’s in our hands to make it happen. Previous Item Next Item

Knowledge Creation- the Symphony of KM When we talk about knowledge creation, we’re actually referring to a variety of Knowledge Management (KM) solutions, each focusing on a different kind of learning and creation process. Lessons Learned / Debriefing sessions Where insights from action are distilled into good practices, usually tactical, helping teams refine what already works. Tacit Knowledge Capture Sensitive conversations with subject matter experts, revealing hidden approaches, methods, and tools. These can range from strategic frameworks to very hands-on techniques. Collaborative Learning Structured series that combine conceptualizing, capturing, organizing, sharing, and documenting knowledge on a defined topic, from shaping a new policy to structuring a professional doctrine to addressing risks and challenges together. Innovation Focused ideation processes that create new products, services, or work methods, turning creativity into structured output. Game-Changing Transformation Where knowledge creation becomes re-creation, proposing new value or a radically different way to fulfill existing needs. In each of these, we, the knowledge managers, are not the ones playing the instruments, but rather the conductors of the orchestra, guiding others to create, connect, and compose new harmonized knowledge together. Previous Item Next Item

Why Knowledge Management? Rudolf DSouza, as always, hits the nail on the head by starting with the most critical question. Simon Sinek has taught us to START WITH WHY. Here’s my quick checklist of typical "whys" for KM, because sometimes it’s easier to start with a guide rather than a blank page: Effectiveness & Efficiency Encouraging knowledge sharing and applying existing knowledge in everyday tasks, problem-solving, and process improvement. Future Capabilities & Innovation Fostering knowledge creation and driving innovation. The key to staying ahead of the curve. Onboarding Improving learning curves and shortening the integration process for new hires and trusted suppliers. An essential part of welcoming talent into the fold. Business Continuity Capturing both tacit and explicit knowledge from experts and key roles, ensures the company mitigates risks when people move on. Safeguarding our intellectual capital. Safety Promoting knowledge learning, sharing, accessibility, and application to prevent accidents and ensure a safe environment. Knowledge can save lives. Knowledge is not just about documentation—it's about dealing with business needs, thus helping them improve performance and growth. Let’s always remember the power of asking WHY and keep pushing forward to unlock the true potential of KM in our organizations. Previous Item Next Item

AI is shaking the foundations of Knowledge Management It’s flooding us with unprecedented amounts of information and documented knowledge — and while knowledge will remain crucial for organizations, that doesn’t automatically mean they will appoint someone to manage it. After all, knowledge is everyone’s responsibility. While some of our current KM tasks will change — and some may even disappear — we also see new KM-based roles emerging: Leader of Strategic Knowledge – guiding how organizations identify, capture, acquire and integrate new knowledge. Leader of Knowledge Curation & Quality – ensuring knowledge is trusted, relevant, and methodologically sound. Leader of Knowledge-Driven Innovation – guiding how to catalyze turning insights into creative solutions and breakthroughs. Leader of Knowledge Risk Management – protecting against the loss of critical expertise and safeguarding organizational knowledge assets. These roles will only exist if we define them, model them, and show their impact. If enough of us step forward, others will follow. Meanwhile, there’s still much to do with today’s KM solutions to deliver business value more efficiently and effectively. (But that’s for another post ) We must start NOW — shaping both the near and distant future. It’s in our hands. It’s our responsibility. Previous Item Next Item

The Value of Tacit Knowledge: A Balanced Perspective YES—Tacit Knowledge is important. NO—Its tacit nature isn’t what makes it valuable. Why do we get so excited when discussing tacit knowledge and its significance? Here are a few reasons: Knowledge creation always starts with tacit knowledge—so yes, it’s important. Experts in our organizations possess undocumented, unshared knowledge that is crucial for problem-solving and decision-making. This tacit knowledge is not only important but vital to business performance. Tacit knowledge was neglected for many years in KM programs because managing explicit knowledge was easier. This gap elevates the importance of tacit knowledge. These are indeed good and important reasons. However: Not all tacit knowledge is equal. While some represent deep, critical knowledge, other portions may be less significant or even shallow. Capturing tacit knowledge and turning it explicit doesn’t diminish its value. Even if some of it remains tacit, successfully codifying 80% of it is a win! So, what’s my suggestion? YES: Aim to handle tacit knowledge by: Fostering interactions and encouraging experts to share their knowledge. Creating new knowledge through various KM activities. Capturing tacit knowledge and turning it explicit. NO: Don’t treat all tacit knowledge equally—it depends on its depth and specific value. Don’t neglect the knowledge we’ve turned explicit; its value is priceless. We’re on the right track by embracing tacit knowledge. Let’s take it a step further by being smarter about how we treat both tacit and explicit knowledge. Previous Item Next Item

Digital Transformation: Praise or Daze? Isn't it true that we often get carried away by tech buzzwords that promise revolutionary changes but often fail to deliver? "Digital transformation" is one such buzzword we're grappling with. Yesterday, I had the privilege of leading a round table discussion as part of our Knowledge Management Global Network roundtable series. Our goal? To decode digital transformation, scrutinize the hype, and explore the role of knowledge managers in this realm. The session kicked off with an insightful definition from Cheng Gong and Vincent Ribiere: "Digital transformation is a fundamental change process enabled by the innovative use of digital technologies. It leverages key resources and capabilities, aiming to radically improve an entity and redefine its value proposition for its stakeholders." (Note: An entity could be an organization, a business network, an industry, or society.) Alongside esteemed panelists Annie Green, Art Murray, Ron Young, and Yesha Sivan, we dove into the promising depths of digital transformation, especially in the exciting era of AGI (Artificial General Intelligence). Thank you, Yesha, for opening our eyes to the impact of the AGI Here are some key takeaways: Business needs should guide our digital transformation journey. Technology-oriented only is an aimless approach that won't get us far. We should put only some eggs in the technology basket; human expertise matters. This is an essential part of our role as KMers, bringing in, again and again, human knowledge. Remember, digital transformation is not a one-off project; it's a perpetual 'Plan-Do-Learn' cycle. Learning through systematic KM-based debriefing makes the process more effective. Never underestimate the power of change management. We all should commit to ongoing learning about new digital technologies and empower our knowledge workers to do the same. A true transformation combines digital, data, knowledge, and collaboration transformations. We must ensure responsible AI and digital transformation implementation. Last but not least, we should embrace Generative AI! It's a golden opportunity to supercharge KM and digital transformation. Previous Item Next Item