How to Retain Tacit Knowledge

A classic Knowledge Management problem for in-house teams is how to access tacit institutional knowledge from long-serving team members. 🤔 If you've worked in an in-house team that's been around for even a short while, you'll know that a significant amount of the inner workings of how the team operates and interacts with the business is stored in the heads of a few individuals. This knowledge can range from the weird and wonderful to super relevant and insightful context that can shine a light on bizarre deals and confusing decisions. Often, this knowledge is most useful for fast decision-making and continuity of operations. ⚙️ From a management perspective, you have to ask: what happens to this knowledge when these team members leave or retire? And how do we ensure other team members can operate effectively by having access to this information without constantly needing to consult their more experienced colleagues? 🤔 It’s crucial to proactively manage this risk and opportunity by systematically capturing and disseminating this knowledge across the team. You could consider the following for you team: Structured Knowledge Audits: Regularly conduct interviews or workshops with long-serving team members to document processes, insights, and decision-making criteria that are often not written down. These sessions should be structured to dig deep into the "why" behind certain practices, not just the "what" or "how." 📝 (Probably don't call them 'audits' and make sure your colleagues understand why you're doing them) Mentorship and Cross-Training: Encourage mentorship programs where senior team members actively share their knowledge with newer colleagues. Additionally, cross-training team members on different roles and responsibilities can ensure that knowledge is spread across the team, reducing dependency on any single individual. 👥 Centralizing Knowledge: Almost every KM topic comes back to this, but consider where and how you can centralize your documented knowledge so it is easily accessible. Start small and strategically, but start somewhere and with a commitment to maintaining what you've done. 📚 #LegalOps #KnowledgeManagement #Law #Legal #Business

🔹 Retention Is a Safety Issue: PKSM in Aging Workforces A brilliant but often-overlooked risk: Process Safety Knowledge Loss in Aging Workforces (Inspired by CCPS Guidelines for PSKM – 2024) 🧠 One of the smartest process safety insights I’ve seen recently — and it’s hiding in plain sight. Most plants are surprised by it year after year: 🎯 “We didn’t know how critical that operator’s knowledge was... until he retired.” 🙄 I’ve personally seen this play out multiple times in my career — Critical know-how just walks out the door. Troubleshooting tips, interlock logic, workaround justifications — gone. And suddenly what seemed like a staffing problem becomes a safety problem. ⚠️ What CCPS Gets Right The 2024 CCPS PSKM Guidelines spell it out clearly: “When a knowledgeable person leaves and knowledge is lost, a hazard may go unrecognized.” (Ch. 1.4) This is why Process Safety Knowledge Management (PSKM) is more than just a records project; it’s how you prevent your plant from forgetting what it once learned. 🧠 What Needs to Happen ✅ Identify who holds tacit knowledge (and what knowledge they hold) ✅ Conduct knowledge transfer sessions — not just exit interviews ✅ Capture insights into structured systems (tagged, retrievable, reviewed) ✅ Assign knowledge stewards to own and update key material ✅ Embed this into training, MOC, and procedures — not just storage 📘 “PSKM systems must proactively account for workforce dynamics.” (Ch. 6) 💥 Example (Based on Field Reality, Inspired by CCPS) After a near miss, one site discovered its reactor upset response existed only in a retiree’s handwritten notes. The solution? A navigator shadowed senior staff and codified their insights into SOPs and training—the result: faster onboarding, better emergency response, and more confident decision-making. 🔁 Final Thought: “People retire. Knowledge shouldn’t.” 👉 If you’re not tracking who holds critical knowledge, you may be one resignation away from your next major risk. ♦️ Let’s stop being surprised by the same thing every year. #ProcessSafety #PSKM #PSM #KnowledgeRetention #AgingWorkforce #CCPS #Leadership #HazardAwareness #RBPS #ChemicalEngineering #WorkforcePlanning #LeadershipDevelopment #TacitKnowledge

We are about to lose one of the most critical assets in manufacturing, and most organizations aren’t ready for it. Not equipment. Not systems. Experience. I was talking with a team at one of our plants recently, and they raised a concern: several key individuals with 20+ years of experience may not be coming back. People who know the process beyond the documentation. People who’ve seen the edge cases. People everyone quietly relies on when things go sideways. So I asked: “How are you capturing and transferring their knowledge today?” The answer: “We pair them with others.” That’s necessary, but it’s not sufficient. Because pairing only transfers what’s happening now. It doesn’t capture the rare failures. The once-a-year issues. The “this only breaks when these three things happen at once” scenarios. And that’s exactly the knowledge you need when it matters most. What we’re trying to preserve isn’t just procedures. It’s judgment. It’s intuition. It’s pattern recognition built over decades. And here’s the challenge: These experts don’t have the time, or interest, to sit down and document everything they know. And even if they did, most of what makes them valuable wouldn’t show up in a standard work instruction anyway. So we need a different approach. Instead of asking: “Can you write this down?” What if we asked: “Tell me about a time this went wrong.” “What’s the first thing you check when something feels off?” “What’s a mistake only experience teaches you to catch?” Stories are the gateway to tacit knowledge. Now this is where technology, especially AI, can change the game. We can capture those conversations and use AI to: - Ask intelligent follow-up questions in the moment - Pull out key decision points and hidden assumptions - Identify patterns across multiple experts and sites - Convert raw conversations into structured, searchable knowledge And most importantly, deliver that knowledge back to the frontline in a way that’s actually usable: In the moment. In context. At the point of need. Not another document buried in a system, but real-time guidance informed by decades of experience. Because this isn’t a documentation problem. It’s an extraction, translation, and delivery problem. And if we get this right, we’re not just preserving knowledge, we’re scaling it. The uncomfortable truth? If your knowledge strategy depends on people having time to document or tell others what they know, you need to ask if you have the right strategy. You have a risk. What are you doing, right now, to reduce it? #DigitalTransformation #SmartManufacturing #KnowledgeManagement #IndustrialAI #ManufacturingLeadership #FutureOfWork #OperationalExcellence #ConnectedWorker

Your Team’s Brain is Leaking. Here’s How to Stop It Your company’s intelligence is leaking every single day. You’re hiring great people, they’re learning on the job, making decisions, solving problems… and then? That knowledge evaporates into thin air the moment they move on, switch roles, or simply forget. Meanwhile, you’re constantly asking, Why are we solving the same problems over and over? The truth is, most organisations treat knowledge like a one-time transaction instead of a strategic asset. → Training programs? Already outdated by the time they’re implemented. → Standard knowledge management? Too rigid. → SOPs? Too static. What we need is 'corporate collective intelligence'. An evolving, self-scaling system that captures, refines, and distributes knowledge seamlessly, so our team gets smarter as it grows. Here’s how you start: - Turn conversations into intelligence. Your best insights happen in Slack threads, meetings, and problem-solving sessions. Capture and refine them as they happen. - Make tacit knowledge explicit. The way your best performers make decisions? That’s gold. Codify it before it disappears. - Use AI and automation wisely. Stop treating AI as a gimmick. It should be actively structuring, indexing, and surfacing knowledge, not just summarising documents. - Create a feedback loop. Your organisation should be learning from itself in real-time. No more one-and-done knowledge drops, continuous refinement is key. → Teams that scale without bottlenecks. → Faster decision-making with fewer mistakes. → Institutional knowledge that doesn’t walk out the door. The companies that master this won’t just scale - they compound. Those that don’t? They will keep reinventing the wheel. Which one do you want to be? Found this useful? Repost ♻️ to help your network.

Expert decision-makers process patterns 6x faster than they can explain them. That's the real reason your team can't close like you—and why your sales playbook will never fix it. Carnegie Mellon University researchers found that experts aren't thinking through steps. They're matching the current situation against thousands of previous situations—instantly, unconsciously. Cognitive scientists call this tacit knowledge. Michael Polanyi named it in 1958: "We know more than we can tell." The uncomfortable part: the more expert you become, the less able you are to explain what you do. It's called the expertise reversal effect. As skills become automatic, the reasoning behind them becomes invisible—even to you. You don't decide to read the room. You just read it. This is why documentation fails as a transfer method. You can't document a pattern-matching engine. You can only create conditions where someone else builds their own. Three conditions research supports: 1️⃣ Exposure to expert decision-making in real time—not after the fact. 2️⃣ Deliberate practice with feedback in realistic scenarios. 3️⃣ Forced verbalization—the expert narrating their own judgment while it's happening. That third one is the hardest. It requires founders to slow down and articulate what's normally automatic. Uncomfortable. Unnatural. And, it's the single most effective method for transferring tacit expertise. What's one judgment call in your sales process you've never been able to explain to your team—even though you do it every time?

When you’ve been at a startup for almost 8 years, you 𝘢𝘤𝘤𝘪𝘥𝘦𝘯𝘵𝘢𝘭𝘭𝘺 become a knowledge center. (Need to know how we used to run PD in 2017? I probably have the doc. Or at least the context. 🗃️) But here’s the thing: I won’t always be around to share my institutional knowledge. And a recent lightbulb moment from the Project Management Institute's 𝘗𝘳𝘰𝘫𝘦𝘤𝘵 𝘔𝘢𝘯𝘢𝘨𝘦𝘮𝘦𝘯𝘵 𝘉𝘰𝘥𝘺 𝘰𝘧 𝘒𝘯𝘰𝘸𝘭𝘦𝘥𝘨𝘦 reminded me how important it is to manage project knowledge on purpose. 📘 PMBOK defines Managing Project Knowledge as the process of using existing knowledge and creating new knowledge to achieve project goals 𝘢𝘯𝘥 contribute to organizational learning. What struck me most? It's not just about collecting documents—it's about capturing both explicit knowledge (the what) and tacit knowledge (the why + how). 💡 Think about it: 👉 Product teams often do this well—retros, documentation, shadowing, etc. 👉 But what about training and enablement? How are we capturing the insights behind what worked (or didn’t)? 👉 What habits or rituals help surface 𝘵𝘢𝘤𝘪𝘵 𝘬𝘯𝘰𝘸𝘭𝘦𝘥𝘨𝘦 before it walks out the door? One tool I’m experimenting with is a lightweight lessons learned register—capturing not just outcomes, but decisions, pivots, and insights along the way. I’m also investing time in building shared practices around documentation, feedback loops, and team retros. Because in startups, speed is great—but shared knowledge is what keeps the speed sustainable. 💬 Curious: How does your team manage knowledge transfer across projects, teams, or turnover? What’s worked (or flopped)? #ProjectManagement #LearningAndDevelopment #Startups #KnowledgeManagement #PMBOK #OrganizationalLearning #Leadership #Enablement

💫 An 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗼𝗻𝘁𝗼𝗹𝗼𝗴𝘆 is just a means, not an end. 👉 Transforming 𝘁𝗮𝗰𝗶𝘁 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 into 𝗲𝘅𝗽𝗹𝗶𝗰𝗶𝘁 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 through an enterprise ontology is a self-contained exercise if not framed within a broader process of knowledge creation, collection, and distribution within the organization. 👇 The 𝗦𝗘𝗖𝗜 𝗠𝗼𝗱𝗲𝗹 effectively describes the various steps of this process, going beyond mere collection and formalization. The SECI model outlines the following four phases that must be executed iteratively and continuously to properly manage organizational knowledge: 1️⃣ 𝗦𝗼𝗰𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: In this phase, tacit knowledge is shared through direct interaction, observation, or experiences. It emphasizes the transfer of personal knowledge between individuals and fosters mutual understanding through collaboration (tacit ➡️ tacit). 2️⃣ 𝗘𝘅𝘁𝗲𝗿𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: In this phase, tacit knowledge is articulated into explicit forms, such as an enterprise ontology. It helps to codify and communicate the personal knowledge that might otherwise remain unspoken or difficult to share (tacit ➡️ explicit). 3️⃣ 𝗖𝗼𝗺𝗯𝗶𝗻𝗮𝘁𝗶𝗼𝗻: In this phase, explicit knowledge is gathered from different sources, categorized, and synthesized to form new sets of knowledge. It involves the aggregation and reorganization of existing knowledge to create more structured and accessible forms (explicit ➡️ explicit). 4️⃣ 𝗜𝗻𝘁𝗲𝗿𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: In this phase, individuals internalize explicit knowledge, turning it back into tacit knowledge through practice, experience, and learning. It emphasizes the transformation of formalized knowledge into personal, actionable knowledge (explicit ➡️ tacit). 🎯 In a world where the only constant is change, it is no longer enough for an organization to know something; what matters most is how fast it learns by creating and redistributing new knowledge internally. 🧑🎓 To quote Nadella, organizations and the people within them should not be 𝘒𝘯𝘰𝘸-𝘐𝘵-𝘈𝘭𝘭𝘴 but rather 𝘓𝘦𝘢𝘳𝘯-𝘐𝘵-𝘈𝘭𝘭𝘴. #TheDataJoy #KnowledgeMesh #KnowledgeManagement #Ontologies

Institutional knowledge is being rebuilt as persistent AI memory. Not documents. Active intelligence that reasons and compounds. Here's why the enterprises capturing this layer will dominate: Right now, your organization loses context every day. Customer insights fade. Tribal knowledge disappears when people leave. Years of learning walk out the door. But there's a fundamental shift happening. Organizations are rebuilding institutional knowledge as a persistent, queryable intelligence layer. This is the competitive advantage that separates fast-moving companies from everyone else. Traditional systems store information. Agentic memory makes it usable for reasoning. You're not searching through Notion pages hoping someone documented the discussion. You're asking an agent who already connects the dots between what happened, why it mattered, and how it applies to today's decision. Staff turnover stops erasing what you know. When your senior engineer leaves, their debugging approaches, architecture decisions, and customer empathy don't walk out with them. The agent layer captured not just what they did, but their reasoning process. The shift requires rethinking your data architecture. Most companies built systems to store and retrieve, not to reason. The counterintuitive part? More data doesn't automatically mean better memory. I've seen organizations with petabytes of stored interactions perform worse than smaller companies with curated context. What matters is whether the agent can distinguish signal from noise and maintain reasoning chains across time. In traditional systems, knowledge half-life is measured in months. Documentation goes stale. Context gets lost. Insights become archaeology. With agentic retrieval and auto-summarization, that half-life extends to years. The system keeps knowledge current by continuously interpreting new information against existing context. The reasoning is preserved, not just the outcome. The pattern is emerging across enterprise software. Systems are moving from "search and retrieve" to "interpret and suggest." The winning implementations don't just have better storage. They have better reasoning about what the stored information means for the current context. The enterprises building this layer now will compound faster. They're capturing tacit knowledge that competitors are losing every time someone quits or a customer insight gets buried in Slack. Building this layer creates a new challenge How do you know it's actually working? When agents interpret context, you need visibility into whether they're reasoning correctly or hallucinating connections. At Kaily.ai, I'm focused on exactly this infrastructure problem. Agent monitoring, conversation analysis, and performance metrics that show whether your AI memory layer is interpreting accurately. If you're evaluating how to deploy agentic memory confidently, let's discuss what trustworthy AI infrastructure looks like.

🧠 Change Amnesia: Your Company is Forgetting Faster Than It's Learning A financial services organization completed a large-scale system modernization, celebrating all milestones. Six months later, they couldn't explain why certain design decisions were made, and within two years, they were solving problems they'd already solved during implementation. They had fallen victim to Change Amnesia – the systematic loss of organizational memory during transformation. 💸 The Hidden Cost of Transformation We track budgets, timelines, and adoption during change initiatives. But we rarely measure what's being lost: institutional knowledge. Research from Deloitte shows financial institutions lose up to 23% of critical knowledge during major transformations. This silent drain has tangible costs: - Key process insights disappear when subject matter experts move on - Workarounds developed during implementation become "the way things are done" - Decision rationales fade, leading to reversed decisions - Hard-earned lessons evaporate, ensuring mistakes will be repeated ☠️ The Four Horsemen of Organizational Forgetting Change Amnesia follows predictable patterns: 1. Knowledge Concentration: Critical information held by few people who may leave 2. Documentation Deprioritization: When timelines tighten, documentation is the first casualty 3. Context Collapse: The "why" behind decisions gets lost while the "what" remains 4. Transformation Layering: New changes begin before the organization has integrated previous ones ING Bank's risk platform implementation saw three different project managers over 18 months. By go-live, no one could explain why certain critical design choices were made – setting the stage for future rework. 🕰️ Building Organizational Memory Resilience Forward-thinking organizations implement memory-preservation strategies: - Decision Journals: Documenting not just what was decided, but why - Knowledge Transfer Programs: Structured approaches for capturing tacit knowledge - Technical Debt Tracking: Monitoring shortcuts that create future vulnerability - Organizational Historians: Dedicated roles focused on preserving institutional memory BBVA reduced rework by 34% through a "memory-first" approach to transformation, treating knowledge preservation as a primary objective rather than an afterthought. 🥫 The Memory Preservation Mindset Change without memory isn't transformation – it's just motion. What's been your experience in organizational change memory loss? How does your organization preserve critical knowledge during periods of change? #ChangeManagement #OrganizationalMemory #KnowledgeManagement

How are you looking at AI for the workforce right now? More importantly: does the workforce see it the same way… or as another tool being pushed at them? For your most experienced workers on the shop floor, AI isn’t “cool.” It’s a way to leave a lasting legacy. The Great Goodbye is already underway. Retirement waves, attrition, role changes. And every time a seasoned tech walks out, so does a playbook you can’t buy from a vendor and can’t replace with a PDF. Your experienced employees want to prevent the younger workforce from learning through the school of hard knocks as they did, and eliminate the physical and mental scars many carry from years of learning from what went wrong. So how do we stop treating AI like a chatbot… and start treating it like a Knowledge Capture Agent? Can we position it as an non-intrusive tool that can cull rich knowledge assets trapped in the minds of your workforce and turn that experience into repeatable performance? My colleague Ryan Cahalane said something thought provoking when the LNS Research team was discussing the disparity in the disconnect between how the C-Suite and workers see AI: AI is like Depends for the senior workforce. Not because you’re done exploring— but because you’re done playing it safe. It’s permission to take on new adventures, start things you wouldn’t have risked before, and say yes without worrying about the occasional mental misfire. AI’s the quiet insurance policy: spotting the leaks, catching the slips, and letting experience run faster than it used to. More risk. More range. Same wisdom—now leak-proof. 🔎 For manufacturing ops and EHS leaders: AI deployment across the shop floor is investing in resilience, safety, and consistent execution. 👉 Capturing tacit process knowledge in the flow of work - to provide expert support to the next generation through workflow insights and guided execution. 👉 Converting implicit knowledge into digital work instructions with photos, data capture, and access to troubleshooting videos in their native language. 👉 Maturing Industrial Knowledge Management across the enterprise for a competitive advantage. 🔎 For software vendors: this is the wedge into measurable value — not “AI features,” but workforce outcomes. It's time to ask ourselves why, and how, we are deploying AI. And what can we do to ensure we protect the knowledge that keeps the plant running and the next generation safe? Share your thoughts with the team, Matthew Littlefield, Niels Erik Andersen, Michael Carroll, Vivek Murugesan, and James Wells, and tell us how you've created a path to success! #IndustrialAI #Transformation #Manufacturing #FutureOfWork #IndustrialKnowledgeManagement