Knowledge Transfer Techniques

Imagine calling training a success when no one uses it on the job. Have you? Most people do not fail the training. They fail to apply it. About 85% of training never gets used on the job. Not because the content was bad. Not because the learner was not engaged. Because learning and doing are two very different things. We have built entire Learning & Development systems around consumption. Videos. Workshops. Courses. Certifications. But knowing something is not the same as doing it. The real gap is not knowledge. It is transfer. Here are 5 ways to actually close it. 1️⃣ Replace content with reps: Stop adding more modules. Build in deliberate practice. Repetition under real conditions is what creates retention. 2️⃣ Make managers part of the design: If a manager does not reinforce it, it dies. Loop them in before the training, not after. 3️⃣ Create accountability structures: Peer check-ins. Follow-up commitments. Application goals. Without accountability, good intentions evaporate. 4️⃣ Shrink the time between learning and doing: The longer the gap, the more fades. Give learners a chance to apply within 48 hours of any session. 5️⃣ Measure behavior, not completion: Finishing a course proves nothing. What changed on the job? That is the only number worth tracking. Active learning feels productive. Active practice is what actually changes performance. Your learners do not need more content. They need more reps. AI makes this matter even more. When information is everywhere and content is easier than ever to generate, the real advantage is not access to knowledge. It is the ability to apply it. Statistic source: The Institute for Transfer Effectiveness ——— ✦ ——— More on AI for Workforce Transformation → Janet Perez

We’ve all heard that testing yourself boosts learning retention. But a new 2025 paper (Corral & Carpenter, 2025) digs deeper, and the findings matter for anyone serious about capability building. Across three experiments using complex research methods concepts, the authors tested four strategies: 1️⃣ Retrieval practice 2️⃣ Studying Q&A pairs 3️⃣ Recognition-based quizzing 4️⃣ Simple restudy The key insight? 👉 Retrieval practice only shows its true power when it’s done multiple times and measured after a delay. Under those conditions, retrieval doesn’t just strengthen recall, it improves people’s ability to apply concepts in scenarios they’ve never seen before. And that’s the holy grail of learning: Not “Do you remember it?”...but “Do you recognise when it matters and use it effectively?” 😮 One finding I found particularly important: Even when learners remembered the concept, only those who practiced retrieval were better at identifying when the concept applied in a new context. That hits directly at the real barrier in workplace learning: recognition, not recall. In other words: People often know what to do, they just don’t realise this is the moment to do it. This aligns perfectly with how I think about behaviour change through readiness, cues, and cognitive accessibility. 🧠 Practical takeaways Don’t rely on restudy or passive review, build retrieval into every learning experience Use multiple rounds of retrieval, not one off quizzes Space retrieval out over time to strengthen transfer Design questions that mirror real decision contexts, not just textbook definitions Treat retrieval as a behaviour activation tool, not just a memory strategy If you want learning to survive beyond the classroom, and actually shift behaviour, retrieval practice must be part of the architecture.

Most are sleeping on the power of 𝗠𝗼𝗱𝗲𝗹 𝗗𝗶𝘀𝘁𝗶𝗹𝗹𝗮𝘁𝗶𝗼𝗻, and every company should have a Distillation Factory to stay competitive This technique is reshaping how companies build efficient, scalable, and cost-effective AI. First, 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗠𝗼𝗱𝗲𝗹 𝗗𝗶𝘀𝘁𝗶𝗹𝗹𝗮𝘁𝗶𝗼𝗻? Also known as knowledge distillation, is a machine learning technique where a smaller, more efficient "student" model is trained to replicate the behavior and performance of a larger, more complex "teacher" model. Think of it as a master chef (the teacher) passing down their culinary expertise to an apprentice (the student) without sharing the exact recipe. The student learns by observing the teacher’s outputs and mimicking their decision-making process, resulting in a lightweight model that retains much of the teacher’s capabilities but requires fewer resources. Introduced by Geoffrey Hinton in his 2015 paper, “Distilling the Knowledge in a Neural Network,” the process involves: 1/ Teacher Model: A large, powerful model trained on massive datasets. 2/ Student Model: A smaller, efficient model built for faster, cheaper deployment. 3/ Knowledge Transfer: The student learns from the teacher’s outputs—distilling its intelligence into a lighter version. There are several types of distillation: 1/ Response-Based: The student mimics the teacher’s final outputs 2/ Feature-Based: The student learns from the teacher’s intermediate layer representations. 3/ Relation-Based: The student captures relationships between the teacher’s outputs or features. The result? A student model that’s faster, cheaper to run, and nearly as accurate as the teacher, making it ideal for real-world applications. 𝗪𝗵𝘆 𝗘𝘃𝗲𝗿𝘆 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗡𝗲𝗲𝗱𝘀 𝗮 𝗗𝗶𝘀𝘁𝗶𝗹𝗹𝗮𝘁𝗶𝗼𝗻 𝗙𝗮𝗰𝘁𝗼𝗿𝘆? In today’s AI landscape, very large LLMs are incredibly powerful but come with significant drawbacks: high computational costs, massive energy consumption, and complex deployment requirements. A Distillation Factory is a dedicated process or team focused on creating distilled models, addressing these challenges and unlocking transformative benefits. Here’s why every company should invest in one: 1/ Cost Efficiency: Distilled models cut costs, running on minimal GPUs or smartphones, not data centers. 2/ Scalability: Smaller models deploy easily. 3/ Faster Inference: Quick responses suit real-time apps. 4/ Customization: Tailor models for healthcare or finance with proprietary data, no full retraining. 5/ Sustainability: Lower compute needs reduce carbon footprints, aligning with green goals. 6/ Competitive Edge: Rapid AI deployment via distillation outpaces costly proprietary models. A Distillation Factory isn’t just a technical process; it’s a strategic move.

Exciting New Research: Injecting Domain-Specific Knowledge into Large Language Models I just came across a fascinating comprehensive survey on enhancing Large Language Models (LLMs) with domain-specific knowledge. While LLMs like GPT-4 have shown remarkable general capabilities, they often struggle with specialized domains such as healthcare, chemistry, and legal analysis that require deep expertise. The researchers (Song, Yan, Liu, and colleagues) have systematically categorized knowledge injection methods into four key paradigms: 1. Dynamic Knowledge Injection - This approach retrieves information from external knowledge bases in real-time during inference, combining it with the input for enhanced reasoning. It offers flexibility and easy updates without retraining, though it depends heavily on retrieval quality and can slow inference. 2. Static Knowledge Embedding - This method embeds domain knowledge directly into model parameters through fine-tuning. PMC-LLaMA, for instance, extends LLaMA 7B by pretraining on 4.9 million PubMed Central articles. While offering faster inference without retrieval steps, it requires costly updates when knowledge changes. 3. Modular Knowledge Adapters - These introduce small, trainable modules that plug into the base model while keeping original parameters frozen. This parameter-efficient approach preserves general capabilities while adding domain expertise, striking a balance between flexibility and computational efficiency. 4. Prompt Optimization - Rather than retrieving external knowledge, this technique focuses on crafting prompts that guide LLMs to leverage their internal knowledge more effectively. It requires no training but depends on careful prompt engineering. The survey also highlights impressive domain-specific applications across biomedicine, finance, materials science, and human-centered domains. For example, in biomedicine, domain-specific models like PMC-LLaMA-13B significantly outperform general models like LLaMA2-70B by over 10 points on the MedQA dataset, despite having far fewer parameters. Looking ahead, the researchers identify key challenges including maintaining knowledge consistency when integrating multiple sources and enabling cross-domain knowledge transfer between distinct fields with different terminologies and reasoning patterns. This research provides a valuable roadmap for developing more specialized AI systems that combine the broad capabilities of LLMs with the precision and depth required for expert domains. As we continue to advance AI systems, this balance between generality and specialization will be crucial.

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?

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

What Netflix, TikTok, Escape Rooms, and Video Games Taught Me About Designing a Breakthrough Learning Journey Most training focuses on content. But real impact comes from designing the entire learning experience—from the first click to on-the-job mastery. Here’s how I think about the full journey, using the entertainment we can’t stop consuming: ⸻ 1. Attention – Think Netflix trailers. Start with curiosity, not content. A great trailer teases value in seconds—you have to know more. Your learning hook should do the same. No more “Welcome to this training…” Try: “What if you could solve this in 5 minutes?” ⸻ 2. Interest – Think TikTok. Once you’ve got their attention, keep it with fast, focused, value-packed moments. TikTok works because it’s punchy, paced, and addictive. In learning? Use microformats, crisp storytelling, and emotional connection. ⸻ 3. Understanding – Think How to Get Away with Murder (or Squid Game). These shows are masterclasses in layered storytelling. Each episode builds tension, teaches something new, and deepens the stakes. In learning: • One key concept per module • Clear through-line • Questions that pull learners forward People don’t need less content—they need better structure. ⸻ 4. Retention – Think escape rooms. You don’t just observe—you do. You make choices, fail, adjust, and try again. Learning sticks when people wrestle with content. Design challenges, scenarios, and immediate application. Let them work it out, not just watch it. ⸻ 5. Application – Think video games. The best games teach through doing. Level by level, skill by skill. Players get feedback, unlock new abilities, and adapt strategy in real time. Great learning works the same way: • Practice in safe spaces • Level up complexity • Build confidence before real-world play ⸻ 6. Transfer – Think coaching and culture. When the “game” ends, learners need support to apply skills in real life. This is where adult learning theory shines: • Real-world relevance • Social learning and feedback • Autonomy, mastery, purpose Learning doesn’t stop at the module. It lives in mentorship, conversations, and culture. ⸻ Great learning feels like entertainment. But more importantly—it empowers real change. Design for the journey, not just the course. ⸻ Image: A fun workshop I did with the U.S. Department of States where I utilized multiple forms of entertainment to attract attention, support knowledge retention, understanding, and application. #LearningDesign #LXD #InstructionalDesign #ContentStrategy #AdultLearning #LearningJourney #TrangTranLearningDesigner

What if your experts only needed five hours to share knowledge that used to take 200 hours to document? One of the most effective ways to engage subject matter experts in knowledge transfer is to dramatically reduce the size of the ask. At Shepley Bulfinch, they’ve flipped the traditional process. Instead of starting with written documentation—which often means days or weeks of writing, reviewing, editing, and peer QA—they begin with video. A short prep call, a recorded screen-share session where the expert talks through the process in their own words, and that’s it. That’s the expert’s whole involvement other than a final review. From there, others take over. Video editors—often junior team members—cut it down into smaller pieces. Tools like Synthesis AI Search make the content easily discoverable inside the firm. Written documentation can be auto-generated or added as needed by others. This shift does more than save time. It lowers the emotional and cognitive friction that often stops people from sharing what they know. Experts don’t have to think of themselves as “teachers” or “writers” or worry about crafting the perfect explanation. They just have to show what they do and talk through it naturally. And when they know that updating the content later will be as simple as recording a quick new video, the whole thing becomes more maintainable—and far less daunting. Meanwhile, the people who take on the editing and cleanup don’t just process the material. They learn it. By watching the videos closely—pausing, replaying, summarizing—they start to absorb the knowledge themselves. Over time, they move from being consumers of expertise to future experts. This approach not only speeds things up, it changes the way expertise flows through an organization. It makes sharing easier, learning more distributed, and documentation a collective act rather than a solitary chore. This clip is from “Discovering the Value of AI Through Experimentation,” episode 6 in our Welcome to KM 3.0 collaboration with the TRXL Podcast. 👉 You can find a link to the full episode in the comments. Thanks to Jess Purcell and James C. Martin of Shepley Bulfinch for sharing your thoughts! #AEC #KnowledgeManagement #ModernLearningOrganizations

Have you ever felt uneasy about whether your business’s core processes can continue to thrive under new ownership—and wondered if that doubt might lower your selling price? Years of refining internal workflows, unique knowledge bases, and specialized handoff steps can lose their impact if potential buyers aren’t convinced that vital operational details will transfer smoothly. Hoping for a hassle-free transition is one thing, but lacking a concrete plan for “how it all really works” can leave them uncertain about the true depth of your organization’s strengths. When you outline structured Knowledge Transfer Sessions, you reassure buyers that your operations won’t collapse the moment you depart. You also show that these processes aren’t locked in your head but live within an open, transparent framework. This level of clarity fosters trust, making it easier for a new owner to envision how they’ll step in and keep things running. When you schedule thorough Knowledge Transfer Sessions: ⇢ You showcase a business model that won’t fall apart the moment you step away. ⇢ You build confidence in your buyer, who sees that daily operations won’t skip a beat. ⇢ You create a transparent environment where critical details are shared proactively, not withheld or forgotten. 🤔 So, how do you run effective transfer sessions? Start by identifying your “mission-critical” processes—those that directly impact revenue, client relations, or product quality. Gather the right people: key managers, team leads, or any specialists who hold unique information. Use structured agendas and checklists so nothing slips through the cracks. Record or document each session, creating reference materials that outlast the meeting. Encourage Q&A, ensuring buyers fully grasp the reasoning behind each step, not just the steps themselves. Don’t let your well-oiled operations become a mystery that kills buyer confidence. By scheduling detailed Knowledge Transfer Sessions, you’re reinforcing the operational backbone that earned you success in the first place. Because when potential acquirers realize they won’t be left in the dark, you transform fear of the unknown into tangible trust—ultimately safeguarding the value of your enterprise and paving the way for a more rewarding sale. Join our (Intro to Exiting) Success To Selling Your SaaS Business webinar on April 17, 2025. 📩 Registration link in the comments! #BusinessGrowth #MergersAndAcquisitions #KnowledgeTransfer #BusinessValuation #Entrepreneurship #BusinessStrategy #HighValueExit

Following up on my post on training transfer, here's the breakdown of the four critical factors you need to consider:  1. Analyze the Work Environment: Before training begins, identify barriers to applying new skills. Are there policies that block implementation? Will supervisors actively support transfer of learning? What about resource availability? I've seen cases where existing approval processes made it impossible for trained staff to use new skills. Also consider workplace stressors—being understaffed, hierarchy issues, or team dynamics can prevent even well-trained employees from performing. If decision-making under stress is critical, train under realistic pressure conditions. 2. Understand Your Learners: Develop diverse personas based on experience levels, prior knowledge, and cultural backgrounds. A novice needs a completely different pathway than an expert. If behavior change efforts have failed before, dig into why—more training may not be the answer. Use pre-tests, learner interviews, or interviews with SMEs in direct contact with learners in case you can't reach the learners to uncover the real barriers. 3. Design Skills-Based Experiences: Tie learning directly to real tasks using frameworks like Cathy Moore's Action Mapping and Richard Clark's Cognitive Task Analysis. Go beyond observable actions to uncover invisible cognitive processes and decision-making strategies. Create scenario-based assessments, demonstrations, or role-plays that test application, not just recall. Use spaced repetition for mastery and provide job aids like task-centric checklists for post-training support. 4. Measure Learning Effectiveness and Transfer: Start your design with evaluation metrics, but don't stop at course completion. Follow up 2-3 months after training to measure if learning was actually applied and identify any barriers preventing transfer. Interview with SMEs in direct contact with learners in case you can't reach the learners. #trainingeffectiveness #trainingevaluation #trainingdesign #trainingtransfer #learninganddevelopment