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Für die neue Folge des Podcasts "neues lernen" hat sich Redakteurin Kristina Enderle da Silva auf der Learntec in Karlsruhe umgehört. Der Fokus: Wie KI Lernen wirksamer macht.

Most companies say they invest in learning. What they actually invest in is training. There's a difference and it's costing them more than they know. Andrew Barry's Learning Culture Lotus captures that gap beautifully. Three overlapping circles. Four outer forces. Four intersection zones. Learning Culture at the center, not as a program, but as an emergent property of something deeper. Here's my read on each element through the lens of strategy, culture, and human potential: The four outer forces: 1. Individual Curiosity This is the pre-condition everything else depends on. You cannot build a learning culture on top of disengaged people. Curiosity isn't a soft skill, it's a strategic signal. When it's absent, no curriculum in the world will compensate for it. 2. Human Connection People learn more from one another than from any formal content. Peer-to-peer learning, real dialogue, and psychological safety aren't perks, they are the infrastructure of organizational intelligence. Connection is how learning moves from individual insight to collective capability. 3. Continuous Learning This is the operational discipline, not a one-time initiative, but a rhythm baked into how work gets done. The organizations that outperform aren't the ones that run the best annual training. They're the ones where learning happens between the meetings, not just inside them. 4. Company Mission The North Star. Without it, learning becomes a collection of individual pursuits with no directional pull. Mission is what transforms personal growth into organizational momentum. It answers the question that most L&D programs never ask: Why are we learning this? The four intersections, where the real culture lives: 5. Shared Vision (Individual Curiosity × Company Mission) When people's personal curiosity aligns with where the organization is going, you don't have to push engagement. It pulls itself. This intersection is why the best leaders don't just communicate strategy, they translate it into something people can personally connect to. 6. Shared Assumptions (Company Mission × Continuous Learning) Every culture runs on invisible beliefs. What we think is possible. What we think success looks like. What we think is worth questioning. Shared assumptions are the operating system most companies never open and never update. 7. Shared Stories (Continuous Learning × Human Connection) Cultures don't scale through policies. They scale through stories. The moments that get told in hallways, in onboarding, in 1:1s, those are the true architecture of a learning culture. They encode what we value, what we celebrate, and what we're willing to learn from. 8. Shared Experiences (Human Connection × Individual Curiosity) This is the one most organizations skip entirely. Not content. Not modules. Experiences live, collaborative, challenging enough to create real learning. The kind you still talk about two years later. Learning culture isn't built through HR programs. It's cultivated at the intersection of curiosity, connection, and a mission worth serving. The lotus blooms from the center, not from the top down. Where does your organization go weak in this model? #learningorganization #cultureoflearning #leadingwithculture #culture Image Credit: Jeroen Kraaijenbrink | 89 comments on LinkedIn

The next wave of skills management will not be about building a better skills taxonomy. It will be about building a better skills signal system. For years, companies treated skills like a static database: define the taxonomy, tag the content, ask employees to self-assess, then hope managers use the data. AI changes the equation - but only if we stop treating skills as HR vocabulary and start treating skills as living signals. A stronger skills strategy connects: * Work signals - what people actually do * Learning signals - what people are building * Career signals - where people want to go * Business signals - what capabilities the company needs next The real opportunity is not “AI-generated learning recommendations.” The real opportunity is helping leaders answer better questions: - Where are our capability gaps emerging? - Which skills are becoming strategically obsolete? - Which employees are ready to grow into adjacent roles? - Which learning investments are actually changing workforce capability? My point of view: the companies that win with AI-powered skills management will not be the ones with the biggest taxonomy. They will be the ones with the most "trusted skills signals" - and the operating discipline to act on them. #SkillsManagement #LearningAndDevelopment #AIinHR #FutureOfWork #LearningTechnology

I finally got around to reading the Insight222 PA & AI benchmarking report (https://lnkd.in/gGVBPTBb, yes, I still read reports rather than just the AI summaries of them... There's a time for a quick scan and themes and then a time to really wallow in meaning and patterns....) Lots of nuggets in here but the one that I think broke my heart the most? Single digit percentages of my PA friends and compatriots are using these advanced tools to forecast their talent pipelines. All of you people wringing your hands about SWP and you're not using what you already have in front of your faces? Come on folks. If I'm honest, the low utilization of skills inference kind of killed me too. People! Let's get on the stuff that lets us shepherd the organization (and people into it!) into the future! Also - L&D personalization is such low hanging fruit! There are such great tools! Super surprised by this one. There's lots more in this 88 (!) page report. Notably - as reported here, PA ratios and workloads aren't really changing that much, and mostly aren't shrinking... Appreciation to Madhura Chakrabarti, PhD, Jay Dorio, Heidi Binder-Matsuo, Jonathan Ferrar for their tireless work in support of this community. AI tools notwithstanding, building a report that's meaningful, insightful and useful is harder than it looks! Happy reading!

If you work in L&D, AI is likely devaluing you and your role. Here’s why, and how to fight back. As someone who works in L&D, you probably feel something I hear a lot: that your role is being quietly flattened by AI. AI promised innovation and growth, but in practice its impact has been to increase the volume of content you’re expected to build in compressed timelines. If you feel this too, you're definitely not alone. As one person on my bootcamp said last month: “I’m a learning designer, but since AI appeared I’ve never felt more like a content developer”. Here’s what I learned when I dug into the details..... __________________________ 👉 The Problem: → L&D has access to <15% of the AI tools available to most other industries. → Over 90% of the AI tools built for L&D help to do one thing: develop more content. Almost none help us with the strategic work — diagnosis, prioritisation, impact measurement — that elevate our roles and get a function taken seriously. → The result is a narrowing of our roles, job losses and flat-lining salaries. 👉 The Solution: → Stop waiting for vendors to build the AI tools you need to drive real value. They're not coming — or at least not soon enough for you. → Start building your own AI tech stack to augment tasks outside content development using tools you already use: ChatGPT, Claude and Microsoft Copilot. Think: GPTs for data analysis, research bots to select optimal design approaches and agents to automate slow but critical tasks like monitoring completion data and running QA. → Treat building with AI as a core L&D skill, not a technical side-quest. It's what's separating the L&D pros getting promoted (complete with a ~20% salary premium) from the ones getting flattened. This is exactly the pattern we've seen play out in Product Management over the last two years, where the rise of self-taught “AI PMs” has driven a 340% increase in hiring and 50%+ increase in salaries. __________________________ In this week's blog post, I explore: → Why L&D's AI tech stack is so much smaller than other industries' → What a fully-formed AI tech stack for L&D would actually look like → How to build your own AI tech stack in ~a month, with the tools you already use & pay for. TLDR: building with AI is what separates the L&D pros getting promoted & feeling fulfilled from the ones getting flattened. Start flexing your builder muscles this week, with help from this post (link in comments). Happy building! Phil 👋 | 13 comments on LinkedIn

Unternehmenswissen erschließen?

🎬 Employee happiness has too often been treated as a soft topic. The Happiness Dividend from Reward Gateway and Edenred in partnership with The London School of Economics and Political Science (LSE) refreshingly makes the opposite case. Happiness is measurable. It is material. And it is value-creating. The report answers four important questions: 1️⃣ What is happiness at work? 🔎 It is both how people feel day to day and how they evaluate their job overall. 2️⃣ How should happiness be measured? 🔎 With four practical questions on satisfaction, purpose, happiness and stress. 3️⃣ What does employee happiness deliver? 🔎 Happier employees are 10 to 12% more productive and 30% less likely to leave. 🔎 Business units with higher happiness see an 18% increase in productivity and a 21% rise in profitability. 🔎 Firms with higher employee happiness are linked to a 20% increase in firm value. 4️⃣ What drives employee happiness? 🔎 Not pay alone. The biggest drivers are connection, quality of relationships, manager support, recognition, flexibility, work-life balance and reduced financial stress. The line from the report that really resonated with me is: 🎙️ “Employee happiness is genuinely value-creating and the stock market has historically been slow to fully recognise this previously intangible asset.” That should change the CHRO conversation. The task is not to make work artificially cheerful. It is to build organisations where people feel valued, connected, supported and able to do meaningful work. That is not a perk. It is a performance system. Read the full report here https://hubs.ly/Q04bmLY-0 #FutureofWork #EmployeeHappiness #HRInsights | 16 comments on LinkedIn

A quiet vindication. A year ago I published "Learning in the Flow of Context" and argued that Palantir's Forward Deployed Engineer (FDE) was actually L&D's Trojan Horse for AI advancement in disguise. I named the L&D analogue the Forward Deployed Learning Designer or FDLD. Today we have some big news. OpenAI launched the $4 billion OpenAI Deployment Company leveraging the FDE model. Anthropic announced a parallel platform within minutes. Salesforce, EY, Stripe, Workday, Adobe, Databricks, Poolside... all now running the same FDE play. I am not going to pretend I predicted the scale of this and any tie to it's cousin, the FDLD. Nobody did. "Deployment Is L&D's New Moat" -- the second part of last year's prediction -- is out today. It opens with the OpenAI and Anthropic news, walks through the broader wave, suggests the Top 10 critical FDE > FDLD skills, and includes the original Part 1 so you have the full context. Heck, maybe it's time for a new "L&D"? Learning and Deployment? I encourage sharing and critique, and special thanks to the folks in comments for enhancing, challenging my thinking and ongoing encouragement. https://lnkd.in/euZ-WSFz

In meinem Vortrag "𝗕𝗲𝘆𝗼𝗻𝗱 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆: 𝗪𝗶𝗲 𝗞𝗜 𝗪𝗶𝗿𝘁𝘀𝗰𝗵𝗮𝗳𝘁 (𝘂𝗻𝗱 𝗔𝗿𝗯𝗲𝗶𝘁) 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗲𝗿𝘁" auf der TechShow in Frankfurt ging es um die Auswirkungen der KI auf Wirtschaft und Arbeit.

KI verändert, wie Menschen sich Themen erschließen. Einer dieser Ansätze ist "Vibe Learning", also Lernen mit Vibe. Was dieser Ansatz bedeutet und welche Falle er birgt.

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Learn how global trends like tech innovation and green transition will transform jobs, skills, and workforce strategies in The Future of Jobs Report 2025

New research shows that offloading learning tasks to AI can improve - rather than erode - human thinking and learning

Really solid insights in this new report diving deep into 51 successful enterprise AI deployments from Stanford Digital Economy Lab led by Elisa Pereira Alvin Wang Graylin Erik Brynjolfsson. Some of the most important takeaways with a little of my own commentary: 🧱 Technology is not the hardest part. The model is rarely the bottleneck. The real work is change, data, and process redesign, which accounted for 77% of the hardest challenges. Failed attempts are often part of the hidden cost of success. This is ultimately about organizational transformation. ⏱️ Timeline variance is organizational, not technical. Similar AI use cases can take weeks in one company and years in another. The accelerators are sponsorship, existing foundations, user willingness, and the ability to iterate rather than over-plan. It is and has to be understood as a journey. 🔁 Escalation-based models were associated with better results. The highest gains came when AI handled routine work and humans reviewed exceptions. Escalation models showed 71% median productivity gains, compared with 30% for approval or collaboration models. However this should not be generalized across decision types. 🚀 Executive sponsorship is about actions, not approval. Effective sponsors do not just endorse AI; they clear blockers, align business and technology, and make experimentation safe. The key implication is that AI adoption has to be led, not merely funded. 🛡️ Staff functions are the most frequent source of resistance, but some parts may become enablers after buy-in. Legal, HR, Risk, and Compliance were the biggest blockers at 35%, ahead of end users. But when they are brought into governance early, their caution is what makes scaling possible. Governance is a fundamental enabler of transformation, but for that the usual blockers need to understand the AI context. 🤖 Agentic AI works, but most firms have not used it, yet. Agentic AI represented only 20% of cases, yet showed strong gains. The real shift is not a new interface, but a redesign of who does what: AI takes larger chunks of work, humans move toward judgment and oversight. This requires redesign of workflows. 🗂️ Messy data is not a blocker if you design around it. Waiting for perfect data is often the wrong strategy. LLMs can help make unstructured, inconsistent, and scattered data usable, so the priority is to connect and expose data rather than clean everything first. Start with what you have and work at continually improving data integrity and architecture. 🔐 Security enables more than it blocks. Security did not kill successful projects; it made higher-value uses possible. Strong controls are a front-loaded cost, but once in place they allow AI to work with sensitive data at enterprise scale.

A data-driven report on the skills professionals are learning right now.

KI und grüne Technologien verändern die Arbeitswelt. Zwei aktuelle Reports zeigen, wie sich gefragte Kompetenzen verschieben und neue Herausforderungen entstehen.

Drei unbequeme Wahrheiten für die Bildungsbranche — direkt von der LXW26:

siehe eEight.io für technische Umsetzung.

𝗔𝗜 𝘀𝗸𝗶𝗹𝗹𝘀 𝗮𝗿𝗲𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗳𝗼𝗿 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀. 𝗧𝗵𝗲𝘆’𝗿𝗲 𝗳𝗼𝗿 𝗲𝘃𝗲𝗿𝘆𝗼𝗻𝗲. If your execs can’t articulate AI’s value — you’re stuck. If your experts can’t translate use cases — you’re stalled. If your employees don’t trust the tools — adoption fails. This is the AI literacy gap — and it’s killing transformation before it even begins. 𝗪𝗵𝘆 𝗔𝗜 𝗹𝗶𝘁𝗲𝗿𝗮𝗰𝘆 𝗺𝗮𝘁𝘁𝗲𝗿𝘀? It’s not just about new roles or flashy tools. It’s about enabling everyone to understand, trust, and challenge AI. Gartner calls AI literacy a major trend for 2026 — and here’s why: → It’s tied to regulation (like the EU AI Act) → It drives responsible, real-world adoption → It prevents the two biggest risks: blind trust and blind rejection The idea is simple: The more people understand AI, the better they use it. That includes non-technical teams too. AI literacy means: → Knowing where AI fails (hallucinations, misuse) → Navigating compliance, ethics, and governance → Cutting through hype to focus on business value Gartner’s framework breaks it down into four key level: 𝗟𝗲𝘃𝗲𝗹 𝟭 – 𝗡𝗼𝗻𝗲: → No clue how AI works. Still far too common. 𝗟𝗲𝘃𝗲𝗹 𝟮 – 𝗕𝗮𝘀𝗶𝗰: → Understands AI concepts. Can follow, not lead. 𝗟𝗲𝘃𝗲𝗹 𝟯 – 𝗜𝗻𝘁𝗲𝗿𝗺𝗲𝗱𝗶𝗮𝘁𝗲: → Applies AI meaningfully in their work. The SME sweet spot. 𝗟𝗲𝘃𝗲𝗹 𝟰 – 𝗦𝘁𝗿𝗼𝗻𝗴: → Leads AI strategy. Evaluates trade-offs. Connects models to mission-critical goals. There’s no one-size-fits-all training when it comes to AI literacy. A tailored approach is essential. Technical teams need different training than executives or middle management. So what’s needed? Targeted upskilling — by role, by depth, by design. Because AI success isn’t just about smarter models. It’s about smarter people. ⇣ 𝗘𝘃𝗲𝗿𝘆 𝘄𝗲𝗲𝗸 𝗜 𝗯𝗿𝗲𝗮𝗸 𝗱𝗼𝘄𝗻 𝘁𝗵𝗲 𝗺𝗼𝘃𝗲𝘀 𝘀𝗵𝗮𝗽𝗶𝗻𝗴 𝗔𝗜 - 𝗹𝗲𝗮𝗸𝘀, 𝗹𝗮𝘂𝗻𝗰𝗵𝗲𝘀, 𝗳𝗶𝗲𝗹𝗱 𝗻𝗼𝘁𝗲𝘀 - 𝗶𝗻 𝗺𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 𝗛𝘂𝗺𝗮𝗻 𝗶𝗻 𝘁𝗵𝗲 𝗟𝗼𝗼𝗽 [𝗙𝗿𝗲𝗲]: https://lnkd.in/dbf74Y9E | 68 comments on LinkedIn

Soeben hat Sal Khan (Khan Academy) gemeinsam mit TED und ETS ein radikal neues Studienmodell angekündigt: das Khan TED Institute.

An eight-month study of 2,500 employees at KPMG discovered indicators that can help identify superusers.

Every CEO is talking about AI. I surveyed 703 about AI at work. The scary part? Most executives deciding what to automate and who to cut?  They don't use AI for their own work. And they'll never admit it. In big companies, the AI strategy starts like this: Inside the room: ↳ CEO: read about AI in the airport lounge. ↳ Board Member: loves the Wall Street hype. ↳ CIO: sees this as a chance to be seen as more strategic. ↳ Consultant: has the perfect slide to make you feel behind. ↳ Vendor: built a beautiful wrapper and called it AI. Outside the room: ↳ The middle managers who are the glue who cover years of ops debt. ↳ The director still waiting 9 weeks for a business intelligence report. ↳ The Customer Success lead who hears what frustrates customers daily. Then nothing in that AI strategy scales but the PowerPoint. But the execs already announced the transformation. Got the stock bump from Wall Street. 👉 The people expected to make it work? We find out from the headline. Adoption flatlines. Six months later HBR writes the case study: ‼️ How 95% of AI fails in enterprise. Here is how to join the 5% heroes 📌 : 1️⃣ Start where people won't block AI to protect their 19 years of doing work the same way. Let them build AI in, not defend how they have always done it. 2️⃣ Assume everyone is a little worried about looking foolish and admitting what they don't know. Small masterminds allow people to learn and apply AI together. 3️⃣ Don't listen to what the AI vendor can do, start with how people actually work. The manager knows what breaks when you delete the workaround. Listen to them first. 4️⃣ Build AI operating agreements for what the AI can and should never decide. A client gave an AI agent a simple prompt: clean test data. It ran the SQL command perfectly and incorrectly deleted 1.3M rows of real customer data. 5️⃣ Start with a specific work where failure is cheap. Internal FAQs. Meeting notes. Formatting documents. The stuff people do that no one has time to check twice. 6️⃣ One team. Real feedback. Not KPIs to impress the board with how fast AI is. A test to see how people adapt so you can go deeper. The press release doesn't fail when you try to scale it. The work does. Who's the smartest person about AI in your company that no one's asked yet? ♻️ Repost to help other leaders trying to work out AI strategies. ➕ Follow Betsy Tong for frameworks on how AI works. | 279 comments on LinkedIn

DIE LOGIK DER KI PASST NICHT ZU UNSEREN ORGANISATIONEN

𝗦𝗼 𝘆𝗼𝘂 𝗺𝗶𝗴𝗵𝘁 𝗵𝗮𝘃𝗲 𝗵𝗲𝗮𝗿𝗱 𝗼𝗳 𝘁𝗵𝗶𝘀... ⇣ A guy just became the first 1 person $1B+ company.  One guy. Zero employees. $1.8B run rate. Read that again. Medvi - selling weight loss drugs - did $400M+ in its first 12 months. One person. No team. No office. No org chart. Year two? Pacing nearly $2B. This isn't a startup success story. It's a structural shift in what a single human with the right tools can build. And here's what gets me. Every week I talk to enterprise leaders managing teams of hundreds of people who tell me they're "exploring" AI. Meanwhile one guy automated an entire pharmaceutical business end to end - customer acquisition, fulfillment, compliance, support - and printed a billion in revenue. The leverage gap between people who use AI and people who "plan to use AI" is becoming obscene. You don't need to build a billion-dollar company. But if this guy can automate everything, you can automate the thing you've been putting off since January. This is your weekly reminder to get started on using AI in your business. You can just do things. Start. | 18 comments on LinkedIn

Every aspect of organizational value creation will be Humans + AI. Our focus needs to be on architecting how they are integrated across all layers, and building loops where humans, AI, and systems all continually improve their capabilities. The Individual Augmentation layer is already happening apace by giving GenAI tools to knowledge workers. What is often still missing are the skills and mindsets for effective use and leverage. Many are now recognizing the reality that Humans + AI teams are moving to the center of work, requiring redesigned workflows, new skills, effective governance and accountability structures, and structures for better decision-making. Workflows must be redesigned to bring to bear the most relevant capabilities across the organization as work becomes more fluid, requiring Humans + AI orchestration processes to dynamically address challenges and opportunities. Every aspect of the organization needs to evolve based on data, insight, and learning. Learning loops need to encompass humans, AI systems, and how each of the other layers are architecting in constantly evolving systems. And since value creation largely happens across organizational boundaries, not just within companies, Humans + AI processes need to enable shared value creation, based on interoperability, trust layers, and platform structures. This diagram is an evolution from a previous one I created, simplifying and consolidating based on extensive work with leaders in helping them apply Humans + AI principles to transform their organizations. Would love any thoughts and comments