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Von OpenAI gibt es ein geleaktes Strategiepapier. Hier meine wichtigsten Erkenntnisse! OpenAI baut kein Produkt. Sie bauen eine Plattform. Das interne Memo zur ChatGPT-Strategie 2025/26 macht klar, worum es wirklich geht: Kein besserer Chatbot. Keine klügere Antworten. Sondern ein neues Betriebssystem für Menschen. Der Plan: Bis 2026 soll ChatGPT zur Schnittstelle für alles werden. Da steht: "ChatGPT wird Suchmaschinen, Browser & Co. ersetzen. Schritt für Schritt." ➡️ Internet ➡️ Kommunikation ➡️ Tools ➡️ Entscheidungen OpenAI wollte nie ein SaaS-Modell bauen. Das Plus- und das Team-Abo waren nie das Ziel, sondern eher ein Nebeneffekt. Laut Memo sogar eher ein Hindernis. 🔴 Weil sie etwas Größeres bauen wollen Eine Analogie könnte sein: Sie wollen nicht in iOS oder in Android rein, sondern wollen wie ein neues iPhone sein. Deswegen haben sie auch IO Products übernommen, wollen also mit einem eigenen Gerät neue Wege gehen. Sie beschreiben auch eine direkte Angst vor Apple, Google, Microsoft, weil sie befürchten, blockiert zu werden (indem diese eigene AIs pushen und Nutzer abschirmen). Deshalb wollen sie ihre Plattform selbst bauen. Alles andere macht sie zu abhängig. Für Anbieter von Software-Produkten heißt das: ⭕ Deine App braucht bald kein User-Interface mehr, nur noch eine API. ChatGPT macht den Rest! Die Frage ist für mich: Welche Apps überleben diese Veränderung, wenn der Zugang nur über ChatGPT erfolgt? Ein wichtiger Kernsatz aus dem Papier: 🔺 Alle Mensch-Computer-Interaktionen können über ChatGPT laufen. 🔺 (Oder zumindest orchestriert werden!) Ich sehe es im Moment so: OpenAI hat bereits die Nutzer, aber noch keine Plattform. Bei der Entwicklungsgeschwindigkeit würde es mich aber nicht wundern, wenn mit GPT-5 im August bereits die ersten Vorzeichen sichtbar werden und heute in einem Jahr schon wieder alles ganz anders sein wird.

If We Want To Understand The Future Of AI, Just Watch Star Trek: The Next Generation And I am dead serious. For those unfamiliar, Star Trek: TNG ran from 1987 to 1994. It didn’t just predict technology, it reimagined our relationship to it. And it got something right we’re still getting wrong. We’ve misunderstood what AI actually is, And since GPT we've been distracted by a shiny and seductive object We keep calling it an intern, an assistant, a tool, a shortcut. For many it's a potential threat, or a get rich quick scheme Silicon Valley loves those metaphors because they’re cheap. But they’re not just misleading, they’re limiting. The real problem? Strategy. Because the people shaping AI strategy for the enterprise are management consultants-- and this technology is as new to them as it is to anyone but they pretend they know what they're doing and they don't Here’s the formula they sell to CEOs and CFOs: We’ll implement this tech to reduce costs We’ll treat your office like a factory We’ll measure tasks, optimize bottlenecks, and speed up cycle times We’ll replace humans wherever possible You’ll save money, signal to the market, and boost your share price Sounds smart. But it’s junior-high thinking. Because this entire logic assumes AI’s greatest value is in efficiency. It views humans as bottlenecks, not assets. It assumes replacing judgment with pattern-matching is strategic progress. It’s rear-view mirror thinking dressed up as innovation. And it’s not working. Error rates remain high.¹ Hallucinations persist.² Most GenAI pilots fail to scale.³ And internal backlash is growing.⁴ Why? Because AI isn’t about automation. It’s about augmentation. And that means imagining new ways of thinking, creating, and deciding, not just faster ways to do what we already do. And Star Trek: TNG showed us what that could look like. The ship’s computer wasn’t a task engine, it was a thinking partner. Data wasn’t a replacement for the crew, he was part of the crew. AI didn’t strip humanity, it deepened it. Oh and was Data sentient? Who cares for me he was ******************************************************************************** The trick with technology is to avoid spreading darkness at the speed of light Sign up: Curiouser.AI is the force behind The Rogue Entrepreneur, a masterclass series for builders, misfits, and dreamers. For those of us who still realize we need to work hard to be successful and that there are not magic shortcuts. Inspired by The Unreasonable Path, a belief that progress belongs to those with the imagination and courage to simply be themselves. To learn more, DM or email stephen@curiouser.ai (LINK IN COMMENTS) Sources: ¹ [MIT Sloan] 85% of GenAI projects fail to deliver ROI ² [Stanford/Princeton 2024] Hallucination rates range 3%–27% depending on task ³ [McKinsey, 2023] Most enterprise AI pilots fail to scale ⁴ [Korn Ferry, 2024] 54% of knowledge workers report productivity declines from GenAI tools | 177 comments on LinkedIn

Microsoft 𝗷𝘂𝘀𝘁 𝗮𝗻𝗮𝗹𝘆𝘇𝗲𝗱 𝟮𝟬𝟬,𝟬𝟬𝟬 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗔𝗜 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝘀 — 𝗮𝗻𝗱 𝗿𝗮𝗻𝗸𝗲𝗱 𝗵𝗼𝘄 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗮𝗯𝗹𝗲 𝘆𝗼𝘂𝗿 𝗷𝗼𝗯 𝗿𝗲𝗮𝗹𝗹𝘆 𝗶𝘀. ⬇️ MS Research studied how people actually use Microsoft Copilot — and what kinds of tasks AI performs best. Then they mapped that usage onto real job data across the occupation classifications. 𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁? A first-of-its-kind AI applicability score across 800+ occupations. And some surprising findings. But what does “AI-applicable” even mean? Microsoft used a 3-part score: → Coverage – How often AI touches a job’s tasks → Completion – How well AI helps with those tasks → Scope – How much of the job AI can actually handle 𝗠𝗼𝘀𝘁 𝗔𝗜-𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝗯𝗹𝗲 𝗷𝗼𝗯𝘀? → Interpreters, Writers, Historians, Sales Reps, Customer Service, Journalists 𝗟𝗲𝗮𝘀𝘁 𝗔𝗜-𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝗯𝗹𝗲 𝗷𝗼𝗯𝘀? → Phlebotomists, Roofers, Ship Engineers, Dishwashers, Tractor Operators 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝟲 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: ⬇️ 1. AI is not doing your job — it’s helping you do it better → In 40% of conversations, the AI task and the user’s goal were completely different. People ask AI for help gathering, editing, summarizing. The AI responds by teaching and explaining. This is augmentation at scale. 2. Information work is the real frontier → The most common user goals? “Get information” and “Write content.” The most common AI actions? “Provide information,” “Teach others,” and “Advise.” 3. Jobs most affected are not just high-tech — they’re high-communication → Interpreters, historians, journalists, teachers, and customer service roles all scored high. Why? Because they involve information, communication, and explanation — all things LLMs are good at. 4. AI can’t replace physical work — and probably won’t → The bottom of the list? Roofers, dishwashers, tractor operators. Manual jobs remain least impacted — not because AI can’t help, but because it can’t reach. 5. Wage isn’t a strong predictor of AI exposure → Surprising: there’s only a weak correlation (r=0.07) between average salary and AI applicability. In other words: this wave of AI cuts across income levels. It’s not just a C-suite story. 6. Bachelor’s degree jobs are most exposed — but not most replaced → Occupations requiring a degree show more AI overlap. But that doesn’t mean these jobs disappear — it means they change. AI is refactoring knowledge work, not deleting. This transformation is moving faster than most realize. The question isn’t whether AI will change how we work — it already is. Study in comments. ⬇️ 𝗣.𝗦. 𝗜 𝗿𝗲𝗰𝗲𝗻𝘁𝗹𝘆 𝗹𝗮𝘂𝗻𝗰𝗵𝗲𝗱 𝗮 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 𝘄𝗵𝗲𝗿𝗲 𝗜 𝘄𝗿𝗶𝘁𝗲 𝗮𝗯𝗼𝘂𝘁 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 𝘁𝗵𝗲𝘀𝗲 𝘀𝗵𝗶𝗳𝘁𝘀 𝗲𝘃𝗲𝗿𝘆 𝘄𝗲𝗲𝗸 — 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀, 𝗲𝗺𝗲𝗿𝗴𝗶𝗻𝗴 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀, 𝗮𝗻𝗱 𝗵𝗼𝘄 𝘁𝗼 𝘀𝘁𝗮𝘆 𝗮𝗵𝗲𝗮𝗱: 𝗵𝘁𝘁𝗽𝘀://𝘄𝘄𝘄.𝗵𝘂𝗺𝗮𝗻𝗶𝗻𝘁𝗵𝗲𝗹𝗼𝗼𝗽.𝗼𝗻𝗹𝗶𝗻𝗲/𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 | 21 comments on LinkedIn

Over 40% of agentic AI projects will be canceled by the end of 2027, due to escalating costs, unclear business value or inadequate risk controls, according to Gartner.

Since the launch of the L&D Maturity Model in March, I’ve been able to assess the collective maturity of the profession. Some results are surprising - and troubling. Here's a breakdown and my call to action for L&D leaders: BACKGROUND There are 7 themes in the L&D Maturity Model: - Learning strategy - Leadership alignment - SME collaboration - Learner engagement - Learning needs identification - Training processes - Learning metrics PROBLEM Of these themes, L&D professionals have self-assessed their functions as the LEAST MATURE in: - Learning needs identification - Learner engagement - Learning metrics I'm not sure about you, but I see this as alarming because what this tells me is: 1. We don’t know if we’re working on the right things. 2. Learners don’t often engage. 3. We’re not able to measure our impact. The relationship between each of these is fundamental to the success of our function and yet our maturity is lowest on them. SOLUTION Imagine L&D was its own business for a moment. If that was the case, we would see its critical path as: 1) Align on the biggest challenges Before anything else, we need to ruthlessly align to the biggest challenges facing our organisation and our employees. No more assumptions, we need to validate with data and before we spend any time (let alone money, effort and credibility), we need to ensure our intake process is robust. 2) Quantify the challenges and determine what success looks like Put metrics to the problems at the outset and determine what success we’re aiming for, i.e. bake measurement into the initial planning stage and then measure milestones towards it. If it can’t be quantified, don’t do it! 3) Engage learners like problem-solving partners Help learners understand what's at stake and the role they play in the solution. Share the data with them, understand their lived experience and co-create alongside them and their more experienced peers and colleagues. Anything other than this would be a wild swing, the business would burn money and we’d go out of business. TAKEAWAY L&D maturity isn’t taking what we're already doing and just doing it a little bit better. It's about transforming what we're doing entirely. It’s about connecting with both the reason L&D should exist in the organisation and the possibilities that a mature and functioning department can bring. We cannot stop at building an L&D storefront of generic 'solutions'. L&D maturity starts with believing in more, creating a vision of a business aligned function, articulating the benefits and engaging all stakeholders. This versus a shop front approach to L&D is a no-brainer. Often it’s our abilities to articulate the vision and sell it to stakeholders (including our own teams) that’s holding us back. More on that topic in the future.... | 65 comments on LinkedIn

𝗙𝘂𝘁𝘂𝗿𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 – 𝗪𝗮𝘀 𝘄𝗶𝗿𝗱 𝗮𝘂𝘀 𝗱𝗲𝗻 𝗧𝗿𝗮𝗶𝗻𝗲𝗿*𝗶𝗻𝗻𝗲𝗻? 𝘋𝘪𝘦 𝘝𝘰𝘳𝘴𝘵𝘦𝘭𝘭𝘶𝘯𝘨𝘦𝘯 𝘷𝘰𝘯 𝘦𝘪𝘯𝘦𝘳 ›𝘞𝘪𝘴𝘴𝘦𝘯𝘴𝘷𝘦𝘳𝘮𝘪𝘵𝘵𝘭𝘶𝘯𝘨‹ 𝘪𝘯 𝘥𝘪𝘦 𝘒ö𝘱𝘧𝘦 𝘥𝘦𝘳 𝘔𝘪𝘵𝘢𝘳𝘣𝘦𝘪𝘵𝘦𝘯𝘥𝘦𝘯 𝘰𝘥𝘦𝘳 𝘨𝘢𝘳 𝘦𝘪𝘯𝘦𝘳 ›𝘞𝘦𝘳𝘵𝘦- 𝘶𝘯𝘥 𝘒𝘰𝘮𝘱𝘦𝘵𝘦𝘯𝘻𝘷𝘦𝘳𝘮𝘪𝘵𝘵𝘭𝘶𝘯𝘨‹ 𝘪𝘯 𝘚𝘦𝘮𝘪𝘯𝘢𝘳𝘦𝘯 𝘪𝘴𝘵 𝘯𝘢𝘤𝘩𝘸𝘦𝘪𝘴𝘭𝘪𝘤𝘩 𝘧𝘢𝘭𝘴𝘤𝘩. 𝘎𝘭𝘦𝘪𝘤𝘩𝘻𝘦𝘪𝘵𝘪𝘨 𝘦𝘳𝘧𝘢𝘩𝘳𝘦𝘯 𝘸𝘪𝘳, 𝘥𝘢𝘴𝘴 𝘴𝘦𝘭𝘣𝘴𝘵𝘰𝘳𝘨𝘢𝘯𝘪𝘴𝘪𝘦𝘳𝘵𝘦s 𝘓𝘦𝘩𝘳𝘦𝘯 𝘪𝘮 𝘋𝘪𝘢𝘭𝘰𝘨 𝘮𝘪𝘵 𝘥𝘦𝘳 𝘒𝘐 𝘪𝘮 𝘈𝘳𝘣𝘦𝘪𝘵𝘴𝘱𝘳𝘰𝘻𝘦𝘴𝘴 𝘦𝘳𝘩𝘦𝘣𝘭𝘪𝘤𝘩 𝘦𝘧𝘧𝘪𝘻𝘪𝘦𝘯𝘵𝘦𝘳 𝘶𝘯𝘥 𝘯𝘢𝘤𝘩𝘩𝘢𝘭𝘵𝘪𝘨𝘦𝘳 𝘪𝘴𝘵. 𝘐𝘯 𝘥𝘦𝘳 𝘗𝘳𝘢𝘹𝘪𝘴 𝘣𝘦𝘥𝘦𝘶𝘵𝘦𝘵 𝘥𝘪𝘦𝘴, 𝘥𝘢𝘴𝘴 𝘥𝘪𝘦 𝘓𝘦𝘳𝘯𝘦𝘯𝘥𝘦𝘯 𝘥𝘪𝘦 𝘝𝘦𝘳𝘢𝘯𝘵𝘸𝘰𝘳𝘵𝘶𝘯𝘨 𝘧ü𝘳 𝘪𝘩𝘳 𝘦𝘪𝘨𝘦𝘯𝘦𝘴 𝘓𝘦𝘳𝘯𝘦𝘯 𝘴𝘦𝘭𝘣𝘴𝘵 ü𝘣𝘦𝘳𝘯𝘦𝘩𝘮𝘦𝘯. Dabei dürfen die Mitarbeitenden nicht allein gelassen werden. Sie benötigen die Flankierung von Lernbegleitenden. 𝗟𝗲𝗿𝗻𝗯𝗲𝗴𝗹𝗲𝗶𝘁𝗲𝗻𝗱𝗲 𝗲𝗿𝗺ö𝗴𝗹𝗶𝗰𝗵𝗲𝗻 𝘀𝗲𝗹𝗯𝘀𝘁𝗼𝗿𝗴𝗮𝗻𝗶𝘀𝗶𝗲𝗿𝘁𝗲 𝗟𝗲𝗿𝗻𝗽𝗿𝗼𝘇𝗲𝘀𝘀𝗲 d𝗲𝗿 𝗟𝗲𝗿𝗻𝗲𝗻𝗱𝗲𝗻 𝗶𝗺 𝗔𝗿𝗯𝗲𝗶𝘁𝘀𝗽𝗿𝗼𝘇𝗲𝘀𝘀– 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘀𝗶𝗲𝗿𝘁, 𝗸𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝘃 𝘂𝗻𝗱 𝗞𝗜-𝘂𝗻𝘁𝗲𝗿𝘀𝘁𝘂𝘁𝘇𝘁. In professionell begleiteten Lernprozessen steht die Entwicklung der Selbstorganisationsfähigkeit im Vordergrund. Daraus ergibt sich folgendes 𝗔𝘂𝗳𝗴𝗮𝗯𝗲𝗻𝗽𝗿𝗼𝗳𝗶𝗹: 𝟭. 𝗦𝗲𝗹𝗯𝘀𝘁𝗼𝗿𝗴𝗮𝗻𝗶𝘀𝗶𝗲𝗿𝘁𝗲𝘀 𝗟𝗲𝗿𝗻𝗲𝗻 𝗲𝗿𝗺ö𝗴𝗹𝗶𝗰𝗵𝗲𝗻 Gemeinsam mit Learning & Development wird laufend der Ermöglichungsrahmen für selbstorganisiertes Lernen – Learning Experience Platform – weiterentwickelt. 𝟮. 𝗣𝗼𝘀𝗶𝘁𝗶𝗼𝗻𝘀𝗸𝗹ä𝗿𝘂𝗻𝗴 Gemeinsam werden regelmäßig die aktuellen Herausforderungen reflektiert, die beruflichen Ziele geklärt und evtl. die Skills-Diagnostik eingeführt. 𝟯. 𝗞𝗜-𝗯𝗮𝘀𝗶𝗲𝗿𝘁𝗲 𝗦𝗸𝗶𝗹𝗹𝘀-𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗸 Die Lernenden diagnostizieren ihre Skills eigenverantwortlich und planen ihr Lernen selbstorganisiert. Die Lernbegleitenden unterstützen bei Bedarf dabei, die Lernpfade zu strukturieren, Prioritäten zu setzen und eigenständig Entscheidungen zum Lernprozess zu treffen. 𝟰. 𝗟𝗲𝗿𝗻𝗯𝗲𝗴𝗹𝗲𝗶𝘁𝘂𝗻𝗴 𝗮𝘂𝗳 𝗔𝘂𝗴𝗲𝗻𝗵ö𝗵𝗲 Die Lernbegleitenden fördern die Selbstorganisation, geben konstruktives Feedback sowie Impulse und bieten Reflexionsräume. 𝟱. 𝗡𝗲𝘁𝘇𝘄𝗲𝗿𝗸𝗲 𝗮𝘂𝗳𝗯𝗮𝘂𝗲𝗻 𝘂𝗻𝗱 𝗽𝗳𝗹𝗲𝗴𝗲𝗻 Lernbegleitende unterstützen beim Aufbau von Lerngemeinschaften. 𝗔𝗻𝗳𝗼𝗿𝗱𝗲𝗿𝘂𝗻𝗴𝗲𝗻 𝗮𝗻 𝗟𝗲𝗿𝗻𝗯𝗲𝗴𝗹𝗲𝗶𝘁𝗲𝗻𝗱𝗲 Neben fachlich-methodischer und konzeptioneller Expertise benötigen Lernbegleitende die Kompetenz zum werteorientierten, fördernden und impulsgebenden Handeln mit hoher Expertise. Deshalb ist der gezielte, praxisbezogene Skillsaufbau der heutigen Trainer*innen für Future Learning erforderlich. 𝗦𝗽𝗿𝗲𝗰𝗵𝗲𝗻 𝗦𝗶𝗲 𝗺𝗶𝘁 𝘂𝗻𝘀 ü𝗯𝗲𝗿 𝗱𝗶𝗲 𝗞𝗼𝗺𝗽𝗲𝘁𝗲𝗻𝘇𝗲𝗻𝘁𝘄𝗶𝗰𝗸𝗹𝘂𝗻𝗴 𝘇𝘂𝗺/𝗿 𝗭𝗲𝗿𝘁. 𝗦𝗸𝗶𝗹𝗹𝘀𝗺𝗮𝗻𝗮𝗴𝗲𝗿*𝗶𝗻

Groß Lindow, Juli 2025 - Der Bildungs-Stakeholder "Betriebsrat" nimmt eine zentrale Rolle ein, wenn es um Compliance im Unternehmen geht. Der Betriebsrat verfügt nämlich über ein gesetzlich verankertes Mitbestimmungsrecht bei allen Maßnahmen, die compliance-relevante Aspekte betreffen – etwa Regelungen zum Datenschutz oder zur Leistungs- und Verhaltenskontrolle. Und da mittlerweile nahezu jedes IT-System, jedes Tool oder jede Anwendung – insbesondere solche mit KI-Funktionalitäten – personenbezogene Daten erhebt und verwendet, ist es nachvollziehbar, dass der Betriebsrat in diesen Zeiten stark gefordert ist. » MEHR

If I could wave a magic wand over Learning & Development, I’d remove one thing. One thing that would immediately improve everything from our impact to our efficiency. That one thing would be: Everybody’s expectations of what L&D is supposed to do. And I mean everybody… Senior leaders Line managers The workforce HR And yes, even us in L&D Because the biggest barrier to impactful Learning & Development isn’t budget, bandwidth or buy-in… It’s the baggage. We’re carrying around decades of assumptions about what L&D should look like that have very little to do with actually improving performance or closing skills gaps. If we could hit reset and define our role from scratch we’d operate very differently. We’d prioritise: - Support over solutions - Performance over participation - Outcomes over optics But we don’t, because everyone thinks they know what L&D should be. And that’s what’s holding us back. So here’s the real challenge: Can we slowly but surely rewrite the narrative starting with how we talk about both: what we do and the value we bring? Thoughts? | 30 comments on LinkedIn

Druckfrisch aus dem Weissen Haus: Der AI Action Plan der USA. "WINNING THE RACE" ist die Ansage. Ich bin mal sehr gespannt auf die Europäische Antwort. Mein GPT sagt dazu ganz wertfrei: "Wird 2025 zum Jahr der globalen AI-Doktrin? Mit dem 28-seitigen „America’s AI Action Plan“ legt die Trump-Administration ein kompromisslos ambitioniertes Strategiepapier vor – ein geopolitisches Manifest für technologische Vorherrschaft, das Innovation, Infrastruktur und Diplomatie radikal neu denkt. Ziel: globale AI-Dominanz. Kein „Könnte“, kein „Sollte“. Sondern ein „Wird“ – mit einer Regierung, die AI als Schlüssel zur wirtschaftlichen, militärischen und kulturellen Zukunft Amerikas versteht. Das Dokument ruft eine neue industrielle Revolution, eine Informationsrevolution und eine digitale Renaissance gleichzeitig aus. Der Plan umfasst: _ • Deregulierung und Priorisierung von Open-Source-Modellen • Milliarden-Investitionen in Halbleiter, Cloud-Infrastruktur, Energie und AI-Forschung • staatlich geförderte AI-Sandboxes für Healthcare, Bildung, Verteidigung und Industrie • nationale Reallabore, Skills-Offensiven und beschleunigte Adoption im öffentlichen Sektor • Exportoffensive für ein „American AI Stack“ – Hardware, Modelle, Standards • strikte Exportkontrollen und diplomatische Isolierung Chinas in Governance-Gremien • Cyber- und Biosecurity-Maßnahmen gegen Missbrauch von Frontier-Modellen • juristische Anpassung zur Bekämpfung von Deepfakes und synthetischer Evidenz Bemerkenswert ist der offen geopolitische Ton: Die USA verstehen sich wieder als Gestalter einer neuen Weltordnung - mit AI als Hebel. Wer das Rennen macht, schreibt die Regeln. Für Europa stellt sich damit dringender denn je die Frage: Wollen wir nur regulieren - oder auch gestalten?" Quelle: https://lnkd.in/eXwTUGzv

The social signals behind employee retention "Research has long shown that employees at the center of an organizational network—those with many active connections—are 24 percent less likely to leave." 🤔 Michael Arena and Aaron Chasan highlight an important insight: employee connection, not just engagement, is the true bedrock of retention: 👉 “In today’s networked workplace, social withdrawal is often the first—and most reliable—indicator that someone’s already halfway out the door.” For HR to genuinely impact business performance and employee experience, we must leverage social signals to build robust internal networks. Michael and Aaron outline four high-impact ways HR can proactively employee connection and significantly reduce attrition: 🔎 Utilise network analysis: Identify early flight risks by spotting employees with few or declining connections. 🔎 Facilitate connection moments: Deliberately create opportunities for interaction, especially in hybrid settings, using tools like interest-based matching. 🔎 Support relationship-rich teams: Encourage cross-functional initiatives and invest in psychologically safe team cultures. 🔎 Routinely pulse central employees: Their engagement profoundly influences the entire network. "In today’s networked workplace, social withdrawal is often the first—and most reliable—indicator that someone’s already halfway out the door." 👉 This report is featured in the June edition of the Data Driven HR Monthly, which you can access here: https://lnkd.in/exEqY-Hn 👈 #humanresources #organizationalnetworkanalysis #peopleanalytics #leadership #culture #socialcapital | 18 comments on LinkedIn

I was recently looking at how Gen AI shapes and potentially impacts cognitive skills - a topic that matters for education and for work. Here are a few resources I reviewed. 1️⃣ Your Brain on ChatGPT - What Really Happens When Students Use AI MIT released a study on AI and learning. Findings indicate that students who used ChatGPT for essays showed weaker brain activity, couldn't remember what they'd written, and got worse at thinking over time https://shorturl.at/qaLie 2️⃣ Cognitive Debt when using AI - Your brain on Chat GPT There is a cognitive cost of using an LLM vs Search Engine vs our brain in e.g. writing an essay. The study indicates that there is a likely decrease in learning skills, the more we use technology as substantial replacement of our cognitive skills. https://lnkd.in/drVa_YNg 3️⃣ Teachers warn AI is impacting students' critical thinking One of many articles about the importance of using Gen AI smartly, in Education but also at work. https://lnkd.in/dSbGjusu 4️⃣ The Impact of Gen AI on critical thinking Another interesting study on the same topic. https://shorturl.at/74OO6 5️⃣ Doctored photographs create false memories In psychology, research indicated a long time ago that our memory - our recollection of past events - is susceptible to errors, biases, can be fragmentary, contain incorrect details, and, oftentimes, be entirely fictional. Memories are a reconstruction of our past to respond to our need for coherence in life. A rigorous 2023 study shows that doctored photographs – think Photoshop or today, AI – create false memories. Why it matters? Memory is essential for learning, recall of episodical and factual happenings, and it’s a basis for the integrity of sources of truth in organizations.   https://shorturl.at/hdgtN 6️⃣ The decline of our thinking skills Another great article on AI and critical thinking from IE University. https://shorturl.at/rGl99 7️⃣ Context Engineering Ethan Mollick recently wrote a blog on "context engineering" - how we give AI the data and information it needs to generate relevant output. The comments on the post were even more interesting than the post itself. Personally I think that good part of context engineering is not in organizations documents or processes, it is in peoples ability to think critically and understand relevant parameters of their environment to nurture AI/Gen AI. Gotta follow up on this one ;-) https://shorturl.at/sfnuV #GenAI #CriticalThinking #AICognition #AIHuman #ContextEngineering | 29 comments on LinkedIn

Upskilling in the AI era is fundamental for every company regardless of size and is often viewed as a necessary business investment. Additionally, many companies connect workforce upskilling to business functions (operations, manufacturing, legal, etc.

I have commented in my newsletter that what people have been describing as 'ethical AI principles' actually represents a specific political agenda, and not an ethical agenda at all. In this post, I'll outline some ethical principles and work my way through them to make my point.

EY just broke the biggest lie in corporate learning. They trained 44,000 employees internally. Then launched an AI Academy using 200 real-world use cases. The result? 50+ actual AI projects launched across five enterprises. Plus leadership-driven AI manifestos. That's the story I shared recently. It got more reactions and shares than anything I've posted. The support: ↳ Those who liked and shared it ↳ "We're already doing this approach" ↳ "This is exactly what we want to implement" But what I liked the most is how the experts responded. The pushback: ↳ "50 projects means nothing without context" ↳ "What about the projects that failed?" ↳ "Where's your control group?" ↳ "This sounds like survivor bias" ↳ "Did you measure actual skill transfer?" ↳ "Will this work for topics other than AI?" ↳ "Correlation isn't causation" Fair points. All of them. But here's what I'm doubling down on: We're measuring the wrong damn things. L&D isn't in the learning business. We're in the building business. L&D's focus should be simple: ↳ What solutions can our people build? Our measurement strategy should be reimagined around that core ability. Not: ↳ Did they complete the course? ↳ Did they pass the quiz? ↳ Did they like the content? While we debate attribution methodology: ↳ 97% of enterprises still cite talent gaps ↳ $366B spent annually on corporate training ↳ Minimal business impact to show for it The uncomfortable truth? Perfect measurement of learning consumption ≠ Performance change EY's numbers might be messy. ↳ Their attribution might be flawed. ↳ Their methodology might be incomplete. But they're asking the right question: "What did people actually build?" The experts want rigor. I want it too. But let's get rigorous about what matters: ↳ Solutions created ↳ Problems solved ↳ Value delivered I'm building a coalition of L&D leaders ready to abandon traditional metrics. If you're already measuring "what people build" - I want to hear your story. If you're ready to start - let's connect and figure this out together. Who's in? [Check out my original post and the expert responses - link in comments] #WorkplaceLearning #LearningAndDevelopment #PerformanceSupport #ReimagineLND 🔁 Resonates? Share it—let's reimagine L&D together! ➕ Follow me, Santhosh Kumar, for unconventional insights that challenge how we lead and learn.

📊 Welchen Reifegrad hat eure L&D-Arbeit? Wo steht ihr und wie könnt ihr euch gezielt weiterentwickeln? 💡In diesem Video stelle ich euch das L&D-Reifegradmo...

📊 Welchen Reifegrad hat eure L&D-Arbeit? Wo steht ihr und wie könnt ihr euch gezielt weiterentwickeln? 💡In diesem Video stelle ich euch das L&D-Reifegradmo...

L&Ds aren't very happy with their LMS platforms, that's for sure 🥲 We recently launched our first tools report, and below 👇🏻 you can find 7 insights around LMSs & LXPs. Want to read more? － Download the free report 👉 https://lnkd.in/dBZzW6TZ － Join the Offbeat Fellowship to explore all our insights 👉 https://lnkd.in/dx3REqBh Hope you'll find this useful! 💜 #learninganddevelopment #learningmanagementsystem #learningexperienceplatform #learningtools

We’re at a turning point. AI can generate faster, broader, cheaper content than ever. So the real differentiator isn’t knowledge, it’s experience design that actually influences behaviour and builds people's capability. And that’s tough. Because designing for experience means starting with the challenges people face, not topics or content. It means accepting that 'learning' doesn’t always feel good, in fact, it’s often the sting of experience that actually drives change. It’s layered too. Like an onion. The micro layer: our senses and emotions The meso layer: the interactions and activities we’re part of The macro layer: the strategic shifts in thinking and behaviour Some of the best experiences are completely invisible. Others stop us in our tracks and change us forever. But they’re rarely a one-off event. The most powerful ones are embedded in how we work, not added on after the fact. So let’s stop trying to make learning cute or entertaining (ie the Disneyfication effect). Let’s stop pretending every experience needs to feel good. Be honest. Build what’s real. And what actually makes a difference. Because if AI owns the content, then we’ve got to own the context. This means suba diving (see previous post..it will make sense, i promise), not snorkelling. If you’re interested in learning design that genuinely supports performance and growth, feel free to get in touch. As many know, I love talking about it.

Berufswahl im Zeitalter der lernenden Maschinen – Offener Brief an meine Nichte (Abi-Jahrgang 2025) Liebe Anna, als du mich fragtest, ob „Informatik, Medienwissenschaft oder Politik“ noch zukunftssicher sind, merkte ich, wie löchrig die alte Landkarte der Arbeit geworden ist. Code wird von KI vervollständigt, Diagnosen von Algorithmen unterstützt, Routineverträge von Bots geprüft. Laut Weltwirtschaftsforum wird bis 2030 fast jede zweite Kompetenz umgeschrieben. Was also studieren? Meine Empfehlung: Drei Felder, die weniger vom Titel als vom Skill-Mix leben. Warum? Weil sie Eigenschaften bieten, die KI kaum kopieren kann: direkten Menschenkontakt, interdisziplinäres Denken und sinnliche Materialerfahrung. Sie bilden zusammen einen „Human Moat“ – einen Schutzwall gegen reine Automatisierung. 1 | HEALTH & HUMAN SERVICES – BERUFE MIT EMPATHIE-FAKTOR Das ist erwartbar: Pflege, Sozialarbeit, Therapie oder Pädagogik bleiben knapp, weil Demografie und Krisen Resilienz verlangen. Typische Rollen: Pflegefachfrau+, Physician Assistant, Tele-Coach Mental Health. Schlüssel-Skills: evidenzbasierte Pflege, interkulturelle Kommunikation, Basiswissen Medizinrecht und Datenschutz. 2 | TWIN-TRANSITION CAREERS – KLIMA × TECHNOLOGIE Smarte Mash-Ups: Unternehmen brauchen Talente, die CO₂-Reduktion mit Datenkompetenz verbinden. Typische Rollen: Nachhaltigkeits-Data-Analyst, Circular-Economy-Ingenieurin, KI-Policy-Analyst, Energy-Systems-Modeler. Schlüssel-Skills: Life-Cycle-Assessment, Python/R, EU-Regulatorik (CSRD, AI Act), Systemdenken. 3 | CRAFT & EXPERIENCE DESIGN – WERT DES EINZIGARTIGEN Je perfekter Massenware KI-optimiert ist, desto höher steigt der Wert des Nicht-Skalierbaren. Typische Rollen: Produktdesigner*in für Bio-Materialien, Restaurator, Schreinerin mit CNC-Know-how, UX-Designer für phygitale Erlebnisse. Schlüssel-Skills: Materialkunde, CAD/CAM & 3-D-Druck, Storytelling, Customer-Journey-Mapping. Das ist natürlich nur ein Ausschnitt. Aber ich denke, die Muster dahinter sind klar, um es selbst weiterzudenken. WAS VERSCHWINDET? Alles, was rein repetitiv ist: Standard-Reporting, einfache Software-Tests, seitenlange Vertragsprüfungen. Die Maschine erledigt es schneller und billiger – doch jemand muss die Systeme entwerfen, mit Daten füttern und ethisch beaufsichtigen. MEIN RAT IN DREI SÄTZEN >> Suche kein Joblabel, sondern ein Problem, das dich elektrisiert. << Kombiniere digitale Grundfitness, empathische Kommunikation und moralischen Kompass. Dann arbeitest du nicht gegen Maschinen, sondern mit ihnen – und kannst dir jederzeit einen neuen Beruf erfinden. Vielleicht startest du als Pflege-Informatikerin, wirst später KI-Ethikerin und eröffnest irgendwann eine Bäckerei, in der Roboter den Teig kneten, während du den Sauerteig fütterst und Kund:innen berätst. Zukunftssicherheit entsteht nicht aus einem Studium, sondern aus lebenslanger Lernlust. Die Welt bleibt turbulent, doch wer Richtung Sinn steuert, hat immer Rückenwind. Dein Onkel Stefan | 59 Kommentare auf LinkedIn

👉 Prompt-Sammlung: https://simon-eckert.com/prompt-sammlung/👉 Bindungsstil-Selbsttest: https://simon-eckert.com/bindung-selbsttest/-- Notfallkontakte --Tel...

My site: https://natebjones.com/My links: https://linktr.ee/natebjonesMy substack: https://natesnewsletter.substack.com/Takeaways 1. Software 3.0 Paradigm Sh...

An eagerness to learn is essential for innovation. But the way we learn—and the order in which we partake in various learning activities—can make the difference between effective growth and potential missed opportunities. Jean-François Harvey, Johnathan Cromwell, Kevin J. Johnson, and I studied more than 160 innovation teams and found that the key to faster, clearer progress is: Structured learning 👷🏗️ Our research, published in the Administrative Science Quarterly Journal, highlights four distinct types of learning behaviors used by high-performing teams and examines variations in the sequence and blend of these types of team learning. Without a deliberate rhythm, teams risk becoming overwhelmed by continual information intake, leading to confusion and burnout. But by honing a team's ideal 'learning rhythm,' you can avoid overwhelm and instead focus on strategic decision-making and sustainable innovation. Read our research summary now in the Harvard Business Review: https://lnkd.in/e5nU-Kka | 90 comments on LinkedIn

The Learning Journey That Led to Nowhere The carefully curated “learning journey.” Polished decks. Inspiring speakers. Branded workbooks. The kickoff, the modules, the reflection points, the wrap-up. The capstone . It all looked beautiful. But no one changed. No one led differently. No one made a better decision, helped somebody else, solved a harder problem, or grew in any measurable way. They left as they came—only now with a certificate and a champagne toast. And we called it success. Why? Because the survey said they liked it. Because someone said it “landed well.” Because it fit the budget, the time box, and the LMS tracked completion. But deep down, we know better. We know that most learning programs don’t stick. They don’t demand enough. They don’t disturb the old habits. They don’t connect to the real pressures people actually face at work and in life. We’ve made learning comfortable when it’s supposed to be disruptive and difficult. We’ve made it a journey—when it should’ve been an expedition. As practitioners, we carry some of the blame. We built what would be approved, not what was required. We chased polish over power. And we told ourselves that “awareness” was enough. Because if they leave the same way they arrived—was it a journey at all. Or was it a scenic loop.? | 65 comments on LinkedIn

Technology often outpaces the ability of organizations to embrace it. We look at how businesses can overcome the growing challenge of gen AI adoption.

How AI Works 101. Brief morning rant, in case anyone is persistently bamboozled by AI discussions. #AI #learning #education

Your best coach can't be everywhere at once. 𝘉𝘶𝘵 𝘵𝘩𝘦𝘪𝘳 𝘈𝘐 𝘵𝘸𝘪𝘯 𝘤𝘢𝘯. Scaling world-class coaching is one of the biggest headaches in L&D. You bring in a top-tier expert for a workshop, and the C-suite loves it; then what? The knowledge fades, and the cost to retain them for 1-on-1 coaching across the org is astronomical. Well, the ability to have experts available 24/7 is now a reality. Google is quietly testing a potential solution in its Labs. 𝗜𝘁'𝘀 𝗰𝗮𝗹𝗹𝗲𝗱 𝗣𝗼𝗿𝘁𝗿𝗮𝗶𝘁𝘀. It’s more than a chatbot. It’s a library of voice-enabled, AI-powered avatars of real-world experts, trained only on their unique ideas and content. What that means: → Minimal AI hallucinations → No generic advice → Just the expert's authentic perspective, on-demand Check out this screenshot of Google Portraits. That’s an AI version of storytelling expert Matt Dicks. He’s coaching me to find the "heart of a story" in a seemingly dull, everyday moment — cutting grass. It's a very immersive experience as he walks me through finding the "story" in my experience. Think about the possibilities: → Democratize coaching: Assign a storytelling coach or a feedback sparring partner to every new manager. → Practice in private: Let employees rehearse difficult conversations in a safe and controlled environment before the real thing. → Scalable IP: A new model for licensing and deploying the knowledge of the world's best minds across your entire company. This is the future of personalized, scalable learning. It’s moving from static courses to dynamic, conversational experiences. The big question for us in L&D: Is this the scalable future we've been waiting for, or are we losing the essential human element of coaching? | 12 comments on LinkedIn

𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗠𝗖𝗣 𝗶𝘀 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗼𝘀𝗲 𝗿𝗮𝗿𝗲 “𝗼𝗵 𝗱𝗮𝗺𝗻, 𝘁𝗵𝗶𝘀 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴” 𝗺𝗼𝗺𝗲𝗻𝘁𝘀! I’ve been in tech for years, and MCP (Model Context Protocol) is one of those rare innovations that deserves every bit of the hype. I really can’t believe how much smoother everything gets. 𝗜𝗳 𝗜 𝗵𝗮𝗱 𝘁𝗼 𝗯𝗲𝘁 𝗼𝗻 𝗼𝗻𝗲 𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗶𝗻 𝗔𝗜, 𝗶𝘁’𝘀 𝗠𝗖𝗣. MCP sounds complex — but it’s really not. Think of it as a guide that helps your AI agents understand: → what tools exist → how to talk to them → and when to use them 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝟵 𝗳𝘂𝗹𝗹𝘆 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗲𝗱 𝗠𝗖𝗣 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗲𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱 𝘄𝗶𝘁𝗵 𝘃𝗶𝘀𝘂𝗮𝗹𝘀 & 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝗰𝗼𝗱𝗲 (𝘁𝗼 𝗴𝗲𝘁 𝘆𝗼𝘂 𝘀𝘁𝗮𝗿𝘁𝗲𝗱): ⬇️ 1. 100% Local MCP Client → Build a local MCP client using SQLite + Ollama — no cloud, no tracking. → Full docu: https://lnkd.in/gtaEGvFZ 2. MCP-powered Agentic RAG → Add fallback logic, vector search, and agents in one clean flow. → Full docu: https://lnkd.in/gsV62MDE 3. MCP-powered Financial Analyst → Fetch stock data, extract insights, generate summaries. → Full docu: https://lnkd.in/g2\_EaJ\_d 4. MCP-powered Voice Agent → Speech-to-text, database queries, and spoken responses — all local. → Full docu: https://lnkd.in/gweH8Rxi 5. Unified MCP Server (with MindsDB) → Query 200+ data sources via natural language using MindsDB + Cursor. → Full docu:https://lnkd.in/gCevVqKK 6. Shared Memory for Claude + Cursor → Build cross-app memory for dev workflows — share context seamlessly. → Full docu: https://lnkd.in/giDXdtXd 7. RAG Over Complex Docs → Tackle PDFs, tables, charts, messy layouts with structured RAG. → Full docu: https://lnkd.in/gMHqHvBR 8. Synthetic Data Generator (SDV) → Generate synthetic tabular data locally via MCP + SDV. → Full docu:https://lnkd.in/ghyUyByS 9. Multi-Agent Deep Researcher → Rebuild ChatGPT’s research mode, fully local with writing agents. → Full docu: https://lnkd.in/gp3EsrZ2 Kudos to Daily Dose of Data Science! 𝗜 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲𝘀𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁𝘀 — 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 — 𝗶𝗻 𝗺𝘆 𝘄𝗲𝗲𝗸𝗹𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. 𝗬𝗼𝘂 𝗰𝗮𝗻 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗵𝗲𝗿𝗲 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲: https://lnkd.in/dbf74Y9E | 49 comments on LinkedIn

AI can help, or hurt, our thinking