Open New Learning Lab Resources

🚨 Studien zeigen bereits hohe Traffic-Rückgänge.   Was können Redakteure und Publisher tun?   👊 Deshalb bin ich mit Matthäus Michalik in den Podcast-Ring gestiegen:   Wir haben 2 Folgen aufgenommen: 𝗚𝗘𝗢 𝘀𝘁𝗮𝘁𝘁 𝗦𝗘𝗢 & 𝗪𝗶𝗲 𝗽𝗹𝗮𝘁𝘇𝗶𝗲𝗿𝗲𝗻 𝘄𝗶𝗿 𝘂𝗻𝘀 𝗶𝗻 𝗱𝗲𝗻 𝗔𝗜-𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄?   Als Teaser für euch: 4️⃣ Sofort-Tipps für GEO (Generative Engine Optimization) 1. Autorität & Vertrauen belegen 🔸 Quellen, Zitate und fachliche Referenzen explizit nennen. 🔸Ergebnis: bis zu +40 % höhere Wahrscheinlichkeit, in KI-Antworten zitiert zu werden. 2. Zahlen sprechen lassen 🔸Statistiken, Studien-Daten und eigene Benchmarks einbauen. 🔸KI-Modelle gewichten quantitative Infos stärker → +30 % Relevanz-Boost. 3. Klare Struktur, einfache Sprache 🔸Kurze Absätze, Bullet-Points, FAQs, sprechende Zwischen­überschriften. 🔸Erleichtert Parsing durch LLMs und erhöht die Chance auf direkte Übernahme. 4. Gezielter Fachwort-Einsatz 🔸Relevante Terminologie und Branchen-Jargon bewusst einstreuen. 🔸Signalisiert Expertise und verbessert das Matching für spezifische Nutzer­anfragen. ‼️ Kurzformel: Autorität + Daten + Klarheit + Terminologie = Sichtbarkeit Chat-Antworten.   𝗦𝗶𝗰𝗵𝘁𝗯𝗮𝗿𝗸𝗲𝗶𝘁 𝗶𝗻 𝗔𝗜 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄𝘀 – 𝗱𝗮𝘀 𝗺𝘂𝘀𝘀𝘁 𝗱𝘂 𝗯𝗲𝗮𝗰𝗵𝘁𝗲𝗻 🔸Grundvoraussetzung: Deine Seite muss im Google-Index stehen und bereits ein gewisses Vertrauensniveau besitzen. Dann gilt: 🔸Hochwertige, faktenbasierte Inhalte: präzise, recherchiert, aktuell. 🔸Klare Struktur: H-Überschriften, Listen, Tabellen → erleichtert Parsing. 🔸Strukturierte Daten (Schema.org): zeigt der KI, was welche Bedeutung hat. 🔸UX & Performance: schnelle Ladezeiten, sauberes Mobile-Design. 🔸E-E-A-T pflegen: Expertise, Erfahrung, Autorität, Vertrauen kontinuierlich belegen (Autoren­profile, Quellen, Backlinks). 𝟴 𝗣𝗿𝗮𝘅𝗶𝘀-𝗧𝗶𝗽𝗽𝘀 𝗳ü𝗿 𝗱𝗶𝗲 𝗣𝗼𝘀𝘁-𝗦𝗘𝗢-Ä𝗿𝗮 ✔️ Qualität vor Quantität – fewer, deeper pieces mit klarer Expertise. ✔️Struktur first – H-Tags, Bullet-Points, FAQ-Blöcke, Schema. ✔️User Experience optimieren – Speed, Navigation, mobile UX. ✔️Mehrwert über die KI hinaus – eigene Daten, Cases, Meinungen. ✔️Traffic-Quellen streuen – Social, E-Mail, Communities, Partnerschaften. ✔️Monitoring & Anpassung – beobachte, welche Seiten in AI Overviews landen, und iteriere. ✔️Multimedial denken – Videos, Podcasts, Infografiken ergänzen Text. ✔️E-E-A-T kontinuierlich stärken – Fachautor:innen, Referenzen, Reviews, Backlinks. 𝗞𝘂𝗿𝘇­𝗳𝗼𝗿𝗺𝗲𝗹: Qualität + Struktur + Mehrwert + Vertrauen + Channel-Mix = langfristige Sichtbarkeit – auch in der KI-Suche. ❓ Wie geht ihr den Battle um Sichtbarkeit und Traffic an? Lasst uns diskutieren. 👇 | 12 Kommentare auf LinkedIn

Hugging Face 𝗷𝘂𝘀𝘁 𝗱𝗿𝗼𝗽𝗽𝗲𝗱 9 𝗙𝗥𝗘𝗘 𝗔𝗜 𝗰𝗼𝘂𝗿𝘀𝗲𝘀! If you’re trying to level up or pivot into AI — this is pure gold. 𝗔𝗹𝗹 OPEN. 𝗔𝗹𝗹 FREE. 𝗔𝗹𝗹 expert thaugt. Here’s what’s inside (with links): ⬇️ 1. 𝗟𝗟𝗠 𝗖𝗼𝘂𝗿𝘀𝗲 Master large language models fast. Train, fine-tune, deploy with Transformers. → https://lnkd.in/dcCMCs96 2. 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗖𝗼𝘂𝗿𝘀𝗲 Build multi-step reasoning agents with LangChain + HuggingFace. → https://lnkd.in/dJD3QRuT 3. 𝗗𝗲𝗲𝗽 𝗥𝗟 𝗖𝗼𝘂𝗿𝘀𝗲 Teach AI to learn like a human. Reward-based decision-making in real environments. → https://lnkd.in/d8JuRvn8 4. 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲 Image classification, segmentation, object detection — with HF models. https://lnkd.in/dEH8Tx-v 5. 𝗔𝘂𝗱𝗶𝗼 𝗖𝗼𝘂𝗿𝘀𝗲 Turn sound into signal. Voice recognition, music tagging, audio generation. → https://lnkd.in/dZtkA3sw 6. 𝗠𝗟 𝗳𝗼𝗿 𝗚𝗮𝗺𝗲𝘀 𝗖𝗼𝘂𝗿𝘀𝗲 AI-powered game design: NPCs, logic, procedural generation. → https://lnkd.in/d4RhU6pz 7. 𝗠𝗟 𝗳𝗼𝗿 3𝗗 𝗖𝗼𝘂𝗿𝘀𝗲 Work with point clouds, meshes, and 3D data in ML. → https://lnkd.in/dU8T8BPw 8. 𝗗𝗶𝗳𝗳𝘂𝘀𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹𝘀 𝗖𝗼𝘂𝗿𝘀𝗲 The tech behind DALL·E and Stable Diffusion. Generate visuals from noise — step by step. → https://lnkd.in/dFwN_idt 9. 𝗢𝗽𝗲𝗻-𝗦𝗼𝘂𝗿𝗰𝗲 𝗔𝗜 𝗖𝗼𝗼𝗸𝗯𝗼𝗼𝗸 Not a course — a growing library of real-world AI notebooks. Copy, remix, and build. → https://lnkd.in/dQ5BXvSz There’s no excuse left. Save this. Study it. Build. Share this with your network to help them level up! ♻️ Which one will you start with? | 16 comments on LinkedIn

Genau das wollte ich von Claudia Pohlink wissen, die eine beeindruckende Karriere in der Daten- und KI-Welt bei Telekom, Deutsche Bahn und FIEGE hingelegt hat. Also, wie sieht der Anti-Hype Blueprint aus? 𝟭. 𝗦𝘁𝗮𝗺𝗺𝗱𝗮𝘁𝗲𝗻 𝗱𝗲𝗳𝗶𝗻𝗶𝗲𝗿𝗲𝗻 𝘂𝗻𝗱 𝘀𝘁𝗿𝘂𝗸𝘁𝘂𝗿𝗶𝗲𝗿𝗲𝗻 Starte mit der Definition deiner Kerndomänen und Stammdaten. Bestimme führende Systeme für jede Datendomäne, bevor du Tools auswählst. Dies schafft ein stabiles Fundament für alle KI-Aktivitäten. 𝟮. 𝗘𝗿𝘀𝘁𝗲 𝗘𝗿𝗳𝗼𝗹𝗴𝘀𝗴𝗲𝘀𝗰𝗵𝗶𝗰𝗵𝘁𝗲 𝘀𝗰𝗵𝗿𝗲𝗶𝗯𝗲𝗻 Identifiziere einen ersten Use Case, zum Beispiel mit dem Controlling-Bereich, wo bereits Datenaffinität besteht. Zeige schnelle Erfolge, um Management-Support zu gewinnen. 𝟯. 𝗗𝗮𝘀 𝟯-𝗛𝗮̈𝘂𝘀𝗲𝗿-𝗠𝗼𝗱𝗲𝗹𝗹 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗶𝗲𝗿𝗲𝗻 • House of Data: Grundlagen, Governance, Architektur • House of AI: Use Cases, Data Scientists, Engineers • House of 3C: Change, Communication, Community Diese 3 Bereiche sollten zu gleichen Teilen aufgebaut werden. Keiner kann ohne den anderen für nachhaltige Daten- und KI-Implementierung. Die Leads sollten zu Beginn intern aufgebaut werden, extern können operative Ressourcen zugekauft werden. 𝟰. 𝗕𝗮𝗹𝗮𝗻𝗰𝗲 𝘇𝘄𝗶𝘀𝗰𝗵𝗲𝗻 𝘇𝗲𝗻𝘁𝗿𝗮𝗹 𝘂𝗻𝗱 𝗱𝗲𝘇𝗲𝗻𝘁𝗿𝗮𝗹 𝗳𝗶𝗻𝗱𝗲𝗻 Etabliere zentrale Standards und Koordination, befähige aber gleichzeitig dezentrale Teams durch Multiplikatoren-Ideen wie KI-Awards, Schulungen und Hackathons. Laut Claudia ist diese Balance eine der größten Herausforderungen in der Umsetzung. 𝟱. 𝗣𝗿𝗮𝗴𝗺𝗮𝘁𝗶𝘀𝗰𝗵 𝗽𝗹𝗮𝗻𝗲𝗻 𝘀𝘁𝗮𝘁𝘁 𝘁𝗵𝗲𝗼𝗿𝗲𝘁𝗶𝘀𝗶𝗲𝗿𝗲𝗻 Erstelle 6-12-Monats-Pläne statt langfristiger Strategien. Dokumentiere Erfahrungen systematisch, auch Misserfolge, und passe deine Pläne regelmäßig an. Ich weiß, wie viele Mittelständler vor der großen Aufgabe stehen, Daten- und KI-Kompetenzen und Strukturen im Unternehmen aufzubauen. Claudia's Erfahrungen sind eine echte Schatzkiste. Ganz ohne Buzzwords, Hype oder Selbstprofilierung. Claudia, 1000 Dank für deine Offenheit und dass du uns an deinen Erfahrungen teilhaben lässt! Was sagt ihr zum Blueprint? | 22 Kommentare auf LinkedIn

Mit seinem AI Mode und dem Agenten Mariner zieht Google eine Plattformschicht über das offene Web. Google transformiert sich von einer klassischen Suchmaschine zum zentralen Marktplatz, Assistenten und Zahlungsdienstleister. Nutzer können künftig Produkte direkt in der Google-Suche finden, vergleichen, kaufen und bezahlen – ohne die Plattform zu verlassen. Diese Entwicklung hat weitreichende Folgen für das gesamte Internet-Ökosystem. Die Auswirkungen treffen nicht nur klassische Online-Händler, sondern auch Marktplatzgiganten wie Amazon, Verlage, Übersetzungsdienste wie DeepL, Reservierungsanbieter wie OpenTable, Buchungsseiten wie Ticketmaster oder Sprachschulen wie Duolingo. Wer weiterhin sichtbar und relevant bleiben will, muss sich auf die neuen Spielregeln einstellen, in KI-Overviews und Shopping-Graphen präsent sein und seine Inhalte für KI-Systeme optimieren. Denn OpenAI baut etwas Ähnliches auf und auch Amazon bewegt sich in diese Richtung. Der Wettstreit der Plattformen ist damit endgültig im KI-Zeitalter angekommen. Weiterlesen auf F.A.Z. PRO Digitalwirtschaft (€) ▶︎ https://lnkd.in/e-r8k7up€ Frankfurter Allgemeine Zeitung

Erfahrungswerte aus der aktuellen MIT Sloan Management Review (Bahnhofsbuchhandel). „Gehe langsam, wenn Du es eilig hast.“ Diese Erkenntnis war es, die zwei weltbekannte dänische Unternehmen – LEGO und VELUX (Dachfenster) – dazu brachte, ihren Umgang mit Veränderung zu überdenken. Inmitten digitaler Umbrüche und wachsender Komplexität stießen beide an die Grenzen ihres bisherigen Erfolgsmodells: Was früher als effizient galt, erwies sich plötzlich als zu starr und zu oberflächlich. Workshops sind oft Strohfeuer. Externe Berater kamen und gingen. Also die Erkenntnis: Veränderung muss von innen kommen – durch Führung. LEGO und VELUX machten etwas Ungewöhnliches: Sie bildeten ihre Führungskräfte nicht zu besseren Projektmanagern aus, sondern zu besseren Frage-Stellern. Sie machten sie zu Coaches. Zu Lernbegleitern ihrer eigenen Mitarbeitenden. Zu Menschen, die nicht mit Antworten glänzen, sondern mit klugen Fragen Orientierung geben. ⸻ Element 1: Probleme neu denken – mit A3 Beide Unternehmen führten die A3-Methode von Toyota ein – ein strukturiertes Denkformat, das ein Problem auf einer einzigen DIN-A3-Seite abbildet. Klar. Visuell. Jeder arbeitet damit. Das dazugehörige Modell: 🍍 Finding: Das richtige Problem entdecken. 🍍 Facing: Sich ihm mutig stellen. 🍍 Framing: Die eigentliche Herausforderung erkennen. 🍍 Forming: Lösungen entwickeln. Diese vier Phasen führten zu einem neuen Problembewusstsein: Nicht Symptome bekämpfen. Ursachen verstehen. Nicht sofort handeln. Erst gemeinsam denken. Teams lernten langsamer und nachhaltiger. ⸻ Element 2: Lernen im Kollektiv – Gruppen-Coaching als Mikrokosmos Individuelles Lernen reicht nicht. Also bauten LEGO und VELUX einen Raum für kollektive Reflexion: Gruppencoaching. Dort trafen sich Teams aus Führungskräften in festen Rollen: ein Moderator, eine Fallgeberin, ein Coach und stille Beobachter. In 30 Minuten wurde ein reales Problem durchdacht – mit klugen Fragen, ehrlichen Perspektiven, geteilten Einsichten. Diese Sessions stärkten nicht nur die Problemlösefähigkeiten – sie schufen psychologische Sicherheit. Menschen konnten sich verletzlich zeigen. Fehler besprechen. Ideen testen. Und gemeinsam wachsen. ⸻ Element 3: Coaching-Hierarchie – Lernen strukturell verankern Um all das nachhaltig zu machen, entwickelten beide Unternehmen eine dreistufige Coaching-Struktur: 🍓 First Coach: Die direkte Führungskraft begleitet das tägliche Lernen. 🍓 Second Coach: Bereichsleiter coachen die Coaches – und verbessern deren Fragekompetenz. 🍓 Third Coach: Das Top-Management reflektiert die Metaebene und sichert strategische Ausrichtung. So wurde Innovation nicht zur Aufgabe von Externen, sondern zur DNA der Organisation. Lernen wurde nicht delegiert – sondern verkörpert. Erfordert erst Zeit und Geduld, zahlt sich langfristig jedoch aus. | 96 Kommentare auf LinkedIn

“In times of change, the learners inherit the earth, while the learned find themselves beautifully equipped for a world that no longer exists.” – Eric Hoffer In a world of swirling uncertainty, waiting for perfect information is the fastest path to irrelevance.

Are you optimizing for learning, or just chasing certainty? In today’s world, waiting for perfect information is the fastest way to fall behind. True leadership isn’t about always having the answers; it’s about building teams and systems that learn faster than the world changes. In this latest post (Part 2 of 3), I explore why learning, not knowing, is the real driver of performance. Neuroscience shows that in uncertain times, our ability to adapt and learn together is what sets successful organizations apart. How does your organization balance “doing” and “learning”? Are you cultivating a culture where learning is the soil, not just a one-off event? I’d love to hear your experiences and strategies in the comments! Let’s build a conversation around how we can all adapt, grow, and thrive in uncertainty. #Leadership #OrganizationalLearning #ContinuousImprovement #ChangeManagement #FutureOfWork #LearningCulture #Adaptability #HumanCorps | 19 comments on LinkedIn

In a new paper, British philosopher Andy Clark (author of the 2003 book Natural Born Cyborgs, see comment below) offers a rebuttal to the pervasive anxiety surrounding new technologies, particularly generative AI, by reframing the nature of human cognition. He begins by acknowledging familiar concerns: that GPS erodes our spatial memory, search engines inflate our sense of knowledge, and tools like ChatGPT might diminish creativity or encourage intellectual laziness. These fears, Clark observes, mirror ancient worries, like Plato’s warning that writing would weaken memory, and stem from a deeply ingrained but flawed assumption: the idea that the mind is confined to the biological brain. Clark challenges this perspective with his extended mind thesis, arguing that humans have always been cognitive hybrids, seamlessly integrating external tools into our thinking processes. From the gestures we use to offload mental effort to the scribbled notes that help us untangle complex problems, our cognition has never been limited to what happens inside our skulls. This perspective transforms the debate about AI from a zero-sum game, where technology is seen as replacing human abilities, into a discussion about how we distribute cognitive labour across a network of biological and technological resources. Recent advances in neuroscience lend weight to this view. Theories like predictive processing suggest that the brain is fundamentally geared toward minimising uncertainty by engaging with the world around it. Whether probing a river’s depth with a stick or querying ChatGPT to clarify an idea, the brain doesn’t distinguish between internal and external problem-solving—it simply seeks the most efficient path to resolution. This fluid interplay between mind and tool has shaped human history, from the invention of stone tools to the design of modern cities, each innovation redistributing cognitive tasks and expanding what we can achieve. Generative AI, in Clark’s view, is the latest chapter in this story. While critics warn that it might stifle originality or turn us into passive curators of machine-generated content, evidence suggests a more nuanced reality. The key, Clark argues, lies in how we integrate these technologies into our cognitive ecosystems. https://lnkd.in/gUmxE57w | 41 comments on LinkedIn

Watch CEO Satya Nadella unveil all the biggest product moves, including Copilot and Azure updates, developer tools, and more, from Seattle.0:00 Intro0:14 Bui...

𝗔𝘁 𝗜/𝗢 2025, Google 𝘀𝗵𝗼𝘄𝗲𝗱 𝘂𝘀 𝘄𝗵𝗮𝘁 𝗔𝗜-𝗳𝗶𝗿𝘀𝘁 𝗥𝗘𝗔𝗟𝗟𝗬 𝗺𝗲𝗮𝗻𝘀. 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗚𝗼𝗼𝗴𝗹𝗲 𝗮𝗻𝗻𝗼𝘂𝗻𝗰𝗲𝗱: ⬇️ The company's flagship developer event Google I/O 2025 was held last night in Mountain View, California. 𝗧𝗟𝗗𝗥: Google is turning Gemini into the AI operating system for everything — with agents now embedded across Search, Chrome, Workspace, Android, and more. If you don’t have time for the full event, here’s a curated 𝘀𝘂𝗽𝗲𝗿𝗰𝘂𝘁 of the highlights that really matter. 𝗞𝗲𝘆 𝗺𝗼𝗺𝗲𝗻𝘁𝘀 𝗳𝗿𝗼𝗺 𝗚𝗼𝗼𝗴𝗹𝗲 𝗜/𝗢 𝟮𝟬𝟮𝟱: 0:00 𝗜𝗻𝘁𝗿𝗼 – AI-native from the ground up 0:11 𝗚𝗲𝗺𝗶𝗻𝗶 𝗽𝗹𝗮𝘆𝘀 𝗮 𝗣𝗼𝗸𝗲𝗺𝗼𝗻 𝗴𝗮𝗺𝗲 — memory, reasoning, and code 0:30 𝗚𝗼𝗼𝗴𝗹𝗲 𝗕𝗲𝗮𝗺 – Real-time 3D video chat with AI 1:08 𝗚𝗼𝗼𝗴𝗹𝗲 𝗠𝗲𝗲𝘁 – Speech-to-speech translation, live 1:27 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗠𝗮𝗿𝗶𝗻𝗲𝗿 – AI agents that book, plan, filter, decide 2:07 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 – Gemini gets memory and task awareness 2:40 𝗚𝗲𝗺𝗶𝗻𝗶 𝟮.𝟱 𝗣𝗿𝗼 + 𝗙𝗹𝗮𝘀𝗵 – New SOTA models, LMArena leader 4:57 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗔𝘀𝘁𝗿𝗮 – Multimodal, fast-response agent that sees and hears 5:32 𝗔𝗜 𝗠𝗼𝗱𝗲 – Overlay for restaurants, bookings, prices, events 7:10 𝗦𝗵𝗼𝗽𝗽𝗶𝗻𝗴 – Track, compare, and auto-buy with Google Pay 8:34 𝗚𝗲𝗺𝗶𝗻𝗶 𝗟𝗶𝘃𝗲 – Screen sharing + live AI guidance 8:59 𝗗𝗲𝗲𝗽 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗔𝗴𝗲𝗻𝘁 – Upload files, get insights 9:12 𝗖𝗮𝗻𝘃𝗮𝘀 – Live, collaborative AI whiteboard 9:31 𝗚𝗲𝗺𝗶𝗻𝗶 𝗶𝗻 𝗖𝗵𝗿𝗼𝗺𝗲 – AI understands and acts on any webpage 9:51 𝗜𝗺𝗮𝗴𝗲𝗻 𝟰 – Next-gen image generation 10:23 𝗩𝗲𝗼 𝟯 – Ultra-realistic video model 11:01 𝗟𝘆𝗿𝗶𝗮 𝟮 – AI-powered music composition 11:56 𝗙𝗹𝗼𝘄𝘀 – Multimodal, promptable AI video creation 12:39 𝗔𝗻𝗱𝗿𝗼𝗶𝗱 𝗫𝗥 – AI-first spatial computing 12:57 𝗦𝗮𝗺𝘀𝘂𝗻𝗴 𝗠𝗼𝗼𝗵𝗮𝗻 – Google’s XR headset revealed 13:16 𝗟𝗶𝘃𝗲 𝗴𝗹𝗮𝘀𝘀𝗲𝘀 𝗱𝗲𝗺𝗼 – Gemini + XR = real-time AI overlay Super insightful and forward-looking: Google’s AI strategy just went full stack. Even if some of these projects don’t make it past the prototype stage, the direction is obvious: AI is being integrated into everything. LLMs — Gemini, in this case — are rapidly becoming the new operating system and everything will be powered by AI Agents across all products. Full keynote: https://lnkd.in/dPFFtyZ9 Supercut: https://lnkd.in/d-eBNGjw Enjoy watching! | 61 comments on LinkedIn

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One of the most interesting things about the original research is that it provides a clear metric for measuring AI performance improvement that is not tied to benchmarks that keep on being superceded, needing new benchmarks. We can now rank AI models and agents by their half-life - the time for human tasks for which they achieve 50% success rate. Of course we are usually more interested in models that can achieve 99+% success rates - depending on the task - but the relative consistency of the half life decay means the T50 threshold predicts whatever success rate we aim for, both today, and at future dates if the original trend holds Generally the decay is due to cumulative errors or going off course. But the decay is not always consistent, as there can be subtasks of uneven difficulty, or agents can recover from early mistakes. Interestingly, humans don't follow pure exponential decay curves. Our success rate falls off more slowly over very long tasks, suggesting we have broader context, allowing us to recover from early mistakes. The research was applied to tasks in research or software engineering. The dynamics of this performance evolution may or may not apply to other domains. Certainly, this reframing of assessing the development of AI capabilities and its comparison to human work is a very useful advance to the benchmarking approach.

Here’s what I take away: 🚫 HR is no longer just about people. 🚫 IT is no longer just about systems. ✅ The real value lies in how people and systems interact—seamlessly, intelligently, adaptively. Let’s be honest, most organizations still operate in silos: - HR builds talent and culture - IT builds systems and infrastructure But the future of work is all about integration. What if you make that happen? Think about it: Can you redesign work itself? Not roles. Not org charts. But the actual FLOW of work. Because that’s what Moderna’s doing. They are reimagining how humans and machines co-create value. IBM is doing the same. They use HR AI agents that handle questions, routes issues, and manage HR processes. This isn’t about cutting costs. It’s about building a business that adapts faster to the next disruption. They are building resilience. I recognize that HR and IT both have unique complexities, and in many companies are simply too far apart or too large merge shortly. Still, it still got me thinking. As an HR leader: -> How comfortable are you with data, automation, and AI? -> Could you confidently lead both people strategy and digital infrastructure? -> What would need to change for that answer to be yes? This isn’t a tech conversation. It’s an organization and leadership revolution. The next era of HR won’t be like today's HR at all. It will be integrated, tech-savvy, and central to how business gets done. Time to level up. Are you ready? #futureofwork #hrtech #ai Picture and story credits: Isabelle Bousquette 🙏 | 34 comments on LinkedIn

switching to non-augmented tasks. It is definitely interesting research, but I am very cautious about the conclusions reached by the authors, partly since they are to a degree contradictory, and also not necessarily generalizable. The authors implicitly criticize AI for removing the “most cognitively demanding parts” of work, implying that this reduces fulfillment. But the outputs and productivity are clearly improved. Are they suggesting workers create inferior output for the sake of engagement? It is worth noting that other recent research points to improved emotion and engaement with genAI collaboration. The emotional impact of genAI collaboration will vary substantially across use cases, especially with the nature of the task, and certainly with the cultural context. It appears the use case here was performance reviews, which is not representative of many other types of cognitive work. The authors also say that AI-assisted tasks reduce users’ sense of control, thus lowering motivation. But they say this sense of control is restored during subsequent solo tasks, even though those are when boredom and disengagement rise. Having said that, for some tasks and work design the issues they raise could be real and substantial. These are the sound remedies they suggest: ➡️Blend AI and Human Contributions: Use gen AI as a foundation for tasks while encouraging humans to personalize, expand, and refine outputs to retain creativity and ownership. ➡️Design Engaging Solo Tasks: Follow AI-supported work with autonomous, creative tasks to help employees stay motivated and exercise their own skills. ➡️Make AI Collaboration Transparent: Clearly communicate AI’s supporting role to preserve employees’ sense of control and fulfillment in their contributions. ➡️Rotate Between Tasks: Alternate between independent and AI-assisted tasks to maintain engagement and productivity throughout the workday. ➡️Train Employees to Use AI Mindfully: Provide training that helps employees critically and strategically integrate AI, strengthening their autonomy and judgment.

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There is a lot of talk about how L&D needs to be proactive, not reactive. But how do we do that when technology is moving so fast? It starts with having a point of view on where the world of work is headed, and then building a bridge to that future. Because if we only make incremental changes from where we are now, we'll likely be playing catch-up for a long time—and risk preparing people for the work of today, not tomorrow. Here are some of the forces I think about a lot these days: 🎓 AI seems to be denting the supply of entry level jobs. What does that mean for the talent pipeline later down the line? And how should we onboard the graduates that *do* get employed so they can add value on top of AI? 📈 AI gets lower performers closer to higher performers (HBS & BCG study), and individuals working with AI match the performance of *teams* without AI (HBS & P&G study). How do we evaluate, recognise and enhance expertise in such a world? 🏁 Vibe coding/marketing/learning/something else, single founder unicorns, service-as-a-software (not software-as-a-service!) and zero latency economy are just some of the predictions that would affect both the nature and pace of work. What support would our people and organisations need to adapt? L&D isn't short on AI tools. What we need is a vision—to imagine how AI will reshape performance, learning, and the world of work at large. And, ultimately, what L&D needs to 𝘣𝘦𝘤𝘰𝘮𝘦 to have a role in it. Nodes #AI #HR #Learning #Talent #FutureOfWork | 12 comments on LinkedIn

Norwegen, Schweden und Finnland haben viele Dinge gemeinsam: Lange Küsten, viele Wälder und eine ausgeprägte soziale Tradition. Alle drei nordischen Länder s...

In my work with boards and exec teams one of the pointed questions is always the degree of focus in AI initiatives. Johnson & Johnson's divergent-convergent strategy is highly instructive. Some commentators have suggested that this means the use case proliferation was a mistake. J&J's CIO doesn't see it like that. "You had to take an iterative approach to say, ‘Where are these technologies useful and where are they not?’... We had the right plan three years ago, but we matured our plan based on three years of understanding,” Leaders cannot know in advance where the value will emerge. The challenge is to select the right scope of experimenation before selecting focus use cases. Another shift was from centralized AI by a board governance to function-specific ownership such as commercial, R&D, and supply chain, enabling better prioritization and faster iteration. Again, these models suit different phases of the AI adoption journey. Most organizations are far earlier than J&J, which has strong maturity. On metrics: "The company is tracking progress in three buckets: first, the ability to successfully deploy and implement use cases; second, how widely they are adopted; and third, the extent to which they deliver on business outcomes." I strongly suspect that they are not using a "win rate" on their use case success. There are similarities to VC portfolios, where a few big wins make all the investments worthwhile. | 12 comments on LinkedIn

3.000 KI-Assistenten integriert in alle Teams. Das ist die KI-Reise von Moderna: “It’s hard to convey—within the hype—how much AI is changing things and how much Moderna is using it across the board” Dieses Zitat von Wade Davis, Modernas Head of Digital for Business, zeigt sehr schön wie schwer der allumfassende Wandel von KI zu beschreiben ist. Es sind eben nicht 2 - 3 Use Cases ein ein paar Bereichen. Viel mehr geht es um eine Veränderung der Denk- und Arbeitsweise. Während viele Unternehmen noch zögern, hat Moderna bereits konkrete Schritte unternommen, um KI strategisch zu implementieren: 1. Zusammenlegung von HR und IT unter einer Führung 2. Systematische Analyse aller Arbeitsprozesse 3. Klare Entscheidung: Was macht Mensch & Maschine? 4. Entwicklung von 3.000 spezialisierten KI-Assistenten 5. Integration dieser Assistenten in komplexe Workflows Der taktische Ansatz dahinter ist bemerkenswert: ↳ Nicht einzelne KI-Projekte, sondern eine umfassende Transformation ↳ Keine isolierten Tools, sondern vernetzte Systeme ↳ Kein Fokus auf Stellenabbau, sondern auf Neugestaltung der Arbeit KI-Integration ist keine einmalige Initiative, sondern ein fortlaufender Prozess der Organisationsentwicklung. Moderna zeigt, dass der Erfolg nicht von einzelnen Tools abhängt, sondern von der strategischen Neugestaltung der Arbeit selbst. Genau das ist der Weg, den es zu gehen gilt. | 12 Kommentare auf LinkedIn

We used to rely on each other, but then it went a bit sour. Or did it? AI has helped ramp that up too. The context of work ie hybrid and remote has also driven the need for providing digital learning solutions. So what happened to face to face training? It was the staple of L&D pre-Covid but now it is hardly talked about. Have you seen articles or conference sessions on the topic? Latest research from Hemsley Fraser suggests the in-person training is far from forgotten. Indeed, it is the number one learning approach, according to its survey of 822 L&D, HR and talent practitioners, followed by coaching and virtual instructor-led training. The research shows that there has been a 17% year on year growth in in-person training in the US. According to the research, the learning approaches used by L&D are: ⦿ In-person training 87% ⦿ Coaching 62% ⦿ Virtual instructor-led training 60% ⦿ Free online resources 56% ⦿ Learning hub/portal 54% ⦿ Blended learning 51% The report authors say, "When L&D is expected to not only grow skills but boost engagement and experiences, coming together, when structured effectively, can simply make people feel good. It becomes a strategic tool with benefits beyond training alone." This sentiment is reflected in the latest Fosway Group 9-Grid for Digital Learning. One of its trends is titled "The desire to learn together gains momentum". The report, says: "Amongst all this talk of AI, the desire to actually learn together (with other people) remains, arguably, valued more highly. The need for learning solutions for high-value or large-scale transformation programmes is enduring. The best solutions bring cohorts to learn and work together over time. And maybe unsurprisingly, live events are still an important part of the desired blend, as well as virtual and hybrid approaches, led by experts that can interact in real time with their audience. In-group facilitation and collaboration is another core capability." So what is the reality for L&D teams? Is in-person training evolving to find its place alongside learning technology? And is the in-person experience evolving away from content delivery to focus on the value of people being together, sharing and working and learning collaboratively? 🔗 Read the Hemsley Fraser Learning & Development Impact Survey 2025 https://lnkd.in/e33fuTp2 🔗 Read the 2025 Fosway 9-Grid for Digital Learning https://lnkd.in/ebAbqd_x Picture credit: ElisaRiva https://lnkd.in/epFrUtXd #learninganddevelopment #research | 23 comments on LinkedIn

(I didn't even know that was a thing! 🙃 ) Not able to make attend? Here is everything that was covered from theory to application is now available for free... ➡️ Day 1: Foundational Models & Prompt Engineering https://lnkd.in/d-_w3gXj ➡️ Day 2: Embeddings & Vector Stores / Databases https://lnkd.in/dkmfDUcp ➡️ Day 3: Generative AI Agents https://lnkd.in/dd3Zd2-F ➡️ Day 4: Domain-Specific LLMs https://lnkd.in/d6Z39yqt ➡️ Day 5: MLOps for Generative AI https://lnkd.in/dcXCTPVF And, be sure to check out the winners of the course's capstone project: building tools from Generative AI (classroom assistants, schedulers, mock interviewers and more.) https://lnkd.in/dPsXnrct Interested in putting all of those newly-developed AI skills to use? Here are some of the latest job openings here at Google: http://google.com/careers. Hope to see you around! 😊 #google #lifeatgoogle #training #ai #education | 21 comments on LinkedIn

I was interviewed in today's The Wall Street Journal on the impact of AI agents on customer behavior - here's how I believe our lives are about to change: ( ⬇️ From the article by the great Steve Rosenbush ⬇️ ) There is a flywheel effect at work here. The AI agent has access to an enormous amount of data about users that makes it possible to tailor recommendations, information, and insights to their needs. And once they reside in a messaging app, they can create a continuing presence in the user’s life, just like a person would. “Once an AI knows you and remembers your history, it stops feeling like a tool and starts to feel like a companion,” says Conor Grennan, chief AI architect at New York University Stern School of Business. “It starts to blur the line between an AI brand ambassador and just a friend who shares your taste.”" ⬆️ End of quote ⬆️ . The wild part of all this to me is that agents are coming to WhatsApp, where we hang out. It shows us a ton about Meta's strategy: My thoughts: WhatsApp already hosts most of our everyday conversations, so when a brand drops in an AI agent that greets me like the barista who knows my order, it doesn’t feel like marketing—it feels like service. What’s new is the compounding effect: every helpful, context-aware response deposits a little ‘trust capital’ in the relationship bank. Those micro-interactions can become a moat for a brand by helping establish lasting customer loyalty. So: Where do you see this all going? +++++++++++++++++ UPSKILL YOUR ORGANIZATION: When your organization is ready to create an AI-powered culture—not just add tools—AI Mindset would love to help. We drive behavioral transformation at scale through a powerful new digital course and enterprise partnership. DM me, or check out our website. | 56 comments on LinkedIn

Eine empirische Studie zeigt: Der wirtschaftliche Nutzen der generativen KI wird von Führungskräften mit praktischer Erfahrung deutlich positiver bewertet als von solchen ohne. Während 64 Prozent der Erfahrenen von einer schnellen Amortisation ausgehen, glauben dies nur 35 Prozent der Unerfahrenen. Die Wirtschaftlichkeit hängt stark vom Betriebsmodell, der Nutzungstiefe und den unternehmensspezifischen Bedingungen ab. Wer GenAI gezielt einsetzt, steigert Produktivität, Innovationskraft und Arbeitgeberattraktivität – ein realer betriebswirtschaftlicher Vorteil, schreibt Peter Buxmann in seinem Gastbeitrag für F.A.Z. PRO Digitalwirtschaft. 𝗪𝗲𝗶𝘁𝗲𝗿𝗹𝗲𝘀𝗲𝗻: ▶︎ https://lnkd.in/e3faARTd Der Text stammt aus unserem Digitalwirtschaft-Newsletter zur digitalen Ökonomie. Der Newsletter wird jeden Mittwoch um 8 Uhr an 230.000 Abonnenten versendet und erklärt die relevanten Digitalthemen der Woche, aufgeteilt auf die Themenbereiche Künstliche Intelligenz, Zukunft der Arbeit, Digitale Transformation, Plattformen und Digitale Mobilität. Interessenten können den Newsletter zwei Monate 𝗸𝗼𝘀𝘁𝗲𝗻𝗹𝗼𝘀 testen. ▶️ https://lnkd.in/eY_4zwbr Frankfurter Allgemeine Zeitung | 13 Kommentare auf LinkedIn

“𝗔𝗜 𝗶𝘀 𝗰𝗼𝗺𝗶𝗻𝗴 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗷𝗼𝗯. 𝗛𝗲𝗰𝗸, 𝗶𝘁’𝘀 𝗰𝗼𝗺𝗶𝗻𝗴 𝗳𝗼𝗿 𝗺𝗶𝗻𝗲 𝘁𝗼𝗼!" That’s not clickbait. That’s the CEO of Fiverr (Micha Kaufman) in his latest e-mail every employee received yesterday! And he’s not the first writing that: first Shopify, then Duolingo, now Fiverr. Top tech CEOs are one by one speaking out loud. BUT Fiverr did something different: They didn’t just warn their teams — they gave them a blueprint to survive. 𝗧𝗵𝗲 𝗳𝗼𝗹𝗹𝗼𝘄𝗶𝗻𝗴 𝗮𝗿𝗲 𝘁𝗵𝗲 7 𝗔𝗜 𝗦𝘂𝗿𝘃𝗶𝘃𝗮𝗹 𝗧𝗶𝗽𝘀 𝗵𝗲 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝘀 𝗳𝗼𝗿 𝗻𝗮𝘃𝗶𝗴𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲𝘀𝗲 𝗰𝗵𝗮𝗻𝗴𝗲𝘀: ⬇️ 1. 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁, 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁, 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁. ➜ Test every AI tool you can get your hands on. See which ones make you 10x faster. If you’re in coding? Use Cursor. Law? Lexis+. Learn what makes you dangerous. 2. 𝗙𝗶𝗻𝗱 𝘀𝗺𝗮𝗿𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝗮𝗻𝗱 𝘀𝘁𝗶𝗰𝗸 𝗰𝗹𝗼𝘀𝗲. ➜ Surround yourself with folks who already get AI. Ask questions. Watch what tools they use. Shortcut your learning curve. 3. 𝗨𝘀𝗲 𝘆𝗼𝘂𝗿 𝘁𝗶𝗺𝗲 𝗹𝗶𝗸𝗲 𝗶𝘁’𝘀 𝗲𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲. ➜ If you're still working like it's 2024, you're doing it wrong (!!!). Speed and efficiency are the new currency. Cut the fluff, automate the rest. 4. 𝗠𝗮𝘀𝘁𝗲𝗿 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴. ➜ Google is basic now. LLM'S are the new baseline. The better your prompts, the more powerful you become. Learn it like it’s your second language. 5. 𝗛𝗲𝗹𝗽 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗱𝗼 𝗺𝗼𝗿𝗲 𝘄𝗶𝘁𝗵 𝗹𝗲𝘀𝘀. ➜ Don’t just do your job — rethink how the whole org works. Automate stuff. Suggest improvements. Be the person who makes things smoother and smarter. 6. 𝗧𝗵𝗶𝗻𝗸 𝗹𝗶𝗸𝗲 𝗮𝗻 𝗼𝘄𝗻𝗲𝗿. Know what the company’s really trying to achieve. Don’t wait to be asked. Show up with ideas. Pitch improvements. You don’t need a permission slip to contribute. 7. 𝗠𝗮𝗸𝗲 𝘆𝗼𝘂𝗿 𝗼𝘄𝗻 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝗶𝗲𝘀. No one's coming to save you. If you want to grow, build your own path. Take initiative, start small, stay consistent. Those who help themselves get help too. 𝗜 𝗯𝗲𝗹𝗶𝗲𝘃𝗲 𝘁𝗵𝗲𝗿𝗲'𝘀 𝘀𝗼𝗺𝗲 𝘁𝗿𝘂𝘁𝗵 𝘁𝗼 𝘁𝗵𝗶𝘀. 𝗕𝘂𝘁 𝘄𝗲 𝗼𝗳𝘁𝗲𝗻 𝘁𝗲𝗻𝗱 𝘁𝗼 𝗲𝗶𝘁𝗵𝗲𝗿 𝗼𝘃𝗲𝗿𝗲𝘀𝘁𝗶𝗺𝗮𝘁𝗲 𝗼𝗿 𝘂𝗻𝗱𝗲𝗿𝗲𝘀𝘁𝗶𝗺𝗮𝘁𝗲 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆. 𝗧𝗵𝗮𝘁 𝘀𝗮𝗶𝗱, 𝘁𝗵𝗲 𝗺𝗲𝘀𝘀𝗮𝗴𝗲 𝗿𝗲𝗺𝗮𝗶𝗻𝘀 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲: 𝗔𝗜 𝗶𝘀𝗻’𝘁 𝗰𝗼𝗺𝗶𝗻𝗴 — 𝗶𝘁’𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗵𝗲𝗿𝗲. 𝗕𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗮𝗻 𝗔𝗜-𝗳𝗶𝗿𝘀𝘁 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗶𝘀 𝗿𝗮𝗽𝗶𝗱𝗹𝘆 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝘁𝗵𝗲 𝗻𝗲𝘄 𝗻𝗼𝗿𝗺𝗮𝗹. 𝗦𝘁𝗶𝗹𝗹, 𝘁𝗵𝗲 𝘁𝗶𝗽𝘀 𝗮𝗯𝗼𝘃𝗲 𝗮𝗿𝗲 𝘃𝗮𝗹𝘂𝗮𝗯𝗹𝗲. 𝗟𝗲𝘁’𝘀 𝗯𝗲 𝗵𝗼𝗻𝗲𝘀𝘁: 𝗨𝗽𝘀𝗸𝗶𝗹𝗹𝗶𝗻𝗴 𝗶𝘀 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝗮 𝗽𝗲𝗿𝗸. 𝗬𝗼𝘂’𝗿𝗲 𝗲𝗶𝘁𝗵𝗲𝗿 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 — 𝗼𝗿 𝘄𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝗶𝘁 𝗽𝗮𝘀𝘀 𝘆𝗼𝘂 𝗯𝘆. You can read the full e-mail attached! ⬇️ | 152 comments on LinkedIn

*NEW PAPER*: GenAI investments will only pay off if employees adopt the technology and learn to use it effectively. Feelings of psychological threats are common and they are going to be a major obstacle. GenAI deployment therefore needs to be accompanied by a careful talent management strategy. In our new Trends in Cognitive Sciences article, we review the psychological threats that GenAI deployment can trigger in workers, focusing on three areas: competence, autonomy, and relatedness. Moreover, we sketch different types of reactions that such feelings of threats can trigger. The figure below summarizes five coping strategies (both adaptive and maladaptive) for the three types of psychological threat. Link to the article in comment (open access for 50 days). With Erik Hermann and Carey Morewedge The Wharton School Wharton AI & Analytics Initiative Wharton Executive Education | 24 comments on LinkedIn

When I say a ‘traditional course’, I mean learning content (instructor-led or self-service) delivered online or face-to-face either, going from beginning to end with little variation in content or delivery. These courses have been the mainstay of training at work since I started in the classroom in the 1980s. For some, the idea that the traditional course is doomed come as a shock. For others, it’s self-evident. Listening to researchers, experts and practitioners like Dani Johnson, Dr Philippa Hardman and Gregg Collins, I am convinced that within 5 years most organisational training will abandon these traditional courses. Why? It is now easy to personalise content, even in a simple fashion, with AI. With extra effort, you can deliver content via adaptive delivery that understands where you are succeeding and failing and changes what you learn, and how you learn it, to ensure you reach competency faster. It’s more effective, more enjoyable, and faster. All of this is already happening, and it's only going to get easier and more wide-spread. But the real drive will come not from the technology, but from the learners. The technology enables the change. The learners will demand it. Once enough people have experienced content delivered with this flexibility – probably initially in their private lives, as consumers – they will start to ask why their employers aren’t delivering content the same way. So I have three questions for you this Friday: · Do you agree that the traditional course doomed? · If so, is the timescale of 2-5 years reasonable? · What are the implications of all this? I’d love to hear your thoughts. | 77 comments on LinkedIn

Progress is happening all over the world. I couldn’t fit the entire world into my studio though, so built a model. See how I did that with a Nebula subscript...

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