Open New Learning Lab Resources

Open New Learning Lab Resources

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L&D isnโ€™t very happy with their LMS platforms, thatโ€™s for sure ๐Ÿฅฒ
L&D isnโ€™t very happy with their LMS platforms, thatโ€™s for sure ๐Ÿฅฒ
L&Ds aren't very happy with their LMS platforms, that's for sure ๐Ÿฅฒ We recently launched our first tools report, and below ๐Ÿ‘‡๐Ÿป you can find 7 insights around LMSs & LXPs. Want to read more? ๏ผ Download the free report ๐Ÿ‘‰ https://lnkd.in/dBZzW6TZ ๏ผ Join the Offbeat Fellowship to explore all our insights ๐Ÿ‘‰ https://lnkd.in/dx3REqBh Hope you'll find this useful! ๐Ÿ’œ #learninganddevelopment #learningmanagementsystem #learningexperienceplatform #learningtools
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L&D isnโ€™t very happy with their LMS platforms, thatโ€™s for sure ๐Ÿฅฒ
Not content. Experience. Not fun. Impact. Not learning events. Real, transformative interactions that can shift how we think, feel, and perform.
Not content. Experience. Not fun. Impact. Not learning events. Real, transformative interactions that can shift how we think, feel, and perform.
Weโ€™re at a turning point. AI can generate faster, broader, cheaper content than ever. So the real differentiator isnโ€™t knowledge, itโ€™s experience design that actually influences behaviour and builds people's capability. And thatโ€™s tough. Because designing for experience means starting with the challenges people face, not topics or content. It means accepting that 'learning' doesnโ€™t always feel good, in fact, itโ€™s often the sting of experience that actually drives change. Itโ€™s layered too. Like an onion. The micro layer: our senses and emotions The meso layer: the interactions and activities weโ€™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 theyโ€™re rarely a one-off event. The most powerful ones are embedded in how we work, not added on after the fact. So letโ€™s stop trying to make learning cute or entertaining (ie the Disneyfication effect). Letโ€™s stop pretending every experience needs to feel good. Be honest. Build whatโ€™s real. And what actually makes a difference. Because if AI owns the content, then weโ€™ve got to own the context. This means suba diving (see previous post..it will make sense, i promise), not snorkelling. If youโ€™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.
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Not content. Experience. Not fun. Impact. Not learning events. Real, transformative interactions that can shift how we think, feel, and perform.
Berufswahl im Zeitalter der lernenden Maschinen โ€“ Offener Brief an meine Nichte (Abi-Jahrgang 2025)
Berufswahl im Zeitalter der lernenden Maschinen โ€“ Offener Brief an meine Nichte (Abi-Jahrgang 2025)
Berufswahl im Zeitalter der lernenden Maschinen โ€“ Offener Brief an meine Nichte (Abi-Jahrgang 2025) Liebe Anna, als du mich fragtest, ob โ€žInformatik, Medienwissenschaft oder Politikโ€œ noch zukunftssicher sind, merkte ich, wie lรถchrig die alte Landkarte der Arbeit geworden ist. Code wird von KI vervollstรคndigt, Diagnosen von Algorithmen unterstรผtzt, Routinevertrรคge von Bots geprรผft. 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, interdisziplinรคres Denken und sinnliche Materialerfahrung. Sie bilden zusammen einen โ€žHuman Moatโ€œ โ€“ einen Schutzwall gegen reine Automatisierung. 1 | HEALTH & HUMAN SERVICES โ€“ BERUFE MIT EMPATHIE-FAKTOR Das ist erwartbar: Pflege, Sozialarbeit, Therapie oder Pรคdagogik bleiben knapp, weil Demografie und Krisen Resilienz verlangen. Typische Rollen: Pflegefachfrau+, Physician Assistant, Tele-Coach Mental Health. Schlรผssel-Skills: evidenzbasierte Pflege, interkulturelle Kommunikation, Basiswissen Medizinrecht und Datenschutz. 2 | TWIN-TRANSITION CAREERS โ€“ KLIMA ร— TECHNOLOGIE Smarte Mash-Ups: Unternehmen brauchen Talente, die COโ‚‚-Reduktion mit Datenkompetenz verbinden. Typische Rollen: Nachhaltigkeits-Data-Analyst, Circular-Economy-Ingenieurin, KI-Policy-Analyst, Energy-Systems-Modeler. Schlรผssel-Skills: Life-Cycle-Assessment, Python/R, EU-Regulatorik (CSRD, AI Act), Systemdenken. 3 | CRAFT & EXPERIENCE DESIGN โ€“ WERT DES EINZIGARTIGEN Je perfekter Massenware KI-optimiert ist, desto hรถher steigt der Wert des Nicht-Skalierbaren. Typische Rollen: Produktdesigner*in fรผr Bio-Materialien, Restaurator, Schreinerin mit CNC-Know-how, UX-Designer fรผr phygitale Erlebnisse. Schlรผssel-Skills: Materialkunde, CAD/CAM & 3-D-Druck, Storytelling, Customer-Journey-Mapping. Das ist natรผrlich 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 Vertragsprรผfungen. Die Maschine erledigt es schneller und billiger โ€“ doch jemand muss die Systeme entwerfen, mit Daten fรผttern und ethisch beaufsichtigen. MEIN RAT IN DREI Sร„TZEN >> 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 โ€“ und kannst dir jederzeit einen neuen Beruf erfinden. Vielleicht startest du als Pflege-Informatikerin, wirst spรคter KI-Ethikerin und erรถffnest irgendwann eine Bรคckerei, in der Roboter den Teig kneten, wรคhrend du den Sauerteig fรผtterst und Kund:innen berรคtst. Zukunftssicherheit entsteht nicht aus einem Studium, sondern aus lebenslanger Lernlust. Die Welt bleibt turbulent, doch wer Richtung Sinn steuert, hat immer Rรผckenwind. Dein Onkel Stefan | 59 Kommentare auf LinkedIn
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Berufswahl im Zeitalter der lernenden Maschinen โ€“ Offener Brief an meine Nichte (Abi-Jahrgang 2025)
An eagerness to learn is essential for innovation.
An eagerness to learn is essential for innovation.
An eagerness to learn is essential for innovation. But the way we learnโ€”and the order in which we partake in various learning activitiesโ€”can make the difference between effective growth and potential missed opportunities. Jean-Franรงois 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 ๐Ÿ‘ท๐Ÿ—๏ธ 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
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An eagerness to learn is essential for innovation.
The Learning Journey That Led to Nowhere
The Learning Journey That Led to Nowhere
The Learning Journey That Led to Nowhere The carefully curated โ€œlearning journey.โ€ 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 cameโ€”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 โ€œlanded well.โ€ 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 donโ€™t stick. They donโ€™t demand enough. They donโ€™t disturb the old habits. They donโ€™t connect to the real pressures people actually face at work and in life. Weโ€™ve made learning comfortable when itโ€™s supposed to be disruptive and difficult. Weโ€™ve made it a journeyโ€”when it shouldโ€™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 โ€œawarenessโ€ was enough. Because if they leave the same way they arrivedโ€”was it a journey at all. Or was it a scenic loop.? | 65 comments on LinkedIn
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The Learning Journey That Led to Nowhere
Your best coach can't be everywhere at once.
Your best coach can't be everywhere at once.
Your best coach can't be everywhere at once. ๐˜‰๐˜ถ๐˜ต ๐˜ต๐˜ฉ๐˜ฆ๐˜ช๐˜ณ ๐˜ˆ๐˜ ๐˜ต๐˜ธ๐˜ช๐˜ฏ ๐˜ค๐˜ข๐˜ฏ. 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. ๐—œ๐˜'๐˜€ ๐—ฐ๐—ฎ๐—น๐—น๐—ฒ๐—ฑ ๐—ฃ๐—ผ๐—ฟ๐˜๐—ฟ๐—ฎ๐—ถ๐˜๐˜€. Itโ€™s more than a chatbot. Itโ€™s a library of voice-enabled, AI-powered avatars of real-world experts, trained only on their unique ideas and content. What that means: โ†’ Minimal AI hallucinations โ†’ No generic advice โ†’ Just the expert's authentic perspective, on-demand Check out this screenshot of Google Portraits. Thatโ€™s an AI version of storytelling expert Matt Dicks. Heโ€™s coaching me to find the "heart of a story" in a seemingly dull, everyday moment โ€” cutting grass. It's a very immersive experience as he walks me through finding the "story" in my experience. Think about the possibilities: โ†’ Democratize coaching: Assign a storytelling coach or a feedback sparring partner to every new manager. โ†’ Practice in private: Let employees rehearse difficult conversations in a safe and controlled environment before the real thing. โ†’ 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. Itโ€™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
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Your best coach can't be everywhere at once.
For some time now, a few of us L&D loudmouths (me, David James, Guy W Wallace, Bob Mosher, Laura Overton Charles Jennings, Arun Pradhan, et al.) have been encouraging a shift from โ€˜learning objectivesโ€™ to โ€˜performance outcomesโ€™.
For some time now, a few of us L&D loudmouths (me, David James, Guy W Wallace, Bob Mosher, Laura Overton Charles Jennings, Arun Pradhan, et al.) have been encouraging a shift from โ€˜learning objectivesโ€™ to โ€˜performance outcomesโ€™.
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For some time now, a few of us L&D loudmouths (me, David James, Guy W Wallace, Bob Mosher, Laura Overton Charles Jennings, Arun Pradhan, et al.) have been encouraging a shift from โ€˜learning objectivesโ€™ to โ€˜performance outcomesโ€™.
๐—ช๐—ผ๐—ฟ๐—ธ๐—ถ๐—ป๐—ด ๐˜„๐—ถ๐˜๐—ต ๐— ๐—–๐—ฃ ๐—ถ๐˜€ ๐—ผ๐—ป๐—ฒ ๐—ผ๐—ณ ๐˜๐—ต๐—ผ๐˜€๐—ฒ ๐—ฟ๐—ฎ๐—ฟ๐—ฒ โ€œ๐—ผ๐—ต ๐—ฑ๐—ฎ๐—บ๐—ป, ๐˜๐—ต๐—ถ๐˜€ ๐—ฐ๐—ต๐—ฎ๐—ป๐—ด๐—ฒ๐˜€ ๐—ฒ๐˜ƒ๐—ฒ๐—ฟ๐˜†๐˜๐—ต๐—ถ๐—ป๐—ดโ€ ๐—บ๐—ผ๐—บ๐—ฒ๐—ป๐˜๐˜€! Iโ€™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 canโ€™t believe how much smoother everything gets.
๐—ช๐—ผ๐—ฟ๐—ธ๐—ถ๐—ป๐—ด ๐˜„๐—ถ๐˜๐—ต ๐— ๐—–๐—ฃ ๐—ถ๐˜€ ๐—ผ๐—ป๐—ฒ ๐—ผ๐—ณ ๐˜๐—ต๐—ผ๐˜€๐—ฒ ๐—ฟ๐—ฎ๐—ฟ๐—ฒ โ€œ๐—ผ๐—ต ๐—ฑ๐—ฎ๐—บ๐—ป, ๐˜๐—ต๐—ถ๐˜€ ๐—ฐ๐—ต๐—ฎ๐—ป๐—ด๐—ฒ๐˜€ ๐—ฒ๐˜ƒ๐—ฒ๐—ฟ๐˜†๐˜๐—ต๐—ถ๐—ป๐—ดโ€ ๐—บ๐—ผ๐—บ๐—ฒ๐—ป๐˜๐˜€! Iโ€™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 canโ€™t believe how much smoother everything gets.
๐—ช๐—ผ๐—ฟ๐—ธ๐—ถ๐—ป๐—ด ๐˜„๐—ถ๐˜๐—ต ๐— ๐—–๐—ฃ ๐—ถ๐˜€ ๐—ผ๐—ป๐—ฒ ๐—ผ๐—ณ ๐˜๐—ต๐—ผ๐˜€๐—ฒ ๐—ฟ๐—ฎ๐—ฟ๐—ฒ โ€œ๐—ผ๐—ต ๐—ฑ๐—ฎ๐—บ๐—ป, ๐˜๐—ต๐—ถ๐˜€ ๐—ฐ๐—ต๐—ฎ๐—ป๐—ด๐—ฒ๐˜€ ๐—ฒ๐˜ƒ๐—ฒ๐—ฟ๐˜†๐˜๐—ต๐—ถ๐—ป๐—ดโ€ ๐—บ๐—ผ๐—บ๐—ฒ๐—ป๐˜๐˜€! Iโ€™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 canโ€™t believe how much smoother everything gets. ๐—œ๐—ณ ๐—œ ๐—ต๐—ฎ๐—ฑ ๐˜๐—ผ ๐—ฏ๐—ฒ๐˜ ๐—ผ๐—ป ๐—ผ๐—ป๐—ฒ ๐—ฝ๐—ฟ๐—ผ๐˜๐—ผ๐—ฐ๐—ผ๐—น ๐—ฏ๐—ฒ๐—ฐ๐—ผ๐—บ๐—ถ๐—ป๐—ด ๐—ฒ๐˜€๐˜€๐—ฒ๐—ป๐˜๐—ถ๐—ฎ๐—น ๐—ถ๐—ป ๐—”๐—œ, ๐—ถ๐˜โ€™๐˜€ ๐— ๐—–๐—ฃ. MCP sounds complex โ€” but itโ€™s really not. Think of it as a guide that helps your AI agents understand: โ†’ what tools exist โ†’ how to talk to them โ†’ and when to use them ๐—›๐—ฒ๐—ฟ๐—ฒ ๐—ฎ๐—ฟ๐—ฒ ๐Ÿต ๐—ณ๐˜‚๐—น๐—น๐˜† ๐—ฑ๐—ผ๐—ฐ๐˜‚๐—บ๐—ฒ๐—ป๐˜๐—ฒ๐—ฑ ๐— ๐—–๐—ฃ ๐—ฝ๐—ฟ๐—ผ๐—ท๐—ฒ๐—ฐ๐˜๐˜€ ๐—ฒ๐˜…๐—ฝ๐—น๐—ฎ๐—ถ๐—ป๐—ฒ๐—ฑ ๐˜„๐—ถ๐˜๐—ต ๐˜ƒ๐—ถ๐˜€๐˜‚๐—ฎ๐—น๐˜€ & ๐—ผ๐—ฝ๐—ฒ๐—ป-๐˜€๐—ผ๐˜‚๐—ฟ๐—ฐ๐—ฒ ๐—ฐ๐—ผ๐—ฑ๐—ฒ (๐˜๐—ผ ๐—ด๐—ฒ๐˜ ๐˜†๐—ผ๐˜‚ ๐˜€๐˜๐—ฎ๐—ฟ๐˜๐—ฒ๐—ฑ):ย โฌ‡๏ธ 1. 100% Local MCP Client โ†’ Build a local MCP client using SQLite + Ollama โ€” no cloud, no tracking. โ†’ Full docu: https://lnkd.in/gtaEGvFZ 2. MCP-powered Agentic RAG โ†’ Add fallback logic, vector search, and agents in one clean flow. โ†’ Full docu: https://lnkd.in/gsV62MDE 3. MCP-powered Financial Analyst โ†’ Fetch stock data, extract insights, generate summaries. โ†’ Full docu: https://lnkd.in/g2\_EaJ\_d 4. MCP-powered Voice Agent โ†’ Speech-to-text, database queries, and spoken responses โ€” all local. โ†’ Full docu: https://lnkd.in/gweH8Rxi 5. Unified MCP Server (with MindsDB) โ†’ Query 200+ data sources via natural language using MindsDB + Cursor. โ†’ Full docu:https://lnkd.in/gCevVqKK 6. Shared Memory for Claude + Cursor โ†’ Build cross-app memory for dev workflows โ€” share context seamlessly. โ†’ Full docu: https://lnkd.in/giDXdtXd 7. RAG Over Complex Docs โ†’ Tackle PDFs, tables, charts, messy layouts with structured RAG. โ†’ Full docu: https://lnkd.in/gMHqHvBR 8. Synthetic Data Generator (SDV) โ†’ Generate synthetic tabular data locally via MCP + SDV. โ†’ Full docu:https://lnkd.in/ghyUyByS 9. Multi-Agent Deep Researcher โ†’ Rebuild ChatGPTโ€™s research mode, fully local with writing agents. โ†’ Full docu: https://lnkd.in/gp3EsrZ2 Kudos to Daily Dose of Data Science! ๐—œ ๐—ฒ๐˜…๐—ฝ๐—น๐—ผ๐—ฟ๐—ฒ ๐˜๐—ต๐—ฒ๐˜€๐—ฒ ๐—ฑ๐—ฒ๐˜ƒ๐—ฒ๐—น๐—ผ๐—ฝ๐—บ๐—ฒ๐—ป๐˜๐˜€ โ€” ๐—ฎ๐—ป๐—ฑ ๐˜„๐—ต๐—ฎ๐˜ ๐˜๐—ต๐—ฒ๐˜† ๐—บ๐—ฒ๐—ฎ๐—ป ๐—ณ๐—ผ๐—ฟ ๐—ฟ๐—ฒ๐—ฎ๐—น-๐˜„๐—ผ๐—ฟ๐—น๐—ฑ ๐˜‚๐˜€๐—ฒ ๐—ฐ๐—ฎ๐˜€๐—ฒ๐˜€ โ€” ๐—ถ๐—ป ๐—บ๐˜† ๐˜„๐—ฒ๐—ฒ๐—ธ๐—น๐˜† ๐—ป๐—ฒ๐˜„๐˜€๐—น๐—ฒ๐˜๐˜๐—ฒ๐—ฟ. ๐—ฌ๐—ผ๐˜‚ ๐—ฐ๐—ฎ๐—ป ๐˜€๐˜‚๐—ฏ๐˜€๐—ฐ๐—ฟ๐—ถ๐—ฏ๐—ฒ ๐—ต๐—ฒ๐—ฟ๐—ฒ ๐—ณ๐—ผ๐—ฟ ๐—ณ๐—ฟ๐—ฒ๐—ฒ: https://lnkd.in/dbf74Y9E | 49 comments on LinkedIn
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๐—ช๐—ผ๐—ฟ๐—ธ๐—ถ๐—ป๐—ด ๐˜„๐—ถ๐˜๐—ต ๐— ๐—–๐—ฃ ๐—ถ๐˜€ ๐—ผ๐—ป๐—ฒ ๐—ผ๐—ณ ๐˜๐—ต๐—ผ๐˜€๐—ฒ ๐—ฟ๐—ฎ๐—ฟ๐—ฒ โ€œ๐—ผ๐—ต ๐—ฑ๐—ฎ๐—บ๐—ป, ๐˜๐—ต๐—ถ๐˜€ ๐—ฐ๐—ต๐—ฎ๐—ป๐—ด๐—ฒ๐˜€ ๐—ฒ๐˜ƒ๐—ฒ๐—ฟ๐˜†๐˜๐—ต๐—ถ๐—ป๐—ดโ€ ๐—บ๐—ผ๐—บ๐—ฒ๐—ป๐˜๐˜€! Iโ€™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 canโ€™t believe how much smoother everything gets.
๐—™๐˜‚๐˜๐˜‚๐—ฟ๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด: ๐—ช๐—ฎ๐˜€ ๐˜„๐—ถ๐—ฟ๐—ฑ ๐—ฎ๐˜‚๐˜€ ๐—ฑ๐—ฒ๐—ฟ ๐—ฃ๐—ฒ๐—ฟ๐˜€๐—ผ๐—ป๐—ฎ๐—น๐—ฒ๐—ป๐˜๐˜„๐—ถ๐—ฐ๐—ธ๐—น๐˜‚๐—ป๐—ด? In der aktuellen Wirtschaftswoche (1) plรคdieren Julian Kirchherr und Cawa Younosi fรผr โ€žNO HRโ€œ, d. h. die Abschaffung des gesamten Personalbereiches mithilfe Generativer KI und die Rรผckverlagerung von HR-Aufgaben ins Management.
๐—™๐˜‚๐˜๐˜‚๐—ฟ๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด: ๐—ช๐—ฎ๐˜€ ๐˜„๐—ถ๐—ฟ๐—ฑ ๐—ฎ๐˜‚๐˜€ ๐—ฑ๐—ฒ๐—ฟ ๐—ฃ๐—ฒ๐—ฟ๐˜€๐—ผ๐—ป๐—ฎ๐—น๐—ฒ๐—ป๐˜๐˜„๐—ถ๐—ฐ๐—ธ๐—น๐˜‚๐—ป๐—ด? In der aktuellen Wirtschaftswoche (1) plรคdieren Julian Kirchherr und Cawa Younosi fรผr โ€žNO HRโ€œ, d. h. die Abschaffung des gesamten Personalbereiches mithilfe Generativer KI und die Rรผckverlagerung von HR-Aufgaben ins Management.
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๐—™๐˜‚๐˜๐˜‚๐—ฟ๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด: ๐—ช๐—ฎ๐˜€ ๐˜„๐—ถ๐—ฟ๐—ฑ ๐—ฎ๐˜‚๐˜€ ๐—ฑ๐—ฒ๐—ฟ ๐—ฃ๐—ฒ๐—ฟ๐˜€๐—ผ๐—ป๐—ฎ๐—น๐—ฒ๐—ป๐˜๐˜„๐—ถ๐—ฐ๐—ธ๐—น๐˜‚๐—ป๐—ด? In der aktuellen Wirtschaftswoche (1) plรคdieren Julian Kirchherr und Cawa Younosi fรผr โ€žNO HRโ€œ, d. h. die Abschaffung des gesamten Personalbereiches mithilfe Generativer KI und die Rรผckverlagerung von HR-Aufgaben ins Management.
๐™ƒ๐™–๐™—๐™ฉ ๐™ž๐™๐™ง ๐™จ๐™˜๐™๐™ค๐™ฃ ๐™ซ๐™ค๐™ฃ ๐˜ผ๐™„ ๐™‡๐™š๐™–๐™ฅ 2025 ๐™œ๐™š๐™รถ๐™ง๐™ฉ? AI Leap ist eine landesweite KI-Bildungsinitiative aus #Estland, die 20.000 Schรผler:innen der 10. und 11. Klasse sowie 3.000 Lehrkrรคften einen kostenlosen Zugang zu KI-basierten Lernwerkzeugen und entsprechender Schulung gewรคhrt.
๐™ƒ๐™–๐™—๐™ฉ ๐™ž๐™๐™ง ๐™จ๐™˜๐™๐™ค๐™ฃ ๐™ซ๐™ค๐™ฃ ๐˜ผ๐™„ ๐™‡๐™š๐™–๐™ฅ 2025 ๐™œ๐™š๐™รถ๐™ง๐™ฉ? AI Leap ist eine landesweite KI-Bildungsinitiative aus #Estland, die 20.000 Schรผler:innen der 10. und 11. Klasse sowie 3.000 Lehrkrรคften einen kostenlosen Zugang zu KI-basierten Lernwerkzeugen und entsprechender Schulung gewรคhrt.
๐™ƒ๐™–๐™—๐™ฉ ๐™ž๐™๐™ง ๐™จ๐™˜๐™๐™ค๐™ฃ ๐™ซ๐™ค๐™ฃ ๐˜ผ๐™„ ๐™‡๐™š๐™–๐™ฅ 2025 ๐™œ๐™š๐™รถ๐™ง๐™ฉ? AI Leap ist eine landesweite KI-Bildungsinitiative aus #Estland, die 20.000 Schรผler:innen der 10. und 11. Klasse sowie 3.000 Lehrkrรคften einen kostenlosen Zugang zu KI-basierten Lernwerkzeugen und entsprechender Schulung gewรคhrt. Bereits letztes Jahr war ich von der politischen Haltung und konsequenten Umsetzung Estlands fasziniert, als ich u.a. mit der Botschafterin der Republik Estland, Marika Linntam, auf dem Panel der IHK Berlin รผber die Arbeitswelt der Zukunft diskutieren durfte. AI Leap ist Estlands Antwort auf die vielseitigen Herausforderungen im Bildungsbereich und fรถrdert frรผhzeitig notwendige Schlรผsselkompetenzen, die fรผr den Arbeitsmarkt der Zukunft unerlรคsslich sind. Estland hat erkannt, dass ein professioneller Umgang mit KI-Technologien der wichtigste Wettbewerbsfaktor der Zukunft sein wird. Das war auch eine meiner insgesamt 4 Thesen, die ich vorab in einer Keynote vorstellen durfte, den kompletten Vortrag findet ihr hier: https://lnkd.in/dTdXMGuA ๐Ÿ…ฐ๐Ÿ…ฑ๐Ÿ…ด๐Ÿ†: ๐ŸŽฏ WO STEHEN WIR IN DEUTSCHLANDโ“ ๐ŸŽฏ Wie kรถnnen wir trotz Bildungsfรถrderalismus schnell wirksam werdenโ“ Spannende Fragen fรผr unsere neue Regierung v.a. mit Blick auf das Bundesministerium fรผr Digitales und Staatsmodernisierung unter Leitung von Dr. Karsten Wildberger, das die #Digitalisierung und die #KI #KรผnstlicheIntelligenz in Deutschland auf ein nรคchstes Level heben will. Was mir gefรคllt ist die Aufbruchstimmung und ein #WirMachen. Ich hoffe, dass es gelingt, etwas zu bewegen und die entsprechenden Stakeholder einzubinden. Ich bin gerne dabei, denn da gibt es noch VIEL ZU TUN. Estland macht es vor! Es ist zwar viel kleiner als Deutschland, dennoch kรถnnen wir viel von Estland (und anderen Lรคndern) lernen v.a. wenn wir in globale Kooperationen und in Public-Private-Partnership Modelle investieren. Quelle: https://lnkd.in/eUzXiSza #FutureOfWork #FutureSkills #SmartLearning :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: ๐Ÿ”” Du mรถchtest mehr รผber die Arbeitswelt im Wandel zu erfahren? Let's connect! ๐Ÿ’Œ Du interessierst Dich fรผr eine Zusammenarbeit? Schreib mir gerne!
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๐™ƒ๐™–๐™—๐™ฉ ๐™ž๐™๐™ง ๐™จ๐™˜๐™๐™ค๐™ฃ ๐™ซ๐™ค๐™ฃ ๐˜ผ๐™„ ๐™‡๐™š๐™–๐™ฅ 2025 ๐™œ๐™š๐™รถ๐™ง๐™ฉ? AI Leap ist eine landesweite KI-Bildungsinitiative aus #Estland, die 20.000 Schรผler:innen der 10. und 11. Klasse sowie 3.000 Lehrkrรคften einen kostenlosen Zugang zu KI-basierten Lernwerkzeugen und entsprechender Schulung gewรคhrt.
When I think about the future of learning with AI, I donโ€™t imagine it as more content and courses. A rewiring of what we do and how we do it is happening right now.
When I think about the future of learning with AI, I donโ€™t imagine it as more content and courses. A rewiring of what we do and how we do it is happening right now.
When I think about the future of learning with AI, I donโ€™t imagine it as more content and courses. A rewiring of what we do and how we do it is happening right now. While most teams are stuck at the point of innovations from 2 years back, you can be ahead of this. Yet...I still see a lot of talk and not so much action, sprinkled with a lot of misinformation and actual understanding of Gen AI's power and limitations. That creates a problem if the L&D industry wishes to thrive in the new world of work with AI. Thatโ€™s not to say I have โ€œall the answersโ€, coz I donโ€™t What I do have is a barrel load of real-world experiences working with teams on making AI adoptions a success. In tmrw's Steal These Thoughts! newsletter I'm going to share some of that with 5 insights that'll challenge everything you think you know about AI in L&D. Like the sound of that? โ†’ Join us by clicking 'subscribe to my newsletter' on this post and my profile. #education #learninganddevelopment #artificialintelligence
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When I think about the future of learning with AI, I donโ€™t imagine it as more content and courses. A rewiring of what we do and how we do it is happening right now.
Uses can now select the model you want to use with a custom GPT. Which is perfect for those using my performance consulting coach GPT
Uses can now select the model you want to use with a custom GPT. Which is perfect for those using my performance consulting coach GPT
This is the feature I've been waiting for OpenAI to release. It's not "game-changing", but it's incredibly useful. Uses can now select the model you want to use with a custom GPT. Which is perfect for those using my performance consulting coach GPT. Switch the model to o3 and use it as it was intended in my original design. Here's a little how-to video with my GPT in action. Find my GPT: https://lnkd.in/e2pdCKt8 #education #artificialintelligence #learninganddevelopment
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Uses can now select the model you want to use with a custom GPT. Which is perfect for those using my performance consulting coach GPT
What is learning? [3 mins] You donโ€™t really need to understand something to work with it - but it sure does help!
What is learning? [3 mins] You donโ€™t really need to understand something to work with it - but it sure does help!
For a long time I felt that if we wanted to answer questions such as โ€˜how do we design learning experiences?โ€™, โ€˜how do we measure learning?โ€™, โ€˜what is our pedagogy based on?โ€™ - or even just explain to stakeholders what it is that we do - then it would help to have an understanding of learning. Thanks again to Ben Gallacher and the #Inrehearsal team for creating this series. #learning #pedagogy #learningdesign #education #training
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What is learning? [3 mins] You donโ€™t really need to understand something to work with it - but it sure does help!
I spent my long weekend exploring the 2025 AI-in-Education report - two graphs showed a major disconnect!
I spent my long weekend exploring the 2025 AI-in-Education report - two graphs showed a major disconnect!
We might think we have an AI adoption story, but the reality is different: we still have a huge AI understanding gap! Here are some key stats from the report that honestly made me do a double-take: โ–ช๏ธ99% of education leaders, 87% of educators worldwide & 93% of US students have already used generative-AI for school at least once or twice! โ–ช๏ธYet only 44% of those educators worldwide & 41% of those US students say they โ€œknow a lot about AI.โ€ โ€ผ๏ธthis means our usage is far outpacing our understanding & thatโ€™s a significant gap! When such powerful tools are used without real fluency, we would see: โ–ช๏ธcomplicated implementation with no shared strategy (sounds familiar?)! โ–ช๏ธanxious students whoโ€™d fear being accused of cheating (I've heard this from so many students!) โ–ช๏ธoverwhelmed teachers who feel alone, unsupported & unprepared (this one is a common concern by some of my teacher friends)! The takeaway that jumped out at me: โ–ช๏ธthe schools that win won't be the ones that adopt AI the fastest, but the ones that adopt it the wisest! So here's what Iโ€™d think we should consider: โœ…building a "learning-first" culture across institutions & understanding when AI supports our learning vs. when it gets in the way! โ–ช๏ธmore like, we need to swap the question "Are we using AI?" for "Can we show any learning gains?" โš ๏ธso, what shifts does this report data point us to? Here is my takeaway: โœ…Building real AI fluency: โ–ช๏ธmoving beyond simple "prompting hacks" to true literacy that includes understanding ethics, biases & pedagogical purposes, โ–ช๏ธthis may need an AI Council of faculty, IT, learners & others working together to develop institution-wide policies on when AI helps or harms our learning, โ–ช๏ธit's about building shared wisdom, not just industry-ready skills โœ…Creating collaborative infrastructure: โ–ช๏ธthe "every teacher for themselves" approach seems to be failing, โ–ช๏ธshared guidelines, inclusive AI Councils & a culture of open conversation are now needed to bridge this huge gap! โœ…Shifting focus from "using AI tools" to "achieving learning outcomes": โ–ช๏ธthis one really resonated with me because unlike other tech rollouts we've witnessed, AI directly affects how our students think & learn, โ–ช๏ธour institutions need coordinated assessments tracking whether AI use makes our learners better thinkers or just faster task completers! The goal that keeps coming back to us โ–ช๏ธisn't to get every student using AI! โ–ช๏ธbut to make sure every learner & teacher really understands it! โ‰๏ธIโ€™m curious, where is your institution on this journey? 1๏ธโƒฃ individual use: everyone is figuring it out on their own (been there!) 2๏ธโƒฃ shared guidelines: we have policies, but they're not yet deeply integrated (getting closer!) 3๏ธโƒฃ fully integrated strategy: we have a unified approach with a learning-first, outcome-tracked focus (this is the goal!) | 24 comments on LinkedIn
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I spent my long weekend exploring the 2025 AI-in-Education report - two graphs showed a major disconnect!
๐—ง๐—ต๐—ถ๐˜€ ๐—ถ๐˜€ ๐—ต๐—ฎ๐—ป๐—ฑ๐˜€ ๐—ฑ๐—ผ๐˜„๐—ป ๐—ผ๐—ป๐—ฒ ๐—ผ๐—ณ ๐˜๐—ต๐—ฒ ๐—•๐—˜๐—ฆ๐—ง ๐˜ƒ๐—ถ๐˜€๐˜‚๐—ฎ๐—น๐—ถ๐˜‡๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ผ๐—ณ ๐—ต๐—ผ๐˜„ ๐—Ÿ๐—Ÿ๐— ๐˜€ ๐—ฎ๐—ฐ๐˜๐˜‚๐—ฎ๐—น๐—น๐˜† ๐˜„๐—ผ๐—ฟ๐—ธ. | Andreas Horn
๐—ง๐—ต๐—ถ๐˜€ ๐—ถ๐˜€ ๐—ต๐—ฎ๐—ป๐—ฑ๐˜€ ๐—ฑ๐—ผ๐˜„๐—ป ๐—ผ๐—ป๐—ฒ ๐—ผ๐—ณ ๐˜๐—ต๐—ฒ ๐—•๐—˜๐—ฆ๐—ง ๐˜ƒ๐—ถ๐˜€๐˜‚๐—ฎ๐—น๐—ถ๐˜‡๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ผ๐—ณ ๐—ต๐—ผ๐˜„ ๐—Ÿ๐—Ÿ๐— ๐˜€ ๐—ฎ๐—ฐ๐˜๐˜‚๐—ฎ๐—น๐—น๐˜† ๐˜„๐—ผ๐—ฟ๐—ธ. | Andreas Horn
๐—ง๐—ต๐—ถ๐˜€ ๐—ถ๐˜€ ๐—ต๐—ฎ๐—ป๐—ฑ๐˜€ ๐—ฑ๐—ผ๐˜„๐—ป ๐—ผ๐—ป๐—ฒ ๐—ผ๐—ณ ๐˜๐—ต๐—ฒ ๐—•๐—˜๐—ฆ๐—ง ๐˜ƒ๐—ถ๐˜€๐˜‚๐—ฎ๐—น๐—ถ๐˜‡๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ผ๐—ณ ๐—ต๐—ผ๐˜„ ๐—Ÿ๐—Ÿ๐— ๐˜€ ๐—ฎ๐—ฐ๐˜๐˜‚๐—ฎ๐—น๐—น๐˜† ๐˜„๐—ผ๐—ฟ๐—ธ. โฌ‡๏ธ ๐˜“๐˜ฆ๐˜ต'๐˜ด ๐˜ฃ๐˜ณ๐˜ฆ๐˜ข๐˜ฌ ๐˜ช๐˜ต ๐˜ฅ๐˜ฐ๐˜ธ๐˜ฏ: ๐—ง๐—ผ๐—ธ๐—ฒ๐—ป๐—ถ๐˜‡๐—ฎ๐˜๐—ถ๐—ผ๐—ป & ๐—˜๐—บ๐—ฏ๐—ฒ๐—ฑ๐—ฑ๐—ถ๐—ป๐—ด๐˜€: - Input text is broken into tokens (smaller chunks). - Each token is mapped to a vector in high-dimensional space, where words with similar meanings cluster together. ๐—ง๐—ต๐—ฒ ๐—”๐˜๐˜๐—ฒ๐—ป๐˜๐—ถ๐—ผ๐—ป ๐— ๐—ฒ๐—ฐ๐—ต๐—ฎ๐—ป๐—ถ๐˜€๐—บ (๐—ฆ๐—ฒ๐—น๐—ณ-๐—”๐˜๐˜๐—ฒ๐—ป๐˜๐—ถ๐—ผ๐—ป): - Words influence each other based on context โ€” ensuring "bank" in riverbank isnโ€™t confused with financial bank. - The Attention Block weighs relationships between words, refining their representations dynamically. ๐—™๐—ฒ๐—ฒ๐—ฑ-๐—™๐—ผ๐—ฟ๐˜„๐—ฎ๐—ฟ๐—ฑ ๐—Ÿ๐—ฎ๐˜†๐—ฒ๐—ฟ๐˜€ (๐——๐—ฒ๐—ฒ๐—ฝ ๐—ก๐—ฒ๐˜‚๐—ฟ๐—ฎ๐—น ๐—ก๐—ฒ๐˜๐˜„๐—ผ๐—ฟ๐—ธ ๐—ฃ๐—ฟ๐—ผ๐—ฐ๐—ฒ๐˜€๐˜€๐—ถ๐—ป๐—ด) - After attention, tokens pass through multiple feed-forward layers that refine meaning. - Each layer learns deeper semantic relationships, improving predictions. ๐—œ๐˜๐—ฒ๐—ฟ๐—ฎ๐˜๐—ถ๐—ผ๐—ป & ๐——๐—ฒ๐—ฒ๐—ฝ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด - This process repeats through dozens or even hundreds of layers, adjusting token meanings iteratively. - This is where the "deep" in deep learning comes in โ€” layers upon layers of matrix multiplications and optimizations. ๐—ฃ๐—ฟ๐—ฒ๐—ฑ๐—ถ๐—ฐ๐˜๐—ถ๐—ผ๐—ป & ๐—ฆ๐—ฎ๐—บ๐—ฝ๐—น๐—ถ๐—ป๐—ด - The final vector representation is used to predict the next word as a probability distribution. - The model samples from this distribution, generating text word by word. ๐—ง๐—ต๐—ฒ๐˜€๐—ฒ ๐—บ๐—ฒ๐—ฐ๐—ต๐—ฎ๐—ป๐—ถ๐—ฐ๐˜€ ๐—ฎ๐—ฟ๐—ฒ ๐—ฎ๐˜ ๐˜๐—ต๐—ฒ ๐—ฐ๐—ผ๐—ฟ๐—ฒ ๐—ผ๐—ณ ๐—ฎ๐—น๐—น ๐—Ÿ๐—Ÿ๐— ๐˜€ (๐—ฒ.๐—ด. ๐—–๐—ต๐—ฎ๐˜๐—š๐—ฃ๐—ง). ๐—œ๐˜ ๐—ถ๐˜€ ๐—ฐ๐—ฟ๐˜‚๐—ฐ๐—ถ๐—ฎ๐—น ๐˜๐—ผ ๐—ต๐—ฎ๐˜ƒ๐—ฒ ๐—ฎ ๐˜€๐—ผ๐—น๐—ถ๐—ฑ ๐˜‚๐—ป๐—ฑ๐—ฒ๐—ฟ๐˜€๐˜๐—ฎ๐—ป๐—ฑ๐—ถ๐—ป๐—ด ๐—ต๐—ผ๐˜„ ๐˜๐—ต๐—ฒ๐˜€๐—ฒ ๐—บ๐—ฒ๐—ฐ๐—ต๐—ฎ๐—ป๐—ถ๐—ฐ๐˜€ ๐˜„๐—ผ๐—ฟ๐—ธ ๐—ถ๐—ณ ๐˜†๐—ผ๐˜‚ ๐˜„๐—ฎ๐—ป๐˜ ๐˜๐—ผ ๐—ฏ๐˜‚๐—ถ๐—น๐—ฑ ๐˜€๐—ฐ๐—ฎ๐—น๐—ฎ๐—ฏ๐—น๐—ฒ, ๐—ฟ๐—ฒ๐˜€๐—ฝ๐—ผ๐—ป๐˜€๐—ถ๐—ฏ๐—น๐—ฒ ๐—”๐—œ ๐˜€๐—ผ๐—น๐˜‚๐˜๐—ถ๐—ผ๐—ป๐˜€. Here is the full video from 3Blue1Brown with exaplantion. I highly recommend to read, watch and bookmark this for a further deep dive: https://lnkd.in/dAviqK_6 ๐—œ ๐—ฒ๐˜…๐—ฝ๐—น๐—ผ๐—ฟ๐—ฒ ๐˜๐—ต๐—ฒ๐˜€๐—ฒ ๐—ฑ๐—ฒ๐˜ƒ๐—ฒ๐—น๐—ผ๐—ฝ๐—บ๐—ฒ๐—ป๐˜๐˜€ โ€” ๐—ฎ๐—ป๐—ฑ ๐˜„๐—ต๐—ฎ๐˜ ๐˜๐—ต๐—ฒ๐˜† ๐—บ๐—ฒ๐—ฎ๐—ป ๐—ณ๐—ผ๐—ฟ ๐—ฟ๐—ฒ๐—ฎ๐—น-๐˜„๐—ผ๐—ฟ๐—น๐—ฑ ๐˜‚๐˜€๐—ฒ ๐—ฐ๐—ฎ๐˜€๐—ฒ๐˜€ โ€” ๐—ถ๐—ป ๐—บ๐˜† ๐˜„๐—ฒ๐—ฒ๐—ธ๐—น๐˜† ๐—ป๐—ฒ๐˜„๐˜€๐—น๐—ฒ๐˜๐˜๐—ฒ๐—ฟ. ๐—ฌ๐—ผ๐˜‚ ๐—ฐ๐—ฎ๐—ป ๐˜€๐˜‚๐—ฏ๐˜€๐—ฐ๐—ฟ๐—ถ๐—ฏ๐—ฒ ๐—ต๐—ฒ๐—ฟ๐—ฒ ๐—ณ๐—ผ๐—ฟ ๐—ณ๐—ฟ๐—ฒ๐—ฒ: https://lnkd.in/dbf74Y9E | 48 comments on LinkedIn
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๐—ง๐—ต๐—ถ๐˜€ ๐—ถ๐˜€ ๐—ต๐—ฎ๐—ป๐—ฑ๐˜€ ๐—ฑ๐—ผ๐˜„๐—ป ๐—ผ๐—ป๐—ฒ ๐—ผ๐—ณ ๐˜๐—ต๐—ฒ ๐—•๐—˜๐—ฆ๐—ง ๐˜ƒ๐—ถ๐˜€๐˜‚๐—ฎ๐—น๐—ถ๐˜‡๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ผ๐—ณ ๐—ต๐—ผ๐˜„ ๐—Ÿ๐—Ÿ๐— ๐˜€ ๐—ฎ๐—ฐ๐˜๐˜‚๐—ฎ๐—น๐—น๐˜† ๐˜„๐—ผ๐—ฟ๐—ธ. | Andreas Horn
Scientists just published something in Nature that will scare every marketer, leader, and anyone else who thinks they understand human choice.
Scientists just published something in Nature that will scare every marketer, leader, and anyone else who thinks they understand human choice.
Scientists just published something in Nature that will scare every marketer, leader, and anyone else who thinks they understand human choice. Researchers created an AI called "Centaur" that can predict human behavior across ANY psychological experiment with disturbing accuracy. Not just one narrow task. Any decision-making scenario you throw at it. Here's the deal: They trained this AI on 10 million human choices from 160 different psychology experiments. Then they tested it against the best psychological theories we have. The AI won. In 31 out of 32 tests. But here's the part that really got me... Centaur wasn't an algorithm built to study human behavior. It was a language model that learned to read us. The researchers fed it tons of behavioral data, and suddenly it could predict choices better than decades of psychological research. This means our decision patterns aren't as unique as we think. The AI found the rules governing choices we believe are spontaneous. Even more unsettling? When they tested it on brain imaging data, the AI's internal representations became more aligned with human neural activity after learning our behavioral patterns. It's not just predicting what you'll choose, it's learning to think more like you do. The researchers even demonstrated something called "scientific regret minimization"โ€”using the AI to identify gaps in our understanding of human behavior, then developing better psychological models. Can a model based on Centaur be tuned for how customers behave? Companies will know your next purchasing decision before you make it. They'll design products you'll want, craft messages you'll respond to, and predict your reactions with amazing accuracy. Understanding human predictability is a competitive advantage today. Until now, that knowledge came from experts in behavioral science and consumer behavior. Now, there's Centaur. Here's my question: If AI can decode the patterns behind human choice with this level of accuracy, what does that mean for authentic decision-making in business? Will companies serve us better with perfectly tailored offerings, or with this level of understanding lead to dystopian manipulation? What's your take on predictable humans versus authentic choice? #AI #Psychology #BusinessStrategy #HumanBehavior | 369 comments on LinkedIn
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Scientists just published something in Nature that will scare every marketer, leader, and anyone else who thinks they understand human choice.
There is perhaps no industry more fundamentally disrupted by AI than professional services.
There is perhaps no industry more fundamentally disrupted by AI than professional services.
There is perhaps no industry more fundamentally disrupted by AI than professional services. Here are some of the top insights in the excellent new ThomsonReuters Future of Professionals Report, drawing on a survey of over 2,000 professionals globally. The industry is based on professionals, so individual capability development - as shown in the image - is fundamental. However it is also about organizational transformation, with most far behind where they need to be. The report shows: ๐Ÿ“Š Strategy-first adopters dominate ROI. Having a visible AI roadmap makes all the difference: firms with a clear strategy are 3.5 ร— more likely to enjoy at least one concrete benefit from AI, and almost twice as likely to see revenue growth compared with ad-hoc adopters. โฑ๏ธ AI is freeing up 240 hours a year. Professionals expect generative AI to claw back about five hours a weekโ€”240 hours annuallyโ€”worth roughly US $19 k per head and a US-wide impact of US $32 billion for legal and tax-accounting alone. ๐Ÿšฆ Expectations outrun execution. While 80 % of respondents foresee AI having a high or transformational impact within five years, only 38 % think their own organisation will hit that level this year, and three in ten say their firm is moving too slowly. ๐Ÿง  Skill depth multiplies payoff. Employees with good or expert AI knowledge are 2.8 ร— more likely to report organisational gains, regular users are 2.4 ร— more likely, and those with explicit AI adoption goals are 1.8 ร— more likely to see benefits. ๐Ÿ… Leaders who walk the talk win. When leaders model new tech adoption, their people are 1.7 ร— likelier to harvest AI benefits; active tech investors double their odds, and firms that added transformation roles see a 1.5 ร— uplift. ๐ŸŽฏ Accuracy anxieties set a sky-high bar. A hefty 91 % believe computers must outperform humans for accuracy, and 41 % insist on 100 % correctness before trusting AI without reviewโ€”making reliability the top blocker to further investment. ๐ŸŒฑ Millennials are sprinting ahead. Millennials are adopting AI at nearly twice the rate of Baby Boomers, underscoring a generational divide that could widen capability gaps if left unaddressed. ๐Ÿ› ๏ธ Tech-skill shortages stall teams. Almost half (46 %) of teams report skill gaps, with 31 % pointing to deficits in technology and data know-howโ€”outpacing gaps in traditional domain expertise or soft skills. ๐Ÿ”„ Service models are already shifting. Twenty-six percent of firms launched new advisory offerings in the past year, yet only 13 % have rolled out AI-powered services; meanwhile, a third are moving away from hourly billing and a quarter of in-house clients reward flexible fee structures. ๐Ÿ”— Goals and strategy are often misaligned. Two-thirds (65 %) of professionals who set personal AI goals donโ€™t know of any corporate AI strategy, while 38 % of organisations with a strategy give staff no personal targetsโ€”fuel for inconsistent, inefficient adoption
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There is perhaps no industry more fundamentally disrupted by AI than professional services.
You canโ€™t accomplish anything if your stakeholders arenโ€™t on board | Nick Shackleton-Jones
You canโ€™t accomplish anything if your stakeholders arenโ€™t on board | Nick Shackleton-Jones
'You canโ€™t accomplish anything if your stakeholders arenโ€™t on board.' After delivering hundreds of Human Centred Design (5Diยฉ) workshops, โ€˜Stakeholdersโ€™ is one of the topics that comes up time & time again: L&D want to do one thing, the business another. Whatโ€™s required is a consultative approach rather than an adversarial one - partnering not pushback. In response, I added new resources into the latest version version of the 5Diยฉ toolkit released a few months back (https://lnkd.in/eacUSAbc) - here are a couple of them: #learning #education #consulting #learninganddevelopment #HR #stakeholders | 11 comments on LinkedIn
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You canโ€™t accomplish anything if your stakeholders arenโ€™t on board | Nick Shackleton-Jones
๐—™๐˜‚๐˜๐˜‚๐—ฟ๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด: ๐—ช๐—ฎ๐˜€ ๐˜„๐—ถ๐—ฟ๐—ฑ ๐—ฎ๐˜‚๐˜€ ๐—ฑ๐—ฒ๐—ฟ ๐—ฃ๐—ฒ๐—ฟ๐˜€๐—ผ๐—ป๐—ฎ๐—น๐—ฒ๐—ป๐˜๐˜„๐—ถ๐—ฐ๐—ธ๐—น๐˜‚๐—ป๐—ด?
๐—™๐˜‚๐˜๐˜‚๐—ฟ๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด: ๐—ช๐—ฎ๐˜€ ๐˜„๐—ถ๐—ฟ๐—ฑ ๐—ฎ๐˜‚๐˜€ ๐—ฑ๐—ฒ๐—ฟ ๐—ฃ๐—ฒ๐—ฟ๐˜€๐—ผ๐—ป๐—ฎ๐—น๐—ฒ๐—ป๐˜๐˜„๐—ถ๐—ฐ๐—ธ๐—น๐˜‚๐—ป๐—ด?
๐—™๐˜‚๐˜๐˜‚๐—ฟ๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด: ๐—ช๐—ฎ๐˜€ ๐˜„๐—ถ๐—ฟ๐—ฑ ๐—ฎ๐˜‚๐˜€ ๐—ฑ๐—ฒ๐—ฟ ๐—ฃ๐—ฒ๐—ฟ๐˜€๐—ผ๐—ป๐—ฎ๐—น๐—ฒ๐—ป๐˜๐˜„๐—ถ๐—ฐ๐—ธ๐—น๐˜‚๐—ป๐—ด? In der aktuellen Wirtschaftswoche (1)ย plรคdieren Julian Kirchherr und Cawa Younosi fรผr โ€žNO HRโ€œ, d. h. die Abschaffung des gesamten Personalbereiches mithilfe Generativer KI und die Rรผckverlagerung von HR-Aufgaben ins Management. Alles, was besonders viel Fingerspitzengefรผhl verlangt, insbesondere die Entwicklung ihrer Mitarbeitenden, gehรถrt danach in die Verantwortung der Fรผhrungskrรคfte. Routinetรคtigkeiten wie das Erstellen von Arbeitsvertrรคgen, Schulungszertifikaten oder die erste Sichtung von Bewerbungen รผbernimmt die Maschine. Ihre Hauptargumente sind: ย โ€ข Die Fรผhrungskrรคfte sollen Personalaufgaben direkt verantworten, um eine engere Verknรผpfung der strategischen Unternehmensziele mit dem Personalmanagement zu erreichen.ย  ย โ€ข Traditionelle HR-Abteilungen sollten in ihren Strukturen รผberdacht oder sogar abgeschafft werden, um mehr Agilitรคt zu ermรถglichen. Nicht nur administrative Aufgaben wie Arbeitsvertrรคge, werden automatisiert, auch Arbeitszeugnisse oder Umfragen werden mit Hilfe der KI bearbeitet. Was auf den ersten Blick sehr radikal wirkt, beinhaltet aus meiner Sicht diskussionswรผrdige Aspekte. Mit diesem Vorschlag rรผcken viele Aufgaben wieder dorthin, wo sie eigentlich schon immer hingehรถrten, zur Fรผhrungskraft. Zu ihrer Verantwortung rechnen danach Onboarding, Nachfolgeplanung oder die gezielte Skillsentwicklung im Team sowie Fragen von Kultur und Transformation. Sie entwickeln sich zu โ€ž๐—ฃ๐—ฒ๐—ผ๐—ฝ๐—น๐—ฒ ๐— ๐—ฎ๐—ป๐—ฎ๐—ด๐—ฒ๐—ฟ*๐—ถ๐—ป๐—ป๐—ฒ๐—ปโ€œ. Ich bin der รœberzeugung, dass weiterhin eine HR-Funktion benรถtigt wird, aber mit relativ wenigen, hoch spezialisierten Expert*innen, z. B. fรผr die mittelfristige Personalbedarfsplanung und insbesondere fรผr ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด & ๐——๐—ฒ๐˜ƒ๐—ฒ๐—น๐—ผ๐—ฝ๐—บ๐—ฒ๐—ป๐˜. Im Future Learning stehen nรคmlich die Mitarbeitenden im Mittelpunkt, die mit Unterstรผtzung der KI selbst ihre Lernpfade planen und im Arbeitsprozess umsetzen. Ihre Fรผhrungskrรคfte werden deshalb zu ihren Entwicklungspartner*innen, die ihre personalisierten Lernpfade ermรถglichen. Die Personalentwicklung wandelt sich folglich zum ๐˜€๐˜๐—ฟ๐—ฎ๐˜๐—ฒ๐—ด๐—ถ๐˜€๐—ฐ๐—ต๐—ฒ๐—ป ๐—Ÿ๐—ฒ๐—ฟ๐—ป๐—ฎ๐—ฟ๐—ฐ๐—ต๐—ถ๐˜๐—ฒ๐—ธ๐˜๐—ฒ๐—ป, zu ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด & ๐——๐—ฒ๐˜ƒ๐—ฒ๐—น๐—ผ๐—ฝ๐—บ๐—ฒ๐—ป๐˜. Diese baut und optimiert laufend das "Lernhaus" als Ermรถglichungsraum fรผr selbstorganisiertes Lernen im Dialog mit der generativen KI , begleitet Lernprojekte sowie die notwendigen Verรคnderungsprozesse und evaluiert den Lernerfolg. ๐—Ÿ&๐—— ๐˜„๐—ถ๐—ฟ๐—ฑ ๐—ฑ๐—ฎ๐—บ๐—ถ๐˜ ๐˜‡๐˜‚๐—ฟ ๐˜‡๐—ฒ๐—ป๐˜๐—ฟ๐—ฎ๐—น๐—ฒ๐—ป ๐—จ๐—บ๐˜€๐—ฒ๐˜๐˜‡๐—ฒ๐—ฟ๐—ถ๐—ป ๐˜€๐˜๐—ฟ๐—ฎ๐˜๐—ฒ๐—ด๐—ถ๐˜€๐—ฐ๐—ต๐—ฒ๐—ฟ ๐—ง๐—ฟ๐—ฎ๐—ป๐˜€๐—ณ๐—ผ๐—ฟ๐—บ๐—ฎ๐˜๐—ถ๐—ผ๐—ป. Daraus ergibt sich folgende Rollenverteilung im Future Learning. (1) https://lnkd.in/eiupSJae
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๐—™๐˜‚๐˜๐˜‚๐—ฟ๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด: ๐—ช๐—ฎ๐˜€ ๐˜„๐—ถ๐—ฟ๐—ฑ ๐—ฎ๐˜‚๐˜€ ๐—ฑ๐—ฒ๐—ฟ ๐—ฃ๐—ฒ๐—ฟ๐˜€๐—ผ๐—ป๐—ฎ๐—น๐—ฒ๐—ป๐˜๐˜„๐—ถ๐—ฐ๐—ธ๐—น๐˜‚๐—ป๐—ด?
How AI ready ist your L&D team?
How AI ready ist your L&D team?
So, it finally happened, I spent a week โ€˜vibe codingโ€™ an app with an AI app builder. I learnt a ton from this experience, which Iโ€™ll be sharing more on in an upcoming premium edition of the Steal These Thoughts! newsletter. Until then, here's what I built and why. Just over a year ago (feels like an eternity these days), I shared an article with you on how you can assess the AI readiness of your L&D team in 4 levels. At the time, I thought, โ€œThis might be a good use case for an app experimentโ€, but the AI-powered app builders werenโ€™t so great then. Now, itโ€™s a whole new world, and Iโ€™ve spent about 30 hours creating an AI Readiness Assessment tool to live beside this article. The journey felt simple-ish, but it was not easy, friend. I now have a newfound respect for devs because the debugging and constant blockers have been traumatic ๐Ÿ˜‚. While the tool is available to use, it is most certainly a prototype, so expect bugs, glitches and weird things to happen. For now, Iโ€™d love for you to try it out, give me your feedback (worth developing or should I kill?) and any other thoughts. Watch the demo on how to use the tool โ†“ ๐Ÿ”— to the tool: https://lnkd.in/efJaPJF5 ๐Ÿ“ง Share your FB to support@stealthesethoughts.com #education #artificialintelligence
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How AI ready ist your L&D team?