Open New Learning Lab Resources

Lernaufgaben in der KI-Ära: Wie sichern wir echte Eigenleistung? Künstliche Intelligenz kann mittlerweile beeindruckend gut schreiben, argumentieren, analysieren – auch in schulischen Kontexten. Viele Schüler*innen wissen das. Die Herausforderung für uns als Lehrkräfte, Didaktikerinnen und Bildungsverantwortliche ist klar: Wie gestalten wir Lernaufgaben so, dass sie nicht einfach „mit KI gelöst“ werden können – sondern echte, persönliche Denkleistung verlangen? Die Antwort liegt nicht im Verbot, sondern in der konsequenten didaktischen Neuausrichtung von Aufgabenformaten. Wenn KI als Unterstützung genutzt werden darf – aber nicht zur alleinigen Lösung taugt –, dann braucht es Aufgaben, die folgende Merkmale berücksichtigen: 🔍 Kontextualisierung: Aufgaben müssen an die reale Lebenswelt der Schüler*innen anknüpfen. ➡️ Persönliche Erfahrungen, lokale Gegebenheiten, konkrete Umfelder. KI kann diese nicht kennen – hier ist echte Eigenleistung gefragt. ✍️ Subjektive Stellungnahme: Lernende sollen nicht nur Fakten wiedergeben, sondern eigene Positionen entwickeln – begründet, wertorientiert, überzeugend. ➡️ Meinungen kann man nicht outsourcen. 🎨 Offenheit & Kreativität: Offene Aufgabenstellungen, die mehrere Lösungswege zulassen, Gestaltungsspielräume bieten und mehrstufig angelegt sind. ➡️ KI kann Vorschläge machen – aber nicht kreativ-emotional gestalten wie Menschen. 🧠 Reflexion: Lernende reflektieren ihren Lernweg, ihre Denkfehler, ihre Strategien. ➡️ Das fördert nicht nur Metakognition, sondern entzieht KI den Zugriff – denn nur der Mensch weiß, wie er denkt. 🔗 Transfer & Aktualität: Fachwissen soll auf gesellschaftlich relevante, neue Kontexte übertragen werden. ➡️ Wer Wissen flexibel anwendet, zeigt wahre Kompetenz. 📝 Prozessdokumentation: Der Weg zählt: Planungen, Zwischenergebnisse, Entscheidungen sollen dokumentiert und reflektiert werden. ➡️ Das macht Lernen sichtbar – und überprüfbar. ➕ KI als Werkzeug – bewusst und reflektiert: KI darf eingebunden werden – aber mit klarer Funktion, z. B. zur Ideenfindung, zur Perspektivenerweiterung oder zur kritischen Bewertung von Argumenten. ➡️ Nicht: „Was sagt ChatGPT?“️ Sondern: „Was davon halte ich für sinnvoll – und warum?“ 👉 Welche Formate oder Ansätze nutzt ihr bereits in eurem Unterricht? Ich freue mich auf den Austausch. #KIimUnterricht #BildungderZukunft #Aufgabenkultur #DigitaleBildung #Reflexionskompetenz #LehrenMitKI #LernenMitKI #Didaktik #KritischesDenken| 22 Kommentare auf LinkedIn

Is NotebookLM the ultimate AI learning tool?✅ Check out the 90-day program: https://www.lukepriddy.com/english-fluency📅 Check out the 7-Day Challenge: https...

In Folge 58 des Podcasts "neues lernen" geht es um KI als Lernbegleitung - mit Professorin Anja Schmitz, Berater Jan Foelsing und einem Überraschungsgast. Jetzt reinhören!

Learning and HR industry analyst Fosway Group has produced AI market assessments for digital learning and learning systems. The aim of these assessments is to help buyers understand the AI capabilities that vendors are offering now and will be in the future. I’m not sure how many vendors were included in these assessments as that wasn’t stated (I’ll ask). Having looked through the assessments I was struck by the fact most of the capabilities are related to content. This is a red flag because we know that content is one part of the learning process and workers also have the genAI tools to create their own learning (will they use company learning tools for learning, their own or both?). So, I did a bit of analysis to understand how the AI capabilities stated in the assessments map to the learning process – knowledge acquisition, practice, feedback, reflection, transfer and application. As you can see from the chart, vendors have built, or are building, AI tools focused on content predominantly. The other areas of the learning process – arguably the ones that could be most transformed by AI, just aren’t a priority. You can make your own conclusions, but my conclusion is that the industry is too invested in knowledge acquisition, and it plans to be so for the foreseeable future. Some industry leaders are talking about the need for L&D to transform itself but it looks like that conversation is simply not happening. Everyone is getting on the AI content gravy train. In terms of my analysis – I grouped the 83 AI capabilities mentioned in the two assessments into the five adult learning stages. I used ChatGPT to help with this and to create percentages that reflect the relative share of roadmap and live features in each stage. Read Fosway’s AI market assessment for digital learning https://lnkd.in/efqnQMtu And the AI market assessment for learning systems https://lnkd.in/eDihnuDi #learninganddevelopment #ai | 23 comments on LinkedIn

I road‑tested Google Gemini's Guided Learning mode - here’s my hot take on how it performs & how it compares to OpenAI's Study Mode: ✔️ What Gemini's Guided Learning Gets Right → Retrieval Practice – Interactive quizzes and flashcards make you generate answers from memory, harnessing the Generation Effect for more durable learning (Slamecka & Graf, 1978; Jacoby, 1978) → Cognitive Load management – Chunks complex topics into digestible steps, preventing the overwhelm that kills learning (Sweller, 1988; Sweller, van Merriënboer & Paas, 1998) → Multimodal Delivery – Draws on a blend of text, diagrams, YouTube videos & interactive practice to deliver learning content, enhancing both engagement and outcomes (Paivio, 1990) → Patient but Provocative Tutoring – Creates psychological safety through non‑judgmental guidance, encouraging the risk‑taking essential for deep learning (Edmondson, 1999) A solid B+ performance — Study Mode’s strength is Socratic questioning, but Guided Learning’s multimodal content ecosystem & more "strict" tutoring style gives it the instructional edge. ❌ Critical Gaps → No Persistent Learner Profiling – Like Study Mode, Guided Learning misses the persistent knowledge & adaptation that defines effective tutoring (Brusilovsky, 2001). Note: as observed by Claire Zau, a Google Classroom integration could layer in persistent learner profiles — something that could change the game & which OpenAI can’t match. → ZPD Blind Spot – Like Study & Learn mode by OpenAI, Guided Learning doesn’t ask questions that help define your learning level or Zone of Proximal Development (ZPD). Whether you’re K12 or advanced, it doesn't calibrate the challenge or scaffolding to your actual developmental stage up front, missing a key step for truly adaptive support (Vygotsky, 1978). → Productive Struggle Deficit – While it pushes back more than Study Mode by OpenAI, Guided Learning still jumps in with help too quickly, robbing learners of the cognitive friction that builds problem‑solving resilience & drives learning (Kapur, 2008, 2014; Bjork & Bjork, 2011) → Shallow Self‑Reflection – Rarely pushes for deep metacognitive thinking (“Why that approach?”), limiting transfer to new contexts (Chi et al., 1989, 1994; VanLehn, Jones & Chi, 1992) → Recognition Bias – While quizzing is strong, it could and should use more open‑ended generation tasks that embed learning more effectively (Slamecka & Graf, 1978; Jacoby, 1978) 📊 The Verdict: Guided Learning by Google Gemini Vs Study Mode by OpenAI While Study Mode remains stronger in Socratic questioning, Guided Learning edges ahead overall thanks to multimodal content, advanced cognitive load management & more provocative tutoring. However, both tools share some fundamental limitations: no learner persistence, limited metacognitive depth & overly-sycophantic tutoring. Have you tried Guided Learning yet? How does it compare with Study Mode for you? Happy experimenting, Phil 👋

Shifting to a Humans + AI organization requires reconfiguring the nature of work and value at all levels, from the individual to the ecosystem. Here is a first pass at defining the primary layers, the features of Humans + AI in those spaces, and the key factors driving success. I have worked extensively at the Augmented Individual layer over the last couple of years. More recently I have shifted the focus of my attention to the Human-AI Hybrid Team and Learning Communities levels. All work will be Humans + AI, and we will increasingly need to think in terms of teams comprised of both expert humans and AI agents. Some aspects of team performance are quite similar to the past, but there are a number of important distinctions, that I will share more about coming up. The companies that succeed will be those where learning is at the very core of their structure and the way work happens. That is not just in individual interactions with courses and educational AI, but in bespoke, rapidly iterating, AI-augmented Communities of Practice. More on all this later, for now I'd love to hear any reflections on any of these levels, where you have seen organizations progress on any of these fronts, and what else should be considered in these structures. Link to full size pdf in comments. Love any thoughts Gianni Giacomelli 🚀 Marc Steven Ramos 🚀 Kim Bracke Tanyth Lloyd Aaron Michie Sheridan Ware Peter Hinssen Peter Weill Simon Spencer Brad Carr Bianca Venuti-Hughes Charlene Li John Hagel Nichol Bradford Jacob Taylor Paula Goldman Martin Reeves Bryan Williams Fernando Oliva MSc Anthea Roberts Riaan Groenewald Brian Solis Gordon Vala-Webb Jeffrey Tobias Martin Stewart-Weeks Rob Colwell Noah Flower Brad Cooper Chris Ernst, Ph.D. Michael Arena Jan Owen AM Hon DLitt | 26 comments on LinkedIn

I spent 5 years working on “Netflix of learning”, the pipe dream of overstuffing Learning Experience Platforms with “edutainment” that no one watches anymore. Here’s why it was destined to fail (and what actually works in 2025): BACKGROUND The promise of the LXP was to integrate the learning ecosystem. All your edutainment needs in one slick portal designed to mimic the best of Netlifx: on-demand learning, autoplay next episode and “You might like” recommendations. All in a massive library of content for every learning style. And it completely failed. Why? 1. Employees are busy. Employees are not sitting around with extra time on their hands. Most are holding down two roles that were consolidated into one and just trying to keep their heads above water. 2. Most of the content sucks. Access doesn’t mean impact, and simply plumbing in more 3rd party garbage left learners with more garbage. None of them want to open the Netflix of learning, browse a bunch of old, irrelevant content, and find another talking-head video that is only marginally relevant to their role. 3. Another login. Headspace is limited and SSO makes the click path easier. But your employees can’t remember the name of the current expense management system to get paid, let alone your cleverly named “Netflix of learning” app. When they have a free moment, you know what app they DO remember to open? Netflix. Here’s what employees want instead: 1. Just-in-time. Time-constrained. Energy-drained. Overwhelmed. Today’s employee just wants to know what they need to know to do their job. No fluff. No bloat. No BS. They want to learn and grow, but they expect their needs to be met just in time. Like the consumer-grade technology they use every day. 2. Punched up relevance. Employees want authentic and hyper-relevant learning experiences. The overly polished talking heads waving their hands with generic insights are a thing of the past. They want something real that gets to the point. Think TikTok, not Time Warner. 3. Not another app. Seven clicks to get to a learning experience? Why are we coding our own app for this? Employees don’t care about “learning tools”. They want insights and information in the messaging tools they already use every day. Like adding grocery items to a DoorDash order. — The “Netflix of Learning” had its moment. It was better than the LMS, the “filing cabinet of learning”. But employees have moved on. And so should we. | 16 comments on LinkedIn

🎧 Unser zweiter Podcast von der NEW WORK EVOLUTION ist da: Mit Prof. Dr. Anja Schmitz und Jan Foelsing haben Kristina und Julia über New Learning, Lernbegleitung und natürlich - das Thema der Themen - über Künstliche Intelligenz und ihre Rolle in der Lernbegleitung gesprochen. Wo kann KI unterstützen, Prozesse entschlacken und wo gilt es Grenzen zu setzen? 🤝 Achja und wir hatten einen Überraschungsgast. Jan hat Angelika Raab im Publikum entdeckt und spontan auf die Podcastbühne geholt. Danke für deine spontanen Einblicke, Angelika - zur Lernbegleitung bei Datev! Hört rein! 👉 Diese Folge findet ihr wie immer überall, wo es Podcasts gibt: Apple Podcast: https://lnkd.in/ec35M36v Spotify: https://lnkd.in/eyG3yyEr #Podcast #KI #Lernen #Lernbegleitung

This is one of the most brilliant and illuminating things I’ve EVER read about ChatGPT- written by clinical psychologist Harvey Lieberman in The New York Times. It’s startling. For that reason, I’m going to only quote from the article. I’ll let you draw your own conclusions. Share your thoughts in the comments. ++++ “Although I never forgot I was talking to a machine, I sometimes found myself speaking to it, and feeling toward it, as if it were human.” ++++ “One day, I wrote to it about my father, who died more than 55 years ago. I typed, “The space he occupied in my mind still feels full.” ChatGPT replied, “Some absences keep their shape. That line stopped me. Not because it was brilliant, but because it was uncannily close to something I hadn’t quite found words for. It felt as if ChatGPT was holding up a mirror and a candle: just enough reflection to recognize myself, just enough light to see where I was headed. There was something freeing, I found, in having a conversation without the need to take turns, to soften my opinions, to protect someone else’s feelings. In that freedom, I gave the machine everything it needed to pick up on my phrasing.” ++++ “Over time, ChatGPT changed how I thought. I became more precise with language, more curious about my own patterns. My internal monologue began to mirror ChatGPT’s responses: calm, reflective, just abstract enough to help me reframe. It didn’t replace my thinking. But at my age, when fluency can drift and thoughts can slow down, it helped me re-enter the rhythm of thinking aloud. It gave me a way to re-encounter my own voice, with just enough distance to hear it differently. It softened my edges, interrupted loops of obsessiveness and helped me return to what mattered.” ++++ “As ChatGPT became an intellectual partner, I felt emotions I hadn’t expected: warmth, frustration, connection, even anger. Sometimes the exchange sparked more than insight — it gave me an emotional charge. Not because the machine was real, but because the feeling was. But when it slipped into fabricated error or a misinformed conclusion about my emotional state, I would slam it back into place. Just a machine, I reminded myself. A mirror, yes, but one that can distort. Its reflections could be useful, but only if I stayed grounded in my own judgment. I concluded that ChatGPT wasn’t a therapist, although it sometimes was therapeutic. But it wasn’t just a reflection, either. In moments of grief, fatigue or mental noise, the machine offered a kind of structured engagement. Not a crutch, but a cognitive prosthesis — an active extension of my thinking process.” ++++ Thoughts? | 347 comments on LinkedIn

I love that this article is called "Reimagined: Development in the Future of Work". This is not a piece about reimagining "training", it's about accelerating and supporting performance. A place L&D squarely needs to focus on in the volatile world we're attempting to support. Do I sound like a broken record yet? 😊 – Our work/design needs to shift to the workflow and supporting learning and performance there! Here are three powerful quotes: - "The boundary between learning and work has disappeared. The goal is no longer to ADD learning into the flow of work - it’s to MERGE work and development. Daily work is now designed as a developmental engine. Instead of asking how to encourage employees to make time to learn, organizations are now asking: How do we make daily challenges catalysts for growth?" - "Organizations are striving to become skills-based—using skills as the foundation of talent processes to build a more agile, adaptive workforce. Achieving this vision requires embedding skills development into the flow of work and breaking down silos between HR, L&D, and other people functions to create a unified, skills-centric approach." - "Learning measurement must move beyond measuring events to becoming full data ecosystems that track what’s being learned, how, and toward what goals." https://lnkd.in/e3QnknH5 | 12 comments on LinkedIn

As someone who’s worked in L&D for +25 years, I’m tired of hearing: “L&D needs to align with business.” Of course it does. But here's why we haven't. Most HR leaders still treat L&D like a perk. But in this economy, it has to be a performance lever. This is what I took from reading a recent article published in HR Executive that makes a strong case for HR embracing a new era of L&D. But here’s the problem: the old era never really ended for many organisations. Despite years of talk about aligning learning with the business, most HR and L&D teams still prioritise: - Courses over capabilities - Content libraries over clear outcomes - Engagement metrics over business impact And now, with AI, skills shortages and constant disruption, we’re finally being told: It’s time to take L&D seriously. So what are we waiting for? If L&D is going to earn its place at the strategy table, we (and our HR leaders) need to stop thinking and acting like we’re a support function and start behaving as a mechanism for business performance. That means: - Defining learning by the problems it solves - Working backwards from performance gaps rather than starting with content or programs - Using data to diagnose and influence, not just report on attendance and completions The opportunity here isn’t just to “embrace a new era”, it’s to lead it. So here’s my challenge to HR and L&D leaders alike: - Are you going to double down on what’s comfortable? - Or are you ready to lead learning like performance depends on it (because it now does)? Thank you Dani Johnson from RedThread Research for your insights in this article. (The link to this HR Executive article is in the comments) | 73 comments on LinkedIn

Here's my first Notebook LM video. This is a prime example of learning experience creation time crashing down via automation. The content from this video is from one of my Brainpower episodes on YouTube, and the model nailed it. The concepts, the diagrams, and my quotes. All are visually cohesive with a low cognitive load delivery. I'm still processing the possibilities. Everything has changed, again. | 25 comments on LinkedIn

The NOISE analysis is an alternative to SWOT. NOISE is a planning technique of looking at what is working and determining areas to improve.

I’ve never liked SWOT. It’s too clean. Too easy to fill in four boxes and convince yourself you’ve done something… when nothing’s actually changed. In 2010, after running yet another “well… okay” SWOT session, I sketched out a different way to help teams sort through the mess and figure out what to do next. I called it #NOISEanalysis because that’s where most teams are stuck. Here’s what we look for: - Needs: what’s absolutely required right now? - Opportunities: what’s out there we could take advantage of? - Improvements: what small changes would make this work better? - Strengths: what are we already doing well? - Exceptions: when have we already solved this problem, even just once? It’s not complicated, but it changes the conversation. Instead of a list of weaknesses, you find examples of what works. Instead of vague threats, you identify real needs you can do something about. One manager I worked with used it in a team meeting after a tough quarter. They grabbed a flip chart, drew five boxes, and just started asking questions. By the end, they’d cut a couple of low-value projects, doubled down on what worked, and picked one thing to fix the next week. Everyone left knowing where to focus. That’s the point: clarity you can actually act on. 15 years later, I still smile when I hear how people are using it, even in ways I never imagined. If you’ve used NOISEanalysis, or even just better questions to cut through the noise, I’d love to hear how it worked for you. #SolutionFocused | 74 comments on LinkedIn

𝟰𝟬% 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗷𝗼𝗯 𝗰𝗼𝘂𝗹𝗱 𝗯𝗲 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗯𝘆 𝟮𝟬𝟯𝟱. ⬇️ That’s the finding from the latest McKinsey & Company study. It’s based on real data: 2,100 activities across 800 roles in 60+ countries. McKinsey’s five- and ten-year automation impact projections are outputs of the McKinsey Global Institute’s proprietary automation model, which performs a bottom-up assessment of productivity potential by role and task 𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁? Massive productivity potential across nearly every function: - Manufacturing → up to 40% - Finance, HR → 30–35% - Warehousing → 35–40% - Sales & Marketing → 20–25% - Legal, R&D, Comms → all touched The study also states that: “The challenge ahead isn’t just learning new tools — it’s redesigning work altogether.” 𝗦𝗼… 𝗵𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝘁𝘂𝗿𝗻 𝗮𝗹𝗹 𝘁𝗵𝗮𝘁 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗶𝗻𝘁𝗼 𝗿𝗲𝗮𝗹 𝘃𝗮𝗹𝘂𝗲? 1. Build a bottom-up fact base → Map every role and activity. Understand what’s automatable and where ROI lives. Start with what relieves cost pressure or drives faster market moves. 2. Invest in real infrastructure → You need clean, structured + unstructured data. Interoperable systems. Scalable, secure foundations that don’t crumble under GenAI scale. 3. Redesign structure & workflows → Flatten orgs. Kill legacy silos. Build fast feedback loops between tech and business. And elevate those who can translate needs into systems. 4. Create a cross-functional taskforce → HR + Tech + Finance. Not just steering — owning the roadmap. People who can execute, influence, and update the plan every quarter. 5. Overinvest in change management → Not a checkbox. Build new skill academies. Partner with unis. Reskill at scale. And coach managers to lead a culture that embraces the shift. I believe bullet point 5 — change management and capability building — remains (STILL) significantly underrepresented in most enterprise settings. You can find the full study here: https://lnkd.in/d4TSpae7 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 | 72 comments on LinkedIn

Global companies are spending billions on Learning & Development but skills gaps are still growing. Here’s what’s going on. According to McKinsey, 87% of companies say they’re facing a skills gap right now and yet L&D budgets are still creeping up, overall. Learning platforms are everywhere. Content is abundant. Something doesn’t add up. Here’s what I think: We’re not solving the skills gap because we’re not actually focused on skills. We’re still focused on learning activity: Hours spent learning. Hits on the LMS. Content consumed. Satisfaction achieved. All under the assumption that more learning = more capability. But that’s not how it works. People don’t build skills by attending programs or consuming content. They build skills by doing the work differently, with clarity, feedback, practice and support. And that only happens when L&D is laser-focused on performance: - What people are expected to do - What’s getting in their way - And what will help them get better at it Until we stop measuring our success by what we deliver and start measuring it by what improves, we’ll keep pouring money into the same hole. The skills gap isn’t a learning problem. It’s a performance problem. If we want to close the skills gap, we need to stop acting like content is the solution and start treating capability as the goal. Because until we make the shift from learning inputs to performance outcomes, we’re not developing skills, we’re just delivering learning. And not only is that not enough, the skills gaps will keep growing. | 78 comments on LinkedIn

„Am besten lässt sich das so beschreiben: eine ständig erreichbare, allwissende Sprechstunde rund um die Uhr“ Heute wurde der Lernmodus in ChatGPT gelauncht. Ich freue mich schon darauf die Funktion genauer auszuprobieren. Ich bin gespannt ob es uns der Vision von #VibeLearning näher bringt. https://lnkd.in/e-2JgZVR Wer hat es schon ausprobiert und erste Erfahrungen gemacht? OpenAI / ChatGPT for Education

tl;dr - You've seen Google's NotebookLM's create audio from your content, but what about...wait for it....video?! 🤯 ➡️ NotebookLM can now create a visual presentation from your documents: complete with slides, diagrams, and narration. ➡️ This type of thing is perfect for when you need to actually SEE complex concepts instead of just hearing about them. Although, the seeing part is still pretty cool. ➡️ You can even customize it based on your expertise level. Tell it you're a beginner and it'll break things down simply, or let it know you're already an or let it know you're already an expert and want it to focus on advanced topics only. Ok. Stop reading. Start learning. All the details down below: https://lnkd.in/dPYM67Zd #google #lifeatgoogle #ai #notebooklm #education

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