Open New Learning Lab Resources

A few hours ago, Google published a white paper laying out their vision for the Future of Learning. Here's the TLDR: The Headline: 👉 Global learning is at a crossroads: learner outcomes have dropped sharply worldwide, and UNESCO projects a shortage of 44 million teachers by 2030. 👉 AI is positioned as *the* tool to save us from an impending education crisis BUT... 👉 The real "secret weapon" for improving education isn't the tech: it's the learning science we build into it. According to Google, the four biggest opportunities offered by AI in education are: 🔥 Learning Science at Scale – Embed evidence-based methods (retrieval practice, spaced repetition, active feedback) directly into everyday tools. 🔥 Making Anything Learnable – Adjust explanations, examples and complexity to meet each learner where they are. 🔥 Universal Access – Break down language, literacy and disability barriers through AI-powered translation and transformation. 🔥 Empowering Educators – Free up teacher time through AI-assisted lesson planning, resource creation and differentiation. Overall, Google's latest white paper signals an evolving ed-tech culture which centres on a more substantive partnership between ed & tech: 👉 Co-Creation: Google commits to investing in evidence-based approaches to learning design and development and to rigorous evaluation, pilot studies and educator-led research to test and demo impact. 👉 Collaborative Development: Google commits to working with schools, NGOs, researchers and learning scientists to co-design tools for learning. You can read the white paper in full using the link in comments. Happy innovating! Phil 👋 | 26 comments on LinkedIn

We talk a lot about “learning organisations”...but far less about unlearning. I’ve just been reading a great integrative review on organisational unlearning (Klammer et al., 2024), and it makes a simple but powerful point: "Most organisations don’t fail because they can’t learn something new… They fail because they don’t reduce the grip of what they already know" A few insights that stood out to me: 1️⃣ Unlearning isn’t deleting knowledge. It’s about deliberately reducing the influence of old assumptions, routines and stories so they stop driving behaviour by default. 2️⃣ It’s a process, not an event. It unfolds over time, across individuals, teams and the wider system, with feedback loops, resistance, and sometimes the old quietly creeping back in. 3️⃣ Most unlearning is reactive. We tend to unlearn only after a crisis, failure or disruption. Proactive unlearning, letting go before we hit the wall, is rare but strategically vital. 4️⃣ Unlearning is political. Deciding what to unlearn and whose knowledge is obsolete is deeply tied to power, identity and organisational history. For me, this raises a tough question for leaders and L&D/OD professionals: What knowledge, routines or stories in your organisation are quietly past their sell by date and what are you doing to intentionally unlearn them?

The AI opportunity in L&D isn't one thing - it's five different capability unlocks.

“𝐒𝐤𝐢𝐥𝐥𝐬 𝐝𝐨𝐧’𝐭 𝐝𝐞𝐥𝐢𝐯𝐞𝐫 𝐑𝐎𝐈.” 𝐈’𝐦 𝐡𝐞𝐚𝐫𝐢𝐧𝐠 𝐭𝐡𝐢𝐬 𝐟𝐫𝐨𝐦 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐚𝐟𝐭𝐞𝐫 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞.

Mit KI Wissensmanagement das informelle Wissen, der größte Schatz eurer Organisation, speichern und durch AI Assistenten nutzbar machen.

There is no way to candy coat it.. From L&D challenges and strategy, to investment and budgets, to the adoption and emerging adoption of AI, along with new L&D tech decisions and the evolution of the learning experience - it's not getting easier - just yet! Expectations for #AI in L&D are becoming turbo-charged - with the numbers doubling this year for how much AI is already influencing L&D headcount and resourcing plans. Half of L&D teams think that by 2030 half of what they do could be replaced by AI and still be effective. So, what will L&D teams be doing with the time? In the absence of a good value proposition - downsizing beckons! But equally not all supplier AI roadmaps are real, and there is a lot of vapourware and a long list of promises. Who can you trust to be telling the truth about what AI they can really delivery for you? Is AI going to come soon enough to save L&D - anyway? It looks like some of the cavalry may not arrive for some time. Most L&D professionals don't think their learning systems are fit for the modern workforce. Whilst ChatGPT have Study Mode, and Google has Notebook LM, most LMS & LXPs are still stuck in catalogues and AI enhanced catalogue searches. Nothing exists to support learning cycles at a time when skills and blended learning are becoming more and more important. BUT, as 2026 looms large, it really isn't time to hide under the duvet...! It's time to take a deep breath, put your big pants on and start building the capabilities in your team that will enable you to move from being a transactional function into one that uses its deep understanding of learning, learning motivation, feedback culture, skills, tasks ontologies, organisational change, people and work intelligence and start building a route into the high performance organisation of tomorrow, and the new realities ofthe future of work... It's time to be deeply aligned to business transformation, upskilling and business improvement. It's time to be a #IntelligenceLed & #ValueCentred-L&D team, because you'll need to be able to evidence your value-add if you want a smooth ride. It's also time to find the learning solutions that are fit for the future workforce because as sure a night follows day and day follows night... having the right solutions in the right connected ecosystem might be the thing that turns turns #2026 into a great year. Why do I say ALL this? Because that's what you told us in this year’s Fosway Group Digital Learning Realities Research. Naturally if you want help navigating this maelstrom - come and speak to our experts Myles Runham and Fiona Leteney . No-one knows more about the opportunities than they do!

A practical AI adoption framework for HR and L&D leaders: assess readiness, set clear guardrails, and enable employees for responsible AI adoption.

After dozens of conversations with L&D leaders over the past months across industries, org sizes, and wildly different levels of AI maturity, one thing has become painfully clear:

2. Das Transkript in NotebookLM laden 3. Audio und Video davon erstellen lassen 4. Der KI von NotebookLM bei der Erstellung sagen: "Erstelle mir ein Recap zu diesem Transkript einer Session. Was ist passiert? Was wurde besprochen? Was waren die zentralen Erkenntnisse? Was waren die Ergebnisse?" Und boom, kommt da etwas raus, was die Inhalte der Session oftmals direkt beim ersten Versuch wirklich gut zusammenfasst. Aber hört selbst in das Recap zu unserer 4. Session zum Thema Vibe Learning, in welcher wir angefangen haben, Vibe Learning in verschiedenen Lernkontexten zu denken. Konkret: Vibe Learning als Trainingsstarter und Vibe Learning als Bindeglied von größeren Upskilling Programmen. Ich fand das so cool, das musste ich vor dem Wochenende noch mit euch teilen. 😊

Beyond Faster Content: a sneak peek of data from my 2025 survey with Synthesia.

Artificial intelligence (AI) is already driving scientific breakthroughs in a variety of research fields, ranging from the life sciences to mathematics.

Lebensstile: Eine neue Sicht auf Kunden und ihre Bedürfnisse. Die Unterteilung eines Großteils der deutschen Gesellschaft in 18 Lebensstile ermöglicht es, ein gut ausdifferenziertes Bild der eigenen und potenziellen Kunden zu erhalten. Ein gekürzter Artikel aus der Trendstudie und dem Workbook “Lebensstile”.

Explore interactive visualizations of a large scientific research article dataset built in collaboration between Inference.net and LAION

Choose your track and dive in.

👇 The study analyzed AI agents across 50+ occupations, from software engineering to marketing, HR, and design, and compared how they completed human workflows end to end. What they found is both exciting and humbling: • Agents “code everything.” Even in creative or administrative tasks, AI agents defaulted to treating work as a coding problem. Instead of drafting slides or writing strategies, they generated and ran code to produce results, automating processes that humans usually approach through reasoning and iteration. • They’re faster and cheaper, but not better. Agents completed tasks 4 – 8× faster and at a fraction of the cost, yet their outputs showed lower quality, weak tool use, and frequent factual errors or hallucinations. • Human–AI teaming consistently outperformed solo AI.🔥 When humans guided or reviewed the agent’s process, acting more like a “manager” or “co-pilot”, the results improved dramatically. 🧠 My take: The race toward “fully autonomous AI” is missing the real opportunity, co-intelligence. Right now, the biggest ROI in enterprises isn’t from replacing humans. It’s from augmenting them. ✅ Use AI to translate intent into action, not replace decision-making. ✅ Build copilots before colleagues, co-workers who understand your workflow, not just your prompt. ✅ Redesign processes for hybrid intelligence, where AI handles execution and humans handle ambiguity. The future of work isn’t humans or AI. (for the next 5 years IMO) It’s humans with AI, working in a shared cognitive space where each amplifies the other’s strengths. Because autonomy without alignment isn’t intelligence, it’s chaos. Autonomous AI isn’t replacing human work, it’s redistributing it. Humans shifted from doing to directing, while agents handled repetitive, programmable layers. Maybe we are just too fast to shift from "uncool" Copilot to sth more exciting called "Fully Autonomous AI", WDYT? | 36 comments on LinkedIn

🚨 Just dropped! McKinsey report on AI in 2025: the hype is loud, the impact is.... All the CEO must read this: almost everyone is “using AI,” but only a small slice is wiring it deep enough to move the needle. 88% of companies use AI somewhere, yet ~⅔ are still stuck in experiments/pilots, not scale. Agents are real but early: 62% are experimenting; only 23% are scaling in at least one function (and typically just one or two). Only 39% report any impact from AI at the enterprise level. The rest have scattered wins, not system change. High performers (≈6%) think bigger: they aim for transformation, not just cost cuts, and are ~3× more likely to redesign workflows around AI. Leadership matters: where the CEO and senior team own AI, adoption scales and budgets follow (many leaders spend 20% of digital on AI). Value shows up fastest in software eng, IT, mfg (cost ↓) and in marketing/sales, strategy/finance, product (revenue ↑). Risk is real and showing up: inaccuracy and explainability issues top the list, mature orgs pair ambition with stronger guardrails and human-in-the-loop. My take: Most firms bought tools; the few winners rebuilt work. Agent pilots are cool, but without workflow redesign, data plumbing, and clear governance, you’re funding demos, not outcomes. The org that rewires will beat the org that “rolls out.” Leaders should set the bar higher than “efficiency.” Tie AI to growth, new offerings, and customer experience, then go after costs. Redesign 3–5 critical workflows end-to-end (not feature by feature). Ship, measure, harden, repeat. Put ownership at the top. If the CEO isn’t accountable for AI governance and ROI, it will stall. Invest in the platform: data products, evaluation, CI/CD for models/agents, human-in-the-loop checkpoints, risk controls. Skill the workforce for agents: task decomposition, prompt/context ops, verification, and change management, at scale. AI ROI doesn’t come from the model. It comes from the company willing to change its operating system. wdyt? | 76 comments on LinkedIn

Archive ‘How to AI’ (most recent to oldest) Delve, and the many words to ban on ChatGPT. (soon) From Youtube to your own AI. (the last one) Your ChatGPT prompt is too long. Remove em-dashes (and more). How to stop receiving the same ChatGPT answer. The new ChatGPT Atlas is live. Is it any good...

Vor einigen Jahren entwickelte ich zusammen mit Niclas Schaper und Andreas Seifert nicht nur das Oktagon-Modell, sondern auch eine dazu passende Skala zur Messung des informellen Lernens am Arbeitsplatz. Später folgte, in Zusammenarbeit mit Prof. Dr. Michael Knappstein, auch noch die Publikation einer 8-Item-Kurzversion. Die Skalen lagen zwar bereits auf Deutsch vor – allerdings nur schwer zugänglich im Anhang der englischsprachigen Publikationen, und Evaluationen sowie Handhabungshinweise waren ausschließlich in englischer „Wissenschaftssprache“ verfasst.   💡 Aus der HR/PE-Praxis kam daher immer wieder die Bitte: Gibt es ein deutschsprachiges, anwendungsorientiertes Manual? Diesem Wunsch sind wir nun nachgekommen. Die „Zusammenstellung sozialwissenschaftlicher Items und Skalen“ (#ZIS) des GESIS - Leibniz-Institut für Sozialwissenschaften bietet genau die Plattform, um diesen Transfer transparent und praxistauglich umzusetzen. In der ZIS-Datenbank stehen daher ab sofort bereit: 📝  Zwei Versionen der Skala für informelles Lernen am Arbeitsplatz: Kurz (8 Items) für schnelle Erhebungen & Monitoring, Lang (24 Items, 8 Facetten) für differenzierte Analysen. 📝 Praxisnah dokumentiert, alles auf Deutsch: klare Anwendung, Auswertung und Interpretation. 📝 Offen zugänglich (kostenfrei) über ZIS, inklusive Materialien. 📝 Transparenzzertifikat des Diagnostik- und Testkuratoriums (DTK) von BDP und DGPs: bestätigt die vollständige, standardkonforme Dokumentation – als verlässliche Grundlage für informierte Anwendung und Bewertung. Direkt zu den Inhalten: 🔗 ZIS – Langskala: https://lnkd.in/eiMUWhBz 🔗 ZIS – Kurzskala: https://lnkd.in/e_rVMdqs 📄 Originalpublikation der Langversion auf Englisch (2019): https://lnkd.in/gbGpxKY 📄 Originalpublikation der Kurzversion auf Englisch (2023): https://lnkd.in/e5WsWvcK   Mein Dank geht an #GESIS, insbesondere an den betreuenden Reviewer Julian Urban, für die Möglichkeit, die Skalen offen und nachhaltig zu publizieren – das hilft, Brücken zwischen Forschung und betrieblicher Praxis zu schlagen. 🙌 Rückmeldungen aus der Anwendung sind sehr gern willkommen, z.B. zu Einsatzfeldern, Betrachtungen zu Aufwand & Ertrag und Einbindung in organisationale Lernevaluationen. Viel Erfolg beim Nutzen der Skalen! 🙂 #InformellesLernen #NewLearning #HR #Personalentwicklung #Praxistransfer| 19 Kommentare auf LinkedIn

Last time I asked what would happen if L&D went away. This week, we’re asking how it can stay relevant as everything around it changes. We created the L&D Strategy Framework to account for all the things L&D should consider in its strategy. We’re in the process of figuring out what it looks like with an AI lens. What areas do ya'll think are changing, and how? P.S. - We're discussing this in this week’s L&D Huddle on Wed 1PM EDT. RedThread members can RSVP on the platform. I also have a few guest spots. DM me if you want in. P.P.S. - This L&D Strategy Framework is a part of a larger infographic. You can grab the full infographic through our Starter tier (read: free). Link in the first comment. | 33 comments on LinkedIn

It’s finally happening. 🎉 We’re ditching LMS and SCORM and building learning resources right where our learners already spend their time: in the CRM and sales enablement tools they use every day. It’s literally learning in the flow of work. With AI, we’ve created agents that deliver learning exactly when it’s needed, put together role-specific learning paths, and (soon) will really push personalized lessons based on our team’s performance and platform usage. When AI tools first arrived, the fear was real—and honestly, it still is. As an #LXD, I see every day how AI is “taking over” parts of my job: - I no longer have to set up or record audio and video; AI does that for a fraction of the cost. - I don’t need to code interactions anymore; AI handles that with clear instructions. - I don’t have to manually gather and review progress reports or analyze and present data; AI does it faster and more accurately than I could. Since AI is taking care of those routine and time-consuming tasks, I can focus on designing learning experiences. I actually have time to sit down with my team, brainstorm strategy, and think long term. We get to be more creative and experiment with new ideas (whether they work or not) without burning through tons of resources. I never thought I’d see the day, but yep, LMS and SCORM are dead (or about to be). AI image created in Canva. | 134 comments on LinkedIn

The Headline: 👉 Global learning is at a crossroads: learner outcomes have dropped sharply worldwide, and UNESCO projects a shortage of 44 million teachers by 2030. 👉 AI is positioned as *the* tool to save us from an impending education crisis BUT... 👉 The real "secret weapon" for improving education isn't the tech: it's the learning science we build into it. According to Google, the four biggest opportunities offered by AI in education are: 🔥 Learning Science at Scale – Embed evidence-based methods (retrieval practice, spaced repetition, active feedback) directly into everyday tools. 🔥 Making Anything Learnable – Adjust explanations, examples and complexity to meet each learner where they are. 🔥 Universal Access – Break down language, literacy and disability barriers through AI-powered translation and transformation. 🔥 Empowering Educators – Free up teacher time through AI-assisted lesson planning, resource creation and differentiation. Overall, Google's latest white paper signals an evolving ed-tech culture which centres on a more substantive partnership between ed & tech: 👉 Co-Creation: Google commits to investing in evidence-based approaches to learning design and development and to rigorous evaluation, pilot studies and educator-led research to test and demo impact. 👉 Collaborative Development: Google commits to working with schools, NGOs, researchers and learning scientists to co-design tools for learning. You can read the white paper in full using the link in comments. Happy innovating! Phil 👋 | 26 comments on LinkedIn

In this 2025 edition of the annual McKinsey Global Survey on AI, we look at the current trends that are driving real value from artificial intelligence.

Resilience Science Must-Knows: Nine Things every Decision-Maker Should Know about Resilience! I. Resilience Resilience has become a central consideration across practice, policy, and business. It is increasingly integrated into public health strategies, private-sector risk management, corporate planning, development, and financial investment II. Knowledge Decision-makers across regions and sectors urgently need clear, science-based, and actionable knowledge to maintain the resilience of people and the planet and to ensure societies have the capacity to cope, adapt, and transform in order to thrive amid uncertainty. III. Nine Must-Knows are: 1. Navigate accelerating risk: Resilience offers pathways toward more just and sustainable futures for people and the planet. 2. Cope, adapt, and transform: Resilience is more than just bouncing back from shocks. 3. Invest today – benefit tomorrow: Resilience protects and strengthens the foundations that support long-term human well-being and prosperity. 4. Cultivate continuous learning and innovation: Resilience is a cycle that demands continuous experimentation, learning, and innovation. 5. Foster diversity in all its forms: Diversity is both a source of persistence, providing multiple options, and a source of adaptation, and transformation. 6. Nurture relationships: Resilience grows through relationships and these connections strengthen the flow of resources, knowledge, trust, and care. 7. Govern and negotiate trade-offs: Addressing trade-offs is vital to avoid unintended harms, prevent conflict, and build just, lasting resilience. 8. Empower agency: Supporting and developing agency means enabling people and institutions to take intentional and grounded action. 9. Address power imbalances: Failing to address social inequalities, power imbalances, and historical injustices risk reinforcing the very systems that cause vulnerability. IV. From science to impact This report is not an endpoint. In the next phase of the efforts—the Resilience Road to Action—the initiative will work closely with decision- makers across a range of sectors, including food and agriculture, urban development, health, and finance, to translate the Resilience Science Must-Knows into actionable guidelines. Make sure to check out the important report by Stockholm Resilience Centre , Global Resilience Partnership, Future Earth here: https://lnkd.in/dmaARyPU ______ Stay Ahead of Transformative Innovation Follow The Futuring Alliance for regular insights, foresight, and practical tools to help your organization thrive in times of change. We support leaders across industries in turning future-focused ideas into real-world impact—through collaboration, innovation, and bold action. Let’s shape what’s next—together. #reslience #sustaunability #innovation #foresight #system #systemschange #strategy #venturing #impact

Last week, a group of L&D peers gathered to discuss budgeting. The initiative started from a Slack thread and turned into a space where we discussed all our questions about L&D budgets and the budgeting process. There were SO many brilliant questions asked, and I wanted to share my favs: 👉 What percentage of your L&D budget is directly linked to measurable business goals or KPIs? 👉 When the budget gets tight, how do you decide which initiatives to keep and which to pause or cut? 👉 How do you plan or forecast your L&D budget for the year when future needs aren’t yet fully clear? 👉 How do you measure the impact or return on your L&D investments, beyond just participation rates or satisfaction scores? 👉 How do you factor in external shifts, like economic changes, new technologies, or workforce trends, when planning your L&D budget? 👉 Do you ever include the cost of employees’ time spent learning in your budgeting decisions? Could that actually be your biggest investment? 👉 How can L&D teams move from a “repeat last year’s budget” mindset to a more intentional, impact-driven budgeting approach? What other questions would you add to the list? 💡 Big thanks to peers such as Adam House, Chris McLaughlin FLPI, Jen Collins, Lisa-Noreen Kröger, Mair Horscroft Assoc CIPD, Janelle M., Tereza Kenova, Lauren Vecchio, Marine Petitpas, among others, for joining the conversation with so much openness ☂️💜 #learninganddevelopment #learningbudgeting

What happens when learners meet AI? Think of skill development as a road from beginner to expert. You normally start with basic practice, work through tough problems, reflect on what's working, and eventually reach the point where you can handle anything that comes up. Now AI has entered this picture. Depending on how we use it, we end up on completely different roads. Use AI too early and you risk never-skilling. You skip the fundamentals and never develop real capability. Hand over too much and you risk de-skilling. Abilities you once had start to fade. Copy AI outputs without thinking and you risk mis-skilling. You learn the wrong lessons and build on faulty foundations. But there's another path. Use AI while staying critical. Question its outputs. Think through the logic. Verify the answers. This is AI-enhanced adaptive practice. AI becomes a sparring partner that helps you learn faster without replacing your own reasoning. The difference comes down to one thing: who's in control. The people who'll succeed with AI aren't avoiding it or surrendering to it completely. They're the ones who keep thinking while using AI to compress learning cycles and test ideas faster. AI shouldn't replace your thinking. It should make your thinking better. The question isn't whether to use AI when learning. It's whether you're driving or just sitting in the passenger seat. How are you seeing this play out in your work? ✍ Raja-Elie Abdulnour, Brian Gin, Christy Boscardin. Educational Strategies for Clinical Supervision of Artificial Intelligence Use. N Engl J Med. 2025;393(8):786-797. DOI: 10.1056/NEJMra2503232 | 10 comments on LinkedIn

𝗘𝗺𝗯𝗿𝗮𝗰𝗶𝗻𝗴 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗮 𝗗𝗶𝘀𝗿𝘂𝗽𝘁𝗲𝗱 𝗪𝗼𝗿𝗹𝗱 𝗪𝗮𝗿𝘂𝗺 𝗱𝗲𝗿 𝗠𝗲𝗻𝘀𝗰𝗵𝗻 𝗱𝗲𝗿 𝘄𝗶𝗰𝗵𝘁𝗶𝗴𝘀𝘁𝗲 𝗙𝗮𝗸𝘁𝗼𝗿 𝗶𝗻 𝗱𝗲𝗿 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗶𝘀𝘁 In einer Welt voller Unsicherheit, technologischem Wandel und geopolitischer Spannungen ist Transformation längst kein Projekt mehr – sie ist Dauerzustand. Die aktuelle Studie von Arthur D. Little – „Embracing Transformation in a Disrupted World“ (2025) zeigt eindrucksvoll, wie tiefgreifend der Wandel bereits in Unternehmen verankert ist – und wo er scheitert. ➡️ 65 % der Unternehmen befinden sich aktuell in umfassenden Transformationsprozessen. ➡️ 95 % der Führungskräfte glauben an ihren Erfolg. ➡️ Doch nur 7 % schaffen es, eine wirklich kontinuierliche Transformation zu leben. ➡️ Der größte Stolperstein? Nicht Technologie – sondern Menschen. Die Studie belegt: Ohne echte Einbindung der Mitarbeitenden bleiben Strategien, Strukturen und Systeme leere Hüllen. Denn: Transformation gelingt nicht um Menschen herum – sie gelingt nur mit ihnen. 𝗪𝗮𝘀 𝗯𝗲𝗱𝗲𝘂𝘁𝗲𝘁 𝗱𝗮𝘀 𝗳ü𝗿 𝗛𝗥 𝘂𝗻𝗱 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 ➡️ Upskilling & Re-Skilling sind kein „Nice-to-have“ mehr, sondern Voraussetzung für Zukunftsfähigkeit. ➡️ Kulturarbeit muss Teil der Transformationsarchitektur sein – nicht ein Begleitprogramm. ➡️ Führungskräfte sind die entscheidenden Übersetzer zwischen Strategie und Emotion. Sie schaffen Sinn, Vertrauen und Energie. ➡️ Iterative Transformation statt Großprojektdenken: Wandel wird dann nachhaltig, wenn Organisationen lernen, sich selbst permanent weiterzuentwickeln. 📊 Besonders spannend: Nur 5 % der Unternehmen bewerten ihre Lernkultur als „sehr effektiv“. Das zeigt, wie groß der Handlungsbedarf ist – gerade im HR. Hier entscheidet sich, ob Transformation getragen oder gebremst wird. 🎯 𝗠𝗲𝗶𝗻 𝗙𝗮𝘇𝗶𝘁 Technologie mag der Katalysator sein – aber Menschen sind der Motor jeder erfolgreichen Transformation. Wer ihre Potenziale entfesselt, gestaltet nicht nur Wandel, sondern Zukunft. Quelle: Arthur D. Little (2025): “Embracing Transformation in a Disrupted World” – Autoren: Francesco Marsella, Wilhelm Lerner, Ben van der Schaaf, Marten Zieris, Alexander Buirski, Francesco Cotrone, Alexis Ost Duchateau.

Generate use case ideas and identify the most promising ones to implement.

Ich habe mir die Megatrendstudie zu Wissenskultur angeschaut und mit unseren Thesen zu "New Learning" und warum es eine Transformation benötigt, verglichen. Kurz: Die Schnittmenge ist sehr hoch.

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But people have reacted to genrative AI so differently. Some have embraced it with gusto. Many have shrunk away from it. Thre vast majority of AI experimentation and usage still happens outside of work (ChatGPT has 800m weekly mostly-consumer users now). Most firms don’t have a very good idea of where the individuals and teams that make up their workforce are. Well, a 2x2 matrix almost always helps - so simple, so illuminating. It’s my favourite mental model. In this situation, adoption and capability are two pertinent axes to think about this. It gives a sense of where there’s overconfidence, underconfidence and appropriate confidence. And what actions you might take for populations in each of the quadrants. This enables you to better serve your people, and be better served by them. If you’re interested in a 30-question survey which generates the data behind each axis and forms part of and builds on my AI in the Wild use case research, send me a message. ♻️Please REPOST if people you’re connected to may like to be updated on how AI is being used, out in the Wild. #aiinthewild