Open New Learning Lab Resources

This L&D expert draws on cognitive science to create environments where exploration, connection and repetition build mastery at work.

AI Innovation in HR: Listening to People at Scale

How leaders promote self-determination in the workplace

Niemand hat Bock zu lernen.

Ich habe ein Störgefühl.

Learn how AI is transforming work, focusing on the collaboration between humans, agents, and robots.

Very glad to see Humans + AI thinking is now mainstream.

2026 muss das Jahr werden, in dem Unternehmen wirklich „AI-enabled“ werden. Welche Rolle spielt HR dabei?

Digital Learning Realities Research 2025: Fosway Group's latest research into the realities of digital learning, in partnership with Learning Technologies.

🔥 #Meta hat es vertuscht.

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