Open New Learning Lab Resources

Bridging research on human anticipation with the practice of foresight.

What skills do L&D functions need to deliver business impact? 200+ L&D teams answered. Explore the skills map, CLO analysis and 10 essential skills for L&D Operators in 2027.

Siara KI als technische Unterstützung passt ideal 😊

🚨 Important L&D industry announcement 🚨

Der sechste Studienbericht von Taylor & Vinauskaite (Daten bis September 2026) zeigt, dass die Nutzung von KI bei L&D verbreitet und etabliert ist und dass KI für vielfältige Aufgaben genutzt wird.

Meetings sind der Brokkoli der Arbeitswelt – unbeliebt, aber (mit der richtigen Zubereitung!) ziemlich gut für uns.

Most organizations know they need AI agents. Fewer know where to start. The biggest challenge is not technology, but identifying the workflows where autonomous action delivers measurable value. Learn how to map the right use cases and avoid common pitfalls: https://gtnr.it/4yI0MFM | 13 comments on LinkedIn

Erstellt euch eure Lernsessions doch einfach selbst. :)

Is there only one learning culture? When I worked on organizational learning culture during my PhD, I looked for a definition I could build on and did not find a widely accepted one. Our new article shows this was not just my impression. Together with Prof. Dr. Anja Schmitz and Rabea Germanus, I reviewed 210 publications on organizational learning culture. Definitions vary widely, and learning culture is often conflated with organizational learning or the learning organization. And 208 of the 210 publications treat it as shared across the whole organization: the integration perspective in Martin's (1992) terms. That term comes from Joanne Martin's book Cultures in Organizations. Martin questions and extends the dominant integration perspective on organizational culture, which was largely shaped by Edgar Schein. When we came across it, we worked through it the old-fashioned way: printed copies on our desks, pen in hand, taking it apart page by page. In many Zoom meetings we sat with our books, sometimes reading in silence, then discussing single phrases and what exactly they would mean for learning culture. The result is a two-part definition. The first sentence condenses what we found across the literature. The second is our extension based on Martin: "Organizational Learning Culture refers to the system of assumptions, values, norms, and artifacts that shape how learning is interpreted, enacted, and supported within an organization. These cultural meanings may be shared across the organization, consolidated within subgroups, or remain ambiguous, situational, and contested." It is the definition I would have liked to have back then. A case study in a German bank (436 survey respondents, 19 interviews) showed all three patterns side by side: broad agreement, modest but systematic differences between units, and situational coping in the interviews. For practice: ask not only "How strong is our learning culture?" but also "Where does it differ, and where is it unclear?" and use the differences: units can learn from each other how learning works well in their organization! Read it open access: https://lnkd.in/e4gs5dJm #LearningCulture #OrganizationalLearning #OrganizationalPsychology #HRD

New from Mckinsey... Quite rich and robust. My initial takeaway: - 𝗥𝗲𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗻𝗲𝗲𝗱𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗵𝗶𝘀𝘁𝗼𝗿𝗶𝗰, 𝗮𝗻𝗱 𝗺𝗼𝘀𝘁 𝘄𝗼𝗿𝗸𝗲𝗿𝘀 𝗹𝗮𝗰𝗸 𝗮𝗻 𝗲𝗮𝘀𝘆 𝗽𝗮𝘁𝗵. Roughly 11 million US workers in declining occupations may need to move into new jobs, with a range of six to 16 million depending on how fast automation is adopted. Only about one in seven can make the move with little or no retraining, and nearly half face a much harder route. The report also says this will require about 770,000 workers a year to switch occupational groups, roughly 3.6 times the historical average. - 𝗢𝗻𝗴𝗼𝗶𝗻𝗴 𝗿𝗲𝘀𝗸𝗶𝗹𝗹𝗶𝗻𝗴 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗮𝘀 𝗺𝘂𝗰𝗵 𝗮𝘀 𝗷𝗼𝗯 𝘀𝘄𝗶𝘁𝗰𝗵𝗶𝗻𝗴. More than 70 percent of workers could need some level of reinvention, since new tasks may take up more than 15 percent of their time. The report adds that most workers will need to keep building new skills whether their occupation is growing, shrinking, or stable. It also notes that lower-wage workers have far fewer high-quality pathways and will need targeted support such as training and career navigation. - 𝗦𝗸𝗶𝗹𝗹𝘀 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗻𝗲𝗲𝗱𝘀 𝘁𝗼 𝗳𝗼𝗰𝘂𝘀 𝗼𝗻 𝗮𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗯𝗮𝗿𝗿𝗶𝗲𝗿𝘀 𝗯𝗲𝘆𝗼𝗻𝗱 𝘀𝗸𝗶𝗹𝗹𝘀 𝗺𝘂𝘀𝘁 𝗰𝗼𝗺𝗲 𝗱𝗼𝘄𝗻. McKinsey groups skills into three types: essential skills that widen career options, enabling skills that unlock higher pay, and empowering skills that prepare workers for constant change. Still, the authors warn that training is not enough by itself: it cannot remove licensing rules, change hiring practices, or pay workers while they train. About 85 percent of growing jobs require some kind of credential. If only one in seven displaced workers can switch jobs with minimal retraining, we have a lot of work ahead and very little time to prove it can do it. The 2 most compelling charts: Exhibit 20: Skills by average wage and share of occupations The chart plots each skill by how many occupations require it against the average wage, revealing three clear groups: niche high-paying skills cluster near zero percent of occupations but reach wages of roughly $130,000 to nearly $300,000, enabling skills like collaboration, decision-making, innovation, and critical thinking sit in the middle at roughly $85,000 to $115,000, and essential skills like leadership, problem-solving, and people and process management appear in 75 to 100 percent of occupations at around $60,000 to $70,000. Exhibit 24: Barriers to entering growing occupations 85 percent of growing occupations require a credential, 76 percent require physical presence, 25 percent sit at the bottom wage quintiles, and workers in growing occupations average 91 percent English proficiency. Together, they show that credentials and location are near-universal gates, while language is a quieter but real filter for non-native speakers. Reactions?

Companies aren't seeing ROI from AI for two reasons. They're measuring the tool instead of the people, and they're encouraging AI use instead of building it into processes that drive how work gets done. This is why we say stop encouraging use cases and start embedding AI into daily processes - and have those processes created from the bottom up, held accountable by team leaders. Okay, here's what we see: MISTAKE #1: MEASURING THE TOOL A traditional tool comes with its measurement built in: more leads in the CRM, more tickets closed, stuff like that. AI doesn't, because its power runs through your people. Counting how many times Copilot got opened, I mean, it tells you nothing because that could be people just using it as a better search engine, which gives you zero ROI. We know the gains are real. Anyone using AI well knows they can do 5 or 10 times what they did before. But - and this is critical - those gains are in individuals who choose whether or not to use AI, so of course there's no consistent ROI. You can't measure AI, but you can measure a process. How did this work run before, and how does it run now? Decide what good looks like before you start, on your own numbers, and then count. MISTAKE #2: ENCOURAGING INSTEAD OF EXPECTING Most companies rolled out AI by giving Copilot to everyone and waiting for everyone to get efficient. That's like putting a treadmill in every house in America and expecting to cure heart disease. Leaders see what AI can do and confuse that potential with people actually using it. Imagine installing Excel on every computer in finance and then just *encouraging* people to try it. Nobody would do that. Excel is simply how the work gets done. The same goes for use cases, and we've been saying this since 2024. A use case lives in the person, so when your best AI user leaves, everything they figured out leaves with them. THE FIX: BUILD AI INTO THE PROCESS Stop relying on will power to get people using AI. Build it into their processes for them. You can tell everyone to walk 5,000 steps a day, or you can put the parking lot 5,000 steps from the building. Then nobody has to remember. It's just how you get to work. AI works the same way. When a team rebuilds a process around AI and their manager holds it as the standard, nobody can avoid using it, and nobody has to rely on willpower. That's how you get a million little hockey sticks: every team rebuilding its own processes, one at a time, with each gain locked in. Add those up and you get the curve the CFO (or the consultant) has been looking for. +++++++++ UPSKILL YOUR ORGANIZATION AI Mindset drives behavioral transformation at scale for some of the biggest companies in the world. Microsoft independently validated our approach in their WorkLab research - our approach literally doubles the quality of work after just 30 minutes of our training.  DM me, or check out our website at ai-mindset[.]ai | 17 comments on LinkedIn

Future Studies Academy presents 10 Capabilities that will shape what's next! What capabilities will help us navigate change and shape what comes next? Reports on the future of skills often provide long lists of individual skills. At Future Studies Academy, we wanted to understand the larger patterns behind those lists. We reviewed 11 influential future-of-skills reports and conducted a cross-report analysis of the recurring skills and competencies. From this research synthesis, we developed 10 clusters that will guide the Academy’s learning agenda. We call them the 10 Capability Areas: • Creativity: Understanding your creativity conditions, which bring your most creative self out, and behavioral interventions to improve your creativity.  Generating original and useful ideas by seeing alternatives and making new connections.  • Strategy: Understanding strategy as a human competency, identifying and developing strategy strengths.  Making coherent choices about where to focus and how to create meaningful progress. • Leadership: Mobilizing people around a shared direction (North Star) and enabling them to contribute. • AI/Digital Fluency: Understanding AI’s possibilities and limitations and using it effectively, critically and responsibly. • Transformation Management: Translating change into coordinated action across people, processes and systems. • Foresight: Detecting emerging signals, exploring possible futures and preparing for different possibilities. • Critical Thinking: Examining evidence, assumptions and logic to form more grounded judgments. • Decision-Making: Turning insight into timely choices while navigating uncertainty, complexity and trade-offs. • Empathy & Emotional Intelligence: Understanding emotions and perspectives—and responding with greater awareness and care. • Innovation: Converting promising ideas into applied value through experimentation, implementation and learning. These are not 10 standalone skills. Each capability area contains multiple competencies, skills, behaviours and bodies of knowledge. They also overlap, reinforce and depend on one another. Future Studies Academy is a membership-based learning platform building a growing portfolio of certification programs across these capability areas. The Academy is designed primarily for people leaders and professional practitioners, including coaches, facilitators, consultants, project managers and change managers, who want practical development, professional certification and opportunities to earn CCE or CE hours. We also bring these capabilities into organizations through remote and in-person corporate training, coaching, and customized leadership and management development programs. Learn to shape what’s next. #FutureOfSkills #ProfessionalDevelopment #FutureStudiesAcademy

AI is not yet everywhere, but opinions about it are widespread. New research finds that across the 37 countries surveyed, the median percentage of adults who have never used AI is 57%. Yet among those who are aware of it, positive emotions outweigh negative ones in 34 of 37 countries. In 25 of the 37 countries, majorities of AI-aware adults expect AI to improve their daily lives. When it comes to concerns about the technology, however, wealthy Western countries stand apart, combining relatively high AI use with some of the highest levels of worry. The study, which will ultimately cover 140 countries, measures public engagement with AI across five dimensions. In addition to emotional response, the survey captures awareness, frequency of use, expected impact and trust. For leaders navigating AI adoption, the findings point to an important consideration: Adoption is only part of the picture. How people experience AI, whether they trust it and how they expect it to shape their lives matter too. Explore the first findings from Gallup and Microsoft's ongoing research on AI engagement worldwide: https://lnkd.in/ebKQBB-u

ada builds the human capability to lead AI-enabled work. Practice on ada Flow. Ship a real solution in 12 weeks with ada Shift. Meet at ada Growth Days.

I just came across this HBR article from March 2026: “Researchers Asked LLMs for Strategic Advice. They Got ‘Trendslop’ in Return.” Seven AI models were tested across thousands of simulated business scenarios. Their advice showed similar patterns: favouring fashionable ideas over recommendations tailored to the context. A reminder: polished advice isn’t necessarily good strategy. Use AI to challenge your thinking - but keep your judgment. | 36 comments on LinkedIn

Knowing which 5 documents you need is the easy part. Building them is where every SMEs struggle. Here is your full gudie to prove AI governance: Provable AI governance comes down to 5 documents. Built in sequence. Each one feeds the next: 1. AI System Inventory You can't govern what you haven't listed. 2. AI Use Policy Written around what your team already does. Not what you wish they did. 3. Risk Assessment One per system. This is where most companies stall. 4. Oversight Record A log, not a promise. 5. Vendor File Your vendor's compliance isn't your compliance. None of this starts from a blank page. It starts from evidence you already have. Card statements. Contracts. Decisions your team makes every day. The real question isn't "Do we have an AI policy?" It's "Could we prove it the same day someone asks?" → Click "View my newsletter" right under my name 👆 and join my newsletter for actionable AI insights. | 89 comments on LinkedIn

Four patterns to watch out for.

«𝗞𝗹𝘂𝗴𝗲 𝗠𝗲𝗻𝘀𝗰𝗵𝗲𝗻 𝗵𝗮𝗯𝗲𝗻 𝗶𝗻 𝗱𝘂𝗺𝗺𝗲𝗻 𝗢𝗿𝗴𝗮𝗻𝗶𝘀𝗮𝘁𝗶𝗼𝗻𝗲𝗻 𝗸𝗲𝗶𝗻𝗲 𝗖𝗵𝗮𝗻𝗰𝗲.» (Dr.

In a bold shift in how the firm recognizes impact, Ernst & Young LLP (EY US) is investing $100 million to reward EY US professionals who develop future-focused skills, advance the firm’s culture, drive innovation, and deliver innovative and exceptional client service.

The big next opportunity with AI for most organizations is integrating it into teams and teamwork. Giving individuals AI access is already challenging. Creating value in teams with AI can be far more valuable, but it is also far harder. Gabriele Rosani and Elisa Farri ran workshops for over 300 managers in 35 organizations to observe how they used AI in teamwork, before, during, and after working directly with other team members. Their conclusion is that there are two conditions for successful teamwork: INTENTIONALITY Leaders need to be clear and deliberate on their intentions on how they bring AI into teamwork. It requires deciding specifically where and when AI can best be applied, and embedding the practices into ongoing team behaviors. CRAFT Effective team + AI collaboration needs to be designed based on the context of the team and work. This consists of sequencing the Humans + AI roles, prompts, steps, and interactions to ensure that thinking and decision-making are improved. Some of the more specific insights in the article: ➡️ Using AI individually before meetings can improve collective intelligence Good use of AI by participants before interactive sessions, not just to develop their thinking, but also to individualize it, leads to far richer discussions and outcomes. ➡️ AI can expand the cognitive diversity of the team Teams often converge and have the same assumptions. Using AI well can complement the team with external or quite different perspectives. ➡️ AI adoption by teams is about work design The authors say that just 7% of teams use AI, compared to almost all individually. The next phase is redesigning the structure of collective work with humans and AI each having explicit roles at all stages of collaboration. All of this is entirely in line with my original Humans + AI workflow roles and cognitive augmentation framing. Great to see Humans + AI teaming getting the attention it deserves, this is the next phase for many organizations that already have made reasonable progress. I will be sharing a lot more on Humans + AI teaming in the next while.

Prozesse neu gestalten

A new way of working is emerging if your job is to use and create knowledge to solve complex problems with colleagues. Even just in the last 6 months, I have directly seen the jump in knowledge work (not just coding!) that have become AI-first. Quite a few things have become materially better: 1. the ability to meaningfully interact with (not just query) AI assistants strongly grounded in fresh enterprise knowledge, to come up with a first set of ideas - retrievals, iteration, critique. 2. Then use those grounded assistants during meetings where ideas are further refined with colleagues. 3. Then, leverage them to harvest the most important insights. 4. And likely, in the future, systematically analyze the divergence between expected and actual results. It doesn't feel like it is just me becoming more effective. It feels like these cycles build knowledge that makes the next cycle stronger. And this is a collective result, not one in which a single person and a single machine would achieve results. Many things can still go wrong, many are still imperfect, but this truly is a different way of working. It does feel like a synaptic layer is being built, and it is really about creating loops. At least in organizations that give their people the right knowledge and the right tools, knowledge is starting to compound - where *networks* of people and AI make it compound.  In those places, people may be able, often for the first time, to spend much less time memorizing, documenting and retrieving and much more time exploring, building, testing, learning. This is a whole new way of working. I personally can't see myself going back to what was there before. | 18 comments on LinkedIn

AI will either be the greatest equalizer ever invented, or the worst source of injustice. We need to start planning now so it makes the world a fairer place.

Fliege durch die Ergebnisse der Bevölkerungsbefragung "Erster Zukunftskompass der Schweiz" und schaue in die erwünschte Zukunft.

Tasks are not jobs. In a recent talk on Markus Brunnermeier’s Academy, The London School of Economics and Political Science (LSE) Professor Luis Garicano broke down why the line between human augmentation and full AI automation isn't determined by cognitive complexity alone; it’s determined by messiness. The chart below maps out the "autonomy threshold" (i.e., the capability level at which AI can complete tasks independently and replace human labor). Here are the three key takeaways from the conversation: 1️⃣ 𝐂𝐥𝐞𝐚𝐧 𝐓𝐚𝐬𝐤𝐬 𝐯𝐬. 𝐌𝐞𝐬𝐬𝐲 𝐁𝐮𝐧𝐝𝐥𝐞𝐬 Single, verifiable tasks (Tier 1) cross the autonomy threshold early. AI replaces the worker because output is easily isolated and scored. But most high-value jobs aren't single tasks; they are complex bundles (Tier 2) held together by coordination costs, tacit knowledge, and the joint measurement of outcomes. Unbundling them destroys systemic value. 2️⃣ 𝐀𝐮𝐭𝐡𝐨𝐫𝐢𝐭𝐲 & 𝐀𝐜𝐜𝐨𝐮𝐧𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐁𝐨𝐭𝐭𝐥𝐞𝐧𝐞𝐜𝐤 As work shifts toward multi-stakeholder relationships (Tier 3), the autonomy threshold rises steeply. AI cannot hold legal liability, execute organizational authority, or provide procedural fairness when outcomes are ambiguous. Human trust remains the ultimate rate-limiter. 3️⃣ 𝐂𝐨𝐠𝐧𝐢𝐭𝐢𝐨𝐧 𝐃𝐫𝐨𝐩𝐬 𝐭𝐨 𝐌𝐚𝐫𝐠𝐢𝐧𝐚𝐥 𝐂𝐨𝐬𝐭 As cognitive capability scales, it becomes cheap and abundant. The economic rent shifts away from pure task execution to whoever can effectively manage the organizational architecture around AI (rewiring workflows, managing messy trade-offs, and maintaining trusted relationships). Exposure to AI does not equal the extinction of a job (at least not with current capabilities). The real dividing line in the labor market isn't white-collar vs. blue-collar; it's verifiable single tasks vs. messy bundles. Link to the full talk in the comments section below 👇 #AI #Automation #Productivity #FutureOfWork | 14 comments on LinkedIn

More than 60% of senior executives believe AI-related de-skilling will become a material risk to their organization within the next three to five years. New BCG Institute research examines how AI adoption is reshaping capabilities such as judgment, creative thinking and problem understanding and what this means for organizations looking to sustain performance over time. Explore the findings: https://on.bcg.com/454bLfZ #CHROAgenda #PeopleStrategy #FutureofWork | 13 comments on LinkedIn